Gas_prices_predictor1.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 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="c1">#import needed libraries</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">sqrt</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">mean_squared_error</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">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</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">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">bokeh.plotting</span>
<span class="kn">from</span> <span class="nn">bokeh.plotting</span> <span class="k">import</span> <span class="n">figure</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">tree</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">Imputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">MinMaxScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">RobustScaler</span>

<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">LSTM</span>
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<pre>Automatically created module for IPython interactive environment
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We utilized the titanic example to help us preprocess the data</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#read the csv into a dataframe</span>
<span class="k">def</span> <span class="nf">parse</span><span class="p">(</span><span class="n">x</span><span class="p">):</span><span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">&#39;%m/</span><span class="si">%d</span><span class="s1">/%Y&#39;</span><span class="p">)</span>
<span class="n">data_frame</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;GasPricePredictorTrainingSet.csv&#39;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">date_parser</span><span class="o">=</span><span class="n">parse</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Creating additional two columns for months and days and join these two columns to make the dataset more clear</span>
<span class="n">datadates</span> <span class="o">=</span> <span class="n">data_frame</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span>
<span class="n">datamonths</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">month</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">datadates</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">datadates</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Month&#39;</span><span class="p">)</span>
<span class="n">datadays</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">day</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">datadates</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">datadates</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;Day&#39;</span><span class="p">)</span>
<span class="n">datamonths</span> <span class="o">=</span> <span class="n">datamonths</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">datadays</span><span class="o">.</span><span class="n">to_frame</span><span class="p">())</span>
<span class="n">data_frame</span> <span class="o">=</span> <span class="n">datamonths</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_frame</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
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    .dataframe tbody tr th {
        vertical-align: top;
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Month</th>
      <th>Day</th>
      <th>All Grades All Formulations</th>
      <th>All Grades Conventional</th>
      <th>All Grades Reformulated</th>
      <th>Regular All Formulations</th>
      <th>Regular Conventional</th>
      <th>Regular Reformulated</th>
      <th>Midgrade All Formulations</th>
      <th>Midgrade Conventional</th>
      <th>Midgrade Reformulated</th>
      <th>Premium All Formulations Retail</th>
      <th>Premium Conventional</th>
      <th>Premium Reformulated</th>
      <th>Diesel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1990-08-20</th>
      <td>8</td>
      <td>20</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.191</td>
      <td>1.191</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-08-27</th>
      <td>8</td>
      <td>27</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.245</td>
      <td>1.245</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-03</th>
      <td>9</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.242</td>
      <td>1.242</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-10</th>
      <td>9</td>
      <td>10</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.252</td>
      <td>1.252</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-17</th>
      <td>9</td>
      <td>17</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.266</td>
      <td>1.266</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-24</th>
      <td>9</td>
      <td>24</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.272</td>
      <td>1.272</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-01</th>
      <td>10</td>
      <td>1</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.321</td>
      <td>1.321</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-08</th>
      <td>10</td>
      <td>8</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.333</td>
      <td>1.333</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-15</th>
      <td>10</td>
      <td>15</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.339</td>
      <td>1.339</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-22</th>
      <td>10</td>
      <td>22</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.345</td>
      <td>1.345</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-29</th>
      <td>10</td>
      <td>29</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.339</td>
      <td>1.339</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-05</th>
      <td>11</td>
      <td>5</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.334</td>
      <td>1.334</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-12</th>
      <td>11</td>
      <td>12</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.328</td>
      <td>1.328</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-19</th>
      <td>11</td>
      <td>19</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.323</td>
      <td>1.323</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-26</th>
      <td>11</td>
      <td>26</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.311</td>
      <td>1.311</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-03</th>
      <td>12</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.341</td>
      <td>1.341</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-10</th>
      <td>12</td>
      <td>10</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-17</th>
      <td>12</td>
      <td>17</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-24</th>
      <td>12</td>
      <td>24</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-31</th>
      <td>12</td>
      <td>31</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-07</th>
      <td>1</td>
      <td>7</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-14</th>
      <td>1</td>
      <td>14</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-21</th>
      <td>1</td>
      <td>21</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.192</td>
      <td>1.192</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-28</th>
      <td>1</td>
      <td>28</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.168</td>
      <td>1.168</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-04</th>
      <td>2</td>
      <td>4</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.139</td>
      <td>1.139</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-11</th>
      <td>2</td>
      <td>11</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.106</td>
      <td>1.106</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-18</th>
      <td>2</td>
      <td>18</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.078</td>
      <td>1.078</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-25</th>
      <td>2</td>
      <td>25</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.054</td>
      <td>1.054</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-03-04</th>
      <td>3</td>
      <td>4</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.025</td>
      <td>1.025</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-03-11</th>
      <td>3</td>
      <td>11</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.045</td>
      <td>1.045</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-04-09</th>
      <td>4</td>
      <td>9</td>
      <td>2.811</td>
      <td>2.707</td>
      <td>3.024</td>
      <td>2.694</td>
      <td>2.587</td>
      <td>2.918</td>
      <td>2.977</td>
      <td>2.864</td>
      <td>3.196</td>
      <td>3.209</td>
      <td>3.140</td>
      <td>3.338</td>
      <td>3.043</td>
    </tr>
    <tr>
      <th>2018-04-16</th>
      <td>4</td>
      <td>16</td>
      <td>2.863</td>
      <td>2.762</td>
      <td>3.068</td>
      <td>2.747</td>
      <td>2.644</td>
      <td>2.963</td>
      <td>3.025</td>
      <td>2.915</td>
      <td>3.238</td>
      <td>3.256</td>
      <td>3.189</td>
      <td>3.381</td>
      <td>3.104</td>
    </tr>
    <tr>
      <th>2018-04-23</th>
      <td>4</td>
      <td>23</td>
      <td>2.914</td>
      <td>2.814</td>
      <td>3.119</td>
      <td>2.798</td>
      <td>2.696</td>
      <td>3.014</td>
      <td>3.075</td>
      <td>2.967</td>
      <td>3.284</td>
      <td>3.310</td>
      <td>3.243</td>
      <td>3.433</td>
      <td>3.133</td>
    </tr>
    <tr>
      <th>2018-04-30</th>
      <td>4</td>
      <td>30</td>
      <td>2.961</td>
      <td>2.858</td>
      <td>3.170</td>
      <td>2.846</td>
      <td>2.741</td>
      <td>3.068</td>
      <td>3.117</td>
      <td>3.008</td>
      <td>3.330</td>
      <td>3.353</td>
      <td>3.285</td>
      <td>3.479</td>
      <td>3.157</td>
    </tr>
    <tr>
      <th>2018-05-07</th>
      <td>5</td>
      <td>7</td>
      <td>2.960</td>
      <td>2.851</td>
      <td>3.182</td>
      <td>2.845</td>
      <td>2.733</td>
      <td>3.078</td>
      <td>3.119</td>
      <td>3.003</td>
      <td>3.343</td>
      <td>3.354</td>
      <td>3.280</td>
      <td>3.491</td>
      <td>3.171</td>
    </tr>
    <tr>
      <th>2018-05-14</th>
      <td>5</td>
      <td>14</td>
      <td>2.949</td>
      <td>2.846</td>
      <td>3.148</td>
      <td>2.873</td>
      <td>2.786</td>
      <td>3.055</td>
      <td>3.212</td>
      <td>3.076</td>
      <td>3.405</td>
      <td>3.452</td>
      <td>3.347</td>
      <td>3.574</td>
      <td>3.239</td>
    </tr>
    <tr>
      <th>2018-05-21</th>
      <td>5</td>
      <td>21</td>
      <td>2.999</td>
      <td>2.893</td>
      <td>3.201</td>
      <td>2.923</td>
      <td>2.834</td>
      <td>3.109</td>
      <td>3.257</td>
      <td>3.121</td>
      <td>3.448</td>
      <td>3.497</td>
      <td>3.391</td>
      <td>3.617</td>
      <td>3.277</td>
    </tr>
    <tr>
      <th>2018-05-28</th>
      <td>5</td>
      <td>28</td>
      <td>3.039</td>
      <td>2.937</td>
      <td>3.235</td>
      <td>2.962</td>
      <td>2.877</td>
      <td>3.142</td>
      <td>3.296</td>
      <td>3.165</td>
      <td>3.482</td>
      <td>3.536</td>
      <td>3.435</td>
      <td>3.653</td>
      <td>3.288</td>
    </tr>
    <tr>
      <th>2018-06-04</th>
      <td>6</td>
      <td>4</td>
      <td>3.018</td>
      <td>2.910</td>
      <td>3.226</td>
      <td>2.940</td>
      <td>2.849</td>
      <td>3.131</td>
      <td>3.285</td>
      <td>3.146</td>
      <td>3.482</td>
      <td>3.524</td>
      <td>3.417</td>
      <td>3.647</td>
      <td>3.285</td>
    </tr>
    <tr>
      <th>2018-06-11</th>
      <td>6</td>
      <td>11</td>
      <td>2.989</td>
      <td>2.883</td>
      <td>3.194</td>
      <td>2.911</td>
      <td>2.822</td>
      <td>3.099</td>
      <td>3.259</td>
      <td>3.122</td>
      <td>3.453</td>
      <td>3.495</td>
      <td>3.387</td>
      <td>3.619</td>
      <td>3.266</td>
    </tr>
    <tr>
      <th>2018-06-18</th>
      <td>6</td>
      <td>18</td>
      <td>2.958</td>
      <td>2.852</td>
      <td>3.161</td>
      <td>2.879</td>
      <td>2.790</td>
      <td>3.064</td>
      <td>3.230</td>
      <td>3.093</td>
      <td>3.422</td>
      <td>3.470</td>
      <td>3.359</td>
      <td>3.598</td>
      <td>3.244</td>
    </tr>
    <tr>
      <th>2018-06-25</th>
      <td>6</td>
      <td>25</td>
      <td>2.913</td>
      <td>2.808</td>
      <td>3.116</td>
      <td>2.833</td>
      <td>2.745</td>
      <td>3.018</td>
      <td>3.189</td>
      <td>3.055</td>
      <td>3.380</td>
      <td>3.432</td>
      <td>3.324</td>
      <td>3.557</td>
      <td>3.216</td>
    </tr>
    <tr>
      <th>2018-07-02</th>
      <td>7</td>
      <td>2</td>
      <td>2.924</td>
      <td>2.824</td>
      <td>3.117</td>
      <td>2.844</td>
      <td>2.762</td>
      <td>3.019</td>
      <td>3.197</td>
      <td>3.068</td>
      <td>3.382</td>
      <td>3.439</td>
      <td>3.335</td>
      <td>3.559</td>
      <td>3.236</td>
    </tr>
    <tr>
      <th>2018-07-09</th>
      <td>7</td>
      <td>9</td>
      <td>2.937</td>
      <td>2.837</td>
      <td>3.132</td>
      <td>2.857</td>
      <td>2.774</td>
      <td>3.034</td>
      <td>3.207</td>
      <td>3.080</td>
      <td>3.389</td>
      <td>3.454</td>
      <td>3.354</td>
      <td>3.571</td>
      <td>3.243</td>
    </tr>
    <tr>
      <th>2018-07-16</th>
      <td>7</td>
      <td>16</td>
      <td>2.943</td>
      <td>2.852</td>
      <td>3.120</td>
      <td>2.865</td>
      <td>2.790</td>
      <td>3.023</td>
      <td>3.209</td>
      <td>3.093</td>
      <td>3.377</td>
      <td>3.451</td>
      <td>3.362</td>
      <td>3.555</td>
      <td>3.239</td>
    </tr>
    <tr>
      <th>2018-07-23</th>
      <td>7</td>
      <td>23</td>
      <td>2.911</td>
      <td>2.818</td>
      <td>3.092</td>
      <td>2.831</td>
      <td>2.754</td>
      <td>2.994</td>
      <td>3.180</td>
      <td>3.060</td>
      <td>3.352</td>
      <td>3.426</td>
      <td>3.338</td>
      <td>3.529</td>
      <td>3.220</td>
    </tr>
    <tr>
      <th>2018-07-30</th>
      <td>7</td>
      <td>30</td>
      <td>2.924</td>
      <td>2.834</td>
      <td>3.102</td>
      <td>2.846</td>
      <td>2.772</td>
      <td>3.006</td>
      <td>3.189</td>
      <td>3.075</td>
      <td>3.357</td>
      <td>3.433</td>
      <td>3.349</td>
      <td>3.533</td>
      <td>3.226</td>
    </tr>
    <tr>
      <th>2018-08-06</th>
      <td>8</td>
      <td>6</td>
      <td>2.930</td>
      <td>2.843</td>
      <td>3.101</td>
      <td>2.852</td>
      <td>2.781</td>
      <td>3.004</td>
      <td>3.191</td>
      <td>3.080</td>
      <td>3.352</td>
      <td>3.435</td>
      <td>3.354</td>
      <td>3.530</td>
      <td>3.223</td>
    </tr>
    <tr>
      <th>2018-08-13</th>
      <td>8</td>
      <td>13</td>
      <td>2.921</td>
      <td>2.836</td>
      <td>3.089</td>
      <td>2.843</td>
      <td>2.774</td>
      <td>2.993</td>
      <td>3.184</td>
      <td>3.077</td>
      <td>3.339</td>
      <td>3.428</td>
      <td>3.351</td>
      <td>3.518</td>
      <td>3.217</td>
    </tr>
    <tr>
      <th>2018-08-20</th>
      <td>8</td>
      <td>20</td>
      <td>2.900</td>
      <td>2.818</td>
      <td>3.062</td>
      <td>2.821</td>
      <td>2.755</td>
      <td>2.964</td>
      <td>3.171</td>
      <td>3.066</td>
      <td>3.323</td>
      <td>3.407</td>
      <td>3.334</td>
      <td>3.494</td>
      <td>3.207</td>
    </tr>
    <tr>
      <th>2018-08-27</th>
      <td>8</td>
      <td>27</td>
      <td>2.906</td>
      <td>2.824</td>
      <td>3.068</td>
      <td>2.827</td>
      <td>2.761</td>
      <td>2.969</td>
      <td>3.176</td>
      <td>3.069</td>
      <td>3.329</td>
      <td>3.412</td>
      <td>3.336</td>
      <td>3.502</td>
      <td>3.226</td>
    </tr>
    <tr>
      <th>2018-09-03</th>
      <td>9</td>
      <td>3</td>
      <td>2.903</td>
      <td>2.822</td>
      <td>3.064</td>
      <td>2.824</td>
      <td>2.759</td>
      <td>2.964</td>
      <td>3.175</td>
      <td>3.069</td>
      <td>3.328</td>
      <td>3.416</td>
      <td>3.340</td>
      <td>3.505</td>
      <td>3.252</td>
    </tr>
    <tr>
      <th>2018-09-10</th>
      <td>9</td>
      <td>10</td>
      <td>2.912</td>
      <td>2.829</td>
      <td>3.079</td>
      <td>2.833</td>
      <td>2.765</td>
      <td>2.980</td>
      <td>3.184</td>
      <td>3.080</td>
      <td>3.336</td>
      <td>3.426</td>
      <td>3.351</td>
      <td>3.516</td>
      <td>3.258</td>
    </tr>
    <tr>
      <th>2018-09-17</th>
      <td>9</td>
      <td>17</td>
      <td>2.921</td>
      <td>2.834</td>
      <td>3.091</td>
      <td>2.841</td>
      <td>2.771</td>
      <td>2.991</td>
      <td>3.194</td>
      <td>3.081</td>
      <td>3.355</td>
      <td>3.435</td>
      <td>3.350</td>
      <td>3.534</td>
      <td>3.268</td>
    </tr>
    <tr>
      <th>2018-09-24</th>
      <td>9</td>
      <td>24</td>
      <td>2.923</td>
      <td>2.841</td>
      <td>3.082</td>
      <td>2.844</td>
      <td>2.779</td>
      <td>2.982</td>
      <td>3.195</td>
      <td>3.086</td>
      <td>3.349</td>
      <td>3.436</td>
      <td>3.356</td>
      <td>3.528</td>
      <td>3.271</td>
    </tr>
    <tr>
      <th>2018-10-01</th>
      <td>10</td>
      <td>1</td>
      <td>2.947</td>
      <td>2.859</td>
      <td>3.117</td>
      <td>2.866</td>
      <td>2.796</td>
      <td>3.014</td>
      <td>3.223</td>
      <td>3.106</td>
      <td>3.390</td>
      <td>3.468</td>
      <td>3.378</td>
      <td>3.573</td>
      <td>3.313</td>
    </tr>
    <tr>
      <th>2018-10-08</th>
      <td>10</td>
      <td>8</td>
      <td>2.984</td>
      <td>2.892</td>
      <td>3.161</td>
      <td>2.903</td>
      <td>2.829</td>
      <td>3.057</td>
      <td>3.263</td>
      <td>3.136</td>
      <td>3.440</td>
      <td>3.504</td>
      <td>3.405</td>
      <td>3.618</td>
      <td>3.385</td>
    </tr>
    <tr>
      <th>2018-10-15</th>
      <td>10</td>
      <td>15</td>
      <td>2.961</td>
      <td>2.869</td>
      <td>3.141</td>
      <td>2.879</td>
      <td>2.806</td>
      <td>3.034</td>
      <td>3.249</td>
      <td>3.119</td>
      <td>3.431</td>
      <td>3.488</td>
      <td>3.387</td>
      <td>3.603</td>
      <td>3.394</td>
    </tr>
    <tr>
      <th>2018-10-22</th>
      <td>10</td>
      <td>22</td>
      <td>2.925</td>
      <td>2.828</td>
      <td>3.113</td>
      <td>2.841</td>
      <td>2.763</td>
      <td>3.005</td>
      <td>3.221</td>
      <td>3.084</td>
      <td>3.414</td>
      <td>3.463</td>
      <td>3.357</td>
      <td>3.585</td>
      <td>3.380</td>
    </tr>
    <tr>
      <th>2018-10-29</th>
      <td>10</td>
      <td>29</td>
      <td>2.896</td>
      <td>2.799</td>
      <td>3.083</td>
      <td>2.811</td>
      <td>2.732</td>
      <td>2.975</td>
      <td>3.196</td>
      <td>3.064</td>
      <td>3.381</td>
      <td>3.437</td>
      <td>3.334</td>
      <td>3.555</td>
      <td>3.355</td>
    </tr>
  </tbody>
</table>
<p>1472 rows × 15 columns</p>
</div>
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<div class="prompt input_prompt">In&nbsp;[988]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Print statistict to see if we have missing values</span>
<span class="n">data_frame</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>Month                              1472
Day                                1472
All Grades All Formulations        1335
All Grades Conventional            1249
All Grades Reformulated            1249
Regular All Formulations           1466
Regular Conventional               1466
Regular Reformulated               1249
Midgrade All Formulations          1249
Midgrade Conventional              1249
Midgrade Reformulated              1249
Premium All Formulations Retail    1249
Premium Conventional               1249
Premium Reformulated               1249
Diesel                             1285
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#We dropped the data from our dataset that we did not need for our project. </span>
<span class="n">data_frame1</span> <span class="o">=</span> <span class="n">data_frame</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">data_frame1</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;All Grades All Formulations&#39;</span><span class="p">,</span> <span class="s1">&#39;All Grades Reformulated&#39;</span><span class="p">,</span> <span class="s1">&#39;Regular All Formulations&#39;</span><span class="p">,</span> <span class="s1">&#39;Regular Conventional&#39;</span><span class="p">,</span> 
                  <span class="s1">&#39;Regular Reformulated&#39;</span><span class="p">,</span> <span class="s1">&#39;Midgrade All Formulations&#39;</span><span class="p">,</span> <span class="s1">&#39;Midgrade Conventional&#39;</span><span class="p">,</span> <span class="s1">&#39;Midgrade Reformulated&#39;</span><span class="p">,</span> 
                  <span class="s1">&#39;Premium All Formulations Retail&#39;</span><span class="p">,</span> <span class="s1">&#39;Premium Conventional&#39;</span><span class="p">,</span> <span class="s1">&#39;Premium Reformulated&#39;</span><span class="p">,</span> <span class="s1">&#39;Diesel&#39;</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">data_frame1</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Month</th>
      <th>Day</th>
      <th>All Grades Conventional</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1990-08-20</th>
      <td>8</td>
      <td>20</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-08-27</th>
      <td>8</td>
      <td>27</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-03</th>
      <td>9</td>
      <td>3</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-10</th>
      <td>9</td>
      <td>10</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-17</th>
      <td>9</td>
      <td>17</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-09-24</th>
      <td>9</td>
      <td>24</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-01</th>
      <td>10</td>
      <td>1</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-08</th>
      <td>10</td>
      <td>8</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-15</th>
      <td>10</td>
      <td>15</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-22</th>
      <td>10</td>
      <td>22</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-10-29</th>
      <td>10</td>
      <td>29</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-05</th>
      <td>11</td>
      <td>5</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-12</th>
      <td>11</td>
      <td>12</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-19</th>
      <td>11</td>
      <td>19</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-11-26</th>
      <td>11</td>
      <td>26</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-03</th>
      <td>12</td>
      <td>3</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-10</th>
      <td>12</td>
      <td>10</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-17</th>
      <td>12</td>
      <td>17</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-24</th>
      <td>12</td>
      <td>24</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1990-12-31</th>
      <td>12</td>
      <td>31</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-07</th>
      <td>1</td>
      <td>7</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-14</th>
      <td>1</td>
      <td>14</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-21</th>
      <td>1</td>
      <td>21</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-01-28</th>
      <td>1</td>
      <td>28</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-04</th>
      <td>2</td>
      <td>4</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-11</th>
      <td>2</td>
      <td>11</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-18</th>
      <td>2</td>
      <td>18</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-02-25</th>
      <td>2</td>
      <td>25</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-03-04</th>
      <td>3</td>
      <td>4</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1991-03-11</th>
      <td>3</td>
      <td>11</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-04-09</th>
      <td>4</td>
      <td>9</td>
      <td>2.707</td>
    </tr>
    <tr>
      <th>2018-04-16</th>
      <td>4</td>
      <td>16</td>
      <td>2.762</td>
    </tr>
    <tr>
      <th>2018-04-23</th>
      <td>4</td>
      <td>23</td>
      <td>2.814</td>
    </tr>
    <tr>
      <th>2018-04-30</th>
      <td>4</td>
      <td>30</td>
      <td>2.858</td>
    </tr>
    <tr>
      <th>2018-05-07</th>
      <td>5</td>
      <td>7</td>
      <td>2.851</td>
    </tr>
    <tr>
      <th>2018-05-14</th>
      <td>5</td>
      <td>14</td>
      <td>2.846</td>
    </tr>
    <tr>
      <th>2018-05-21</th>
      <td>5</td>
      <td>21</td>
      <td>2.893</td>
    </tr>
    <tr>
      <th>2018-05-28</th>
      <td>5</td>
      <td>28</td>
      <td>2.937</td>
    </tr>
    <tr>
      <th>2018-06-04</th>
      <td>6</td>
      <td>4</td>
      <td>2.910</td>
    </tr>
    <tr>
      <th>2018-06-11</th>
      <td>6</td>
      <td>11</td>
      <td>2.883</td>
    </tr>
    <tr>
      <th>2018-06-18</th>
      <td>6</td>
      <td>18</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-06-25</th>
      <td>6</td>
      <td>25</td>
      <td>2.808</td>
    </tr>
    <tr>
      <th>2018-07-02</th>
      <td>7</td>
      <td>2</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-07-09</th>
      <td>7</td>
      <td>9</td>
      <td>2.837</td>
    </tr>
    <tr>
      <th>2018-07-16</th>
      <td>7</td>
      <td>16</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-07-23</th>
      <td>7</td>
      <td>23</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-07-30</th>
      <td>7</td>
      <td>30</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-08-06</th>
      <td>8</td>
      <td>6</td>
      <td>2.843</td>
    </tr>
    <tr>
      <th>2018-08-13</th>
      <td>8</td>
      <td>13</td>
      <td>2.836</td>
    </tr>
    <tr>
      <th>2018-08-20</th>
      <td>8</td>
      <td>20</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-08-27</th>
      <td>8</td>
      <td>27</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-09-03</th>
      <td>9</td>
      <td>3</td>
      <td>2.822</td>
    </tr>
    <tr>
      <th>2018-09-10</th>
      <td>9</td>
      <td>10</td>
      <td>2.829</td>
    </tr>
    <tr>
      <th>2018-09-17</th>
      <td>9</td>
      <td>17</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-09-24</th>
      <td>9</td>
      <td>24</td>
      <td>2.841</td>
    </tr>
    <tr>
      <th>2018-10-01</th>
      <td>10</td>
      <td>1</td>
      <td>2.859</td>
    </tr>
    <tr>
      <th>2018-10-08</th>
      <td>10</td>
      <td>8</td>
      <td>2.892</td>
    </tr>
    <tr>
      <th>2018-10-15</th>
      <td>10</td>
      <td>15</td>
      <td>2.869</td>
    </tr>
    <tr>
      <th>2018-10-22</th>
      <td>10</td>
      <td>22</td>
      <td>2.828</td>
    </tr>
    <tr>
      <th>2018-10-29</th>
      <td>10</td>
      <td>29</td>
      <td>2.799</td>
    </tr>
  </tbody>
</table>
<p>1472 rows × 3 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame1</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> 
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<pre>Month                      1472
Day                        1472
All Grades Conventional    1249
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Let&#39;s see what we will get if we drop missing values  </span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">data_frame1</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="n">data_frame_without_missing_vals</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Month</th>
      <th>Day</th>
      <th>All Grades Conventional</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1994-11-28</th>
      <td>11</td>
      <td>28</td>
      <td>1.175</td>
    </tr>
    <tr>
      <th>1994-12-05</th>
      <td>12</td>
      <td>5</td>
      <td>1.143</td>
    </tr>
    <tr>
      <th>1994-12-12</th>
      <td>12</td>
      <td>12</td>
      <td>1.118</td>
    </tr>
    <tr>
      <th>1994-12-19</th>
      <td>12</td>
      <td>19</td>
      <td>1.099</td>
    </tr>
    <tr>
      <th>1994-12-26</th>
      <td>12</td>
      <td>26</td>
      <td>1.088</td>
    </tr>
    <tr>
      <th>1995-01-02</th>
      <td>1</td>
      <td>2</td>
      <td>1.104</td>
    </tr>
    <tr>
      <th>1995-01-09</th>
      <td>1</td>
      <td>9</td>
      <td>1.111</td>
    </tr>
    <tr>
      <th>1995-01-16</th>
      <td>1</td>
      <td>16</td>
      <td>1.102</td>
    </tr>
    <tr>
      <th>1995-01-23</th>
      <td>1</td>
      <td>23</td>
      <td>1.110</td>
    </tr>
    <tr>
      <th>1995-01-30</th>
      <td>1</td>
      <td>30</td>
      <td>1.109</td>
    </tr>
    <tr>
      <th>1995-02-06</th>
      <td>2</td>
      <td>6</td>
      <td>1.103</td>
    </tr>
    <tr>
      <th>1995-02-13</th>
      <td>2</td>
      <td>13</td>
      <td>1.099</td>
    </tr>
    <tr>
      <th>1995-02-20</th>
      <td>2</td>
      <td>20</td>
      <td>1.093</td>
    </tr>
    <tr>
      <th>1995-02-27</th>
      <td>2</td>
      <td>27</td>
      <td>1.101</td>
    </tr>
    <tr>
      <th>1995-03-06</th>
      <td>3</td>
      <td>6</td>
      <td>1.103</td>
    </tr>
    <tr>
      <th>1995-03-13</th>
      <td>3</td>
      <td>13</td>
      <td>1.096</td>
    </tr>
    <tr>
      <th>1995-03-20</th>
      <td>3</td>
      <td>20</td>
      <td>1.095</td>
    </tr>
    <tr>
      <th>1995-03-27</th>
      <td>3</td>
      <td>27</td>
      <td>1.102</td>
    </tr>
    <tr>
      <th>1995-04-03</th>
      <td>4</td>
      <td>3</td>
      <td>1.116</td>
    </tr>
    <tr>
      <th>1995-04-10</th>
      <td>4</td>
      <td>10</td>
      <td>1.134</td>
    </tr>
    <tr>
      <th>1995-04-17</th>
      <td>4</td>
      <td>17</td>
      <td>1.149</td>
    </tr>
    <tr>
      <th>1995-04-24</th>
      <td>4</td>
      <td>24</td>
      <td>1.173</td>
    </tr>
    <tr>
      <th>1995-05-01</th>
      <td>5</td>
      <td>1</td>
      <td>1.181</td>
    </tr>
    <tr>
      <th>1995-05-08</th>
      <td>5</td>
      <td>8</td>
      <td>1.204</td>
    </tr>
    <tr>
      <th>1995-05-15</th>
      <td>5</td>
      <td>15</td>
      <td>1.213</td>
    </tr>
    <tr>
      <th>1995-05-22</th>
      <td>5</td>
      <td>22</td>
      <td>1.232</td>
    </tr>
    <tr>
      <th>1995-05-29</th>
      <td>5</td>
      <td>29</td>
      <td>1.234</td>
    </tr>
    <tr>
      <th>1995-06-05</th>
      <td>6</td>
      <td>5</td>
      <td>1.234</td>
    </tr>
    <tr>
      <th>1995-06-12</th>
      <td>6</td>
      <td>12</td>
      <td>1.230</td>
    </tr>
    <tr>
      <th>1995-06-19</th>
      <td>6</td>
      <td>19</td>
      <td>1.224</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-04-09</th>
      <td>4</td>
      <td>9</td>
      <td>2.707</td>
    </tr>
    <tr>
      <th>2018-04-16</th>
      <td>4</td>
      <td>16</td>
      <td>2.762</td>
    </tr>
    <tr>
      <th>2018-04-23</th>
      <td>4</td>
      <td>23</td>
      <td>2.814</td>
    </tr>
    <tr>
      <th>2018-04-30</th>
      <td>4</td>
      <td>30</td>
      <td>2.858</td>
    </tr>
    <tr>
      <th>2018-05-07</th>
      <td>5</td>
      <td>7</td>
      <td>2.851</td>
    </tr>
    <tr>
      <th>2018-05-14</th>
      <td>5</td>
      <td>14</td>
      <td>2.846</td>
    </tr>
    <tr>
      <th>2018-05-21</th>
      <td>5</td>
      <td>21</td>
      <td>2.893</td>
    </tr>
    <tr>
      <th>2018-05-28</th>
      <td>5</td>
      <td>28</td>
      <td>2.937</td>
    </tr>
    <tr>
      <th>2018-06-04</th>
      <td>6</td>
      <td>4</td>
      <td>2.910</td>
    </tr>
    <tr>
      <th>2018-06-11</th>
      <td>6</td>
      <td>11</td>
      <td>2.883</td>
    </tr>
    <tr>
      <th>2018-06-18</th>
      <td>6</td>
      <td>18</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-06-25</th>
      <td>6</td>
      <td>25</td>
      <td>2.808</td>
    </tr>
    <tr>
      <th>2018-07-02</th>
      <td>7</td>
      <td>2</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-07-09</th>
      <td>7</td>
      <td>9</td>
      <td>2.837</td>
    </tr>
    <tr>
      <th>2018-07-16</th>
      <td>7</td>
      <td>16</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-07-23</th>
      <td>7</td>
      <td>23</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-07-30</th>
      <td>7</td>
      <td>30</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-08-06</th>
      <td>8</td>
      <td>6</td>
      <td>2.843</td>
    </tr>
    <tr>
      <th>2018-08-13</th>
      <td>8</td>
      <td>13</td>
      <td>2.836</td>
    </tr>
    <tr>
      <th>2018-08-20</th>
      <td>8</td>
      <td>20</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-08-27</th>
      <td>8</td>
      <td>27</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-09-03</th>
      <td>9</td>
      <td>3</td>
      <td>2.822</td>
    </tr>
    <tr>
      <th>2018-09-10</th>
      <td>9</td>
      <td>10</td>
      <td>2.829</td>
    </tr>
    <tr>
      <th>2018-09-17</th>
      <td>9</td>
      <td>17</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-09-24</th>
      <td>9</td>
      <td>24</td>
      <td>2.841</td>
    </tr>
    <tr>
      <th>2018-10-01</th>
      <td>10</td>
      <td>1</td>
      <td>2.859</td>
    </tr>
    <tr>
      <th>2018-10-08</th>
      <td>10</td>
      <td>8</td>
      <td>2.892</td>
    </tr>
    <tr>
      <th>2018-10-15</th>
      <td>10</td>
      <td>15</td>
      <td>2.869</td>
    </tr>
    <tr>
      <th>2018-10-22</th>
      <td>10</td>
      <td>22</td>
      <td>2.828</td>
    </tr>
    <tr>
      <th>2018-10-29</th>
      <td>10</td>
      <td>29</td>
      <td>2.799</td>
    </tr>
  </tbody>
</table>
<p>1249 rows × 3 columns</p>
</div>
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<div class="prompt input_prompt">In&nbsp;[992]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame_without_missing_vals</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>Month                      1249
Day                        1249
All Grades Conventional    1249
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame_imputed</span> <span class="o">=</span> <span class="n">data_frame1</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1">#Now we impute missing values with mean values.</span>
<span class="c1">#Similar strategy was used in DataPreparation tutorial given in class. </span>
<span class="n">imput</span> <span class="o">=</span> <span class="n">Imputer</span><span class="p">(</span><span class="n">missing_values</span><span class="o">=</span><span class="s1">&#39;NaN&#39;</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;mean&#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="n">data_frame_imputed</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">imput</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data_frame1</span><span class="p">))</span>
<span class="n">data_frame_imputed</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">data_frame1</span><span class="o">.</span><span class="n">columns</span>
<span class="n">data_frame_imputed</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">data_frame1</span><span class="o">.</span><span class="n">index</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[994]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame_imputed</span>
</pre></div>

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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Month</th>
      <th>Day</th>
      <th>All Grades Conventional</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1990-08-20</th>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-08-27</th>
      <td>8.0</td>
      <td>27.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-09-03</th>
      <td>9.0</td>
      <td>3.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-09-10</th>
      <td>9.0</td>
      <td>10.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-09-17</th>
      <td>9.0</td>
      <td>17.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-09-24</th>
      <td>9.0</td>
      <td>24.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-10-01</th>
      <td>10.0</td>
      <td>1.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-10-08</th>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-10-15</th>
      <td>10.0</td>
      <td>15.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-10-22</th>
      <td>10.0</td>
      <td>22.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-10-29</th>
      <td>10.0</td>
      <td>29.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-11-05</th>
      <td>11.0</td>
      <td>5.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-11-12</th>
      <td>11.0</td>
      <td>12.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-11-19</th>
      <td>11.0</td>
      <td>19.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-11-26</th>
      <td>11.0</td>
      <td>26.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-12-03</th>
      <td>12.0</td>
      <td>3.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-12-10</th>
      <td>12.0</td>
      <td>10.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-12-17</th>
      <td>12.0</td>
      <td>17.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-12-24</th>
      <td>12.0</td>
      <td>24.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1990-12-31</th>
      <td>12.0</td>
      <td>31.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-01-07</th>
      <td>1.0</td>
      <td>7.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-01-14</th>
      <td>1.0</td>
      <td>14.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-01-21</th>
      <td>1.0</td>
      <td>21.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-01-28</th>
      <td>1.0</td>
      <td>28.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-02-04</th>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-02-11</th>
      <td>2.0</td>
      <td>11.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-02-18</th>
      <td>2.0</td>
      <td>18.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-02-25</th>
      <td>2.0</td>
      <td>25.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-03-04</th>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>1991-03-11</th>
      <td>3.0</td>
      <td>11.0</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-04-09</th>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707000</td>
    </tr>
    <tr>
      <th>2018-04-16</th>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762000</td>
    </tr>
    <tr>
      <th>2018-04-23</th>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814000</td>
    </tr>
    <tr>
      <th>2018-04-30</th>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858000</td>
    </tr>
    <tr>
      <th>2018-05-07</th>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851000</td>
    </tr>
    <tr>
      <th>2018-05-14</th>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846000</td>
    </tr>
    <tr>
      <th>2018-05-21</th>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893000</td>
    </tr>
    <tr>
      <th>2018-05-28</th>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937000</td>
    </tr>
    <tr>
      <th>2018-06-04</th>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910000</td>
    </tr>
    <tr>
      <th>2018-06-11</th>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883000</td>
    </tr>
    <tr>
      <th>2018-06-18</th>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852000</td>
    </tr>
    <tr>
      <th>2018-06-25</th>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808000</td>
    </tr>
    <tr>
      <th>2018-07-02</th>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824000</td>
    </tr>
    <tr>
      <th>2018-07-09</th>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837000</td>
    </tr>
    <tr>
      <th>2018-07-16</th>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852000</td>
    </tr>
    <tr>
      <th>2018-07-23</th>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818000</td>
    </tr>
    <tr>
      <th>2018-07-30</th>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834000</td>
    </tr>
    <tr>
      <th>2018-08-06</th>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843000</td>
    </tr>
    <tr>
      <th>2018-08-13</th>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836000</td>
    </tr>
    <tr>
      <th>2018-08-20</th>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.818000</td>
    </tr>
    <tr>
      <th>2018-08-27</th>
      <td>8.0</td>
      <td>27.0</td>
      <td>2.824000</td>
    </tr>
    <tr>
      <th>2018-09-03</th>
      <td>9.0</td>
      <td>3.0</td>
      <td>2.822000</td>
    </tr>
    <tr>
      <th>2018-09-10</th>
      <td>9.0</td>
      <td>10.0</td>
      <td>2.829000</td>
    </tr>
    <tr>
      <th>2018-09-17</th>
      <td>9.0</td>
      <td>17.0</td>
      <td>2.834000</td>
    </tr>
    <tr>
      <th>2018-09-24</th>
      <td>9.0</td>
      <td>24.0</td>
      <td>2.841000</td>
    </tr>
    <tr>
      <th>2018-10-01</th>
      <td>10.0</td>
      <td>1.0</td>
      <td>2.859000</td>
    </tr>
    <tr>
      <th>2018-10-08</th>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.892000</td>
    </tr>
    <tr>
      <th>2018-10-15</th>
      <td>10.0</td>
      <td>15.0</td>
      <td>2.869000</td>
    </tr>
    <tr>
      <th>2018-10-22</th>
      <td>10.0</td>
      <td>22.0</td>
      <td>2.828000</td>
    </tr>
    <tr>
      <th>2018-10-29</th>
      <td>10.0</td>
      <td>29.0</td>
      <td>2.799000</td>
    </tr>
  </tbody>
</table>
<p>1472 rows × 3 columns</p>
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<div class="prompt input_prompt">In&nbsp;[995]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_frame_imputed</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Month</th>
      <th>Day</th>
      <th>All Grades Conventional</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>1472.000000</td>
      <td>1472.000000</td>
      <td>1472.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>6.543478</td>
      <td>15.731658</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>std</th>
      <td>3.445725</td>
      <td>8.805049</td>
      <td>0.809445</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.926000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>4.000000</td>
      <td>8.000000</td>
      <td>1.466750</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>7.000000</td>
      <td>16.000000</td>
      <td>2.218114</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>10.000000</td>
      <td>23.000000</td>
      <td>2.776500</td>
    </tr>
    <tr>
      <th>max</th>
      <td>12.000000</td>
      <td>31.000000</td>
      <td>4.102000</td>
    </tr>
  </tbody>
</table>
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<div class="prompt input_prompt">In&nbsp;[996]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#We decided to use the dropped missing values dataset due to the fact that the standard deviation of our imputed dataset(data_frame_imputed), </span>
<span class="c1">#was greater than that of when we dropped all the missing values(data_frame_without_missing_vals).</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Taken from Dr. Jason Brownlee’s function series_to_supervised from: &quot;https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/&quot;</span>
<span class="k">def</span> <span class="nf">series_to_supervised</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">n_in</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_out</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropnan</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="n">n_vars</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">list</span> <span class="k">else</span> <span class="n">data</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">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
    <span class="n">cols</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(),</span> <span class="nb">list</span><span class="p">()</span>
    <span class="c1"># input sequence (t-n, ... t-1)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_in</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
        <span class="n">names</span> <span class="o">+=</span> <span class="p">[(</span><span class="s1">&#39;var</span><span class="si">%d</span><span class="s1">(t-</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_vars</span><span class="p">)]</span>
    <span class="c1"># forecast sequence (t, t+1, ... t+n)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_out</span><span class="p">):</span>
        <span class="n">cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="o">-</span><span class="n">i</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">names</span> <span class="o">+=</span> <span class="p">[(</span><span class="s1">&#39;var</span><span class="si">%d</span><span class="s1">(t)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_vars</span><span class="p">)]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">names</span> <span class="o">+=</span> <span class="p">[(</span><span class="s1">&#39;var</span><span class="si">%d</span><span class="s1">(t+</span><span class="si">%d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_vars</span><span class="p">)]</span>
    <span class="c1"># put it all together</span>
    <span class="n">agg</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">cols</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">agg</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">names</span>
    <span class="c1"># drop rows with NaN values</span>
    <span class="k">if</span> <span class="n">dropnan</span><span class="p">:</span>
        <span class="n">agg</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">agg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">36</span><span class="p">,</span> <span class="mi">37</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">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[999]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reframed</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
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        vertical-align: top;
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        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>var1(t-12)</th>
      <th>var2(t-12)</th>
      <th>var3(t-12)</th>
      <th>var1(t-11)</th>
      <th>var2(t-11)</th>
      <th>var3(t-11)</th>
      <th>var1(t-10)</th>
      <th>var2(t-10)</th>
      <th>var3(t-10)</th>
      <th>var1(t-9)</th>
      <th>...</th>
      <th>var1(t-3)</th>
      <th>var2(t-3)</th>
      <th>var3(t-3)</th>
      <th>var1(t-2)</th>
      <th>var2(t-2)</th>
      <th>var3(t-2)</th>
      <th>var1(t-1)</th>
      <th>var2(t-1)</th>
      <th>var3(t-1)</th>
      <th>var3(t)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1995-02-20</th>
      <td>11.0</td>
      <td>28.0</td>
      <td>1.175</td>
      <td>12.0</td>
      <td>5.0</td>
      <td>1.143</td>
      <td>12.0</td>
      <td>12.0</td>
      <td>1.118</td>
      <td>12.0</td>
      <td>...</td>
      <td>1.0</td>
      <td>30.0</td>
      <td>1.109</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>1.093</td>
    </tr>
    <tr>
      <th>1995-02-27</th>
      <td>12.0</td>
      <td>5.0</td>
      <td>1.143</td>
      <td>12.0</td>
      <td>12.0</td>
      <td>1.118</td>
      <td>12.0</td>
      <td>19.0</td>
      <td>1.099</td>
      <td>12.0</td>
      <td>...</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>1.101</td>
    </tr>
    <tr>
      <th>1995-03-06</th>
      <td>12.0</td>
      <td>12.0</td>
      <td>1.118</td>
      <td>12.0</td>
      <td>19.0</td>
      <td>1.099</td>
      <td>12.0</td>
      <td>26.0</td>
      <td>1.088</td>
      <td>1.0</td>
      <td>...</td>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>1.103</td>
    </tr>
    <tr>
      <th>1995-03-13</th>
      <td>12.0</td>
      <td>19.0</td>
      <td>1.099</td>
      <td>12.0</td>
      <td>26.0</td>
      <td>1.088</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.104</td>
      <td>1.0</td>
      <td>...</td>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>1.096</td>
    </tr>
    <tr>
      <th>1995-03-20</th>
      <td>12.0</td>
      <td>26.0</td>
      <td>1.088</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.104</td>
      <td>1.0</td>
      <td>9.0</td>
      <td>1.111</td>
      <td>1.0</td>
      <td>...</td>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>1.095</td>
    </tr>
    <tr>
      <th>1995-03-27</th>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.104</td>
      <td>1.0</td>
      <td>9.0</td>
      <td>1.111</td>
      <td>1.0</td>
      <td>16.0</td>
      <td>1.102</td>
      <td>1.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>1.102</td>
    </tr>
    <tr>
      <th>1995-04-03</th>
      <td>1.0</td>
      <td>9.0</td>
      <td>1.111</td>
      <td>1.0</td>
      <td>16.0</td>
      <td>1.102</td>
      <td>1.0</td>
      <td>23.0</td>
      <td>1.110</td>
      <td>1.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>1.116</td>
    </tr>
    <tr>
      <th>1995-04-10</th>
      <td>1.0</td>
      <td>16.0</td>
      <td>1.102</td>
      <td>1.0</td>
      <td>23.0</td>
      <td>1.110</td>
      <td>1.0</td>
      <td>30.0</td>
      <td>1.109</td>
      <td>2.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>1.134</td>
    </tr>
    <tr>
      <th>1995-04-17</th>
      <td>1.0</td>
      <td>23.0</td>
      <td>1.110</td>
      <td>1.0</td>
      <td>30.0</td>
      <td>1.109</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>2.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>1.149</td>
    </tr>
    <tr>
      <th>1995-04-24</th>
      <td>1.0</td>
      <td>30.0</td>
      <td>1.109</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>2.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>1.173</td>
    </tr>
    <tr>
      <th>1995-05-01</th>
      <td>2.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>2.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>1.181</td>
    </tr>
    <tr>
      <th>1995-05-08</th>
      <td>2.0</td>
      <td>13.0</td>
      <td>1.099</td>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>3.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>1.204</td>
    </tr>
    <tr>
      <th>1995-05-15</th>
      <td>2.0</td>
      <td>20.0</td>
      <td>1.093</td>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>3.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>1.213</td>
    </tr>
    <tr>
      <th>1995-05-22</th>
      <td>2.0</td>
      <td>27.0</td>
      <td>1.101</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>3.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>1.232</td>
    </tr>
    <tr>
      <th>1995-05-29</th>
      <td>3.0</td>
      <td>6.0</td>
      <td>1.103</td>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>3.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>1.234</td>
    </tr>
    <tr>
      <th>1995-06-05</th>
      <td>3.0</td>
      <td>13.0</td>
      <td>1.096</td>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>1.234</td>
    </tr>
    <tr>
      <th>1995-06-12</th>
      <td>3.0</td>
      <td>20.0</td>
      <td>1.095</td>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>1.230</td>
    </tr>
    <tr>
      <th>1995-06-19</th>
      <td>3.0</td>
      <td>27.0</td>
      <td>1.102</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>1.224</td>
    </tr>
    <tr>
      <th>1995-06-26</th>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.116</td>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>4.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>1.217</td>
    </tr>
    <tr>
      <th>1995-07-03</th>
      <td>4.0</td>
      <td>10.0</td>
      <td>1.134</td>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>5.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>6.0</td>
      <td>26.0</td>
      <td>1.217</td>
      <td>1.209</td>
    </tr>
    <tr>
      <th>1995-07-10</th>
      <td>4.0</td>
      <td>17.0</td>
      <td>1.149</td>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>5.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>6.0</td>
      <td>26.0</td>
      <td>1.217</td>
      <td>7.0</td>
      <td>3.0</td>
      <td>1.209</td>
      <td>1.200</td>
    </tr>
    <tr>
      <th>1995-07-17</th>
      <td>4.0</td>
      <td>24.0</td>
      <td>1.173</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>5.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>26.0</td>
      <td>1.217</td>
      <td>7.0</td>
      <td>3.0</td>
      <td>1.209</td>
      <td>7.0</td>
      <td>10.0</td>
      <td>1.200</td>
      <td>1.189</td>
    </tr>
    <tr>
      <th>1995-07-24</th>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.181</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>5.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>3.0</td>
      <td>1.209</td>
      <td>7.0</td>
      <td>10.0</td>
      <td>1.200</td>
      <td>7.0</td>
      <td>17.0</td>
      <td>1.189</td>
      <td>1.179</td>
    </tr>
    <tr>
      <th>1995-07-31</th>
      <td>5.0</td>
      <td>8.0</td>
      <td>1.204</td>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>5.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>10.0</td>
      <td>1.200</td>
      <td>7.0</td>
      <td>17.0</td>
      <td>1.189</td>
      <td>7.0</td>
      <td>24.0</td>
      <td>1.179</td>
      <td>1.166</td>
    </tr>
    <tr>
      <th>1995-08-07</th>
      <td>5.0</td>
      <td>15.0</td>
      <td>1.213</td>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>17.0</td>
      <td>1.189</td>
      <td>7.0</td>
      <td>24.0</td>
      <td>1.179</td>
      <td>7.0</td>
      <td>31.0</td>
      <td>1.166</td>
      <td>1.164</td>
    </tr>
    <tr>
      <th>1995-08-14</th>
      <td>5.0</td>
      <td>22.0</td>
      <td>1.232</td>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>24.0</td>
      <td>1.179</td>
      <td>7.0</td>
      <td>31.0</td>
      <td>1.166</td>
      <td>8.0</td>
      <td>7.0</td>
      <td>1.164</td>
      <td>1.162</td>
    </tr>
    <tr>
      <th>1995-08-21</th>
      <td>5.0</td>
      <td>29.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>31.0</td>
      <td>1.166</td>
      <td>8.0</td>
      <td>7.0</td>
      <td>1.164</td>
      <td>8.0</td>
      <td>14.0</td>
      <td>1.162</td>
      <td>1.163</td>
    </tr>
    <tr>
      <th>1995-08-28</th>
      <td>6.0</td>
      <td>5.0</td>
      <td>1.234</td>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>6.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>7.0</td>
      <td>1.164</td>
      <td>8.0</td>
      <td>14.0</td>
      <td>1.162</td>
      <td>8.0</td>
      <td>21.0</td>
      <td>1.163</td>
      <td>1.154</td>
    </tr>
    <tr>
      <th>1995-09-04</th>
      <td>6.0</td>
      <td>12.0</td>
      <td>1.230</td>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>6.0</td>
      <td>26.0</td>
      <td>1.217</td>
      <td>7.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>14.0</td>
      <td>1.162</td>
      <td>8.0</td>
      <td>21.0</td>
      <td>1.163</td>
      <td>8.0</td>
      <td>28.0</td>
      <td>1.154</td>
      <td>1.151</td>
    </tr>
    <tr>
      <th>1995-09-11</th>
      <td>6.0</td>
      <td>19.0</td>
      <td>1.224</td>
      <td>6.0</td>
      <td>26.0</td>
      <td>1.217</td>
      <td>7.0</td>
      <td>3.0</td>
      <td>1.209</td>
      <td>7.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>21.0</td>
      <td>1.163</td>
      <td>8.0</td>
      <td>28.0</td>
      <td>1.154</td>
      <td>9.0</td>
      <td>4.0</td>
      <td>1.151</td>
      <td>1.148</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>2018-04-09</th>
      <td>1.0</td>
      <td>15.0</td>
      <td>2.589</td>
      <td>1.0</td>
      <td>22.0</td>
      <td>2.601</td>
      <td>1.0</td>
      <td>29.0</td>
      <td>2.634</td>
      <td>2.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>19.0</td>
      <td>2.612</td>
      <td>3.0</td>
      <td>26.0</td>
      <td>2.659</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>2.707</td>
    </tr>
    <tr>
      <th>2018-04-16</th>
      <td>1.0</td>
      <td>22.0</td>
      <td>2.601</td>
      <td>1.0</td>
      <td>29.0</td>
      <td>2.634</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.661</td>
      <td>2.0</td>
      <td>...</td>
      <td>3.0</td>
      <td>26.0</td>
      <td>2.659</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>2.762</td>
    </tr>
    <tr>
      <th>2018-04-23</th>
      <td>1.0</td>
      <td>29.0</td>
      <td>2.634</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.661</td>
      <td>2.0</td>
      <td>12.0</td>
      <td>2.630</td>
      <td>2.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>2.814</td>
    </tr>
    <tr>
      <th>2018-04-30</th>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.661</td>
      <td>2.0</td>
      <td>12.0</td>
      <td>2.630</td>
      <td>2.0</td>
      <td>19.0</td>
      <td>2.576</td>
      <td>2.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>2.858</td>
    </tr>
    <tr>
      <th>2018-05-07</th>
      <td>2.0</td>
      <td>12.0</td>
      <td>2.630</td>
      <td>2.0</td>
      <td>19.0</td>
      <td>2.576</td>
      <td>2.0</td>
      <td>26.0</td>
      <td>2.561</td>
      <td>3.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>2.851</td>
    </tr>
    <tr>
      <th>2018-05-14</th>
      <td>2.0</td>
      <td>19.0</td>
      <td>2.576</td>
      <td>2.0</td>
      <td>26.0</td>
      <td>2.561</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.582</td>
      <td>3.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>2.846</td>
    </tr>
    <tr>
      <th>2018-05-21</th>
      <td>2.0</td>
      <td>26.0</td>
      <td>2.561</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.582</td>
      <td>3.0</td>
      <td>12.0</td>
      <td>2.574</td>
      <td>3.0</td>
      <td>...</td>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>2.893</td>
    </tr>
    <tr>
      <th>2018-05-28</th>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.582</td>
      <td>3.0</td>
      <td>12.0</td>
      <td>2.574</td>
      <td>3.0</td>
      <td>19.0</td>
      <td>2.612</td>
      <td>3.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>2.937</td>
    </tr>
    <tr>
      <th>2018-06-04</th>
      <td>3.0</td>
      <td>12.0</td>
      <td>2.574</td>
      <td>3.0</td>
      <td>19.0</td>
      <td>2.612</td>
      <td>3.0</td>
      <td>26.0</td>
      <td>2.659</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>2.910</td>
    </tr>
    <tr>
      <th>2018-06-11</th>
      <td>3.0</td>
      <td>19.0</td>
      <td>2.612</td>
      <td>3.0</td>
      <td>26.0</td>
      <td>2.659</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>2.883</td>
    </tr>
    <tr>
      <th>2018-06-18</th>
      <td>3.0</td>
      <td>26.0</td>
      <td>2.659</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>4.0</td>
      <td>...</td>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-06-25</th>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.710</td>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>4.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>2.808</td>
    </tr>
    <tr>
      <th>2018-07-02</th>
      <td>4.0</td>
      <td>9.0</td>
      <td>2.707</td>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>4.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-07-09</th>
      <td>4.0</td>
      <td>16.0</td>
      <td>2.762</td>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>5.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>2.837</td>
    </tr>
    <tr>
      <th>2018-07-16</th>
      <td>4.0</td>
      <td>23.0</td>
      <td>2.814</td>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>5.0</td>
      <td>...</td>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>2.852</td>
    </tr>
    <tr>
      <th>2018-07-23</th>
      <td>4.0</td>
      <td>30.0</td>
      <td>2.858</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>5.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-07-30</th>
      <td>5.0</td>
      <td>7.0</td>
      <td>2.851</td>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>5.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-08-06</th>
      <td>5.0</td>
      <td>14.0</td>
      <td>2.846</td>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>2.843</td>
    </tr>
    <tr>
      <th>2018-08-13</th>
      <td>5.0</td>
      <td>21.0</td>
      <td>2.893</td>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>2.836</td>
    </tr>
    <tr>
      <th>2018-08-20</th>
      <td>5.0</td>
      <td>28.0</td>
      <td>2.937</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>6.0</td>
      <td>...</td>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836</td>
      <td>2.818</td>
    </tr>
    <tr>
      <th>2018-08-27</th>
      <td>6.0</td>
      <td>4.0</td>
      <td>2.910</td>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>6.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836</td>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.818</td>
      <td>2.824</td>
    </tr>
    <tr>
      <th>2018-09-03</th>
      <td>6.0</td>
      <td>11.0</td>
      <td>2.883</td>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>7.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836</td>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.818</td>
      <td>8.0</td>
      <td>27.0</td>
      <td>2.824</td>
      <td>2.822</td>
    </tr>
    <tr>
      <th>2018-09-10</th>
      <td>6.0</td>
      <td>18.0</td>
      <td>2.852</td>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>7.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.818</td>
      <td>8.0</td>
      <td>27.0</td>
      <td>2.824</td>
      <td>9.0</td>
      <td>3.0</td>
      <td>2.822</td>
      <td>2.829</td>
    </tr>
    <tr>
      <th>2018-09-17</th>
      <td>6.0</td>
      <td>25.0</td>
      <td>2.808</td>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>7.0</td>
      <td>...</td>
      <td>8.0</td>
      <td>27.0</td>
      <td>2.824</td>
      <td>9.0</td>
      <td>3.0</td>
      <td>2.822</td>
      <td>9.0</td>
      <td>10.0</td>
      <td>2.829</td>
      <td>2.834</td>
    </tr>
    <tr>
      <th>2018-09-24</th>
      <td>7.0</td>
      <td>2.0</td>
      <td>2.824</td>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>7.0</td>
      <td>...</td>
      <td>9.0</td>
      <td>3.0</td>
      <td>2.822</td>
      <td>9.0</td>
      <td>10.0</td>
      <td>2.829</td>
      <td>9.0</td>
      <td>17.0</td>
      <td>2.834</td>
      <td>2.841</td>
    </tr>
    <tr>
      <th>2018-10-01</th>
      <td>7.0</td>
      <td>9.0</td>
      <td>2.837</td>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>7.0</td>
      <td>...</td>
      <td>9.0</td>
      <td>10.0</td>
      <td>2.829</td>
      <td>9.0</td>
      <td>17.0</td>
      <td>2.834</td>
      <td>9.0</td>
      <td>24.0</td>
      <td>2.841</td>
      <td>2.859</td>
    </tr>
    <tr>
      <th>2018-10-08</th>
      <td>7.0</td>
      <td>16.0</td>
      <td>2.852</td>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>8.0</td>
      <td>...</td>
      <td>9.0</td>
      <td>17.0</td>
      <td>2.834</td>
      <td>9.0</td>
      <td>24.0</td>
      <td>2.841</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>2.859</td>
      <td>2.892</td>
    </tr>
    <tr>
      <th>2018-10-15</th>
      <td>7.0</td>
      <td>23.0</td>
      <td>2.818</td>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>8.0</td>
      <td>...</td>
      <td>9.0</td>
      <td>24.0</td>
      <td>2.841</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>2.859</td>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.892</td>
      <td>2.869</td>
    </tr>
    <tr>
      <th>2018-10-22</th>
      <td>7.0</td>
      <td>30.0</td>
      <td>2.834</td>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836</td>
      <td>8.0</td>
      <td>...</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>2.859</td>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.892</td>
      <td>10.0</td>
      <td>15.0</td>
      <td>2.869</td>
      <td>2.828</td>
    </tr>
    <tr>
      <th>2018-10-29</th>
      <td>8.0</td>
      <td>6.0</td>
      <td>2.843</td>
      <td>8.0</td>
      <td>13.0</td>
      <td>2.836</td>
      <td>8.0</td>
      <td>20.0</td>
      <td>2.818</td>
      <td>8.0</td>
      <td>...</td>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.892</td>
      <td>10.0</td>
      <td>15.0</td>
      <td>2.869</td>
      <td>10.0</td>
      <td>22.0</td>
      <td>2.828</td>
      <td>2.799</td>
    </tr>
  </tbody>
</table>
<p>1237 rows × 37 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># The following was taken from two seperate tutorials: </span>
<span class="c1">#&quot;Multivariate Time Series Forecasting with LSTMs in Keras&quot; - https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/</span>
<span class="c1">#and</span>
<span class="c1"># &quot;Using Machine Learning to Predict Retail Gasoline Prices&quot; - https://www.linkedin.com/pulse/using-machine-learning-predict-retail-gasoline-prices-jacob-bourne/</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 36) (1040,) (197, 1, 36) (197,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">91</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 197 samples
Epoch 1/50
 - 10s - loss: 0.2086 - val_loss: 0.1073
Epoch 2/50
 - 0s - loss: 0.2071 - val_loss: 0.0950
Epoch 3/50
 - 0s - loss: 0.1733 - val_loss: 0.0575
Epoch 4/50
 - 0s - loss: 0.0955 - val_loss: 0.0379
Epoch 5/50
 - 0s - loss: 0.0496 - val_loss: 0.0425
Epoch 6/50
 - 0s - loss: 0.0597 - val_loss: 0.0371
Epoch 7/50
 - 0s - loss: 0.0438 - val_loss: 0.0390
Epoch 8/50
 - 0s - loss: 0.0484 - val_loss: 0.0338
Epoch 9/50
 - 0s - loss: 0.0409 - val_loss: 0.0347
Epoch 10/50
 - 0s - loss: 0.0449 - val_loss: 0.0522
Epoch 11/50
 - 0s - loss: 0.0589 - val_loss: 0.0325
Epoch 12/50
 - 0s - loss: 0.0378 - val_loss: 0.0333
Epoch 13/50
 - 0s - loss: 0.0419 - val_loss: 0.0475
Epoch 14/50
 - 0s - loss: 0.0583 - val_loss: 0.0308
Epoch 15/50
 - 0s - loss: 0.0369 - val_loss: 0.0357
Epoch 16/50
 - 0s - loss: 0.0450 - val_loss: 0.0435
Epoch 17/50
 - 0s - loss: 0.0557 - val_loss: 0.0306
Epoch 18/50
 - 0s - loss: 0.0362 - val_loss: 0.0398
Epoch 19/50
 - 0s - loss: 0.0496 - val_loss: 0.0405
Epoch 20/50
 - 0s - loss: 0.0477 - val_loss: 0.0328
Epoch 21/50
 - 0s - loss: 0.0358 - val_loss: 0.0381
Epoch 22/50
 - 0s - loss: 0.0461 - val_loss: 0.0364
Epoch 23/50
 - 0s - loss: 0.0440 - val_loss: 0.0370
Epoch 24/50
 - 0s - loss: 0.0395 - val_loss: 0.0376
Epoch 25/50
 - 0s - loss: 0.0463 - val_loss: 0.0339
Epoch 26/50
 - 0s - loss: 0.0407 - val_loss: 0.0415
Epoch 27/50
 - 0s - loss: 0.0445 - val_loss: 0.0335
Epoch 28/50
 - 0s - loss: 0.0408 - val_loss: 0.0300
Epoch 29/50
 - 0s - loss: 0.0366 - val_loss: 0.0506
Epoch 30/50
 - 0s - loss: 0.0518 - val_loss: 0.0310
Epoch 31/50
 - 0s - loss: 0.0357 - val_loss: 0.0285
Epoch 32/50
 - 0s - loss: 0.0366 - val_loss: 0.0471
Epoch 33/50
 - 0s - loss: 0.0488 - val_loss: 0.0298
Epoch 34/50
 - 0s - loss: 0.0347 - val_loss: 0.0273
Epoch 35/50
 - 0s - loss: 0.0354 - val_loss: 0.0494
Epoch 36/50
 - 0s - loss: 0.0520 - val_loss: 0.0284
Epoch 37/50
 - 0s - loss: 0.0304 - val_loss: 0.0270
Epoch 38/50
 - 0s - loss: 0.0306 - val_loss: 0.0431
Epoch 39/50
 - 0s - loss: 0.0441 - val_loss: 0.0266
Epoch 40/50
 - 0s - loss: 0.0312 - val_loss: 0.0290
Epoch 41/50
 - 0s - loss: 0.0359 - val_loss: 0.0507
Epoch 42/50
 - 0s - loss: 0.0511 - val_loss: 0.0262
Epoch 43/50
 - 0s - loss: 0.0289 - val_loss: 0.0286
Epoch 44/50
 - 0s - loss: 0.0338 - val_loss: 0.0399
Epoch 45/50
 - 0s - loss: 0.0471 - val_loss: 0.0265
Epoch 46/50
 - 0s - loss: 0.0308 - val_loss: 0.0334
Epoch 47/50
 - 0s - loss: 0.0455 - val_loss: 0.0422
Epoch 48/50
 - 0s - loss: 0.0399 - val_loss: 0.0267
Epoch 49/50
 - 0s - loss: 0.0294 - val_loss: 0.0309
Epoch 50/50
 - 0s - loss: 0.0403 - val_loss: 0.0440
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse</span><span class="p">)</span>
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<pre>Test RMSE: 0.169
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">GasPricePrediction</span> <span class="o">=</span> <span class="n">inv_yhat</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 1, We changed the number of variables to 1 to have a single input for each feature(columns)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed1</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed1</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed1</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 71) (1040,) (196, 1, 71) (196,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model1</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model1</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model1</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model1</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">91</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 196 samples
Epoch 1/50
 - 10s - loss: 0.1549 - val_loss: 0.1853
Epoch 2/50
 - 0s - loss: 0.2941 - val_loss: 0.0717
Epoch 3/50
 - 0s - loss: 0.2052 - val_loss: 0.1407
Epoch 4/50
 - 0s - loss: 0.0490 - val_loss: 0.0750
Epoch 5/50
 - 0s - loss: 0.0679 - val_loss: 0.0406
Epoch 6/50
 - 0s - loss: 0.0553 - val_loss: 0.0375
Epoch 7/50
 - 0s - loss: 0.0613 - val_loss: 0.0850
Epoch 8/50
 - 0s - loss: 0.0997 - val_loss: 0.0350
Epoch 9/50
 - 0s - loss: 0.0393 - val_loss: 0.0395
Epoch 10/50
 - 0s - loss: 0.0406 - val_loss: 0.0444
Epoch 11/50
 - 0s - loss: 0.0387 - val_loss: 0.0349
Epoch 12/50
 - 0s - loss: 0.0366 - val_loss: 0.0335
Epoch 13/50
 - 0s - loss: 0.0354 - val_loss: 0.0486
Epoch 14/50
 - 0s - loss: 0.0404 - val_loss: 0.0365
Epoch 15/50
 - 0s - loss: 0.0362 - val_loss: 0.0325
Epoch 16/50
 - 0s - loss: 0.0362 - val_loss: 0.0362
Epoch 17/50
 - 0s - loss: 0.0398 - val_loss: 0.0506
Epoch 18/50
 - 0s - loss: 0.0342 - val_loss: 0.0338
Epoch 19/50
 - 0s - loss: 0.0400 - val_loss: 0.0407
Epoch 20/50
 - 0s - loss: 0.0452 - val_loss: 0.0525
Epoch 21/50
 - 0s - loss: 0.0345 - val_loss: 0.0404
Epoch 22/50
 - 0s - loss: 0.0546 - val_loss: 0.0423
Epoch 23/50
 - 0s - loss: 0.0678 - val_loss: 0.0418
Epoch 24/50
 - 0s - loss: 0.0520 - val_loss: 0.0473
Epoch 25/50
 - 0s - loss: 0.0706 - val_loss: 0.0356
Epoch 26/50
 - 0s - loss: 0.0468 - val_loss: 0.0698
Epoch 27/50
 - 0s - loss: 0.0763 - val_loss: 0.0325
Epoch 28/50
 - 0s - loss: 0.0408 - val_loss: 0.0301
Epoch 29/50
 - 0s - loss: 0.0383 - val_loss: 0.0716
Epoch 30/50
 - 0s - loss: 0.0662 - val_loss: 0.0340
Epoch 31/50
 - 0s - loss: 0.0461 - val_loss: 0.0295
Epoch 32/50
 - 0s - loss: 0.0428 - val_loss: 0.0788
Epoch 33/50
 - 0s - loss: 0.0868 - val_loss: 0.0294
Epoch 34/50
 - 0s - loss: 0.0316 - val_loss: 0.0369
Epoch 35/50
 - 0s - loss: 0.0311 - val_loss: 0.0337
Epoch 36/50
 - 0s - loss: 0.0298 - val_loss: 0.0300
Epoch 37/50
 - 0s - loss: 0.0293 - val_loss: 0.0300
Epoch 38/50
 - 0s - loss: 0.0287 - val_loss: 0.0320
Epoch 39/50
 - 0s - loss: 0.0282 - val_loss: 0.0311
Epoch 40/50
 - 0s - loss: 0.0280 - val_loss: 0.0318
Epoch 41/50
 - 0s - loss: 0.0276 - val_loss: 0.0306
Epoch 42/50
 - 0s - loss: 0.0273 - val_loss: 0.0289
Epoch 43/50
 - 0s - loss: 0.0271 - val_loss: 0.0280
Epoch 44/50
 - 0s - loss: 0.0265 - val_loss: 0.0274
Epoch 45/50
 - 0s - loss: 0.0262 - val_loss: 0.0293
Epoch 46/50
 - 0s - loss: 0.0271 - val_loss: 0.0346
Epoch 47/50
 - 0s - loss: 0.0294 - val_loss: 0.0404
Epoch 48/50
 - 0s - loss: 0.0279 - val_loss: 0.0310
Epoch 49/50
 - 0s - loss: 0.0266 - val_loss: 0.0281
Epoch 50/50
 - 0s - loss: 0.0265 - val_loss: 0.0253
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse1</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse1</span><span class="p">)</span>
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<pre>Test RMSE: 0.031
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 2, We changed number of EPOCH(estimated predication of current history) on the base model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed2</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed2</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">36</span><span class="p">,</span> <span class="mi">37</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">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 933) (1040,) (184, 1, 933) (184,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model2</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model2</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model2</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model2</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">91</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 184 samples
Epoch 1/100
 - 11s - loss: 0.1843 - val_loss: 0.2884
Epoch 2/100
 - 0s - loss: 0.3554 - val_loss: 0.1896
Epoch 3/100
 - 0s - loss: 0.0665 - val_loss: 0.2140
Epoch 4/100
 - 0s - loss: 0.0894 - val_loss: 0.1039
Epoch 5/100
 - 0s - loss: 0.1493 - val_loss: 0.0925
Epoch 6/100
 - 0s - loss: 0.1185 - val_loss: 0.0651
Epoch 7/100
 - 0s - loss: 0.0693 - val_loss: 0.0532
Epoch 8/100
 - 0s - loss: 0.0725 - val_loss: 0.1111
Epoch 9/100
 - 0s - loss: 0.1053 - val_loss: 0.0745
Epoch 10/100
 - 0s - loss: 0.0740 - val_loss: 0.1004
Epoch 11/100
 - 0s - loss: 0.0805 - val_loss: 0.0598
Epoch 12/100
 - 0s - loss: 0.0535 - val_loss: 0.0587
Epoch 13/100
 - 0s - loss: 0.0531 - val_loss: 0.0845
Epoch 14/100
 - 0s - loss: 0.0564 - val_loss: 0.0751
Epoch 15/100
 - 0s - loss: 0.0496 - val_loss: 0.0503
Epoch 16/100
 - 1s - loss: 0.0550 - val_loss: 0.1074
Epoch 17/100
 - 1s - loss: 0.0720 - val_loss: 0.0546
Epoch 18/100
 - 1s - loss: 0.0492 - val_loss: 0.0526
Epoch 19/100
 - 1s - loss: 0.0539 - val_loss: 0.1128
Epoch 20/100
 - 0s - loss: 0.0654 - val_loss: 0.0553
Epoch 21/100
 - 0s - loss: 0.0590 - val_loss: 0.0588
Epoch 22/100
 - 0s - loss: 0.0591 - val_loss: 0.1098
Epoch 23/100
 - 0s - loss: 0.0698 - val_loss: 0.0687
Epoch 24/100
 - 0s - loss: 0.0717 - val_loss: 0.0810
Epoch 25/100
 - 0s - loss: 0.0491 - val_loss: 0.0688
Epoch 26/100
 - 0s - loss: 0.0478 - val_loss: 0.0542
Epoch 27/100
 - 0s - loss: 0.0453 - val_loss: 0.0564
Epoch 28/100
 - 0s - loss: 0.0446 - val_loss: 0.0812
Epoch 29/100
 - 0s - loss: 0.0471 - val_loss: 0.0521
Epoch 30/100
 - 0s - loss: 0.0476 - val_loss: 0.0429
Epoch 31/100
 - 0s - loss: 0.0459 - val_loss: 0.0735
Epoch 32/100
 - 0s - loss: 0.0556 - val_loss: 0.1035
Epoch 33/100
 - 0s - loss: 0.0521 - val_loss: 0.0705
Epoch 34/100
 - 0s - loss: 0.0835 - val_loss: 0.0903
Epoch 35/100
 - 0s - loss: 0.0503 - val_loss: 0.0462
Epoch 36/100
 - 1s - loss: 0.0449 - val_loss: 0.0444
Epoch 37/100
 - 1s - loss: 0.0572 - val_loss: 0.1417
Epoch 38/100
 - 1s - loss: 0.0828 - val_loss: 0.0893
Epoch 39/100
 - 1s - loss: 0.0809 - val_loss: 0.0941
Epoch 40/100
 - 0s - loss: 0.0502 - val_loss: 0.0421
Epoch 41/100
 - 0s - loss: 0.0467 - val_loss: 0.0777
Epoch 42/100
 - 0s - loss: 0.0494 - val_loss: 0.0498
Epoch 43/100
 - 0s - loss: 0.0444 - val_loss: 0.0437
Epoch 44/100
 - 0s - loss: 0.0496 - val_loss: 0.1092
Epoch 45/100
 - 0s - loss: 0.0643 - val_loss: 0.0680
Epoch 46/100
 - 0s - loss: 0.0765 - val_loss: 0.0760
Epoch 47/100
 - 0s - loss: 0.0461 - val_loss: 0.0416
Epoch 48/100
 - 0s - loss: 0.0518 - val_loss: 0.0895
Epoch 49/100
 - 0s - loss: 0.0535 - val_loss: 0.0501
Epoch 50/100
 - 0s - loss: 0.0581 - val_loss: 0.0538
Epoch 51/100
 - 0s - loss: 0.0432 - val_loss: 0.0752
Epoch 52/100
 - 0s - loss: 0.0494 - val_loss: 0.0432
Epoch 53/100
 - 0s - loss: 0.0517 - val_loss: 0.0475
Epoch 54/100
 - 0s - loss: 0.0498 - val_loss: 0.0914
Epoch 55/100
 - 0s - loss: 0.0593 - val_loss: 0.0560
Epoch 56/100
 - 0s - loss: 0.0729 - val_loss: 0.0728
Epoch 57/100
 - 0s - loss: 0.0450 - val_loss: 0.0422
Epoch 58/100
 - 0s - loss: 0.0545 - val_loss: 0.0852
Epoch 59/100
 - 0s - loss: 0.0548 - val_loss: 0.0499
Epoch 60/100
 - 0s - loss: 0.0609 - val_loss: 0.0621
Epoch 61/100
 - 0s - loss: 0.0406 - val_loss: 0.0384
Epoch 62/100
 - 0s - loss: 0.0417 - val_loss: 0.0655
Epoch 63/100
 - 0s - loss: 0.0413 - val_loss: 0.0378
Epoch 64/100
 - 0s - loss: 0.0426 - val_loss: 0.0467
Epoch 65/100
 - 0s - loss: 0.0432 - val_loss: 0.0706
Epoch 66/100
 - 0s - loss: 0.0434 - val_loss: 0.0364
Epoch 67/100
 - 0s - loss: 0.0455 - val_loss: 0.0477
Epoch 68/100
 - 0s - loss: 0.0450 - val_loss: 0.0737
Epoch 69/100
 - 0s - loss: 0.0441 - val_loss: 0.0397
Epoch 70/100
 - 0s - loss: 0.0532 - val_loss: 0.0707
Epoch 71/100
 - 1s - loss: 0.0437 - val_loss: 0.0364
Epoch 72/100
 - 1s - loss: 0.0415 - val_loss: 0.0354
Epoch 73/100
 - 1s - loss: 0.0587 - val_loss: 0.0837
Epoch 74/100
 - 1s - loss: 0.0651 - val_loss: 0.0541
Epoch 75/100
 - 0s - loss: 0.0791 - val_loss: 0.0601
Epoch 76/100
 - 0s - loss: 0.0467 - val_loss: 0.0392
Epoch 77/100
 - 0s - loss: 0.0642 - val_loss: 0.0591
Epoch 78/100
 - 0s - loss: 0.0552 - val_loss: 0.0541
Epoch 79/100
 - 0s - loss: 0.0831 - val_loss: 0.0516
Epoch 80/100
 - 0s - loss: 0.0628 - val_loss: 0.0497
Epoch 81/100
 - 0s - loss: 0.0971 - val_loss: 0.0396
Epoch 82/100
 - 0s - loss: 0.0524 - val_loss: 0.0661
Epoch 83/100
 - 0s - loss: 0.0559 - val_loss: 0.0688
Epoch 84/100
 - 0s - loss: 0.0453 - val_loss: 0.0528
Epoch 85/100
 - 0s - loss: 0.0515 - val_loss: 0.0680
Epoch 86/100
 - 0s - loss: 0.0458 - val_loss: 0.0505
Epoch 87/100
 - 0s - loss: 0.0562 - val_loss: 0.0593
Epoch 88/100
 - 0s - loss: 0.0463 - val_loss: 0.0489
Epoch 89/100
 - 0s - loss: 0.0625 - val_loss: 0.0540
Epoch 90/100
 - 0s - loss: 0.0551 - val_loss: 0.0497
Epoch 91/100
 - 0s - loss: 0.0796 - val_loss: 0.0435
Epoch 92/100
 - 0s - loss: 0.0502 - val_loss: 0.0537
Epoch 93/100
 - 0s - loss: 0.0541 - val_loss: 0.0496
Epoch 94/100
 - 0s - loss: 0.0455 - val_loss: 0.0511
Epoch 95/100
 - 0s - loss: 0.0531 - val_loss: 0.0507
Epoch 96/100
 - 0s - loss: 0.0462 - val_loss: 0.0498
Epoch 97/100
 - 0s - loss: 0.0575 - val_loss: 0.0480
Epoch 98/100
 - 0s - loss: 0.0470 - val_loss: 0.0472
Epoch 99/100
 - 0s - loss: 0.0547 - val_loss: 0.0484
Epoch 100/100
 - 0s - loss: 0.0449 - val_loss: 0.0477
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse2</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse2</span><span class="p">)</span>
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<pre>Test RMSE: 0.057
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 3, #We changed number of EPOCH(estimated predication of current history) when  when we changed the number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed3</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed3</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed3</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed3</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 1865) (1040,) (183, 1, 1865) (183,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model3</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model3</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model3</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model3</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model3</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">91</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 183 samples
Epoch 1/100
 - 10s - loss: 0.4117 - val_loss: 0.0705
Epoch 2/100
 - 0s - loss: 0.1964 - val_loss: 0.0680
Epoch 3/100
 - 0s - loss: 0.0731 - val_loss: 0.0555
Epoch 4/100
 - 0s - loss: 0.0602 - val_loss: 0.0854
Epoch 5/100
 - 0s - loss: 0.0662 - val_loss: 0.0670
Epoch 6/100
 - 1s - loss: 0.0603 - val_loss: 0.0675
Epoch 7/100
 - 1s - loss: 0.0640 - val_loss: 0.0851
Epoch 8/100
 - 1s - loss: 0.0565 - val_loss: 0.0525
Epoch 9/100
 - 1s - loss: 0.0508 - val_loss: 0.0835
Epoch 10/100
 - 0s - loss: 0.0627 - val_loss: 0.0705
Epoch 11/100
 - 0s - loss: 0.0651 - val_loss: 0.0485
Epoch 12/100
 - 0s - loss: 0.0537 - val_loss: 0.1182
Epoch 13/100
 - 0s - loss: 0.0706 - val_loss: 0.1008
Epoch 14/100
 - 0s - loss: 0.0685 - val_loss: 0.0919
Epoch 15/100
 - 0s - loss: 0.0888 - val_loss: 0.1596
Epoch 16/100
 - 1s - loss: 0.1172 - val_loss: 0.1058
Epoch 17/100
 - 1s - loss: 0.1092 - val_loss: 0.1165
Epoch 18/100
 - 1s - loss: 0.0717 - val_loss: 0.0719
Epoch 19/100
 - 1s - loss: 0.1390 - val_loss: 0.0451
Epoch 20/100
 - 0s - loss: 0.0811 - val_loss: 0.0805
Epoch 21/100
 - 0s - loss: 0.0738 - val_loss: 0.0752
Epoch 22/100
 - 0s - loss: 0.0686 - val_loss: 0.0791
Epoch 23/100
 - 1s - loss: 0.0626 - val_loss: 0.0682
Epoch 24/100
 - 1s - loss: 0.0811 - val_loss: 0.0916
Epoch 25/100
 - 1s - loss: 0.0586 - val_loss: 0.0621
Epoch 26/100
 - 1s - loss: 0.0712 - val_loss: 0.0720
Epoch 27/100
 - 0s - loss: 0.0632 - val_loss: 0.0799
Epoch 28/100
 - 0s - loss: 0.0983 - val_loss: 0.0622
Epoch 29/100
 - 0s - loss: 0.0552 - val_loss: 0.0701
Epoch 30/100
 - 0s - loss: 0.0625 - val_loss: 0.0691
Epoch 31/100
 - 0s - loss: 0.0548 - val_loss: 0.0743
Epoch 32/100
 - 0s - loss: 0.0939 - val_loss: 0.0717
Epoch 33/100
 - 1s - loss: 0.0587 - val_loss: 0.0686
Epoch 34/100
 - 1s - loss: 0.0833 - val_loss: 0.0529
Epoch 35/100
 - 1s - loss: 0.0583 - val_loss: 0.0824
Epoch 36/100
 - 1s - loss: 0.0636 - val_loss: 0.0701
Epoch 37/100
 - 0s - loss: 0.0502 - val_loss: 0.0660
Epoch 38/100
 - 0s - loss: 0.0513 - val_loss: 0.0590
Epoch 39/100
 - 0s - loss: 0.0479 - val_loss: 0.0568
Epoch 40/100
 - 0s - loss: 0.0469 - val_loss: 0.0599
Epoch 41/100
 - 0s - loss: 0.0512 - val_loss: 0.0481
Epoch 42/100
 - 0s - loss: 0.0521 - val_loss: 0.0729
Epoch 43/100
 - 1s - loss: 0.0514 - val_loss: 0.0628
Epoch 44/100
 - 1s - loss: 0.0750 - val_loss: 0.0613
Epoch 45/100
 - 1s - loss: 0.0524 - val_loss: 0.0540
Epoch 46/100
 - 1s - loss: 0.0752 - val_loss: 0.0556
Epoch 47/100
 - 0s - loss: 0.0605 - val_loss: 0.0597
Epoch 48/100
 - 0s - loss: 0.0850 - val_loss: 0.0439
Epoch 49/100
 - 0s - loss: 0.0523 - val_loss: 0.0659
Epoch 50/100
 - 0s - loss: 0.0529 - val_loss: 0.0629
Epoch 51/100
 - 0s - loss: 0.0488 - val_loss: 0.0647
Epoch 52/100
 - 0s - loss: 0.0537 - val_loss: 0.0612
Epoch 53/100
 - 0s - loss: 0.0488 - val_loss: 0.0613
Epoch 54/100
 - 1s - loss: 0.0566 - val_loss: 0.0577
Epoch 55/100
 - 1s - loss: 0.0492 - val_loss: 0.0516
Epoch 56/100
 - 1s - loss: 0.0568 - val_loss: 0.0561
Epoch 57/100
 - 1s - loss: 0.0497 - val_loss: 0.0556
Epoch 58/100
 - 0s - loss: 0.0643 - val_loss: 0.0512
Epoch 59/100
 - 0s - loss: 0.0509 - val_loss: 0.0601
Epoch 60/100
 - 0s - loss: 0.0606 - val_loss: 0.0515
Epoch 61/100
 - 0s - loss: 0.0494 - val_loss: 0.0608
Epoch 62/100
 - 0s - loss: 0.0562 - val_loss: 0.0519
Epoch 63/100
 - 0s - loss: 0.0486 - val_loss: 0.0602
Epoch 64/100
 - 1s - loss: 0.0561 - val_loss: 0.0529
Epoch 65/100
 - 1s - loss: 0.0484 - val_loss: 0.0512
Epoch 66/100
 - 1s - loss: 0.0550 - val_loss: 0.0577
Epoch 67/100
 - 1s - loss: 0.0488 - val_loss: 0.0634
Epoch 68/100
 - 0s - loss: 0.0527 - val_loss: 0.0590
Epoch 69/100
 - 0s - loss: 0.0474 - val_loss: 0.0588
Epoch 70/100
 - 0s - loss: 0.0566 - val_loss: 0.0583
Epoch 71/100
 - 1s - loss: 0.0488 - val_loss: 0.0534
Epoch 72/100
 - 1s - loss: 0.0633 - val_loss: 0.0513
Epoch 73/100
 - 1s - loss: 0.0498 - val_loss: 0.0582
Epoch 74/100
 - 1s - loss: 0.0589 - val_loss: 0.0530
Epoch 75/100
 - 0s - loss: 0.0533 - val_loss: 0.0646
Epoch 76/100
 - 0s - loss: 0.0646 - val_loss: 0.0499
Epoch 77/100
 - 0s - loss: 0.0486 - val_loss: 0.0690
Epoch 78/100
 - 0s - loss: 0.0511 - val_loss: 0.0509
Epoch 79/100
 - 0s - loss: 0.0471 - val_loss: 0.0598
Epoch 80/100
 - 0s - loss: 0.0565 - val_loss: 0.0500
Epoch 81/100
 - 0s - loss: 0.0450 - val_loss: 0.0564
Epoch 82/100
 - 1s - loss: 0.0454 - val_loss: 0.0480
Epoch 83/100
 - 1s - loss: 0.0425 - val_loss: 0.0473
Epoch 84/100
 - 1s - loss: 0.0457 - val_loss: 0.0492
Epoch 85/100
 - 0s - loss: 0.0443 - val_loss: 0.0481
Epoch 86/100
 - 0s - loss: 0.0545 - val_loss: 0.0484
Epoch 87/100
 - 0s - loss: 0.0508 - val_loss: 0.0559
Epoch 88/100
 - 1s - loss: 0.0698 - val_loss: 0.0441
Epoch 89/100
 - 1s - loss: 0.0471 - val_loss: 0.0657
Epoch 90/100
 - 1s - loss: 0.0508 - val_loss: 0.0425
Epoch 91/100
 - 1s - loss: 0.0489 - val_loss: 0.0665
Epoch 92/100
 - 0s - loss: 0.0469 - val_loss: 0.0481
Epoch 93/100
 - 0s - loss: 0.0423 - val_loss: 0.0438
Epoch 94/100
 - 1s - loss: 0.0467 - val_loss: 0.0542
Epoch 95/100
 - 1s - loss: 0.0437 - val_loss: 0.0515
Epoch 96/100
 - 1s - loss: 0.0524 - val_loss: 0.0532
Epoch 97/100
 - 1s - loss: 0.0447 - val_loss: 0.0545
Epoch 98/100
 - 0s - loss: 0.0483 - val_loss: 0.0522
Epoch 99/100
 - 0s - loss: 0.0457 - val_loss: 0.0524
Epoch 100/100
 - 0s - loss: 0.0537 - val_loss: 0.0464
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model3</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse3</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse3</span><span class="p">)</span>
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<pre>Test RMSE: 0.055
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 4, #We changed the batch size on the LSTM algorithm on the base model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed4</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed4</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed4</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">36</span><span class="p">,</span> <span class="mi">37</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">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed4</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 24255) (1040,) (171, 1, 24255) (171,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model4</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model4</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model4</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model4</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model4</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 171 samples
Epoch 1/50
 - 20s - loss: 1.2255 - val_loss: 1.0191
Epoch 2/50
 - 7s - loss: 0.9149 - val_loss: 0.6581
Epoch 3/50
 - 9s - loss: 0.5644 - val_loss: 0.3188
Epoch 4/50
 - 9s - loss: 0.3744 - val_loss: 0.0931
Epoch 5/50
 - 9s - loss: 0.3073 - val_loss: 0.0720
Epoch 6/50
 - 8s - loss: 0.2831 - val_loss: 0.0891
Epoch 7/50
 - 9s - loss: 0.2769 - val_loss: 0.0939
Epoch 8/50
 - 9s - loss: 0.2756 - val_loss: 0.0950
Epoch 9/50
 - 9s - loss: 0.2752 - val_loss: 0.0950
Epoch 10/50
 - 8s - loss: 0.2751 - val_loss: 0.0949
Epoch 11/50
 - 9s - loss: 0.2753 - val_loss: 0.0952
Epoch 12/50
 - 9s - loss: 0.2751 - val_loss: 0.0951
Epoch 13/50
 - 8s - loss: 0.2751 - val_loss: 0.0949
Epoch 14/50
 - 8s - loss: 0.2751 - val_loss: 0.0948
Epoch 15/50
 - 8s - loss: 0.2752 - val_loss: 0.0952
Epoch 16/50
 - 9s - loss: 0.2750 - val_loss: 0.0951
Epoch 17/50
 - 9s - loss: 0.2750 - val_loss: 0.0949
Epoch 18/50
 - 8s - loss: 0.2750 - val_loss: 0.0948
Epoch 19/50
 - 9s - loss: 0.2752 - val_loss: 0.0952
Epoch 20/50
 - 9s - loss: 0.2750 - val_loss: 0.0951
Epoch 21/50
 - 9s - loss: 0.2750 - val_loss: 0.0949
Epoch 22/50
 - 9s - loss: 0.2750 - val_loss: 0.0948
Epoch 23/50
 - 9s - loss: 0.2751 - val_loss: 0.0952
Epoch 24/50
 - 8s - loss: 0.2750 - val_loss: 0.0951
Epoch 25/50
 - 8s - loss: 0.2750 - val_loss: 0.0949
Epoch 26/50
 - 8s - loss: 0.2750 - val_loss: 0.0948
Epoch 27/50
 - 9s - loss: 0.2751 - val_loss: 0.0952
Epoch 28/50
 - 8s - loss: 0.2750 - val_loss: 0.0951
Epoch 29/50
 - 8s - loss: 0.2750 - val_loss: 0.0950
Epoch 30/50
 - 8s - loss: 0.2750 - val_loss: 0.0949
Epoch 31/50
 - 9s - loss: 0.2751 - val_loss: 0.0952
Epoch 32/50
 - 9s - loss: 0.2750 - val_loss: 0.0952
Epoch 33/50
 - 8s - loss: 0.2750 - val_loss: 0.0950
Epoch 34/50
 - 8s - loss: 0.2750 - val_loss: 0.0949
Epoch 35/50
 - 9s - loss: 0.2751 - val_loss: 0.0953
Epoch 36/50
 - 9s - loss: 0.2750 - val_loss: 0.0952
Epoch 37/50
 - 8s - loss: 0.2750 - val_loss: 0.0951
Epoch 38/50
 - 8s - loss: 0.2749 - val_loss: 0.0950
Epoch 39/50
 - 8s - loss: 0.2751 - val_loss: 0.0953
Epoch 40/50
 - 9s - loss: 0.2750 - val_loss: 0.0953
Epoch 41/50
 - 9s - loss: 0.2749 - val_loss: 0.0951
Epoch 42/50
 - 8s - loss: 0.2749 - val_loss: 0.0950
Epoch 43/50
 - 8s - loss: 0.2749 - val_loss: 0.0949
Epoch 44/50
 - 9s - loss: 0.2751 - val_loss: 0.0953
Epoch 45/50
 - 9s - loss: 0.2750 - val_loss: 0.0952
Epoch 46/50
 - 8s - loss: 0.2749 - val_loss: 0.0950
Epoch 47/50
 - 8s - loss: 0.2749 - val_loss: 0.0950
Epoch 48/50
 - 8s - loss: 0.2751 - val_loss: 0.0953
Epoch 49/50
 - 9s - loss: 0.2749 - val_loss: 0.0952
Epoch 50/50
 - 9s - loss: 0.2749 - val_loss: 0.0951
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model4</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse4</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse4</span><span class="p">)</span>
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<pre>Test RMSE: 0.115
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 5, #We changed the batch size on the LSTM algorithm when we changed the number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="n">reframed5</span> <span class="o">=</span> <span class="n">series_to_supervised</span><span class="p">(</span><span class="n">data_frame_without_missing_vals</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="n">reframed5</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">reframed5</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">(</span><span class="n">feature_range</span><span class="o">=</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">scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">reframed5</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="n">data_frame_without_missing_vals</span> <span class="o">=</span> <span class="n">scaled</span>
<span class="n">n_train_days</span> <span class="o">=</span>  <span class="mi">20</span> <span class="o">*</span> <span class="mi">52</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[:</span><span class="n">n_train_days</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">data_frame_without_missing_vals</span><span class="p">[</span><span class="n">n_train_days</span><span class="p">:,</span> <span class="p">:]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span> <span class="o">=</span> <span class="n">train</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">train</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">test</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">test</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="n">train_X</span> <span class="o">=</span> <span class="n">train_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">train_X</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">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">test_X</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">train_y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(1040, 1, 48509) (1040,) (170, 1, 48509) (170,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="n">model5</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model5</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">train_X</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">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
<span class="n">model5</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">model5</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model5</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>Train on 1040 samples, validate on 170 samples
Epoch 1/50
 - 28s - loss: 1.8198 - val_loss: 1.6655
Epoch 2/50
 - 19s - loss: 1.5601 - val_loss: 1.3042
Epoch 3/50
 - 15s - loss: 1.1992 - val_loss: 0.9435
Epoch 4/50
 - 17s - loss: 0.8388 - val_loss: 0.5839
Epoch 5/50
 - 16s - loss: 0.5107 - val_loss: 0.2660
Epoch 6/50
 - 17s - loss: 0.3550 - val_loss: 0.0928
Epoch 7/50
 - 16s - loss: 0.3047 - val_loss: 0.0682
Epoch 8/50
 - 17s - loss: 0.2838 - val_loss: 0.0810
Epoch 9/50
 - 15s - loss: 0.2778 - val_loss: 0.0884
Epoch 10/50
 - 17s - loss: 0.2763 - val_loss: 0.0926
Epoch 11/50
 - 15s - loss: 0.2755 - val_loss: 0.0944
Epoch 12/50
 - 17s - loss: 0.2750 - val_loss: 0.0946
Epoch 13/50
 - 15s - loss: 0.2749 - val_loss: 0.0944
Epoch 14/50
 - 17s - loss: 0.2750 - val_loss: 0.0948
Epoch 15/50
 - 15s - loss: 0.2749 - val_loss: 0.0946
Epoch 16/50
 - 17s - loss: 0.2749 - val_loss: 0.0946
Epoch 17/50
 - 16s - loss: 0.2749 - val_loss: 0.0945
Epoch 18/50
 - 17s - loss: 0.2748 - val_loss: 0.0944
Epoch 19/50
 - 15s - loss: 0.2750 - val_loss: 0.0947
Epoch 20/50
 - 19s - loss: 0.2749 - val_loss: 0.0946
Epoch 21/50
 - 16s - loss: 0.2748 - val_loss: 0.0945
Epoch 22/50
 - 17s - loss: 0.2750 - val_loss: 0.0949
Epoch 23/50
 - 16s - loss: 0.2747 - val_loss: 0.0945
Epoch 24/50
 - 17s - loss: 0.2750 - val_loss: 0.0947
Epoch 25/50
 - 16s - loss: 0.2748 - val_loss: 0.0946
Epoch 26/50
 - 17s - loss: 0.2748 - val_loss: 0.0945
Epoch 27/50
 - 16s - loss: 0.2750 - val_loss: 0.0949
Epoch 28/50
 - 17s - loss: 0.2747 - val_loss: 0.0945
Epoch 29/50
 - 16s - loss: 0.2750 - val_loss: 0.0947
Epoch 30/50
 - 17s - loss: 0.2748 - val_loss: 0.0946
Epoch 31/50
 - 16s - loss: 0.2748 - val_loss: 0.0945
Epoch 32/50
 - 17s - loss: 0.2750 - val_loss: 0.0949
Epoch 33/50
 - 17s - loss: 0.2747 - val_loss: 0.0945
Epoch 34/50
 - 16s - loss: 0.2749 - val_loss: 0.0947
Epoch 35/50
 - 17s - loss: 0.2748 - val_loss: 0.0946
Epoch 36/50
 - 15s - loss: 0.2748 - val_loss: 0.0945
Epoch 37/50
 - 17s - loss: 0.2750 - val_loss: 0.0949
Epoch 38/50
 - 16s - loss: 0.2747 - val_loss: 0.0945
Epoch 39/50
 - 18s - loss: 0.2749 - val_loss: 0.0948
Epoch 40/50
 - 16s - loss: 0.2748 - val_loss: 0.0947
Epoch 41/50
 - 17s - loss: 0.2748 - val_loss: 0.0946
Epoch 42/50
 - 16s - loss: 0.2748 - val_loss: 0.0946
Epoch 43/50
 - 17s - loss: 0.2748 - val_loss: 0.0944
Epoch 44/50
 - 16s - loss: 0.2750 - val_loss: 0.0948
Epoch 45/50
 - 16s - loss: 0.2748 - val_loss: 0.0947
Epoch 46/50
 - 16s - loss: 0.2748 - val_loss: 0.0946
Epoch 47/50
 - 17s - loss: 0.2748 - val_loss: 0.0945
Epoch 48/50
 - 17s - loss: 0.2750 - val_loss: 0.0948
Epoch 49/50
 - 19s - loss: 0.2748 - val_loss: 0.0948
Epoch 50/50
 - 16s - loss: 0.2748 - val_loss: 0.0946
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model5</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="p">)</span>
<span class="n">test_X</span> <span class="o">=</span> <span class="n">test_X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">test_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">yhat</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">inv_yhat</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">)</span>
<span class="n">inv_yhat</span> <span class="o">=</span> <span class="n">inv_yhat</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:],</span> <span class="n">test_y</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">inv_y</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">inv_y</span><span class="p">)</span>
<span class="n">inv_y</span> <span class="o">=</span> <span class="n">inv_y</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="n">rmse5</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">inv_yhat</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">inv_yhat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">)</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse5</span><span class="p">)</span>
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<pre>Test RMSE: 0.115
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<div class=" highlight hl-ipython3"><pre><span></span><span class="ch">#!!!!!!!The following tweaks were not usefull for the scope of this project. They did not improve our accuracy!!!!!!!</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 6, #We used the LSTM with 40 neurons for predicting prices on the base model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="c1">#reframed6 = series_to_supervised(data_frame_without_missing_vals, 12, 1)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="c1">#reframed6.drop(reframed6.columns[[36, 37]], axis=1, inplace=True)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#scaler = MinMaxScaler(feature_range=(0, 1))</span>
<span class="c1">#scaled = scaler.fit_transform(reframed6)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="c1">#data_frame_without_missing_vals = scaled</span>
<span class="c1">#n_train_days =  20 * 52</span>
<span class="c1">#train = data_frame_without_missing_vals[:n_train_days, :]</span>
<span class="c1">#test = data_frame_without_missing_vals[n_train_days:, :]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="c1">#train_X, train_y = train[:, :-1], train[:, -1]</span>
<span class="c1">#test_X, test_y = test[:, :-1], test[:, -1]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="c1">#train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))</span>
<span class="c1">#test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="c1">#model6 = Sequential()</span>
<span class="c1">#model6.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2])))</span>
<span class="c1">#model6.add(Dense(1))</span>
<span class="c1">#model6.compile(loss=&#39;mae&#39;, optimizer=&#39;adam&#39;)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="c1">#history = model6.fit(train_X, train_y, epochs=50, batch_size=50, validation_data=(test_X, test_y), verbose=2,</span>
 <span class="c1">#                   shuffle=False)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="c1">#yhat = model6.predict(test_X)</span>
<span class="c1">#test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="c1">#inv_yhat = np.concatenate((test_X[:, 0:], yhat), axis=1)</span>
<span class="c1">#inv_yhat = scaler.inverse_transform(inv_yhat)</span>
<span class="c1">#inv_yhat = inv_yhat[:, -1]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="c1">#test_y = test_y.reshape((len(test_y), 1))</span>
<span class="c1">#inv_y = np.concatenate((test_X[:, 0:], test_y), axis=1)</span>
<span class="c1">#inv_y = scaler.inverse_transform(inv_y)</span>
<span class="c1">#inv_y = inv_y[:, -1]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="c1">#rmse6 = sqrt(mean_squared_error(inv_y, inv_yhat))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="c1">#pyplot.plot(inv_y, label=&#39;test&#39;)</span>
<span class="c1">#pyplot.plot(inv_yhat, label=&#39;predicted&#39;)</span>
<span class="c1">#pyplot.legend()</span>
<span class="c1">#pyplot.show()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print(&#39;Test RMSE: %.3f&#39; % rmse6)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Tweak number 7, #We used the LSTM with 55 neurons for predicting prices when we change the number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># frame as a supervised learning problem</span>
<span class="c1">#reframed7 = series_to_supervised(data_frame_without_missing_vals, 12, 1)</span>
<span class="c1"># Here we dropped the columns we did not want to predict (month and day)</span>
<span class="c1">#reframed7.drop(reframed6.columns[[36, 37]], axis=1, inplace=True)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#scaler = MinMaxScaler(feature_range=(0, 1))</span>
<span class="c1">#scaled = scaler.fit_transform(reframed7)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we split our dataset into train and test sets. 20 years for our training set and 3 years for our test set</span>
<span class="c1">#data_frame_without_missing_vals = scaled</span>
<span class="c1">#n_train_days =  20 * 52</span>
<span class="c1">#train = data_frame_without_missing_vals[:n_train_days, :]</span>
<span class="c1">#test = data_frame_without_missing_vals[n_train_days:, :]</span>
<span class="c1"># Here we split the data into input and outputs for training and testing our model</span>
<span class="c1">#train_X, train_y = train[:, :-1], train[:, -1]</span>
<span class="c1">#test_X, test_y = test[:, :-1], test[:, -1]</span>
<span class="c1"># Here we reshaped input to be in a 3-Dimensiontional format [samples, timesteps, features]</span>
<span class="c1">#train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))</span>
<span class="c1">#test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the LSTM neural network</span>
<span class="c1">#model7 = Sequential()</span>
<span class="c1">#model7.add(LSTM(40, input_shape=(train_X.shape[1], train_X.shape[2])))</span>
<span class="c1">#model7.add(Dense(1))</span>
<span class="c1">#model7.compile(loss=&#39;mae&#39;, optimizer=&#39;adam&#39;)</span>
<span class="c1"># fit the LSTM neural network</span>
<span class="c1">#history = model7.fit(train_X, train_y, epochs=50, batch_size=50, validation_data=(test_X, test_y), verbose=2,</span>
 <span class="c1">#                   shuffle=False)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we predict the gas prices</span>
<span class="c1">#yhat = model7.predict(test_X)</span>
<span class="c1">#test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))</span>
<span class="c1"># invert scaling for the gas prices</span>
<span class="c1">#inv_yhat = np.concatenate((test_X[:, 0:], yhat), axis=1)</span>
<span class="c1">#inv_yhat = scaler.inverse_transform(inv_yhat)</span>
<span class="c1">#inv_yhat = inv_yhat[:, -1]</span>
<span class="c1"># invert scaling to get the correct values</span>
<span class="c1">#test_y = test_y.reshape((len(test_y), 1))</span>
<span class="c1">#inv_y = np.concatenate((test_X[:, 0:], test_y), axis=1)</span>
<span class="c1">#inv_y = scaler.inverse_transform(inv_y)</span>
<span class="c1">#inv_y = inv_y[:, -1]</span>
<span class="c1"># calculate the RMSE to see how accurate our model is</span>
<span class="c1">#rmse7 = sqrt(mean_squared_error(inv_y, inv_yhat))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Here we plotted the results to see it visually</span>
<span class="c1">#pyplot.plot(inv_y, label=&#39;test&#39;)</span>
<span class="c1">#pyplot.plot(inv_yhat, label=&#39;predicted&#39;)</span>
<span class="c1">#pyplot.legend()</span>
<span class="c1">#pyplot.show()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print(&#39;Test RMSE: %.3f&#39; % rmse7)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of base model: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse</span><span class="p">)</span>
<span class="c1">#We changed number of variables to 1 to have a single input for each feature(columns)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of tweak #1: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse1</span><span class="p">)</span>
<span class="c1">#We changed number of EPOCH(estimated predication of current history) on the base model</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of tweak #2: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse2</span><span class="p">)</span>
<span class="c1">#We changed number of EPOCH(estimated predication of current history) when number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of tweak #3: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse3</span><span class="p">)</span>
<span class="c1">#We changed the batch size on the LSTM algorithm on the base model</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of tweak #4: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse4</span><span class="p">)</span>
<span class="c1">#We changed the batch size on the LSTM algorithm when we changed the number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test RMSE of tweak #5: </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">rmse5</span><span class="p">)</span>
<span class="c1">#We used the LSTM with 40 neurons for predicting prices on the base model</span>
<span class="c1">#print(&#39;Test RMSE of tweak #6: %.3f&#39; % rmse6)</span>
<span class="c1">#We used the LSTM with 55 neurons for predicting prices when we change the number </span>
<span class="c1">#of variables to 1 to have a single input for each feature(columns)</span>
<span class="c1">#print(&#39;Test RMSE of tweak #7: %.3f&#39; % rmse7)</span>
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<pre>Test RMSE of base model: 0.169
Test RMSE of tweak #1: 0.031
Test RMSE of tweak #2: 0.057
Test RMSE of tweak #3: 0.055
Test RMSE of tweak #4: 0.115
Test RMSE of tweak #5: 0.115
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#From this we can see that tweak #1 is the best</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">GasPricePrediction</span><span class="p">)</span>
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<pre>[2.54493441 2.516984   2.48471102 2.45846369 2.44686804 2.51108869
 2.49029985 2.54983494 2.55210394 2.51530298 2.51457132 2.55882727
 2.52511898 2.56424024 2.62561082 2.66599436 2.67501755 2.73021245
 2.75958567 2.78995842 2.80931839 2.83307248 2.84551868 2.8499501
 2.88255797 2.86463122 2.86325612 2.88079971 2.83921068 2.8294757
 2.82760708 2.80698843 2.75464312 2.71421775 2.65104234 2.60744404
 2.60359737 2.54209674 2.55915098 2.52361155 2.47592247 2.43850258
 2.42420727 2.39708582 2.34046943 2.28197259 2.2471775  2.23708077
 2.21453244 2.20602283 2.23063192 2.2754397  2.21920883 2.1871402
 2.2214093  2.13068368 2.08530136 2.06956591 2.01934584 2.07825565
 2.07606615 2.08082839 2.11603777 2.14219064 2.11961022 2.18093433
 2.22775478 2.29881074 2.30481093 2.33119617 2.36168941 2.39196239
 2.46261759 2.46325337 2.48239024 2.50312806 2.48984401 2.47987827
 2.48266256 2.45741967 2.43319525 2.41284711 2.39532529 2.38131573
 2.37097498 2.37851771 2.36016362 2.3589463  2.373741   2.35487086
 2.40045071 2.39012699 2.37961814 2.36143811 2.36254951 2.35529225
 2.33905514 2.34002958 2.30711088 2.36878028 2.36392349 2.3644453
 2.47219885 2.50828679 2.52480625 2.51274198 2.58232297 2.52558618
 2.5487966  2.58608142 2.58235534 2.60363713 2.60158696 2.56187659
 2.56182718 2.57864157 2.57558714 2.58519244 2.60434285 2.60835535
 2.58715137 2.57958676 2.57873527 2.56440323 2.59501886 2.55185898
 2.54244676 2.54682272 2.50957558 2.50863407 2.50247364 2.49455693
 2.48234301 2.50629001 2.49456602 2.50214141 2.5258319  2.57117124
 2.64455696 2.67763449 2.66971286 2.65118849 2.65622152 2.66267491
 2.63572724 2.60884659 2.61364981 2.63063571 2.63808427 2.64656454
 2.58782964 2.6332625  2.59777834 2.5752428  2.66453179 2.69131911
 2.7057036  2.70478982 2.80102837 2.75654109 2.78657517 2.81983746
 2.81299184 2.82745431 2.83746113 2.79756145 2.81626169 2.83890666
 2.83909956 2.85000103 2.87881656 2.89606105 2.94509996 2.92414075
 2.94575855 3.00316869 3.01619132 3.01190736 3.01923836 3.00126372
 2.99102974 2.99775572 2.98887962 2.96424374 2.93919442 2.95009002
 2.93335438 2.92483739 2.9451418  2.90965026 2.93572182 2.93037151
 2.91606105 2.89494869 2.91247449 2.90833365 2.87899545]
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