Linear Regression in Python.html

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

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

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

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

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

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

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<style type="text/css">
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<h1 id="Linear-Regression-on-MTCARS-Dataset">Linear Regression on MTCARS Dataset<a class="anchor-link" href="#Linear-Regression-on-MTCARS-Dataset">&#182;</a></h1><p>The dataset, <em>mtcars</em> is an in-built data set about cars which contains 32 observations and 11 variables.</p>
<p><a href="https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html">https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html</a></p>
<p>The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).</p>
<table>
<thead><tr>
<th>Sl No</th>
<th>Variable</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>mpg</td>
<td>Miles/(US) gallon</td>
</tr>
<tr>
<td>2</td>
<td>cyl</td>
<td>Number of cylinders</td>
</tr>
<tr>
<td>3</td>
<td>disp</td>
<td>Displacement (cu.in.)</td>
</tr>
<tr>
<td>4</td>
<td>hp</td>
<td>Gross horsepower</td>
</tr>
<tr>
<td>5</td>
<td>drat</td>
<td>Rear axle ratio</td>
</tr>
<tr>
<td>6</td>
<td>wt</td>
<td>Weight (1000 lbs)</td>
</tr>
<tr>
<td>7</td>
<td>qsec</td>
<td>1/4 mile time</td>
</tr>
<tr>
<td>8</td>
<td>vs</td>
<td>Engine (0 = V-shaped, 1 = straight)</td>
</tr>
<tr>
<td>9</td>
<td>am</td>
<td>Transmission (0 = automatic, 1 = manual)</td>
</tr>
<tr>
<td>10</td>
<td>gear</td>
<td>Number of forward gears</td>
</tr>
<tr>
<td>11</td>
<td>carb</td>
<td>Number of carburetors</td>
</tr>
</tbody>
</table>
<h4 id="a.-Construct-a-scatter-plot-to-show-the-relationship-between-the-variables-cars.mpg-and-cars.wt">a. Construct a scatter plot to show the relationship between the variables cars.mpg and cars.wt<a class="anchor-link" href="#a.-Construct-a-scatter-plot-to-show-the-relationship-between-the-variables-cars.mpg-and-cars.wt">&#182;</a></h4><h4 id="b.-Measure-the-relative-strength-of-a-linear-relationship-between-the-cars.mpg-and-cars.wt-by-calculating-the-sample-correlation-coefficient">b. Measure the relative strength of a linear relationship between the cars.mpg and cars.wt by calculating the sample correlation coefficient<a class="anchor-link" href="#b.-Measure-the-relative-strength-of-a-linear-relationship-between-the-cars.mpg-and-cars.wt-by-calculating-the-sample-correlation-coefficient">&#182;</a></h4><h4 id="c.-Give-your-inference-on-the-relationship-between-these-variables">c. Give your inference on the relationship between these variables<a class="anchor-link" href="#c.-Give-your-inference-on-the-relationship-between-these-variables">&#182;</a></h4><h4 id="d.-Give-the-simple-linear-regression-equation-between-cars.mpg-and-cars.wt">d. Give the simple linear regression equation between cars.mpg and cars.wt<a class="anchor-link" href="#d.-Give-the-simple-linear-regression-equation-between-cars.mpg-and-cars.wt">&#182;</a></h4><h4 id="e.-Predict-the-mpg-of-the-car-for-wt-=-3000-lbs">e. Predict the mpg of the car for wt = 3000 lbs<a class="anchor-link" href="#e.-Predict-the-mpg-of-the-car-for-wt-=-3000-lbs">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Importing MTCARS Dataset from statsmodel.api</span>
<span class="n">mtcars</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_rdataset</span><span class="p">(</span><span class="s2">&quot;mtcars&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">data</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">mtcars</span><span class="p">)</span>
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<pre>pandas.core.frame.DataFrame</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Checking the First 05 Rows of the Dataset</span>
<span class="n">mtcars</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

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        text-align: right;
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>mpg</th>
      <th>cyl</th>
      <th>disp</th>
      <th>hp</th>
      <th>drat</th>
      <th>wt</th>
      <th>qsec</th>
      <th>vs</th>
      <th>am</th>
      <th>gear</th>
      <th>carb</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Mazda RX4</th>
      <td>21.0</td>
      <td>6</td>
      <td>160.0</td>
      <td>110</td>
      <td>3.90</td>
      <td>2.620</td>
      <td>16.46</td>
      <td>0</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>Mazda RX4 Wag</th>
      <td>21.0</td>
      <td>6</td>
      <td>160.0</td>
      <td>110</td>
      <td>3.90</td>
      <td>2.875</td>
      <td>17.02</td>
      <td>0</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>Datsun 710</th>
      <td>22.8</td>
      <td>4</td>
      <td>108.0</td>
      <td>93</td>
      <td>3.85</td>
      <td>2.320</td>
      <td>18.61</td>
      <td>1</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
    </tr>
    <tr>
      <th>Hornet 4 Drive</th>
      <td>21.4</td>
      <td>6</td>
      <td>258.0</td>
      <td>110</td>
      <td>3.08</td>
      <td>3.215</td>
      <td>19.44</td>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>1</td>
    </tr>
    <tr>
      <th>Hornet Sportabout</th>
      <td>18.7</td>
      <td>8</td>
      <td>360.0</td>
      <td>175</td>
      <td>3.15</td>
      <td>3.440</td>
      <td>17.02</td>
      <td>0</td>
      <td>0</td>
      <td>3</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print the mtcars info</span>
<span class="n">mtcars</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
Index: 32 entries, Mazda RX4 to Volvo 142E
Data columns (total 11 columns):
mpg     32 non-null float64
cyl     32 non-null int64
disp    32 non-null float64
hp      32 non-null int64
drat    32 non-null float64
wt      32 non-null float64
qsec    32 non-null float64
vs      32 non-null int64
am      32 non-null int64
gear    32 non-null int64
carb    32 non-null int64
dtypes: float64(5), int64(6)
memory usage: 3.0+ KB
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mtcars</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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        vertical-align: middle;
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        vertical-align: top;
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>mpg</th>
      <th>cyl</th>
      <th>disp</th>
      <th>hp</th>
      <th>drat</th>
      <th>wt</th>
      <th>qsec</th>
      <th>vs</th>
      <th>am</th>
      <th>gear</th>
      <th>carb</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.000000</td>
      <td>32.0000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>20.090625</td>
      <td>6.187500</td>
      <td>230.721875</td>
      <td>146.687500</td>
      <td>3.596563</td>
      <td>3.217250</td>
      <td>17.848750</td>
      <td>0.437500</td>
      <td>0.406250</td>
      <td>3.687500</td>
      <td>2.8125</td>
    </tr>
    <tr>
      <th>std</th>
      <td>6.026948</td>
      <td>1.785922</td>
      <td>123.938694</td>
      <td>68.562868</td>
      <td>0.534679</td>
      <td>0.978457</td>
      <td>1.786943</td>
      <td>0.504016</td>
      <td>0.498991</td>
      <td>0.737804</td>
      <td>1.6152</td>
    </tr>
    <tr>
      <th>min</th>
      <td>10.400000</td>
      <td>4.000000</td>
      <td>71.100000</td>
      <td>52.000000</td>
      <td>2.760000</td>
      <td>1.513000</td>
      <td>14.500000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>3.000000</td>
      <td>1.0000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>15.425000</td>
      <td>4.000000</td>
      <td>120.825000</td>
      <td>96.500000</td>
      <td>3.080000</td>
      <td>2.581250</td>
      <td>16.892500</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>3.000000</td>
      <td>2.0000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>19.200000</td>
      <td>6.000000</td>
      <td>196.300000</td>
      <td>123.000000</td>
      <td>3.695000</td>
      <td>3.325000</td>
      <td>17.710000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>4.000000</td>
      <td>2.0000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>22.800000</td>
      <td>8.000000</td>
      <td>326.000000</td>
      <td>180.000000</td>
      <td>3.920000</td>
      <td>3.610000</td>
      <td>18.900000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>4.000000</td>
      <td>4.0000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>33.900000</td>
      <td>8.000000</td>
      <td>472.000000</td>
      <td>335.000000</td>
      <td>4.930000</td>
      <td>5.424000</td>
      <td>22.900000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>5.000000</td>
      <td>8.0000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Construct a scatter plot to show the relationship between the variables cars.mpg and cars.wt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.collections.PathCollection at 0x1c22b640f0&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Printing the Correlation Coefficient</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="n">text_cc</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n\n\n\n\n</span><span class="s1">correlation coefficient is </span><span class="si">%1.4f</span><span class="s1">&#39;</span><span class="o">%</span><span class="k">corr</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Scatter plot MPG Vs Weight&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">text_cc</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Printing the Correlation Coefficient</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mtcars</span><span class="p">[</span><span class="s2">&quot;wt&quot;</span><span class="p">],</span> <span class="n">mtcars</span><span class="p">[</span><span class="s2">&quot;mpg&quot;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;coral&quot;</span><span class="p">,</span><span class="n">s</span> <span class="o">=</span> <span class="mi">65</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Scatterplot - MTCARS wt vs mpg&quot;</span><span class="p">)</span>
<span class="n">corrtext</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n\n\n\n</span><span class="s2">Correlation Coefficient is </span><span class="si">%1.4f</span><span class="s2">&quot;</span> <span class="o">%</span><span class="k">corr</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">corrtext</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Creating the Scatterplot and Correlation Coefficient</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;hotpink&quot;</span><span class="p">)</span>
<span class="n">cortext</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n\n\n\n</span><span class="s2">Correlation Coefficient is </span><span class="si">%1.4f</span><span class="s2">&quot;</span><span class="o">%</span><span class="k">corr</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Scatterplot - MTCARS mpg vs wt&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">cortext</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Creating the Scatterplot and Correlation Coefficient</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lmplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;wt&quot;</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="s2">&quot;mpg&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">mtcars</span><span class="p">)</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</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">cortext</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n\n\n\n</span><span class="s2">Correlation Coefficient is </span><span class="si">%1.4f</span><span class="s2">&quot;</span><span class="o">%</span><span class="k">corr</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Scatterplot - MTCARS mpg vs wt&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">cortext</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<h1 id="Finding-the-Slope-and-Intercept">Finding the Slope and Intercept<a class="anchor-link" href="#Finding-the-Slope-and-Intercept">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ybar</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">xbar</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="o">.</span><span class="n">mean</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">xbar</span><span class="p">,</span> <span class="n">ybar</span><span class="p">)</span>
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<pre>3.2172499999999995 20.090624999999996
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">yi_ybar</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="o">-</span><span class="n">ybar</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xi_xbar</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="o">-</span><span class="n">xbar</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">product</span> <span class="o">=</span> <span class="p">(</span><span class="n">xi_xbar</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">yi_ybar</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># powering the xi_xbar</span>
<span class="n">xi_xbarsq</span> <span class="o">=</span> <span class="n">xi_xbar</span><span class="o">**</span><span class="mi">2</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">sum</span><span class="p">(</span><span class="n">product</span><span class="p">)</span>
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<pre>-158.617225</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">sum</span><span class="p">(</span><span class="n">xi_xbarsq</span><span class="p">)</span>
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<pre>29.678748</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">slope_m</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">product</span><span class="p">)</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">xi_xbarsq</span><span class="p">)</span>
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<pre>-5.344471572722677</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Lets Find the Linear regression of the same</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">stats</span>
<span class="n">stats</span><span class="o">.</span><span class="n">linregress</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">)</span>
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<pre>LinregressResult(slope=-5.344471572722678, intercept=37.28512616734204, rvalue=-0.8676593765172279, pvalue=1.2939587013504974e-10, stderr=0.559101045099323)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Intercept = ybar - m*xbar</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">ybar</span> <span class="o">-</span> <span class="n">slope_m</span><span class="o">*</span><span class="n">xbar</span>
<span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
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<pre>37.28512616734203
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LinearRegression</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
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<pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ypred</span> <span class="o">=</span> <span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Sum of Squared Error</span>
<span class="nb">sum</span><span class="p">((</span><span class="n">y</span><span class="o">-</span><span class="n">ypred</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
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<pre>278.32193754334327</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mtcars</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;mtcars.csv&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span>
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<pre>&#39;/Users/mukul.singh&#39;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">ypred</span><span class="p">)</span>
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<pre>0.7528327936582646</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Multiple Linear Regression </span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;mpg&quot;</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">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xtrain</span><span class="p">,</span><span class="n">xtest</span><span class="p">,</span><span class="n">ytrain</span><span class="p">,</span><span class="n">ytest</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.30</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">xtrain</span><span class="p">,</span><span class="n">ytrain</span><span class="p">)</span>
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<pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)</pre>
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<pre>12.521343823205275</pre>
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<pre>array([ 0.05642263,  0.00913237, -0.0191569 ,  1.26952627, -2.29502804,
        0.22435961, -0.85832351, -0.4642907 ,  2.57751704, -0.87829683])</pre>
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<pre>0.6986516223548618</pre>
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<pre>[ 0.05642263  0.00913237 -0.0191569   1.26952627 -2.29502804  0.22435961
 -0.85832351 -0.4642907   2.57751704 -0.87829683]
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<pre>12.521343823205275
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print SSE</span>
<span class="n">sse</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">((</span><span class="n">ytest</span><span class="o">-</span><span class="n">pred</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Find SST and R Squared</span>
<span class="c1"># SST is sum of (y - ybar)^2</span>
<span class="n">sst</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">((</span><span class="n">ytest</span><span class="o">-</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">rsq</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="n">sse</span><span class="o">/</span><span class="n">sst</span><span class="p">)</span>
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<pre>0.7112071329301184
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<pre>0.6986516223548618</pre>
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<pre>array([-0.11144048,  0.01333524, -0.02148212,  0.78711097, -3.71530393,
        0.82104075,  0.31776281,  2.52022689,  0.65541302, -0.19941925])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># SSE and R2</span>
<span class="n">sse</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">((</span><span class="n">y</span><span class="o">-</span><span class="n">ypred</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">sst</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">((</span><span class="n">y</span><span class="o">-</span><span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">r2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="n">sse</span><span class="o">/</span><span class="n">sst</span><span class="p">)</span>
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<pre>0.8690157644777647</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ypred</span><span class="p">)</span>
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<pre>0.8690157644777647</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Step1 - Import Data from Library</span>
<span class="c1"># Define X and Y</span>
<span class="c1"># Split it if required</span>
<span class="c1"># apply linear regression.fit and .predict</span>
<span class="c1"># lm.coef_ and lm.intercept_</span>
<span class="c1"># Find r2 by sklearn.metrics import r2_score</span>
<span class="c1"># r2_score(y, ypred)</span>

<span class="c1"># Another way to find r2 score is to calculate by formula r2 = 1-(SSE/SST)</span>
<span class="c1"># sse = sum((y-ypred)**2) and sst = sum((y-y.mean())**2)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
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<pre>0.8690157644777647</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mtcars</span><span class="p">[</span><span class="s2">&quot;wt&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
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<pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">residual</span> <span class="o">=</span> <span class="n">y</span><span class="o">-</span><span class="n">pred</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span><span class="n">residual</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c2ab04a90&gt;</pre>
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AyKxpD1byJi2v8BUimqsi/i1LNLkf3hNlgb7DhKRO5LsFqRs/MLnF66HEatFlKlEr0WPop+zz1tTwyAe28Y2azkUFxcjF27dmH58uVYunQpvvjiC0gkEmzfvh3ffPONs2P0KDKVCj0fnIMbX3sFPt0jAZsNl3ftRvITT6Es9YzY4RHRVRiLi5H64ku49PEOwGaDz/XdMXjN6+jSyJR1d94epVndSjqdDmFhYfbroKAg6HQ6KJVKyOUesbGry/n37YPBb/4LuZ/vQu7OL2DIy0fKshfQZeIEXH//faItqSfnKtEkwvTRdvy2cUuH2VnWk5T88mvN2oWKCgBAl0kT0OOBptcuuMtU+4aa1XKIjIzEmjVrkJOTg5ycHKxduxbdu3fHyZMnIZVyTNtZpAoFut97Nwa/+S/4XVnN/fu+b5C06AmUJiaJHB21tdodhG0Vereb1khNs5lMyNz8Hs6uWg1LRQXk/n7ov+w59Jr3SJssahNDsz72v/LKK1i5ciWmT58OmUyGcePGYeXKldi7dy+ee+45Z8coCnc6t9e3x/W48fVXkffl17i04z8wFRXhzIqVCBt3G3o+/AAU/v6ixEVtq3YHYYlUYt9B2F2mNdIfq8rJRdobb6Iq+yIAoFP0QPRd8jhUoSFt8vPFehY1KzkEBwfjzTffhE6ng1wuh9+VAZW//vWvTg1OLK46A6IlJDIZrpseh+DhNyMj/l2Up55B4eHvoUtMQtS8RxA6yjWrzcl57DsIV//vOEh3mdZIjgRBwO/f7Ef2+x/AZjLZ1y50mzkDEpmsTe4h5rOoWX1CmZmZuOuuuzBq1CgMHz4c9913n1O32RabO5/b6x0RgeiVKxD16COQennBXFaGtNffwLnVr8NUWip2eNQKHfUc7I7IVFKKsy+vQubGzbCZTFCFhWLQKy8j8u5ZbZYYAHGfRc1KDkuXLsWsWbOQnJyMpKQk3Hnnnfj73//u7NhE4+7n9kqkUnSdOAHD4tchcNhQAEDx8RNIeuwJaA8dhkgb7VIr1U5rFExmt5vWSP9TfPxnJC1eglJNzbhfWOxYDFn3JjoN6N/m9xLzWdSs5FBdXY177rkHCoUCSqUSs2fPRlFRkbNjE017+QSnCgvDDf/4O/o8sQhyfz9Y9HqcfyseZ1ashEHrHomMmq92WqPU38/tpjUSYKmsxPm33sa51f+qGXT280Pfp59E3ycfd9oxwGI+i5o15hAVFYXExEQMG1ZTSdPT09GtWzenBiam9nRur0QiQfi42xA4dAgyN29B8dHj0CUlI2nREvS4/z50mXgnJJxR1m4ExwyDEkK73466oylLTcX5dW/DqC0EAAQOGYzeixdCFdI2g85/RMxnUbOSQ15eHmbPno1+/fpBLpfj7NmzCA0NxdSpUwEAX331lVODdLXgmGHAvLluM1upOZSBgej/7NMoPn4CFzZthrlUh8zNW1B05Ch6P7YA3tdFiB1ik9xpdhhRLZvJhEs7/oPLu78EBAFSpRLXz5mNrpMmuORDl5jPomYlh6effhparRZlZWUAgLi4mqxV98jPjsZdF6ZcTcjI4QgYNBBZ738I7cFDKD9zFkmPP4nu996N66b9X5sOlrUVd5wdRlR+9hwy3t6A6ss1k298e0Wh75LH4RPp2l4TsZ5FzUoOBw8exPbt2+1TWIGa7ozjx487LTC6dnI/P/RZvBChY0bhwjsbYdQW4uJHH6Po6HH0WbQAvj17iB1iPXVnZADutW0xeR5rdTUufrwD+f/dBwhCzTTyu6Yj8i8zIVUoxA7PZZqVHA4cOICffvoJQUFBzo6H2lDQ0CEYun4tLm7bgfy9+1B54QJOPvWs21V0+/z+Otxpdhh5Dl3ySWRs2GjfRdW3VxT6LFrodh+oXKFZyaFHjx7o1KmTs2MhJ5B5eyNK/TBCR9+KjPh3UH05D7mffY7i4z+jz6KF8O/XV+wQ3XrbYvIMFn0lsrZ+CO13BwEAEoUC3e/5C66bHueWXbGu0KzkMHv2bNx3330YPnx4vY32HnvsMacFRm2r0w0DMGTdGlz6z2e4nLAH1Tm5OPXcMkRMnYzuf7u33oPZ1drT7DDqeEp++RUX3t0MU0kJAMC/fz/0XrQAPh14RmZzNCs5bN68GX5+fqi4stMgtU9SpRI97r8PoaNuRcbbG1CZlY28L79GyS+/otfC+Qi8cZAocbXH2WHU/pnLy5H57/dR9ONPAGq6Mq+f/beamUge2lqoq1nJobq6Gp988omzYyEX8esVhRvfeA2Xd+1Gzqc7Yfi9AKkv/BOdx9+BHg/cD7mvcxb0NKW9zg6j9kcQBBQdOYasf2+BuawcABBw4yD0XvgovLp0ETk699Gs5NCzZ0+cO3cO/fu3/fJwEodULkfkX2YiZMRwZMS/g4q0dBTs/w6lmkT0mj8PwTffJHaIRG3OVFKKCxs3oeTErwAAmY8Pejw4B53//CeHg3g8XbOSQ35+PmbOnInrrrsOSqXS/npHW/zmiXy6R2LQqyuRv3cfLm7bAVNxCc6ufBWhY0cjau5DUAQEiB0iUasJggDtwcPIev8DWCsrAQBBN8eg1/x5Ta5y9uTFmc1KDk8++aSz4yARSWQyREydguCbb0LGho0oO3UaRT8egS75FKIeeQihY0bzU1UzePKDxJ0ZtFpc2LARuuSTAAC5vz+iHnkYoWObrteevjizWcnhlltucXYc5Aa8unTBwJdehPa7g8ja+iEs5eVIX7MOhT8eQa/5aqfvI9OeefqDxB3ZLBbkf70Xlz75FDaDAQAQOnoUej7yMJSBV28Re/riTO7IRvVIJBJ0/vMdGPr2Wwi+5WYAQOmvvyHpsSfw+/4D3A78D7jzGSCeqPxcGk4+9Syyt35YkxikUvh0746w229rVmIA3H/rfmdjcqBGqUKC0X/Zc+j79JNQBHSCtaoKFzZsROo/VsDw++9ih+d2PP1B4i7MFRXIeGcjTj+3zH5sp8zXF96RkbBZrS06k7u9bN3vLKIkh6+++gqTJk3C+PHjsX37djFCoGaQSCQIGzMKQ+PfQujYMQCAslOnkbT4SeR9+TUEq1XkCN2Hpz9IxCYIArSHDiNp4WIUfHsAACD18oIyLAxeXbpAWtuqa0FrrvbwJavB4JGHL7k8ORQUFGDt2rXYsWMHdu/ejU8//RQZGRmuDoNaQNGpE/o99QQGLF8KZUgwbEYjst7bitNLl6PqUo7Y4bkFT3+QiKnq0iWkLHsB59+Kh7msHFIvL/Sc+xDk/v6Q+/vXe29LWnO1hy+pgoM88vClZg1It6Vjx45hxIgRCAwMBADceeed+Oabb7gVRzsQfPNN6HTDAGR/uA0F3x5ARVo6kpc8jci7Z+G6GdMglbu8OrWYs2YUcZW361kNBuR8uhN5e76yt2JDR49Cj4fmQBUSgpITv7R6zy5PXpzp8paDVqtFWFiY/To8PBwFBQXN/v6DB2s2xrJYLFCr1di7dy8AwGAwQK1WY//+/QAAvV4PtVqNQ4cOAQB0Oh3UajV+/PFHAEBRURHUajWOHTsGAPj999+hVqtx4sQJAEBubi7UajU0Gg0AIDs7G2q1GidP1kyHy8jIgFqtRmpqKgAgLS0Na9asQVpaGgAgNTUVarXa3io6efIk1Go1srOzAQAajQZqtRq5ubkAgBMnTkCtVuP3K/35x44dg1qtth/H+uOPP0KtVkOn0wEADh06BLVaDb1eDwDYv38/1Go1DFdmZezduxdqtRoWiwVATVeeWq22l2NCQgIWLFhgv965cycWL15sv/7kk0+wZMkS+/W2bdvwzDPPQO7ri94LHkXu6JGoVCggWCy4tP0THH5wLv711NP292/cuBErVqywX8fHx2PVqlX263Xr1uG1116zX69ZswZr1qyxX7/22mtYt26d/XrVqlWIj4+3X69YsQIbN260X7/wwgvYsmWL/Xrp0qX44IMP7NfPPPMMNr26GpmbtsBYUop3L2Zi75lUex/04sWLsXPnTvv7FyxYgISEBPu1Wq22r+v5o7r3W3ERoleuQP+1/8J6bT6Sy2r+rVpa9woLC1tc99RqdYeve7U+2LoV37wdj8SFj+Pyrt0QrFZUKpW44Z8voN8zT2Lrzp1YsWKFvTX3+fk0fJh+1t6a+9pqcnnd27Ztm/16yZIl9XacaIu61/C5l5hYM66i0+mwdOlSXAuXf9Sz2Wz15hYLgtCiOfS5ubnQaDSwWq3Q6/XIysqCRqOByWSCXq9HZmYmNBoNqqurodfrceHCBQQEBECv10Ov1yMjIwO+vr4oKyuDXq/H+fPnoVKpUFJSYr+Wy+UoLCyEXq9Heno6gJr/A+v1eqSlpcFiseDy5cvQ6/U4d+4cDAYDcnJqulfOnj0LvV6P7Oxs6PV6nDlzBmVlZbhw4QL0ej1SU1NRXFyM9PR06PV6pKSkoKCgAOfPn4der8fp06dx+fJl+/WpU6cQEBCAjIwM6PV6nDx5En5+fvafl5ycDG9vb2RmZkKv1yMpKQlKpRJZWVnQ6/VITEyETCazx1P7wLl48SLKy8vt15cuXUJZWZn9Oicnp951bm4udDqd/TrDZEReSABm94iC9cSv8CqvwIjyCvz82huQx45Gfn4+SkpK7O+vLT+g5uFUUFAAk8lk//vaDwi111qtFkql0n5dWFiI6upq+3VRURFsNpv9uri4GAqFwn5dWlqKy5cv2691Oh0Ks7Jh7BQEiVQCq9UKiyDAaLEg7aOPUVZWhkuXLtnfX15ejosXL9qva/9Nm6p7GcePw/+/36CyuBjlebk4f/TYNdW92vu1pO7p9XqPqHu2Uh2CD/+IPmXlMAGATIbM8FCckAgIsVkBjcZe97IgwPan22Dd8QksVdUwKeSQxY5GWXISTFqtS+te7XMLAMrKypCTk1PvurV1r+Fzr/b30ev1qKqqwrWQCC6em5iQkIDffvvN/ilyw4YNEAThqt1KRqMRKSkpiI6OhqrBrBB3odFoPPLs34q0dJx/ewOqc2o+iXpFRKDPogXodMMAh/eKWUa/PTIfMj8/hw8nVr0eN/373Vb97LrrHOruLHstfdSeWo+aIlituJywBzmf7oTNZAIABA4dgqh5c+HdtavI0bmfunXoWp+dLu9WuvXWW3H8+HGUlJSguroa+/fvx9ixY10dBrUh/359MWTtG+j2l5mQyGQw5OXh9LIXkLl5CyxV1WKHZ+fMGUVc5+A8xqJipPxjBS5u2w6byQRlSDAUM6fjhheXMzE4kcuTQ+fOnbFkyRLcf//9mDZtGqZMmYIbb7zR1WFQG5MqFLj+b/di8Juvw7dXL0AQkP/ffUhe/ARKk5LFDg+Ac2cUcZ2DcxT/fALJTzyJ8pSa8ZUukyZgaPx6yG4YwC1dnEyU6SVTp07F1KlTxbg1OZlvjx4Y/K9XcXnPV8j55FMYC4tw5p8vI/z2cej58AOixubMGUU8za5tWY1GZG/9EL/v+xZAzX5IfRYvtK/aJ+dz/7mH1O5IZDJ0mzENIcNvQUb8Oyg/cxbaQ4dRmpQE3HE7IGJ/urOmJvI0u7ZTefES0tesRdXFSwCAgEHR6LNkMff2cjFun0FO431dBKJXvYSoRx+B1MsL5lIdzDt34dxrb8B0ZVpkR+HpC6bagiAIyN/3LU49/VxNYpBKcf3sv2Hgin8wMYiALQdyKolUiq4TJyD4phhkvLMJusQkFB87jrJTp9Hz4QcRNi62w/Qde/KCqdYyV1QgI/5dlPxcs9ZD1Tkc/Z5aAv9+fUWOzHOx5UAuoQoLww3/+DsUcVMh9/ODRa/H+bfexpmXVsFYWCh2eCSistRUJD/+lD0xhI4ZhSFr32BiEBlbDuQyEokEssGDMGjGNGRu2oLiY8ehS0xC4mNPoMec2egyYTwk0vb3eYWH/Fwbq9GI3M93IffzXYDNBqmXF6LUDyP89nEdpjXZnjE5kMspAwPR/7mnUXz8Z1zY+G+YdTpkbvo3io4cRa8F8+DTrZvYITYbD/lpOXN5BfL37kP+f/fBUl4OAPCN6ol+Tz8J7+siRI6OajE5kGhCRo5Ap+iByH7/Q2gPHUZ56hkkLXwcnQbegPBxsQi5dSTkvr5ih9kkTz8trCUMWi3y9nyFggMH7YsRJQoFIqZMQve/3QupQiFyhFQXkwOJSuHvjz6PP4bQMaPxgCrMAAAPQ0lEQVRw4d3NMGq1KE89g/LUM8jc/B6Ch9+M8HG3IXDIYEhkMrHDdWAo0ELm51fvNS5+q68yOxuXd+1B4U9HAJsNACDz9UHXiRPQdcokKIOCRI6QGsPkQG4haNhQxGyMh+7kKWgP/4CSn0/AZjKh6KejKPrpKBRBgQiLHYvwcbHw7dFD7HDtnL34zVJVhbLTqTDk5cFSVQW/3r0RMCgach/vVv9sZ46VCIKA8pRU5O7aDV1ikv11ZUgwIv5vKjqPvwNyH582uRc5B5MDuQ2JTIagYUMRNGwoLFVVKD72M7SHv0d5SirMpTrk7f4Sebu/hG/PHggbF4uwsWNE/9TZVovfBKsVppJSGIuKYE1JxaW08yg7nYKKtHSHE/ckMhn8B/RH0NAhCBwyGL5RPVs8kH+1sZJrTRyC1Yrin3/B5YTd0J//3yFe3t264brpcQiLHcPuo3aCyYHcktzHB53vuB2d77gdhgItCn/4EdrD38OQl4/KrGxUZmUj+4NtCBo6GGG33Ybg4TdDJsJuvc3ZkkMQBFgqKmAsLIKxqAjGwiKYrnw1FhXBWFQMU0mJvcsFAOqeryeRyeAd2Q1ShRKVmZkQrFaUp6SiPCUVF7dthyKgEwIGD65JFkMHNythNjVWAqDFg+xVubkoOnIMhYd/qHfGuP+A/rhu+jQE3xzTLmeieTImB3J7Xp3DEfmXmeg26y7o089De/h7FP10FBa9HqWaJJRqkiDz8UHIrSMRfnssOg0Y4NIHUXDMMAQOvhHaQ98j/6uvkf7GWkhVSigCA2EzmmAqKrJvM90s3t7wjeiKTgP6I+DGG+t1I1mqqlB26jR0SckoTUqGsUALc1k5in78CUU//gQA8O3ZA4FDhyBw6BB0GtC/0U/qTY2VNHeQvTo/v6bb7+gxVGVfrF8mt9yM62ZMQ6cB/Zv/e5NbYXKgdkMikcC/X1/49+uLng8/iNLfNNAe/h6lvyXCWlUF7XcHof3uIJShofDq0hlyPz/I/Xxrvvr6QublBamXClKlCjKVEkDNp/qaP1z5H0GA/YQTQbjyWs37bCYjrFXVsFRWwlpVVdMNdOXTv7m0tF6s1qoqmEsdtwiRKpVQhYVCGRoKVVgoVHW+KkNDoQoNQXJqKob8wf5Tch8fhIwYjpARwyEIAgz5+fZEUXY6FTaDwd6yurxrN6ReXgiIHojAoUMQNHQIvCK6QiKRNDlW0lTiMBRoUXT0GIqOHEXlhcx671GGhiJ09K3o/Kfb4dM9stn/ruSemByoXZIqFAgZOQIhI0fAXFaGoiNHoT30PfQZF2Aqqum2EYVEAqlSCcmVT+sKf3/0eHAOVGEhUIWGQe7v12YLvCQSCbwjIuAdEYGukyfBZjaj/Ow56JJPQpeUjMrMLNgMBpT+pkHpbxpkAVCFhyNw6BD4DeiH6sM1x5Y2HCvJS9hTL3HYLBaYdToIJhM06vn1YlAEBSF01EiEjh4F/3592XXUgTA5ULunCAhA18mT0HXyJFRdyoHu5EmYyytg0eth0VfCWqmHRV8Fq9EAm8FY89VkBlDzgIX9WS0BJBLUPLuvvGj/ewlkKiVkPj41/3l7QxkUCFVYGFShIcj+8GPI/P0hVSjsD//aU+ZChrtmm2mpQoHAGwch8MZBwP33waTT2ROFLukkzGVlMGq1KPh2v/13k3l7Q7BaIVUqoQwKwqWPd8Ck08GsK6tpOTVyUKQiIAAht45E6Jhb0al/f7ecYkytx+RAHYpP90hRujS0h76HsaS0XqtA7PMclIGBCL8tFuG3xUKw2VCZnQ1dYk0XVMW5tJqDj66cL2wzGmGpqPjDnyXz9kbomFEIHT0KAdEDmRA8AJMDURtw9/McJFIp/KKi4BcVhW4zZ8BSVY3ylBToTp2GzWCEzNsLMm/v//3n421vVci8vdFp4A2Qyvm48CT81yZqA848Zc4Z5D7eCL7lZp6sRn+IyYGojfA8B+pIOLWAiIgcMDkQEZEDJgciInLA5EBERA6YHIiIyAFnK1GHxzOeiVqOLQfq0GrPLTCWlNbbfrpEkyh2aERuzeXJQaPRYObMmYiLi8OcOXNw+fJlV4dAHqTu9tMSiaTmq1xuP7eAiBrn8uTwzDPPYOXKldizZw+mTp2KlStXujoE8iCGAi2kDQ4B4hnPRFfn0jEHk8mExx9/HP371xwA0q9fP3z88ceuDIE8jLPPeCbXaDhuZB00EPiDMy+obUgEoZE9eV3AZrNh/vz5GDRoEB577LGrvt9oNCIlJcUFkVFHYs24APPebwGZDFDIAbMFsFqhmHQnZL17iR0eNQP/DdtGdHQ0VC04StdpLYd9+/bh1VdfrfdaVFQUPvjgA5hMJjz//POwWCyYN29ei35uS39BV9JoNIjhp5kmubyMYmJQ0rv3/z51RnR1+9lKrEf1pSR8CeOVk/xqVep08Dmdiui7/yJiZO6rbh261g/WTksOEydOxMSJEx1er6ysxPz58xEYGIh3330XikbOtyVqS9wQzzWcNWW4sWNLoZBz3MjJRBmQvv7667Fu3ToolUpX356InMCZU4a9OofDZjTWf9Fs4biRk7k0OZw5cwYHDx5EYmIipk+fjri4ODzyyCOuDIGInMCZU4YjpsfVnFpnMNQcvWowAFar2xyk1FG5dLbSDTfcgLS0NFfekohcoLGun7aaMtzYQUrW2NHsKnQybp9BRK3m7CnDDceNNBpNm/xc+mPcPoOIWq2xrh93OkObWo4tByJqtWs5Q5sbIro3JgciahMtmTJcO7tJIpfXm92EeXOZINwEu5WIyOW4IaL7Y8uBiBrlzG4fZ85uorbBlgMROXD2ORiNLWzjhojuhcmBiBw4u9uHs5vcH7uViFrIE2bZOLvb51pmN5FrMTkQtYCnzLJxxTkY3BDRvbFbiagFPGWWDbt9iMmBqAU85djR4JhhiJo3F6rgIFj1eqiCgxDVwVpH1DR2KxFdRd0xBktlJWwWC5RBQfa/76izbNjt49mYHIia0HCMQbBaYdLpAACKwEDYjEZ2t1CHxORA1IS6YwxATUIAAKvBAKtez1k21GExORA1obEpnfKAAEhkMtz073dFiorI+TggTdQEruQlT8WWA3Uobb1ALWJ6HDI3bYHVYIBUpeIYA3kMthyow3DGfkCc0kmeii0H6jAaDh7LvLxgNRiQl7CnVQ9zTukkT8SWA3UYnrJAjcgV2HKgDsMV+wE1xRM25CPPwZYDdRhi7gfk7PMPiFyNyYE6DDEHjz1lQz7yHOxWog7FGYPHzeku4rGX1NGI1nI4c+YMoqOjxbo9UbM0t7uIi+WooxElOVRXV+Pll1+G2WwW4/ZEzdbc7iKef0AdjSjJYfXq1ZgzZ44YtyZqkeZOj+ViOepoXD7mcPDgQRgMBkyYMMHVtyZqsZZMj+ViOepIJIIgCM74wfv27cOrr75a77WoqCjo9Xp88MEH8PPzQ79+/ZCWltasn2c0GpGSkuKMUIn+kDXjAsx7vwVkMkAhB8wWwGqFYtKdkPXuJXZ4RM0WHR0NVYNWcFOclhwas3PnTmzatAm+vr4AgHPnzqF///7Yvn07/BrM9GioNjm09Bd0JY1Gg5iYGLHDcGvtsYxcvbitPZaRq7GMmla3fK712enSbqVZs2Zh1qxZ9ut+/fphzx7OAyf3xu4i8kRcBEdERA5ETQ7NHW8gIiLXYsuBiIgccPsMIjfGnV5JLGw5ELkp7vRKYmJyIHJT3OmVxMTkQOSmeLIdiYnJgchNcadXEhOTA5Gb4k6vJCYmByI3xZ1eSUycykrkxrh1B4mFLQciInLA5EBERA6YHIiIyAGTAxEROWg3A9K1ZxKZTCaRI2mascG8dHLEMro6ltHVsYyaVls+tc/Mlp7r5tKT4FqjoqIC6enpYodBRNQu9e3bF/7+/s1+f7tJDjabDZWVlVAoFJBIJGKHQ0TULgiCALPZDF9fX0ilzR9JaDfJgYiIXIcD0kRE5IDJgYiIHDA5EBGRAyYHIiJywORAREQOmByIiMgBkwMRETlgcmglvV6PKVOmIDc3FwBw7NgxTJ06FePHj8fatWtFjk58Dctn6dKlGD9+POLi4hAXF4cDBw6IHKG44uPjMXnyZEyePBmvv/46ANahhhorI9aj+t566y1MmjQJkydPxtatWwG0QT0S6JolJycLU6ZMEQYOHCjk5OQI1dXVQmxsrHDp0iXBbDYLDz30kPD999+LHaZoGpaPIAjClClThIKCApEjcw9Hjx4V7r77bsFoNAomk0m4//77ha+++op1qI7Gymj//v2sR3WcOHFCuOeeewSz2SxUV1cL48aNE86ePdvqesSWQyt89tlnePHFFxEeXnPg+6lTp3D99dcjMjIScrkcU6dOxTfffCNylOJpWD7V1dXIy8vDsmXLMHXqVKxfvx42m03kKMUTFhaG559/HkqlEgqFAr169UJ2djbrUB2NlVFeXh7rUR233HILPvroI8jlchQXF8NqtaK8vLzV9YjJoRVWrVqFm266yX6t1WoRFhZmvw4PD0dBQYEYobmFhuVTVFSEESNG4JVXXsFnn32G3377DZ9//rmIEYqrT58+GDJkCAAgOzsb+/btg0QiYR2qo7EyGjNmDOtRAwqFAuvXr8fkyZMxcuTINnkWMTm0IZvNVm9TQEEQuElgHZGRkdiwYQPCw8Ph7e2N2bNn44cffhA7LNGdP38eDz30EJ599llERkayDjWibhlFRUWxHjVi8eLFOH78OPLz85Gdnd3qesTk0Ia6dOmCwsJC+3VhYaG9S4WAtLQ0fPvtt/ZrQRAgl7ebI0WcQqPR4IEHHsBTTz2F6dOnsw41omEZsR7Vd+HCBZw9exYA4O3tjfHjx+PEiROtrkdMDm1o8ODByMrKwsWLF2G1WvH1119j7NixYoflNgRBwCuvvIKysjKYzWZ8+umn+POf/yx2WKLJz8/HwoUL8cYbb2Dy5MkAWIcaaqyMWI/qy83NxfLly2EymWAymXDw4EHcc889ra5HnptunUClUmH16tVYtGgRjEYjYmNjMWHCBLHDchv9+/eHWq3GvffeC4vFgvHjx2PKlClihyWa9957D0ajEatXr7a/ds8997AO1fFHZcR69D+xsbE4deoUpk2bBplMhvHjx2Py5MkIDg5uVT3ieQ5EROSA3UpEROSAyYGIiBwwORARkQMmByIicsDkQEREDpgciIjIAZMDERE5YHIgIiIH/w920eeGzlscAQAAAABJRU5ErkJggg==
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Residual Plots</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sms</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ols_pred</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">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">residuals</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">resid</span>
<span class="n">fittedvalues</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fittedvalues</span>
<span class="n">rsq</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rsq</span><span class="p">)</span>
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<pre>0.7528327936582646
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">ols_pred</span><span class="p">)</span>
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<pre>0.7528327936582646</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="n">residuals</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Fitted Values&quot;</span><span class="p">,</span> <span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Residuals&quot;</span><span class="p">,</span><span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Residual Vs Fitted Values PLOT&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Multiple Linear Regression plot</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;mpg&quot;</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">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
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<pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create the Residual Plot</span>
<span class="n">ix</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pred</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">-</span><span class="n">pred</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">)</span>
<span class="n">ix</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Fitted Values&quot;</span><span class="p">,</span> <span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Residual Vs Fitted Values PLOT&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># From statmodels.api</span>
<span class="n">multimodel</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">multipred</span> <span class="o">=</span> <span class="n">multimodel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[138]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fitted_values</span> <span class="o">=</span> <span class="n">multimodel</span><span class="o">.</span><span class="n">fittedvalues</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">residuals</span> <span class="o">=</span> <span class="n">multimodel</span><span class="o">.</span><span class="n">resid</span>
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<div class="prompt input_prompt">In&nbsp;[142]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">fitted_values</span><span class="p">,</span> <span class="n">residuals</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;magenta&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c2b44f9b0&gt;</pre>
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<div class="prompt input_prompt">In&nbsp;[148]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">multipred</span><span class="p">)</span>
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    <div class="prompt output_prompt">Out[148]:</div>


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<pre>0.8663209139912303</pre>
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<h4 id="a.-Construct-a-scatter-plot-to-show-the-relationship-between-the-variables-cars.mpg-and-cars.wt">a. Construct a scatter plot to show the relationship between the variables cars.mpg and cars.wt<a class="anchor-link" href="#a.-Construct-a-scatter-plot-to-show-the-relationship-between-the-variables-cars.mpg-and-cars.wt">&#182;</a></h4><h4 id="b.-Measure-the-relative-strength-of-a-linear-relationship-between-the-cars.mpg-and-cars.wt-by-calculating-the-sample-correlation-coefficient">b. Measure the relative strength of a linear relationship between the cars.mpg and cars.wt by calculating the sample correlation coefficient<a class="anchor-link" href="#b.-Measure-the-relative-strength-of-a-linear-relationship-between-the-cars.mpg-and-cars.wt-by-calculating-the-sample-correlation-coefficient">&#182;</a></h4><h4 id="c.-Give-your-inference-on-the-relationship-between-these-variables">c. Give your inference on the relationship between these variables<a class="anchor-link" href="#c.-Give-your-inference-on-the-relationship-between-these-variables">&#182;</a></h4><h4 id="d.-Give-the-simple-linear-regression-equation-between-cars.mpg-and-cars.wt">d. Give the simple linear regression equation between cars.mpg and cars.wt<a class="anchor-link" href="#d.-Give-the-simple-linear-regression-equation-between-cars.mpg-and-cars.wt">&#182;</a></h4><h4 id="e.-Predict-the-mpg-of-the-car-for-wt-=-3000-lbs">e. Predict the mpg of the car for wt = 3000 lbs<a class="anchor-link" href="#e.-Predict-the-mpg-of-the-car-for-wt-=-3000-lbs">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sms</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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LinearRegression</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mtcars</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_rdataset</span><span class="p">(</span><span class="s2">&quot;mtcars&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">data</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot of Relationship between the WT and MPG</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">)[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span><span class="p">,</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="n">text</span> <span class="o">=</span> <span class="s2">&quot;Correlation Coefficient is </span><span class="si">%1.4f</span><span class="s2">&quot;</span> <span class="o">%</span><span class="k">corr</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Develop SMS Model OLS</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">mpg</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">wt</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">pred_model</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">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Find the Fitted Values, Residuals and R Squared Values</span>
<span class="n">fittedvals</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fittedvalues</span>
<span class="n">residual_wt</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">resid</span>
<span class="n">Rsquared</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">rsquared</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">Rsquared</span><span class="p">)</span>
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<pre>0.7528327936582646
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">r2_score</span>
<span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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<pre>0.7528327936582646</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mtcars</span><span class="o">.</span><span class="n">head</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;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>mpg</th>
      <th>cyl</th>
      <th>disp</th>
      <th>hp</th>
      <th>drat</th>
      <th>wt</th>
      <th>qsec</th>
      <th>vs</th>
      <th>am</th>
      <th>gear</th>
      <th>carb</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Mazda RX4</th>
      <td>21.0</td>
      <td>6</td>
      <td>160.0</td>
      <td>110</td>
      <td>3.90</td>
      <td>2.620</td>
      <td>16.46</td>
      <td>0</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>Mazda RX4 Wag</th>
      <td>21.0</td>
      <td>6</td>
      <td>160.0</td>
      <td>110</td>
      <td>3.90</td>
      <td>2.875</td>
      <td>17.02</td>
      <td>0</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>Datsun 710</th>
      <td>22.8</td>
      <td>4</td>
      <td>108.0</td>
      <td>93</td>
      <td>3.85</td>
      <td>2.320</td>
      <td>18.61</td>
      <td>1</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
    </tr>
    <tr>
      <th>Hornet 4 Drive</th>
      <td>21.4</td>
      <td>6</td>
      <td>258.0</td>
      <td>110</td>
      <td>3.08</td>
      <td>3.215</td>
      <td>19.44</td>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>1</td>
    </tr>
    <tr>
      <th>Hornet Sportabout</th>
      <td>18.7</td>
      <td>8</td>
      <td>360.0</td>
      <td>175</td>
      <td>3.15</td>
      <td>3.440</td>
      <td>17.02</td>
      <td>0</td>
      <td>0</td>
      <td>3</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Residual Plot</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">fittedvals</span><span class="p">,</span> <span class="n">residual_wt</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;magenta&quot;</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Fitted Values&quot;</span><span class="p">,</span><span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;RESIDUAL PLOT&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Lets Do the Same from Scikit Learn</span>
<span class="n">lm</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
<span class="n">model_lm</span> <span class="o">=</span> <span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Find the Fitted Values, Residuals</span>
<span class="n">fitted_values</span> <span class="o">=</span> <span class="n">model_lm</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">y</span><span class="o">-</span><span class="n">model_lm</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">model_lm</span><span class="p">)</span>
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<pre>0.7528327936582646</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Residual Plot</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">fitted_values</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c2b42d9e8&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># QQ PLOT</span>
<span class="kn">from</span> <span class="nn">statsmodels.graphics.gofplots</span> <span class="k">import</span> <span class="n">ProbPlot</span>

<span class="c1"># fittedvals = model.fittedvalues</span>
<span class="c1"># residual_wt = model.resid</span>
<span class="c1"># Rsquared = model.rsquared</span>
<span class="n">st_residual</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span> <span class="c1"># residual/st.dev</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">QQ</span> <span class="o">=</span> <span class="n">ProbPlot</span><span class="p">(</span><span class="n">st_residual</span><span class="p">)</span>
<span class="n">plot</span><span class="o">=</span> <span class="n">QQ</span><span class="o">.</span><span class="n">qqplot</span><span class="p">(</span><span class="n">line</span> <span class="o">=</span> <span class="s2">&quot;45&quot;</span><span class="p">,</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mf">1.5</span><span class="p">)</span>
<span class="n">plot</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Theoretical Quantiles&quot;</span><span class="p">)</span>
<span class="n">plot</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Standardized Residuals&quot;</span><span class="p">)</span>
<span class="n">plot</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Normal Q-Q Plot&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[251]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
<span class="kn">import</span> <span class="nn">pylab</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[253]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">st_residual</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h2 id="Shapiro-Wilk-Test">Shapiro Wilk Test<a class="anchor-link" href="#Shapiro-Wilk-Test">&#182;</a></h2><h3 id="The-Shapiro-Wilk-test-for-normality-is-used-to-examine-a-continuous-variable.">The Shapiro-Wilk test for normality is used to examine a continuous variable.<a class="anchor-link" href="#The-Shapiro-Wilk-test-for-normality-is-used-to-examine-a-continuous-variable.">&#182;</a></h3><h3 id="The-null-hypothesis-for-this-test-is-that-the-data-are-normally-distributed">The null hypothesis for this test is that the data are normally distributed<a class="anchor-link" href="#The-null-hypothesis-for-this-test-is-that-the-data-are-normally-distributed">&#182;</a></h3><p>If the chosen alpha level is 0.05 and the p-value is less than 0.05, then the null hypothesis that the data are normally distributed is rejected. If the p-value is greater than 0.05, then the null hypothesis is not rejected.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Normality</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">shapiro</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span> <span class="o">=</span> <span class="n">shapiro</span><span class="p">(</span><span class="n">mtcars</span><span class="o">.</span><span class="n">drat</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">if</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">&lt;</span><span class="mf">0.05</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Reject the Null Hypothesis&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fail to Reject the Null Hypothesis&quot;</span><span class="p">)</span>
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<pre>Fail to Reject the Null Hypothesis
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Creating a Correlation Matrix</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">mtcars</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> 
            <span class="n">xticklabels</span><span class="o">=</span><span class="n">corr</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
            <span class="n">yticklabels</span><span class="o">=</span><span class="n">corr</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x113901978&gt;</pre>
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<div class="prompt input_prompt">In&nbsp;[307]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scale Location Plot is a plot of Fitted Values vs Sqrt of Standardized Residuals</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">fittedvals</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">st_residual</span><span class="p">),</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Fitted Values&quot;</span><span class="p">,</span> <span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;$\sqrt{Standardized Residuals}$&quot;</span><span class="p">,</span> <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Scale Location Plot&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<pre>/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt
  
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<img 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
"
>
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<div class="prompt input_prompt">In&nbsp;[345]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Cement Data</span>
<span class="n">cement</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="s2">&quot;~/Downloads/concrete.csv&quot;</span><span class="p">)</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[346]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cement</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[346]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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>cement</th>
      <th>slag</th>
      <th>ash</th>
      <th>water</th>
      <th>superplastic</th>
      <th>coarseagg</th>
      <th>fineagg</th>
      <th>age</th>
      <th>strength</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>141.3</td>
      <td>212.0</td>
      <td>0.0</td>
      <td>203.5</td>
      <td>0.0</td>
      <td>971.8</td>
      <td>748.5</td>
      <td>28</td>
      <td>29.89</td>
    </tr>
    <tr>
      <th>1</th>
      <td>168.9</td>
      <td>42.2</td>
      <td>124.3</td>
      <td>158.3</td>
      <td>10.8</td>
      <td>1080.8</td>
      <td>796.2</td>
      <td>14</td>
      <td>23.51</td>
    </tr>
    <tr>
      <th>2</th>
      <td>250.0</td>
      <td>0.0</td>
      <td>95.7</td>
      <td>187.4</td>
      <td>5.5</td>
      <td>956.9</td>
      <td>861.2</td>
      <td>28</td>
      <td>29.22</td>
    </tr>
    <tr>
      <th>3</th>
      <td>266.0</td>
      <td>114.0</td>
      <td>0.0</td>
      <td>228.0</td>
      <td>0.0</td>
      <td>932.0</td>
      <td>670.0</td>
      <td>28</td>
      <td>45.85</td>
    </tr>
    <tr>
      <th>4</th>
      <td>154.8</td>
      <td>183.4</td>
      <td>0.0</td>
      <td>193.3</td>
      <td>9.1</td>
      <td>1047.4</td>
      <td>696.7</td>
      <td>28</td>
      <td>18.29</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cement</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 1030 entries, 0 to 1029
Data columns (total 9 columns):
cement          1030 non-null float64
slag            1030 non-null float64
ash             1030 non-null float64
water           1030 non-null float64
superplastic    1030 non-null float64
coarseagg       1030 non-null float64
fineagg         1030 non-null float64
age             1030 non-null int64
strength        1030 non-null float64
dtypes: float64(8), int64(1)
memory usage: 72.5 KB
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<pre>Index([&#39;cement&#39;, &#39;slag&#39;, &#39;ash&#39;, &#39;water&#39;, &#39;superplastic&#39;, &#39;coarseagg&#39;,
       &#39;fineagg&#39;, &#39;age&#39;, &#39;strength&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">cement</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;strength&quot;</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">y</span> <span class="o">=</span> <span class="n">cement</span><span class="o">.</span><span class="n">strength</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#x = sms.add_constant(x)</span>
<span class="n">model_cement</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">cement_pred</span> <span class="o">=</span> <span class="n">model_cement</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Importing Variance Inflation Factor</span>
<span class="kn">from</span> <span class="nn">statsmodels.stats.outliers_influence</span> <span class="k">import</span> <span class="n">variance_inflation_factor</span>
<span class="n">vif</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">vif</span><span class="p">[</span><span class="s2">&quot;VIF Factor&quot;</span><span class="p">]</span><span class="o">=</span> <span class="p">[</span><span class="n">variance_inflation_factor</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
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<pre>Index([&#39;cement&#39;, &#39;slag&#39;, &#39;ash&#39;, &#39;water&#39;, &#39;superplastic&#39;, &#39;coarseagg&#39;,
       &#39;fineagg&#39;, &#39;age&#39;, &#39;strength&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ce</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cement&#39;</span><span class="p">,</span> <span class="s1">&#39;slag&#39;</span><span class="p">,</span> <span class="s1">&#39;ash&#39;</span><span class="p">,</span> <span class="s1">&#39;water&#39;</span><span class="p">,</span> <span class="s1">&#39;superplastic&#39;</span><span class="p">,</span> <span class="s1">&#39;coarseagg&#39;</span><span class="p">,</span>
       <span class="s1">&#39;fineagg&#39;</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Boston Data - Check if the model measures such as R-square and adjusted R square have </span>
<span class="c1"># improved after transforming the predictor variables. Here our target variable is MEDV.</span>

<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_boston</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">boston</span> <span class="o">=</span> <span class="n">load_boston</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bostonn</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">boston</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bostonn</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="n">bostonn</span><span class="p">[</span><span class="s2">&quot;MEDV&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">boston</span><span class="o">.</span><span class="n">target</span>
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<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>CRIM</th>
      <th>ZN</th>
      <th>INDUS</th>
      <th>CHAS</th>
      <th>NOX</th>
      <th>RM</th>
      <th>AGE</th>
      <th>DIS</th>
      <th>RAD</th>
      <th>TAX</th>
      <th>PTRATIO</th>
      <th>B</th>
      <th>LSTAT</th>
      <th>MEDV</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>0.00632</td>
      <td>18.0</td>
      <td>2.31</td>
      <td>0.0</td>
      <td>0.538</td>
      <td>6.575</td>
      <td>65.2</td>
      <td>4.0900</td>
      <td>1.0</td>
      <td>296.0</td>
      <td>15.3</td>
      <td>396.90</td>
      <td>4.98</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.02731</td>
      <td>0.0</td>
      <td>7.07</td>
      <td>0.0</td>
      <td>0.469</td>
      <td>6.421</td>
      <td>78.9</td>
      <td>4.9671</td>
      <td>2.0</td>
      <td>242.0</td>
      <td>17.8</td>
      <td>396.90</td>
      <td>9.14</td>
      <td>21.6</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.02729</td>
      <td>0.0</td>
      <td>7.07</td>
      <td>0.0</td>
      <td>0.469</td>
      <td>7.185</td>
      <td>61.1</td>
      <td>4.9671</td>
      <td>2.0</td>
      <td>242.0</td>
      <td>17.8</td>
      <td>392.83</td>
      <td>4.03</td>
      <td>34.7</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.03237</td>
      <td>0.0</td>
      <td>2.18</td>
      <td>0.0</td>
      <td>0.458</td>
      <td>6.998</td>
      <td>45.8</td>
      <td>6.0622</td>
      <td>3.0</td>
      <td>222.0</td>
      <td>18.7</td>
      <td>394.63</td>
      <td>2.94</td>
      <td>33.4</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.06905</td>
      <td>0.0</td>
      <td>2.18</td>
      <td>0.0</td>
      <td>0.458</td>
      <td>7.147</td>
      <td>54.2</td>
      <td>6.0622</td>
      <td>3.0</td>
      <td>222.0</td>
      <td>18.7</td>
      <td>396.90</td>
      <td>5.33</td>
      <td>36.2</td>
    </tr>
  </tbody>
</table>
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<pre>(506, 14)</pre>
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<pre>Boston House Prices dataset
===========================

Notes
------
Data Set Characteristics:  

    :Number of Instances: 506 

    :Number of Attributes: 13 numeric/categorical predictive
    
    :Median Value (attribute 14) is usually the target

    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000&#39;s

    :Missing Attribute Values: None

    :Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing


This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. &#39;Hedonic
prices and the demand for clean air&#39;, J. Environ. Economics &amp; Management,
vol.5, 81-102, 1978.   Used in Belsley, Kuh &amp; Welsch, &#39;Regression diagnostics
...&#39;, Wiley, 1980.   N.B. Various transformations are used in the table on
pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression
problems.   
     
**References**

   - Belsley, Kuh &amp; Welsch, &#39;Regression diagnostics: Identifying Influential Data and Sources of Collinearity&#39;, Wiley, 1980. 244-261.
   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">bostonn</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;MEDV&quot;</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">x</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">boston</span><span class="o">.</span><span class="n">target</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stndrdresid</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">stndrdresid</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span><span class="n">plot</span> <span class="o">=</span> <span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[411]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">shapiro</span><span class="p">(</span><span class="n">stndrdresid</span><span class="p">)</span>
</pre></div>

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<pre>(0.8987566232681274, 8.601320139985676e-18)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">if</span><span class="p">(</span><span class="n">shapiro</span><span class="p">(</span><span class="n">stndrdresid</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">&lt;</span><span class="mf">0.05</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Reject the Null&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accept the Null&quot;</span><span class="p">)</span>
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<pre>Reject the Null
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<div class="prompt input_prompt">In&nbsp;[413]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">rsquared</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="p">)</span>
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<pre>0.7406077428649427
0.7337538824121872
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Improve R Squared</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">PolynomialFeatures</span>
<span class="n">poly</span> <span class="o">=</span> <span class="n">PolynomialFeatures</span><span class="p">()</span>
<span class="n">x</span><span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">rsquared</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="p">)</span>
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<pre>0.9291464466583611
0.9109924267723193
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import Data - Insurance</span>
<span class="n">insurance</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="s2">&quot;~/Downloads/Excel and CSVs/insurance.csv&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get Dummies</span>
<span class="n">insurance</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">insurance</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">insurance</span><span class="o">.</span><span class="n">head</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;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>age</th>
      <th>bmi</th>
      <th>children</th>
      <th>charges</th>
      <th>sex_female</th>
      <th>sex_male</th>
      <th>smoker_no</th>
      <th>smoker_yes</th>
      <th>region_northeast</th>
      <th>region_northwest</th>
      <th>region_southeast</th>
      <th>region_southwest</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>19</td>
      <td>27.900</td>
      <td>0</td>
      <td>16884.92400</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>18</td>
      <td>33.770</td>
      <td>1</td>
      <td>1725.55230</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>28</td>
      <td>33.000</td>
      <td>3</td>
      <td>4449.46200</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>33</td>
      <td>22.705</td>
      <td>0</td>
      <td>21984.47061</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>32</td>
      <td>28.880</td>
      <td>0</td>
      <td>3866.85520</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">insurance</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;charges&quot;</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">y</span> <span class="o">=</span> <span class="n">insurance</span><span class="o">.</span><span class="n">charges</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ins_m</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ins_p</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="p">)</span>
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<pre>0.7509130345985207
0.7494136397729286
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test of Linearity</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">ins_p</span><span class="p">,</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c340fc1d0&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Normality</span>
<span class="n">st_res</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
<span class="n">shapiro</span><span class="p">(</span><span class="n">st_res</span><span class="p">)</span>
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<pre>(0.8990878462791443, 8.723982369294297e-29)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">st_res</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span> <span class="n">plot</span> <span class="o">=</span> <span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Lets Apply Polynomial Transformation</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">PolynomialFeatures</span>
<span class="n">poly</span> <span class="o">=</span> <span class="n">PolynomialFeatures</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ins_m</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">ins_p</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="p">)</span>
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<pre>0.8477668880499397
0.8435544422158104
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test of Linearity</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">ins_p</span><span class="p">,</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c33dec940&gt;</pre>
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AmgQvX5tI86ZaHyP7d3z1M0XnThuMslCeYWpZS/KheixNXcQWhMs5xiO9L2nz/o7hwVfxXYMNzVhSRJaHV1bq7PeHev+eay1tbWad8VC2YP0+lvFPfh3EFoTLOcQjtSyzCId3SQDIVBloh3HCV+9KjTcdaySPX1YaTbhJRj9zqdu2LB7KKcSdQnQtyHcwehMc1yCu1IjXAYWXN2jomuTqR0rUDbtsG2sU2TZHc3hs+L4g9QlRe8MBb5NnzzrJX4RQ6RoAjTGS0pctnmDkIwzXIKBSRYRro6OWDpBpIsY6dfb5tm+g8bM6VjpiI0nP//lnSuYgm2NR9dT2LPUyKHSFCQ6YqWFLlscwdhypvl5AckyKqK7NFI9va52lJGUwKQFMV5oyQhaxpabS1DL5dWfLFQPToUhaGX2ybctDCDaBgomCyTaZ4pqCyExjQHyOxIMxqN4g9gDw5ipnRXICFJzt+yjKSqeBYuRA0EnDYTBWzwhcJux0qwncyuWOS6CMqFyGWbGwiNaQ6R0Wg88+fjWbgQWdNczci/dCmSqiLJsiuUoLANvljYreKvGlX9HN2YtA1fVAYXCATZCI1pDpGt0aiBgKsRmdEo77rzByOaiaJg23ZRG3yxsFts3AgrN8F2eBh9aIgXL7sypwL5iZzd2RpZKhzGs6Au53kRTSUQnLwIwTSHOFFUUqkRUsWKsJrRKO+44rKR/KiqKpAkLN1wNas3fvgjR+sJBIqa5fJNd1IkQrK3z3mfv7gmJxCMB1EFYvYiBNMspNgPrpSopGwbfOZzDt3705zPUaqqSBw9im1ZSKqKFgwiKcooQZHs6wNFztGsUr29IIFnwQLnRZaJHglz4OZbmHfG6TRedOEojUyrqyPZ20sqFMJX5RfRVIJJI/yWsxshmGYZxX5wQ+nadFYygRU1kFSVwLLmorvEsT5HHxjAMk0nYMIwSPb2olZXs3Dd33Lwzrswh+NYhgnp16RUFWt4GEs3sA0D0nlTZjxGsrcPG5DAPYeZSIwILnBNjqneXoY7OgCoamp0xyl2vYLxIqpAzG6EYJplFPrBpcJhOh9+FG9DA1rdghyNo9iPsNgP99jOx9GCQZQqH3okgqUbyKqCZ36Q0N4/og8NIckysqJgWRbYNnoojKRpI/lSloUZj6FHIiBJSDhh6plz2IaBlUzmmBxtXUdSFLwNDa62V6pZUAguQT6io/HsRkTlzTIKlV0x43FsyxpXVFux8i3m8DCy16kI4Wtswr98Ob6mpZjxYYY7uxxBI8uONpXJiQKQJCdfSpZBlkmFQpjJFLauY+s6lmlixGLIXi+ypo4qU2MMDaHNm5czB2t4GHM4XnReominoBi+RQ2jIkiF33L2IATTLKPgD07XkbVc5TezOwy17uPPV3+DvR//JHs//kn+fM03CLXuK/rDVapGh4Rn/6Cl7HNkCybLcjUeT309tmGCZTnPKQoSkOrrQ49E8Dc3j0qEVAN+1NranPPaluWYDAvMC0SYuaA401mjT1B+hClvllEowEGSZdRANUbMMZ/ZhlOGSA3W8sYPf4QRjSJJEjYQ7zjKwTvvYlGRMkJLLtxIb5HyQvrPHyR+9KgjcDIJuwCqgn/5cneMZiKB7POh+HzoAwOOliVJWJaFMTTkmtuyTW5tW7aOiiiUZBkpWxICxsAAZiLBi5ddOWVh5sI8OPsRHY1nN0IwzTIK/eAWrvtbjv/nkxiRiCuAbNNE7w85gQey7JjfLBPbtNAjEY7tfJwlF24s2MyvZsUpRX/Qb/zwR1jDw07Enixjezxofv8oQSZrKmptLbJHG/FVpQvLFlocCglcuaoKSZLcY8bAAKlIBK22dsrCzM2Db3Joz9MimmsOIKpAzF6EYJqFFPrBhfb+ESsxjG1aKJoT4p3o6XW0G0XBtkxs3XBfbyYS9O55qmAtsWI/6LrVqzj1mq/mCK34WStZsWLFKEHWtX2nowH5A67QMBMJvHXzi84pX+C+/QufBUaSdc1EAq22Fs985zOmIszc3PsciojmEghmFCGY5ghmfBhf01KkLNuXrIWxEkmn1YWRDv8GsG1kTXP9MeNZcAs1CiwmyMZb6Tm75l+h/KoXL7syJ9IqE2auh0KY0WhZzDVWJIJctyDn2GyM5gq17iN1/y948d77hBlLMOsQgmkWMZbvo1DVBzVQja47zQFdfxCAoqAFg1O64GZrQLG3OlzzXnZgQqG5jJUYWWiOsqoy74zTablxW1nGLQeDo0LZZ1s0V+Y7tAwDpba2osyRwn8nKAURlTdLOFFodCYKSY9EGO48SuzIEfTBQer+9v34ly51tSXJ48FbX48aCEz5gpupRqH4fGjBIFrdApKhMAfvvIs3fvijUXNp//XDvH7bHST7+tBD/ViZUPG0ZjcdkVbK+94zZeeYrtYemWhFyaNVVLSiCO8XlIoQTLOEE4VG161eRf2HP4g+OJgONNBQa2qI7n+N5Z/5NEs/9QkkTXOCIiIRUuHwlIXPZi/Ar992h1MNwjJJHusi2d2NPjCAGYvlzMU2DDoffhQzkUCSZSzDJNnbhxmPuZrddPTbUVacMiXnmM5FuVJbjIvwfkGpCFPeLKGUTPahl9vwNjTkmKHMRIL2+x/EHE6gzZuHEYti6Tr20BBNmz5WdjPKKFNcfz/m8LDj+0pHB9qGU7pouKMD27KQNRXLMNN/O8JTkmVsy4kg1OSROn3TEWk1FeeYzhI5GZNnNpVgjhTVGASlIjSmWUIpmezFdsrDnV1uMdaqpqUE3vY2vA0NJXeuHQ/5u2JZ08Cy3PDy7MQkS9dd7chO/60Fg06wRjo510zpcyIxcjq1mIzJ007pFZVcKqoxCEpFCKZZQin+lWI/fGDUomibBoMHXiu7vyN/AdaCwfQJbewsgeMeyx6TZaEGAngWLnR6RqXLLL3jS18EmNWt16dzUc6YPOWa6opqMS6qMQhKRQimWUIp/pVCP3wzFgNZJt7eznBnJ0Ys5lb9liSp7P6O/AVYDQScmnqS5JYtytTTQ5bdY2qwFtJjVvx+PAsW4F24kNP+8WsAs95pPt2Lct3qVXgu3cyan95Dy43bZlwoZcY01T5CwdxgXD6mwcFB5s2bN1VjEaQpFlJ7It9HfpKq4q/CsG206mpSkQiWrpPq6yOjp2h1da4T+kT+jlLDfAtVcFCrq90q4bLXS6LzKJZhutGB4PjCPMEgWk3NqHO0bdk661sYiBI5DqIag6AUShJMhw4d4qtf/SpDQ0M88sgjfPazn+VHP/oRp5xyylSP76Rjsg3Osn/4bVu2Ot1lfT63NJCZ0sGy8DQ0uEIBxvZ3jDWmQucvVMFh6OCbHNv5uFO9XNOQvd5RLd6Xf+GzBec4V5zmYlEWCEqjJFPejTfeyPXXX8+CBQtYtGgRn/70p7nhhhumemwnJeUMqc3292S3sZBUFVl19iRGLMZwZyfx9naMWKygeWy8Y8rkL/kWNZDo7qH9/gc5/p9PogWD+N/2NjwLFzrCSVNLMukIp7lAcHJRkmCKRCK8//3vdx9v3ryZaDQ6ZYM6mSln9FaxBd1TN59kTw+xQ4dIHj+OlUiAaWIOD3Pg5lto//XDkxpTfs5O4tgxjGjUCQNPCza1uhqtpqYkH4hwmgsEJxclBz8kk0m3Dltvb6/TvVRQdsqpHRRa0FPhMKm+fmzTzC1TJMtIqoptWXQ+/GiO5jTeMeVrWLZpIUmS09E2c7pxCFvhNBcITi5K8jFdcsklfOELX6C/v5/bbruN//zP/+SLXxztXxBMnkLBAxPVDkYFQ1RVYSeTTisMVcVOpUZebNtIkoSkKFimmRNYMNaYDucEfDvk+4RkTcXUDTBGqpuPV9gK/4xAcPJQkmD6+Mc/zrJly3jmmWcwDIN/+7d/yzHtCcpHuaO38oMh4qQ7z0oSdnazv/T/tm0ja2qONlNsTEDBCtb5xVa1YBCzp3dUsIMwxQkEgkKUHC5+zjnncPrpp2OnF7BIJEIwkzwpKCtTpR0kunuQNdU1raEoI1qMJLlVyNVA9ShtJn9MY1WwztewkBW0mhq02tqytacQCARzl5IE069+9StuvvlmdF0HnF21JEns379/SgcnKC++RQ1OEdeBAacunSzjegolCUlxWrRLqkrNWS20bdlaVGtzK1jL0qhcqJYbt43WsL5QOBR8sog2CgLB3KOk4Ief/exn/OpXv2L//v3s37+fAwcOTFooRaNRLrjgAo4ePQrA3r172bhxI+eddx633367+7r9+/dz8cUXc/7553P99ddjpHf4XV1dbN68mfXr13PllVcSi8UAJwn48ssv5yMf+QibN2+mt7d3UuOcSzRedKFTM6+2FkmRsUwTSdNY8IG11K78G7R5tVQ1LqH+wx+kd89TY1ZaOFGkXt3qVbTcuG1KKw+INgoCwdykJMG0cOFCVq5cWbaT/vWvf+VTn/oUR44cASCRSHDddddx9913s2vXLtra2njmmWcA+OY3v8kNN9zAb3/7W2zb5qGHHgJg27ZtXHLJJezevZuWlhbuvvtuAO644w7WrFnDk08+yaZNm7jpppvKNu7ZTia6rapxCdq8Wmr/5kzO+M63OOPr1+QIkaGX24rmLWVaWqTCYRJHj2InEu7nT3dukWijICjEdPW9EkwdJQmmc889l1/+8pd0d3cTiUTcfxPloYceYuvWrTQ0OIvYSy+9xPLly2lubkZVVTZu3Mju3bvp7OwkkUhwzjnnAHDxxReze/dudF3nhRde4Pzzz885DvD000+zceNGAC644AKeffZZ1wQ5F5jsjy5bk2m86EK6tu8c9VnFtKHYWx2uhuJZUIdlmjAwiB6NzkhuUaX2HRLMHEKLnhuU5GP6yU9+QiqV4l//9V/dY5PxMeVrMT09PdTX17uPGxoa6O7uHnW8vr6e7u5uwuEw1dXVqOnqBZnj+Z+lqirV1dWEQiEWLVo0obFWEpMtV1TqZxVqYW4lk1iJBFZiGNt0eihptfPQB4fQQyHmnXH6lPh3xttOXlSEOLmZzr5XgqmjJMH00ksvTekgLMtyk3dhJLii2PHM/9nkP85+jyyPr4h6W1v5+xTl09raOu73pO7/BZZhIMkSDMcBsA2D1+5/EE+BfKJxf9ZwnP233AqKCskk+HygKhCNQSYhNx3NZ6Z0p+7evBps2yZ50d9xGJvDE5hXMcyDb6Lv+q0TPaipJLuOMXDnXWgfPR9lxSmYZ610nk8kQFNBN5wKFuvOndD3m2Ey7x0P5sE3Mfc+hxWJIAeDKO97D8qKE9efHM/7pmsu00Epc0l0dIDPhxSPucds2ybR0VFR30UljWWyTMVcShJMlmXxs5/9jGeffRbDMHj/+9/PFVdc4Wosk2Xx4sU5QQq9vb00NDSMOt7X10dDQwN1dXUMDQ1hmiaKorivB0fb6uvrY/HixRiGQSwWG3dYe0tLC948E1E5aW1tZfXq1eN+34v33odSW5srrKtszGh03J+X/1lmPEYi5ggof2MTxsAAqXAYbBvZo2FLErauO4m44FaJsIei1J55Bi0TmM+JaNv+GMlAYFRHXu/Lr9DyiX+A1asJrVgxolE1Lpm01jbRazNeQq37OLTnaRRVRa5b4OR27Xmat69YMeb4x/O+6ZrLdFDqXNqam0dp0WYigbe5eUru0YkwF69LMpks64a+JFXitttu47nnnuMzn/kMn/vc5/jzn//M97///bIN4uyzz+bw4cO0t7djmiZPPPEEa9eupampCa/X60rknTt3snbtWjRNY82aNezatQuAHTt2sHbtWgDWrVvHjh07ANi1axdr1qxB07SyjXUmyAk46DyKmbUbnKjpKr/MUKZckKxpSJKEFgwiqyqyR6NqabOjLSkK4Ghptm07xyxzTL/SZHxipfiQpiP6byqYaOCGCPgYG1FXcW5Qksrz+9//nkcffdRd4D/wgQ/wd3/3d2UbhNfr5Xvf+x5XXXUVyWSSdevWsX79egBuvfVWtmzZQjQaZeXKlVx66aUAbN26lWuvvZZ77rmHJUuW8IMf/ACAa665hmuvvZYNGzZQU1PDrbfeWrZxzgTZviCtro5UXx+Jnl689TaSomJEo8iayouXXVkwj6eYjyY/CdZM6a5AymBblptQLWuq0wIdwLKcBn+yjF03f8weTpPxic1lH9JEW3mFnKMqAAAgAElEQVTMlRYgU4XoezU3KEkw2bado3V4PJ6yaCG/+93v3L/f+9738thjj416zRlnnMEjjzwy6nhTUxMPPPDAqOPBYJB777130mOrFLJ3yAqOL00PhUj1h6hqbHR8cbpRcOHPFgy2JDH02uvsv/Fm/MuaWX7pp3lHdh09nw/Z683p0STJMhmroRYMkuztA5yF0LNgAbZhYH34AyWNHcbviJ5o3cDZkHQ7UaE7l4V1uRB1FWc/JZnyzjjjDL773e/y1ltv0dHRwXe/+11OO+20qR6bgNHmLDUQwLd0KZ7589Hm1aCkfTCFzDoZwWCbJnp/v6P9yDKJY8ccAQauGey0f/wasqblmEDkqiqUKn/6b7+TmCvLyF6vW+F7LGd9Kaa4sUx9E6kqPlvChSdqchKmKsHJQEka09atW7nxxhv51Kc+hWVZnHvuuaJR4DQx1g4526xjxmNuh9pkTw+h1n3u84muLqfkkCwj2bZTKy8twDKLfLHOs4B7rKpxCad85YocwTBWFF6hseuRCFYyyYuXXYlSVYU+MOAI1+pq4p1dHLj5FtSAH39zc05L+VKZLeHCEzU5CVOV4GRgTMH0ne98x/1bkiQ3wADg+9//Pt/97nenbmQCYGxzVtf2nSRDYbBMx8wmOXXrJEni0I/vQ/FXua8nHTKfqR5eyC9RTAhMJEeqa/tO4h0dGLE4ak0NWjCIHomgDwzgCQYdgXn0KJZpolT5MONxjMFBVwuYaI7WbPLBTNTkNBtMVbPBnCqoXMYUTKeeeuqoY+FwmJ///Oc0NTVN2aAEI5xoh3zox/eR6u93hE8aJRhEUlWwnQg6SZadQIZ0mwstGJwyv0RusMYCJEVFHxzENk1sw8ATDLoBFrZlQbqBoG0DkoQsy9iGOWFNR/hgZp5yJoILTk7GFEyf//zncx7v3buXb3/722zcuJEtW7ZM6cAEI2TvkDM70UP3/hTfogaqzzyd/qefdV4oSSBJmNEosteLreu844rLaP/5g8Q7OpxqDfPrQFamzC+Rb0rTgkFknw9v3fxRpseMMDUzQlVRQJaRNee2nIimU85Gi4KJMVvMqYLKpSQfk2EY3HbbbWzfvp1t27a5NeoE00uhnejggddAVZAkGSljrrMsjHCYmtNPc4VatmnFWz9/ykwrY5nSMtoMlllY4JimEwG6cAEwMU1H+GBmntlkThVUJicUTEeOHOEb3/gGgUCAHTt2sHjx4ukYl6AAhXaitmUhKTJYtvN3umSTbeYmvk6XX2IsU1rjRRdy8M670AcGRjrngqPppR9LsoycjgScTEt5IYhmDmFOFUyWMQXTo48+yve//30+97nPceWVV07XmARFKLgT1VQs3cDXUO9EvOkGsiLjW9pUtsV5PI7sE5nS3IoRWUhpjc8yDNcUOdc1nXIEB1RqgMFMmFMr9bsQTIwx85iuv/56otEoP/nJT1i1apX7713veherVomLPt3klxECnI6zsgyygpppAKgbYFOW3J3x5gWNlXvUtX0nanU1ss+Hm7krSdiG6UYLzjvj9FlXXmi8lCPXqpLztSaSfzYZKvm7EEyMMTWmPXv2TNc4BCVQaCcqqSpNmz5GaO8fiR87lm6RrhA/epQDN99C06aPsfwTmyZ8zok4souZ0jIanxYMkuzpcTSntPZkWxZqoOakCFI40Xdayu6/3AEG+eesOauFoZfbij4+kUaSfw9kEqmnQqMRwRZzjzEFkwgJryzGcuwPvdyGFgxiDA4CICkKtmnS+fCj1Kw4peQFL59yOrJ9ixqId3ZhxuO5T0gS/ualLL/00yfFQjLWd1oowOXgnXeh1dZiDg+7QmLwwGvYloXi0dCCQRR/oOB1KeWa558z3tnFwKv78QSDqLW1DHcdY+DV/Wi1tc6mYpzh3+UKHy82FxFsMTkq0Qxanr4VgmljLG3EjMfdCg/gCCfLNN0SRRNZHMrpyK45q4WBV/e7Y7PT5ryln9g0Ka1utjHWd5q/+8cy0YeGMONxfEuXEm1vZ+DlkfYCVgqSvX146wFZybku5sE3ObTn6RNe8/xzZjYOxtAQZjyGmUiCbWNEo3jmzx+3RlIOjWasuYhgi4lTqTln4+ugJ6hYfIsasHR9xHfDSJWHeEcHr992B8m+PlL9/ZjxeMntEspZm23o5Tan6oNHA9tG8Wh4gkGGXp76xoyVxFjfaX59QT0ScfxwloU1HMcciuZ8lm2a2JZFKhQadV3Mvc+V1CIj/5yZ/DIrlcIyzBFzayqFEXNarhTTSDImu+c/83meu+RS/nTp5xk88JoT2JLFeDWaseYi6gdOnEptoyI0pjlC40UXOuYd03S0kbT/RvJ4MGJxJ5RclrFNk1SfUyVc8ftPuDiUMy8o0d2DmjYHZbBt+6QzuYz1nXbl7f4t3XAbM+qRSG5Eo6qCYYBlYSVTKFW+nPNYkQhy3YKcY4UEQr7GIakqdiLhat+ZiiGZKh1qIOBqJNlmIMVfRSocQVZV9LQAlSQJG5tUXx+SJLnV68er0Vi9faRMA9swnUTxYBC5yrl/Re7axKlUM6gQTHOEutWraNr0MToffhTLdH68aqAafXAQtaYGaziOZZhueSI9EkFSlKKLw1TYnYXJZYRiJtn8ABdJkbEME08w6GwosnK+yNJCZJ8XSzdyzDByuvTUib7v/HMqfj9WIgGy7FakxzTTEZSGq5HUnNWSYwZKdB5177HMP9uykCUZyzTRQyHns7PCx0v1gZFMYtk2sqJgGU5tSK22lqrGJWN+n4KxqdTfpDDlzSGWf2ITZ3znW9T+zZlo85wfreL3o2Xq09lOEi6ApetFzR1TFX47EZPLZDrgzkbyQ619S5agVlcjKQqSqozKAcugVFWNMsMo73tPSd93/jn9TY146uuRFQUsC1nT0OYHnVJRkuSGfw+93JZjBrLNdIK3YSBlpwNYFt76hc4YssLHgZLus67tOyFzjrTmZts2+uCgMNdNkko1gwqNaY6Rv3Ns27LV+eH7A3jrcVtjKD7fqNySzO518MBrSLKEp67OtTuXI/x2vCaXSnXMTjWFQq27tu/EjEWxkqmRF2aElCxjDiecP7PMMMqKU1iIxLGdj2MODyNrGmrtPA7d+1O60t89kHM93nHFZaOaTGZSE2SP1xUoXdt3MvDKq8gezQmI8AeQNRVTNwAb27Yd4WTbSKqKpDg5ai03bnOH37Zla0lBEYnuHqgO4Kmudgr+GgaypiF7vXP6PpgOKtUMKgTTHCfHTFPlR5MVVMMoKJQyCxG2jWXabrRXfihytvlF91cRQir5Rh6PyUXkpzhkf2fPf+bzmImEk0Rtms4LLAtLdwRWthnGPPgmvXuedsLJq6tJ9fWR6g/hrV9IMhTmjR/+yNl4pPthjRL8BRYsGInulD0eLF137xMtGMTs6UVSVKeafbpEluL3F9yFl+rf8C1qIHn4CLphONqYqqL4/fibGkd9V5UY+lzpVKIZVAimOU6pO6Kc6BxVdRc9PRJB8ec6u7PbtVtHO3PatZfzBq9Ux+xM4m9uZrjrmFNvMJ2rBoBpETt8GIDk8ePs/fgnsbHRaubhqa4m1dnpphGk+kNOKkE6wEHOisjKFvyFFqxsLUcLBkn29mIbBoljx5G9HhSfD+/ChegDEUeApnSMoSGqGkcLkVL9GzVntTDwyqtY6WAMS9exBgaoWX9ezutOVg17LiIE00lAKTuibCGgZRztgJnSc+zO+e3ayW/XXsZFoFIdszNJ40UXcuDmW9K+lrwn0/5DO92pGMNAD4cxhoawdd15jSy7r3NebLvXWg0UTtLNZtRmIe3zId0Z2dKHSUUiyF4P9lDUrUSiDw2Nuj9Krak39HIbVAdQdB1LN1A8Goo/QGjvH3OqUeiDQ0LDniOI4AcBkFuHTw0E8Cxc6EZWZdc6y+S8ZPJrkNPN/bLatZeLSnXMziR1q1eh+P3ImuZGyqGqOflrpKvLu39n57elhVJGewInbynZ04MZj51Q8GffJ5nIzoxZz/lgCTMeJ9XT647BNk2MwUEsXc+5P0qtqZfo7gG/H19jE/7ly/E1NiFpKvG3OnICJ+IdHZPOlxJUBkJjEgCFwpQVtPmjF4qMFuO2a7ftnHbt8Y6OstVEq1TH7EwTWNZMMhQm2d3tCBhJyl2QM1rMGNh5CziWRaKnF62mhsYvfLbo+7LvE9swsEkrbukmlZIsY6eyAjQsZ8NiWxZmPO6WXRrPNfUtaiDZdSznmB4OI+dpR7KmYoTDaFkanZVMolRVTVmdPsHUIDQmAVD67jWjxUgZk5Blu+3ajYEBjFi8rGHmdatX0XLjtjlfcXw8uNdAkbHSQQYnEkQAaJr7Otu2HW1HUUaetywkj3bC4qyZ+wRJQlZkJxQ8HegwKpw9o71JEpauo1RVjTsVofGiC8E0czRnSzdQ58/Pnd78+Vh5GrYRjaIPDIjK47MMoTEJXE7ki8rsdK1kAluSnAVHUfAsXACygj7Yj1pTI2z8U0xGk2y//0Hib3U4prR58zCHhkb8R/kCQpIILFtGKuNzsixsGInsUxSQJFJ9/YRa9xVMcs3XchpxIvSQnRwrS8/TwtJktGtJUUBiQtXqtY+ej/flV9zzy6o6ymwnKSr+Zc1oNTUjr0v3KxP35OxCCKYycDKEqGZHPGl1C1CSSYxoFNPnBcvGWz8fIxrNKTcEwsY/VWQ2Edn3njcYZLirayTQIRvbJhUOI2saTZs+5uQ2xWKOBqUoyIriaD1pP2GxVIJsraP+wx9EqfIx3NkFtuUIw+zAirzzN236GL3/87sJRVoqK06h5RP/QKh1nyOQOzpcM6Fn4QInRN0wWP6Fz+aM/cXLrhSRnbMQIZgmyckSoloopwjA0lTW/PAHQFYy7yyIopsrm4lsLTfUuo/2nz9IvL3deTLtf8poRVYyyaL15zH0cpuTDAsjfiHLESxqXd2oRbvQtU+Fw3Q+/CjehgaqmpsxBgZIhUKjB6jIyJoH2etl+Sc2MfRy24TvkVDrPg7eeRf60JAzN9KBG909+JcvGyWUQER2zlaEj2mSVGp13nKTX4EanJ2nFYm4jycTRTedpYfmYsfTzJysTFCKJDnlhBQF7+LF+N/+diRVpXfPUyRDYbS6BU6+mmWl/VUKnoULkVV11KKdf+2NWMypwKDr6KF+p+p5POacV5aR0lUZJE1D8XjwLFhAYFkzMLl7pGv7TszhOJIsIysKsqY559I0tJqaorUHRWTn7EMIpklSbMGea6aCQm3drWQSOct0N9GW2tMtKObiZiJ7TrLH4wgdRUFSZLcauFs1IT1vb/1Cx4ynqvgaG52q9AUW7exrb8RiTt5TpuZiuqCqldJHQtCzajKaqdyajKXcI8U2KYnuHqdIbHagR7oWX7Hf23S3eReUB2HKmySVZiqYKhNVsWRIZd25Oa+bSHmTYqWH2u9/cMy5FJpr5vPGmv9YFSVKiG2rSEpJkM6E9Gdw6ifapPpDTsHYIt9X9rXXIxG3kCpZFcRty3I0NI8HpcqHPjDoCC9Zpv7DH8z5zLHukWKmcevDH8C/qAF9IIJt2SPCybaRZHnMkPBKLLkjGBvlX/7lX/5lpgdRKZimSU9PDw0NDahqaTJbra0l/MKLjuNYUdwFe9mnL3FL8udz7NgxGguUaJksmR+1mUyh+P0Y0SjhF17E17ik6FhKpapxCb7GJQy3t6OHw/jqF7Ls05cwUDd/0nM58vMHMIYG0cNhzHjM2blbFomuY25dtPy5FJpr///+gd7f/4FUX69jbgqHiez7C1XLmnPmH3r+BYxoFDnrGlvJJL76hZhnnj4l12aqyZ6T7PEgaRrm8DCSLBNoXsqyT19CouvYqHnblkWgeSnn3HErDR/6YMH7JPvaJ44fB0UGHN9VJhQccMLHvV6nmWC64oNWW8tw+1sl34Nv3n2vc03TWp2czoEyurpYuGY1g22vOPlTGWEISB4PtmFgmea47/tQ6z7evPte3vrVQ4SefwE1q5XGVDFVv/+ZIDOXiaydYyFMeZOkkkwFU22imoqcolDrPoxY3GmIJzu9hxI9vSSPd4NlFe24W2iuRjSKFY9jW3Y6ysxGHxqi/f4Hc845F/0O+XOSFAWqqznjO99yr9Vk5p259v7mZid3LRvTTLfHUJ227Kbp5i/pkQjJnh5ev+2OHNPsWOa6gr7M3j569zyFVluLpGkjEYC2DZbTHHO89/1c9DXOFYQprwxMxFQwFSa32Vj0tGv7TtSaGozBQeyMbyKTWzNGx91Cc81UM8j4OjL9e4Y7u3JeN1ZFicOtrVM53Smj0JzMdeeOMqFNupKGRNGwcCulj+RPpTsoZx6b8bgbrQrkFAIeeu11txCwUlVVsLkhpuMfU6urkTSNVF+fW3HE0g2nPl+6hh6Udt+fqHr9XIncnI0IwTQDmAff5NCep8seYl5p/q5SSHT3OG2yPR430itTFFRSlKIddwvNFRhVAaGYz2gu+h3y59RaQMiWmkRdbDE248OuqXUUWYIopyxS+pgeDnPg5luQPU74OKkkengkqnP4aCdqdbXjw4IcXyay4mpSmTqNsiynW61rWLruVsKH0u77sTZyJ0saSKUiTHkzgLn3uSkxuWWbavRolOGODhLHjqEPDY3bPJExtfzp0s/z3CWX8vxnPj8lYdyZiC81EKCqqcmNJpM9njE77hYyS2XK69iW5Wpftm0XbLkgGE0ppi3foobRdfYyFChHlC2oMn4hM5FAHxjIEUrgaLxGNIri8+aYxus//EGwTOLt7U6h1kQCW9edeyJT8sg0MYcTGLFoySbKYpGmvkUNczJyczYhBNMMYEUiZQsxzxYgr992B0Y06jSE6+4GCbwN9Vi6MS7beWaBind2YcZizkIyFGW461jZbfCjfCPpwrCeBXV46xciq87uPL/jbiHfXtMnNqHOm+dEbKVrt6nV1Sz/zKfLNt65TP5ijGWiRxwtJ7MpqTmrpWh79zFJF3lVPBqKRxsx1+ZhGwbJ3j4aL7qQNT+9h8aLLqR3z1OQ2aik8jr4ZgIw0rlbyZ5eZE0tyc87ls8t3tFBqr+PeHs7ia5OzHis4s3icwlhypsB5GCwoB19vCY3N6lS1zFjMdcEIqXLzGjz61ADjmljPPXBMguUOTDgRFfhtC5IhULIHo32+x8s6XMyZqHYWx1uuLK/uXlUKG+O32PJEqcJXtp0U6zjbua9+cdqVpwi/AITJNu0ZcZjJHv73IoQA6/uZ/DAa2jzg8hVVVjDw2N/WH5B1/TjTMkqc/h40bfKWWWR2u9/ED0SBsN0yh4VIm329Sxc6EQCFki2LWqiLNKl14jFsdMJyplcLW0aIvYEDkIwzQDK+96Dvefpog3SxlrQYSRPx4g5uzgzHk9Xena0i0wmvx6JuIJpPLu9zAJlG4Yj7LJ2t5ZpEX+rY1Shz/wffs1ZLfTueSpHaJoJiLa3c+DmW1D8fgLLcoVU5v1aba0TjTxGbk0x5qLvaLrI9tvpkYibmwQ4fiXTJNXTC9rYy4YaDGIlEk67d9sGywlS8NTVuT4gSVWLmgQtw2DwwGu0//ph4m91uOWHKKao2TaehQtRAwFs2x51n5/IX1SoS292QE4miVcfHOSUr1xxgm9RUA6EYJoBlBWn8PYVK9yFWPFXgapy6N6f0l5VldYY5JwFfbjrGAfvvAszlYJMHodhYA4Pu7Z8G0a6iQKkf/hGLIYeCmHbNm1btroLfbFdZGaBklQVO91+O4MkSaMKfRb64Q8+/ChqTU2u0DQMzKEoyDJWMukuEEMf/iC9e55y358R1O+44rIZETInazRWdiKtmRVhJ6mqc90VBcs0QTdGNKK8BoWSqmIn0wm9ippuyWFjWzZWSkeusp2KIV4vZpbgc8mUNZIlOh9+1O03Zeeb/vI0smR3N0lVRaupwd+U61M8UfRdPonuHickPdNbKt2MUfH7T4r7oBIQgmmGyOzU2n/9MJ0PP+qYDTSNVFqAyOk2BBktyIzHMHUdDNP90QC5P+zsH6thYEsSib4+zGgUAG/9QuKdXW5klJVKodbUoAWDObvImrNaGHz40ZEdbdYCYOs6tqYRe6vDPVboh+/ksIRHcl401TULYZpYpoke6kfxBzi283G0YBDbNEl0dbn9ntp/XprJcLyMJXimKmJyNpBt2kr29LhtTdygEttG0rTc6uV55jrbNDHTxzL+INnvx06lSPX3ow9E0OrqnCr1waBTkDWr7bukKI4GVFdHoqfXOZYdRFEM2wZdRw+HqVl/Xs5T402jUKqqiB896nRwzgrC8eRVzj8RJ+sGpxzMWcH0+OOPc88992AYBp/5zGfYvHnzTA9pFKHWfa5QkhQFS9dHKkGbJqgKEjKSJDm9bgznObcmmaZi63mdS7ORJMdPBKAopPr6XWFj6jrIMnoohB4KOYLOstj/rze55WYKLgaKAoaBMTjI85/5PGYwiBGO4FmwwH1JKhwatRO283v1SBKWYWKGw5Duboptu+OwgXh7Oy988QpOufLysv2gT2TWMfc+hzLOfkGlnnc2LFLZ7TQO3HyLu5mw08JBq61FUhVS/aHRpjjbRq6uBsPAymjato0VjzubKVVFUmT0cAS1pgbP/Pl45s/HiMVIHj/uJOl6PWjBIIo/gKyFsXQDT309ye7u4sIpe0Mmy3T9ZjudDz8KQFVjI4q/cG5UUZ+ulPunXeD4iRjrPhOcmDkZldfd3c3tt9/OL3/5S3bs2MGvf/1rDh48ONPDGkXX9p2ug9U2jNGRSoaJlUxipVI5i4CVTDrmriKN2Vyyf8imWbCddsG/8/xKOaSz+rFt9IFBrFAIMxYl/lY78fZ2hjs60EPhsccFjoCjQKJm3uNUby/7//Um/nzNN8oSDXiiMOD8iEkzHiPV38fAK69OOFx+NlYYqFu9iqZNH3OqcZgmkiKnhZLKO750GVVNTW6xWNnnw7t4sbtp0YLBUaY2SZadvCPTctusZ1ADAWSfDxQFX2OT64dSA9WO1qIo4K8aGVxaq3I+GCQ1neOkKo42nki6gjR+9CiJnl43ujQTfWfGYuiDQwWr2Zvx4ZyIUFlV8NYvxIyfIOCDkSjZAzffkrYYmCLcfALMSY1p7969vOc97yGYVr3PP/98du/ezVe/+tUZHlkuie4eJ3M9pRdvsDYZJhLWO97PHxxyd5Q2xf3TozCM3NeeYKyJY8fKYlI7kVknO2LSjUyzbWSPZ8JmvbF8HJnnK1GTWv6JTUWjHA/d+1N8S5fmVPrWI2GsZNKp1JF3PTM19WRNxbZtxzqQheL3Yw8N5QQESapK06aPEdr7R4gPO2ZFCWwja9Nkp7VxDdeikDGBA87vyjDQ6uvR5tW4Pl3DtrEMo6DWnPGx+hqb3NM441JHFYqFkeunpP3DSiDgaIqmTbK3D2+9UzB3thcKnk7mZBHXZ599Fo/Hw3vf+14A2tvbOXToEB/60IfGfF+mEOHLL7/MqaeeimEYXHHFFSiKwqmnnkoikeDLX/4yXq+XU045hWg0yle/+lUCgQBvf/vbiUQiXH311dTW1rJ8+XL6+vr42te+xoIFC2hubub48eN84xvfIBAI8M53vpMDTz/DPW+8Rp0kU6eodOs6/xHqpUFVma+qdOkp/m+ol8WaRlBROZpK8fNQL02ah3mKQnsqyQOhPpo9HmoUhcPJBA+G+3ibx0u1onAwmeAX4T5O8XrxywqvJYb5Vbif07w+qmSZ/Ylh/r9wP2f6qvDKMm3DcX4d6WelrwqPLPPX4TgPR/p5Z5UfTZLYF4/xSCTEu6r8KJLEC/Eov4mEWOMPIEsSf4pF2T4Q4v8EnIV/b2yIxwYjvNvvPP59dIgnByOsST9+OjrIfw8NsDq9Q/7d0AC/iw6xKv34v4cG+H1siHOqAtiWxe5wiF3PPsNHL/kUAPfeey+/+fn9LNr7HG/96iHu+dnPePzBB1n49O8JPf8C//7f/8UfWls591ynAvptt91G6+uvcabPj6yq/OKN1zgwEOZMfwBf/UJ++se9tA1EONNwdvX/fuBVOhIJTqvy46mr4z/eOsyx4WGWhiI0fOiDfOc73+Ho0aOcc845AHzzm9+ku7ubs88+G4Cvf/3rhMNhatteRfH7+WHbXxk2Dd5eMw9JUbjlT3vpfbGVpYqG4vfz/ReeI/RiK6euWIHWUD/pe++GG25gyZIlOfdeQ0MDS5cu5ejRo/zTP/0TjY2NNDY2cuTIEb71rW+xbNkyFi9ezMGDB7n22mtZee77afnkPzB0+qnc8T//xdkfWMfChQt58b//m5+89BfeNm8e8zweDg4M8H/fOsJyVSOgKBzUU/yiv3fk3ovH+GWol5VLluD3eGmLhHmwo52VdXVohsHLAxEeNZK8a0kj0uAgbabOI7FBPvaP32DohRf5Y18vvxkIsypQjSLLvBCL8ptI/8i9NzQ4cu8pCnujg+wM9fHumnlgWTwbCfM7n8YX7rmL/j/s5X86j7L7+DH+z6LFyKrKbzs7ePR//oeNmy9Bra3lV48/xu+6jrK6fhFWMsnjh9/k2eNdnB2oQfH7+c2BV9n15C6Wvvoatmmxs/sYz7Uf5kwkzHic7eE+Xo7HWRmoxk6leKTnOC/39rJ6xQrMM0/ngQce4MUXX+Q973kPADfddBMvvfQS7373uwHYtm0bBw4cYM2aNQD88z//M4cOHWLVKmfTUuq9d9ZZZwFw9dVXE41GWblyJQBf/vKXSaVSnHnmmQBcfvnlAJx++unjWvd8Ph9+v58bbriBd77znWUr4jonNSYrnVyZwbbtnMcn4ujRo7S2tmKaJtFolMOHD9Pa2koqlSIajXLo0CFaW1sZHh4mGo3y5ptvUltbSzQaJRqNcvDgQQKBAAMDA0SjUd544w28Xi+hUIhoOhChtbWVxOmnYr/4fNnnPyexbazhOCnD4A+XXYH2/3yIzldeIbL/AAPL3u4kgw4OYlgWyVSSZNcx+t86gvofC9YAACAASURBVK6q/OHV15CDQY4nYrBwAYlYDBIJdEMHHRKxGOa6c+n9416q58/HOuudmHufwzYt8CjYNdWkJDANA0NVGejooLW1lXA4TGdnp1v6JxKJjNw7B98k3PYKh954k2WBakgMY5omqVSKeDyGndKdqtmmRdIyYTiOZdsYpsVr9z+IsvmTk773gFH33htvvIGqqvT29hKNRnn99dcBOH78ONFolNdeew3DMOjs7CQajXLgwAESiQQdHR1Eo1H2799PNBolcco7sP76F+JDQ8SARHTIMZ95NNdPWOgaJuNxUt4qpJVnYrW2MhwO462vxzr7LPRX2khtWI+nuhp93z4STz3FX/7yF6SODkhHBJopHUVRRvw9qjpids74RsE15WU0NdOjMTAw4PzuOjrQLQvTNJxrkXAqRujDw/zxmm846Rwr3oH16n7ioX5Hi1ZVLD3lXivDtrF0AyMWw/RXocdi2OlAH9s00+YDywkGMU30ZBIsm/hZK1GAnp4ePB6Pe+/09vYyPDzsPu7r68OyLPdxf38/mqa5j8e69wAGBgboSN+nmcdvvfWW+3hwcJD29nb3cTQa5ciRI+Ne91atWsVf//pX4lmm2XIg2fZU23umn+3bt/Piiy9y0003AXDXXXdh2/YJTXnJZJK2tjZaWlrw5lVmKCetra2sXr0acGzSr992B2YsNmXnm3NIEnJVFVgmlm6geDTHd5EJb1YUJ9KwtxdZVfA1LcUYGEAfHETx+53oKglS4cioPLHD2O61KdQq3kwk8NbNp+XGbUUDGrId37LXix6JoA8M4AkGUWtr3XB4K5lwOsnmbaLMaJQ1P71n0l9T9n02FRSaf9f2nSRDYaezrWE6PqqM2S7tazntH79WsrnS/X0MDyOrqvNZmVp6Hg9Vzc3uNWm86MKR1uuZxHDbRq2u5tRrvuqeM/u6ZhofZgrCehYsdFIV8sy1L152pZPKMBxHj0SwdCNdw0/G29DgBG8UIp1eMe+M0937Y6qvy3SSmUu51845Gfzwvve9jz/+8Y+EQiGGh4f5r//6L9auXTvTwypI3epVnPaPX5vpYcwu0pFeViLptsqwUik3rNdKJJwoLtN0nhuOow8MOM8lk1iGQSocQZIktGAQrW6B62cwD77pnmaskjVjBTTkB1h45s9Hq611nO5ZrVH8zc1Fa7UVYjrbz5dCoTYo7neWcoSRbVlI6cXbv3w5aiAwLqF06Mf3ucEomWucIVO4NXNN6lavYsXVX8HfnPZ9SRL+pUtzhBLk1ZRMNz7MXKdiQQq+RQ0YAwNOt960wHUGYZEKhQpPID2G7NYjgtKYk6a8RYsW8fWvf51LL70UXdf5+Mc/zjvf+c6ZHlZR6lavovqsFqIvt830UGYd2Tk1btRhdkSYZZHqDzk7V1l2uu76fKR6e0HCDXPPBCSYe58jlJ/8bI+uQtG2ZWvRgIZCARZaMIgZjfKOKy6ja/tODt37U8cJH41ipU1JmZ5UC9f97ah5zpZq15lcqNdvu8MJGNA0tGAQNRDATCTGVXYrI+A91dVO0nl2Uq+qOmkGiUSOBlZK5Y+cUkTHjiF7NEcojdEyo/GiC53w+XQ9R9u2nehAy8qt3wcj47Rt/MuWVdT1mS3MScEEsHHjRjZu3DjTwyiZs2/cxoHbf0j/08/O9FDmFraNlUohezzOopJ2zGYqj2cje71Yx4/nCAArmcSMxdBqa0l097g76WLRffGODizdINnfn7MoW8kkSlVVzmcbAwMY0SEw0/4YTUOtqaF3z1PUrDglZ0Ebb/WCmSRjBcg2Z06kGWP+dyxrmhPJaVn4ly1zzZ4TmX9GgOWba4tVSalbvQrF73fNsJKqpjc1NsnunpFWLenwdst0tCpRQHhizElT3mzljK9fw5k3XD/Tw5j9FHCbWoaRU0RUkmVkVXGfN2IxEkePQjyOHg5jm+ZIhe2hIRLHjuVoKpmkzWyMgQGMWNxd5Cxddyq9h8OONifhCpeMeRHTcrQ5TUNKh6UXMiUV6+xaqdWuy9HZWfFXkeg8Sry93dFM0jl0mc1FOXqN1ZzVQrKnh9iRI8TeaifZ04NlmngW1I3KNwssa8azYAH+5cupampCDQSQFBX/smV46+vx1NUhaaorlJo2faziNg2zhTmrMc1W6lavwrt8Ocn29pkeytzCspD8fhS/3zExVVUhSRJmIuH4nNJdcp2KFIZTey07KCEd6ZnRVLBx/RSZvBt9cNAt8SR7NPRIBDOlYyWTrPjqlY75Lq0BZJrdOR9u5zRE9DU2jhI4s7EJ5GQK6oZa95EKRxx/Tvp7yggmra5uQhpYoXP07nnKreloJRIAaHXzUdMpD9laaXYtweziy8u/8FlgYvlos6UiyHQjNKYKZM2dP3Bs6IKyYsVixA8dQo9EWLzhI6y4+it46+ajh0JO9F5D/Uios52uSpH+Z2cJKdnrxRweHqURKH6/q5Ep/gC+xqYch392YzpLN0ai8bL+d6L1RgucsQIx5iJd23eiVlfjra93NCTZCSTAskh1d5M4dozqM0+f1CLu+rDmzx9pUqmqOS09srXSsbTAQoEgJ2I2VgSZLsTqV6Gced23OfD9W7HzzEWCSSBJTo+nYND147TcuC0nFHhU/k3Gd5AlmDKCI18jyPgrnAK2EbcYrW+J08Mne8ctqYpT/zBdXscNg7Ztkj09owIgivUOmqu764x/SZIk1ECAWPdxiDopFZKmYVsW/b//A+2NjSz/xKZJnSODpKpOnT89t/xX9iahnG1VZpPfcLoRGlOFUrd6FWd8+5+E5lROLAsrlRzVmVXxV7mhwPn+KUlRnPYHpnlCTaXxogsxolGSvb1OLysc39Zw51H+dOnn6dq+k/oPf9DRrnw+JNmpP6fOq3G1NEnT0ObNo3fPUyf1znlU2/NYOoEzHV0pp38Xx3Y+XrZzaMGgEyCjyNOilc42v+F0Ila9CqZu9SrOvO7bTtJgdGikFphg4ljpRnKyjAQkQ2GMaBQzFnVkUlY5aUlRkDUVxe+EOuuhfscElxWckL2zrVu9Cs/8IFZiGNu0nI+yLGwLjKFBhruOkejpdc0/Gf/C4IHXkL2enEZ6+bX04h0dGLF4wTYlxRo26v4qQkizcved789xe0MpIwErkiw7/cjKdA5JUVCrq/HMD06oSWU2pfiOZqPfcLqYk7XyJkqmVl656j0V49ix/7+9c4+Osrzz+Pe9zTU3JjcSBIS1dl2hFZPWxWWTE2hDLgREZbHLpVvOlupxi9vuUS619nAqckjdVQG31l5gvbBaqhBhoWWXmmorVQi2lHMQNEISCJCEyWUyt/f27B/vJTOTSTJJZpKZ8fmcw9F5887kefLO+/6ey+/3/V5FcXHxyCcCsBcXwT5jOqSuG1CCQSBSxJIyJhiO0/YXXC4wLAvZ49FSyhV9GYfjzOJdPiMDhdWL4W+9DM7hAJ+ZiWBnJzqOv42r/3ME3U2nweu221fePAhLXh44iwDZ4xn4hUTL0mMtFgSuXEHBwgrYi4tQsLAC1//3OCx5eWAtFvN0JRCAv7UVnb97F1J3t2YQqRIQUQQrCOAdDhBVhb+lBQULKwAM7FkoQRGcwwGxzwPPh3+Crbgo5SzB7cVFsBUXwd/SovXf7wOYgZkSoCVDcHY7HDfPRPN/voDW//4l3B+cNK/FaH+HLT8PN//TWsxe908oXrrEvEajJfI6yP396D55yrwOxv3PZ2ej++SpAdsbPZlixup/TJnrZfQl3s9OOmNKAaKta7e8vh9XfvmrIe2pKcNgeAvpiQrGaNmSm4ugqsBCtKw5VS+cVAIBXG04BNZqhSUjA7LXa9puK4FA2OzFGAVLPT0DxaDGfxkGsrd/xIw7QyYHqgpwnFZvZexHMQyknh7NKiJi2Sdyz4KxCGBYLmX3LEK/9yeeeQ7qu3+Aqu/bEX0vMOdLJeMqPI7nnpFBLHtHxoxKDQag9muzcOeM6Wm9bzgaaGBKUUItCbytbdroPBHWGakGz2m1QSwb7ikVkpoNhoElLw+8U1s2U4NB2IuLzT0FLjsbqihClSRT387X0qJZglssA6nehEANighev2467s78+mpttKxL8ph7VkaSQyAIUekOK96MXFKSDIkbhtF8tIzP0B1ljcFI5LLPaJ1aUwlL2QLkFRfjasMhKH4/OLsdRcvq4PnL2aSzFBnpOoS6JAuuXHOmFK19n9V0chqYUpjI0d7Jbz4IsaNzElsUQoRRXMJgWThmTNdkg/x+2AoLkDl3Djx/OYv+Ty+ahnSs1QLemWEmMTD6TCSyFuX8S69A6e+HGgzCkpMzkP5tEaCIA9psJNRMUR/B+9o0u/nZhiSP3x82ayL6+YLLBX/7VXy0vR6cwwHnjOnIX1QBz1/OInC9QxMezcqE3NMbPuuCVjvFWixRN+bTfc9i5soVgzLwTv3fb4dU4JgsCaeRrkMsLsnuptNo/vGLmnQW9ExERUlKGapEQANTGvFXD67XrNGHYgKCBcNxEHJdYAULOJsNwRs3IPf2hllfCznZUP1+TaEZBAyjadiNtm2c0zm0UrX+AIs24gSGHklbdHVxI4Vc9np1NWkRUFSoqgrWagXRizEZgQfDMCC6CV77gQbMeXKrKcmjSlJ48eaUHLAsg4BuKS/39sJz3g9va5spNnr28R/Ac+FCmOaaCSFgrVZTTTtSnDR05kVECYQnaVvrBAwdBFRJBuecnFTsQTPgnh7IHg8Ubz/OPv4DqJ1dYAsLw94TOqNyN53Gx8/t1u4bHaIL1go5OSm7NDsaaGBKI1wld+K2J76Hc9u2D+ivAZo0f34+Zut1MJ4LF0BUYiokE1UNE0ONBmO1jlxTxTCw33TTwHJWIACLywXe6QSRZeQvqkDn8be1JYycKeZsZfa3/hlAuBOo2NsDubdvwOLAZtX+V7casE8rxsy1q2MS7Ix2zkjvsxUWwHelHXJfn5aizPEgRA+ehrI0y2pBVU/1Fqa44GtrM11OOYcdnM0Gluch6jI3vDMD3kuXwpZdVUWB2t+Plv96xVzaO/fDp6IGatZux10v/WLIvobWOrGZGZgdw98oFCOQe1vbBlmCJOPDcCg1BkOjL5SJWtYMvQ7e1jYoPh+ErCzw2dkIuruBYBByb685Gwe0YMo57Dj7+A/Q99H58L1jQ/lCVaPuUaYjNDClGa6SO/F3b+4fdm3a8KwhhJgpzcPCMuDtNsiq5uwatncD6JbZgmbk5vcPWww6lFW30fbJIFqKdaiaNKurSRt1R/biIlM/jygqWIGHkJMDVZQge31mJb8ZeHVF8aC7G4rPO/jvp7/2tbbC3XQarpI7wVgsAwMBhjH3qEYyvAwNxE1NTaMOSuYsz+vVkzsAf3t8bO0TwVDfNePvPVnLmkOJxHI2G2CzabOofg9UWQHLc2B4AQzPa6sIhvJIKPprNRCEzHnN70m6QgNTmjLcTOGWDQ+j5aVX4L/SDkBfvw5RHoiEczghZOeAtVo1JeUIjP0alufMG3+4359MN9QgO4neXnz6k58hf1EFQLQgrKoqGJ6HNT8PrN2BwPUOzH7wm2Hq2ZpW3g3wmZlRl48y585B3/43hp+ZEmIGAN5ug6RnoDEMA0KIaWY32v4Nt3ke+nPZ69Xa7PNpgw2OA1FVKD4vBFdu0i4hDfWdijaTmuhlzWiJEOA5TSSY47SBhr4/avhBcRYBSiAkOEXekwyTtAOFeEGVHz6DuEruxLzn/gN3/+o13P2r12DJzjZVrRE5Itdl/KXeXjC8AOvUQlPd2fi55iKtgrM7Um4/I9LUj7EIUCUJV/a/oRVz8rzWXz01PHDlMsTu7jAVh2haeQas1Qpvaxs6j78NIStr2LaEKos7pk+HJScHLM+ZM1qGYSB7fTGbBCqfNA+rxRap1aYEAhCN/TT9e8AwDFRJTrnsvniom8eDQQoWANDvBWvV3HcdM2fCPn26WUoAaAoU5nJxFFQ98SVSgT6doDMminbzyDLkvj4wPB+2XOeYPh1gNNFRYybAOzP0pYh+qIomRmq/aVpMez7JRrQRreLzgagqrHn5mkwRNItuqbsH4DhY8vIQdHcjcPztsP0xxedDwO+HoO+rAQjb82BYZkAkNgqCy2UGAGNGJrhyw9TPrXm5MWeYjZT9FVlvwwqC9hAlBEQRNeFahgFntaRkdl8yzM6j7YFBUSDk5YadxwqCaUHPOZywFQDBzq7wvSaeN2ex6b7XRAMTxbx5+Kws86HMCAKmrbgPM1euMDPUQuGzs7WliAf/GSUlJZPU8vETNatLkkwpImu+Vmyr+LWHBp+ZCbm31xRobf7xi9pyG89rHj6dXZpWHiFged5MICCKDLHrhrYsF9kIhoGQkx3m8hq6d9L30XmwPBdVsiiyYDN0yU7t6QHringAhsx8Bhnx2Wxm9iCAgaVdjk9rJfNEEm0PLChLYLjwRy/ncIB4PAMSTKxW8K0EAtrgRhmwczdmsak2UBgNNDBRRlSuHq4uI9W1z6OlWDMsa/rxcA4nOIdTy6RjWSj9/doyl167JHZ2QnC5YNEf8AzDQHS7IbndyPrrz5sb8Ub6tyYKy4BIAzbwFpcLfHb2oNokY8T//tp1UINBBDu7wPBayjDncISlF0er2WH0EfpQCQCR11UNBAaWkEJLCwiZlGWwiSSRhayRM7cPXv8lyPHGsFkUqw8EjVq20CQOIzuUqJqppBGk0nmgQAMTBcDwyx5DpeQWL1+Gi4PH/ylFtBTr4sVfRefxt8P6y7CsWexqptnr+zCKzwdMmQJAX4axO6D092POk1vN33Puye2alI6uFMvwPITcXKiBAOzFRcMmJ5izWE7bNBe7usDabAAhOPXNhyB7vaZcEjCwZEckcZCZYWjgG3RdJQlgGNgK8s2Z2Xjsy1OFoQL7SEulo0ksCf05d8tfYdYtt0R/bxQLj9DVDFWSPhPuuDQwUUZkuBnVxaamyW7euIlMsZ5ZUjIorT2v/O9x+bVfaskgeoacYVOhRmTaRfPwseTlQuzsMuuwhOwssDwPPiJZIpL2Aw0QsrI0G3Y9XVxVFCj9/RBcLu1BeuOGKZdk7m3JMuB2Q83IGFKLLfK6cjab9k8PStH6Ekk6SOaMxRdppGA23M+B2Pe/Pms+XAY0MFFiIhk2kieSaP11v3ciau2S1Nc3bFqyu+k01KAmCGu4sErdPZC8PnCCgMC1q1BlBVJvDz7Z+Txu2fCw+XDr++j8gPU6IQM1LiwLizFLC5FL4p3OARFYhoHgyoXc2wuprw/e1rZBdh2h/TRVsWNMsR7rTCPZGIvG4EjBbLifY/nSUbXvs3bvATRdnEKJmZlrV0PImQJrYSGsRcUAq1lnTFtx37Bpye0HGkzDwbCaFFGE4vVCFSUtKUIlkDwetLz0ysBDn9WSJYwZmiUvD2DZMIsMI0VdlSQte1AXgWWyMqH6fZB6e0FUVdunGsa+e7Qp1pGp9pzNFuZVlSpES+keaaY4ksnfeE0A3U2ncfbxH+DUNx+KuTwgnaAzJgolRoZdVhnG3jtM/V1PwY5MGSeyDEbQ6sj8V9rRfqBB1w8EIMvafhbLQnK7wbAsOIfDfC/ncMKSI0EJBKD094MQAmt+HkTdIsPYFyOyPOIy1WhG597WNk2XTrcHAbRaLLm/P6b3JwvD7aEOxUhCreNJGEqXmeh4oDMmCmUUuEruxJwnt6L0pz82kxuGGtm6m07jww3f1cQ4jUBESPQ6JoYBkRUY5c2+tjaIPT2aFJJR0KwoIIqCaSvuAysIWoKDXvjL8Dxu/bd/RelPf4ysv/68mY6sSrKuLqAtBwbaryBw7Rr6Pjo/rlG4kZShBoNaf/R/qihC8flSaoQ/lmLc4uXLzMSSaDbsI/18ONJlJjoe6IyJQhkjw41sPZ8048rr+02ri2EJWd4jenKE1Ocxf0YUZcBanOfDvLiibYgbMwAiy2B5Dqqemk4IAUM0GRxmnLI2RlKGGOodZRgwZmUlrXzRUIx2H2ekpITxJAyls69WrNDARKGMkdCRreLT7DEUUcL5HU9rmXqxGDeGGhjqry35+RA7OrSgFPEZajCIltf3Y+bKFUM+SI2H4vmXXgGnyCCKD4SoADOwQCK4XGC4sbvbBq53gM/OhtjTDSgkLJWez84e8SGaDtl8IwWzsSYtpLuvVizQwEShjBFjZKv4vJp0kf5gHqSNNhw8r6Wf67JFhrOu0tcLxR+I+pbL//06rh85GmZHEe1Bb1m7CrPAoOW/XoGvpQUAwFgsEHJzNSsSQsY8CjcenqxgAWEV0+6c5bmYUsw/63sowzGWPa90gwYmCmWMGA/nsAQDI8EhVtNDWQZ4HpzTCWthoWlrIeTkQPFfi/4efc/CeKB7QnyuQh/00m2fx6fnzmsK6DablrWnquYkLTKAxDKLMc7xtbVB9vrAWK2ALGt1UwA4R9aIKeYX/v1ZKIEAOIugq1g4J8zELxX4rNYuhUIDE4UyRsyRrS5jZNYY8TwwgvEiAFOiiM/M1LLbQuSDho1reqKEkWF3teGQ9oCPqJlR//g+mNw8cDYbhJwciF1dIIRA7O6GheXCAkgss5jQcwRXLhiOh9TXB8ZiAas7+NqLi4Z8iIbWSTEsC1VWEOzsgjUfpp0IReOzWLsUCg1MFMoYMUa2xgyAFQSzpijY0RHzHpPi82k6fSHyQcHOzmHfY/gysVYrFL8f1ihW3QiKZi2NoQgh9fRAFcVB1uyxqB9EnmP0VQkEwDudI47sjfezgmDqvRFV1SzDWS4t9lDSYe8sGaDp4hTKOHCV3Ilb/+1fYc3LgyU3F5zDYc6ChvPUAaAlNxAVqiTBOWO6mbIs3rih2Y4YRbkRMCw7UFQbDIKz26MWiEK3qzDgnU5YcnORffvfYM6TW8MemLEUhEaeo/i8EHt6tGW5KH5PkRjvF3JywhI7FFFKiz2USH+rkf4elKGhgYlCGSfR6mA+98i/IPv2v4Ft2jRNcHUo5AGlaKNGyjljOlirBQzLghUErfDWgGEgZGeDtTvM2piiZXVRa2bYv70r5lqaWNQPIs+RenoAaF5CsdTbGO/nnU5Y8vI0UVpVBWezpYV6Oa0/ih90KY9CiQPD2XtzDke4z1EEkUrRgesdEKZMgdh1Q09WYEF4HlBV3PTAPwyyRnCV3Bm1rukiCGaV/X1MS0uxZIJFnqPoUkqhrr3D1duEvt+YWRJZTougBND6o3hCAxOFkiBCs6t6e3r0jAZi1hOxPA9bURFmRsgZGdl+1vw8bU9IksFyLGw3TdPOjSJ/FC0wXmxqGiTS2n6gAZ++8NNRFYRG64+hRs5areb+FTB8vU26Z5vR+qP4QQMThZJAjMAQmtEWOiOZ+fXVg95jzCwYnoe1qHjg3LWDz42VWLLuYskEG48aeay/I1Wh9Ufxg+4xUSgTwGj02KKdm7+oAu0HGsasNp2I/Y+xaMylM/TvET/ojIlCmSBGM1uINjMZj1JCovY/0nkGNBbo3yM+0MBEoSQ5Y3FYjSR0/yNU14+z2eBuOk0fpmlIKtdU0aU8CiXJGa/pHDBgwyD19CDQ0Wlm1LFWK621SUNSvaZq0gLTs88+i127dpmv+/r6sH79elRXV2PVqlXo1CvfRVHEo48+iurqaixfvhzNzc0ANAn/HTt2oKqqCjU1NWgKkZL/xS9+gaqqKixevBjHjh2b2I5RKHFmLA6rkRj7H4qets4KAix5ebBMmUJrbdKQVK+pmvDA5PF4sGXLFuzZsyfs+LPPPovS0lIcPXoUK1aswLZt2wAAL7/8Mux2O44ePYotW7Zg8+bNAIDf/OY3aG5uxpEjR/D8889j8+bNkGUZZ86cwVtvvYWGhgbs27cP9fX16NELASmUVGQ8pnOhuEruBO90wjFzJuzTpplp3rTWJv2Ixyx7MpnwwHT8+HHcfPPN+MY3vhF2vLGxEXV1dQCAJUuW4J133oEkSWhsbMTSpUsBAF/60pfgdrvR3t6O3/3ud6ipqQHLspg1axaKiorw4Ycf4p133sFXv/pVWK1W5Obm4stf/jIaGxsnupsUStyIZ7ZXPGZflOQn1a/zhCc/3HPPPQAQtowHAB0dHcjPz9caxfPIyMiA2+0OOw4A+fn5uHbtGjo6OlBQUBD1+Ny5cwcdHw1nz54ddb9GS9MILpapRDr1BUji/ixfCgZAEMBFkBGdUIHBfVHm3g7pyG+AQAAQeECSAUWBUr4gefutk+ztGw2J7IvySTPkjg6Qjk5NbzHDCXB8wq5zIvqSsMB09OhRbN++PezY7NmzsXfv3pjeTwgBy7KaHbRhIBNyXFXVIY9Hwo4kphnBnDlzYI2YBseTpqYmlJSUJOzzJ5J06guQXv2J2peSErhvuWUgW2sYm4pkIu2vS5xwN53Gp8cbYeUFkMICSN3dUPs8cEyfjplfXx3362z0JRgMxnVAn7DAVF1djerq6pjPLygoQFdXF6ZOnQpZluH1epGTk4PCwkJ0dHRgxowZAICuri4UFBRg6tSp6OgYWC8NPd4ZYhnQ2dmJWbNmxa9jFEqKQ2tt0pfI0gLemQElEICQlZlS1zxp0sXLy8tx8OBBAMCRI0dQWloKQRBQXl6OhgYtk+TUqVOwWq0oLi5GWVkZDh06BEVR0NLSgkuXLmHu3LkoKyvDsWPH4Pf74Xa78cc//hHz58+fzK5RKBTKhJDqSQ8GSVNg+8gjj2DTpk2ora1FZmYmnn76aQDAmjVr8MQTT6C2thYWiwX19fUAgKqqKpw5c8ZMjNi2bRtsNhu+8IUvYOnSpbj//vshyzI2bNiAwggTNQqFQklH0kVIdtIC07e//e2w1zk5OXjhhRcGnWe1WrFjx45BxxmGwcaNG7Fx48ZBP1u3bh3WrVsXv8ZSKBRKCpAuQrJJs5RHoVAolPGRLkKySbOUR6FQKJTxkw7JLXTGRKFQKJSkggYmCoVCoSQVNDBRKBQKJamggYlCoVAoSQVNfgiBEAJAs9pIiAeuswAAB5RJREFUNMEIgcVUJp36AqRXf2hfkpN064vxzDSeoeOFIfH6pDTA4/HgwoULk90MCoVCSUluvfVWZGZmjvtzaGAKQVVVeL1eCIIQJhBLoVAolKEhhECSJDidzlGLZkeDBiYKhUKhJBU0+YFCoVAoSQUNTBQKhUJJKmhgolAoFEpSQQMThUKhUJIKGpgoFAqFklTQwEShUCiUpIIGJgqFQqEkFTQwTTCHDh1CTU0NKisr8eqrr052c8Lo7+/HkiVLcPnyZQDAe++9h7q6OlRWVuKZZ54xzzt37hzuvfdeLF68GN/73vcgyzIAoL29HatWrUJVVRUeeugheL1eAEBfXx/Wr1+P6upqrFq1Cp2dnQnvy+7du1FbW4va2lrU19endH+ee+451NTUoLa2Fnv27Enpvhjs2LEDmzZtimubRVHEo48+iurqaixfvhzNzc0J7cOaNWtQW1uLZcuWYdmyZfjzn/885P0dr+uVKH7729/i3nvvRXV1NZ588sm4tnlM3zFCmTCuXbtGKioqSHd3N/F6vaSuro58/PHHk90sQgghf/rTn8iSJUvI7bffTtra2ojf7yfl5eWktbWVSJJE1q1bRxobGwkhhNTW1pIPP/yQEELI5s2byauvvkoIIWT9+vXk8OHDhBBCdu/eTerr6wkhhGzdupX85Cc/IYQQcuDAAfLII48ktC9/+MMfyMqVK0kwGCSiKJK1a9eSQ4cOpWR/3n//ffLAAw8QSZKI3+8nFRUV5Ny5cynZF4P33nuP3HXXXWTjxo1xbfPPfvYz8v3vf58QQsgHH3xAVqxYkbA+qKpKFixYQCRJMo8NdX/H815KBK2trWTBggXk6tWrRBRF8rWvfY00NjZO6neMBqYJ5M033ySbN282X+/evZvs2rVrEls0wJYtW8jJkydJRUUFaWtrI++//z5Zu3at+fMDBw6QTZs2kcuXL5NFixaZx0+ePEnWrFlDRFEk8+bNM2/U9vZ2snDhQkIIIRUVFaS9vZ0QQogkSWTevHlEFMWE9eXChQvmjUOIdmPs2rUrZftjfPbly5dJWVlZSl+b7u5usmLFCrJnzx6ycePGuLZ59erV5OTJk+ZnLVq0iFy5ciUh/fjkk0/IggULyJo1a0hdXR15+eWXh7y/43m9EsHPf/5z8tRTT5mvr127NunfMbqUN4F0dHQgPz/ffF1QUIDr169PYosG2LZtG0pLS83XQ7U18nh+fj6uX7+O7u5uZGRkgOf5sOORn8XzPDIyMuB2uxPWl8997nO44447AACXLl3C0aNHwTBMyvZHEATs3LkTtbW1mD9/fkpfmyeeeALf+c53kJWVNej3j7fN0T7r2rVrCelHX18f5s+fj+effx579+7Fa6+9hvb29piuy3iuVyJoaWmBoih48MEHsWzZMuzbt2/Sv2M0ME0gqqqGicMSQpJWLHaotg51PFpfhuobISQuQo8j8fHHH2PdunV47LHHMH369JTuz4YNG3DixAlcvXoVly5dSsm+7N+/H0VFRZg/f755LJ5tjnxPIvsyb9481NfXIzMzEy6XC/fffz927tw5qusSj77HA0VRcOLECTz11FN4/fXXcebMGbS1tU3qd4z6MU0gU6dOxalTp8zXnZ2dKCgomMQWDc3UqVPDNimNtkYe7+rqQkFBAVwuFzweDxRFAcdxYX0rKChAV1cXpk6dClmW4fV6kZOTk9D2NzU1YcOGDdiyZQtqa2vxwQcfpGR/mpubIYoibrvtNtjtdlRWVuLXv/41OI5Lub4cOXIEnZ2dWLZsGXp7e+Hz+cAwTNzaXFhYiI6ODsyYMSPssxLBqVOnIEmSGWQJIZg2bVpM37HxXK9EkJeXh/nz58PlcgEAvvKVr0z6d4zOmCaQu+++GydOnIDb7Ybf78exY8dQVlY22c2Kyhe/+EVcvHjRnOYfPnwYZWVlmDZtGqxWK5qamgAADQ0NKCsrgyAIKC0txZEjRwAABw8eNPtWXl6OgwcPAtAeTqWlpRAEIWFtv3r1Kh5++GE8/fTTqK2tTen+XL58GY8//jhEUYQoijh+/DgeeOCBlOzLnj17cPjwYTQ0NGDDhg1YuHAhtm/fHrc2l5eXo6GhAYAWOKxWK4qLixPSF4/Hg/r6egSDQfT39+PAgQP40Y9+FPX+jud3LxFUVFTg97//Pfr6+qAoCt59911UVVVN7ndsdNtklPHy1ltvkdraWlJZWUlefPHFyW7OIIzkB0K07Km6ujpSWVlJtm3bRlRVJYQQcu7cOXLfffeRxYsXk+9+97skGAwSQrTN+dWrV5Pq6mqybt060tPTQwjRNry/9a1vkZqaGrJy5Urz8xPFD3/4Q3LHHXeQpUuXmv/27duXsv3ZuXMnqa6uJkuWLCE7d+4khKTutTF44403zKy8eLU5EAiQxx57jNTU1JB77rmHnD17NqF9eOaZZ0hVVRWprKwke/fuJYQMfX/H63oliv3795vt3rp1K1EUZVK/Y9SPiUKhUChJBV3Ko1AoFEpSQQMThUKhUJIKGpgoFAqFklTQwEShUCiUpIIGJgqFQqEkFTQwUSgUCiWpoIGJQqFQKEkFDUwUCoVCSSr+HyLAI/uF7ovCAAAAAElFTkSuQmCC
"
>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[447]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Normality</span>
<span class="n">st_res</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
<span class="n">shapiro</span><span class="p">(</span><span class="n">st_res</span><span class="p">)</span>
</pre></div>

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    <div class="prompt output_prompt">Out[447]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(0.6726958751678467, 5.605193857299268e-45)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">st_res</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span> <span class="n">plot</span> <span class="o">=</span> <span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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>
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<div class="prompt input_prompt">In&nbsp;[463]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">insurance</span><span class="o">.</span><span class="n">columns</span>
</pre></div>

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    <div class="prompt output_prompt">Out[463]:</div>


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<pre>Index([&#39;age&#39;, &#39;bmi&#39;, &#39;children&#39;, &#39;charges&#39;, &#39;sex_female&#39;, &#39;sex_male&#39;,
       &#39;smoker_no&#39;, &#39;smoker_yes&#39;, &#39;region_northeast&#39;, &#39;region_northwest&#39;,
       &#39;region_southeast&#39;, &#39;region_southwest&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class="prompt input_prompt">In&nbsp;[458]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[464]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ss</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">insurance</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">insurance</span><span class="p">)</span>
<span class="n">insurance</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">insurance</span><span class="p">)</span>
<span class="n">insurance</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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      <th>6</th>
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      <th>0</th>
      <td>-1.438764</td>
      <td>-0.453320</td>
      <td>-0.908614</td>
      <td>0.298584</td>
      <td>1.010519</td>
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      <td>1.970587</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
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      <td>1.765481</td>
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      <td>-1.509965</td>
      <td>0.509621</td>
      <td>-0.078767</td>
      <td>-0.953689</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
      <td>1.635795</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-0.797954</td>
      <td>0.383307</td>
      <td>1.580926</td>
      <td>-0.728675</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
      <td>1.635795</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>3</th>
      <td>-0.441948</td>
      <td>-1.305531</td>
      <td>-0.908614</td>
      <td>0.719843</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>1.765481</td>
      <td>-0.611324</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-0.513149</td>
      <td>-0.292556</td>
      <td>-0.908614</td>
      <td>-0.776802</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>1.765481</td>
      <td>-0.611324</td>
      <td>-0.566418</td>
    </tr>
  </tbody>
</table>
</div>
</div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">insurance</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;bmi&#39;</span><span class="p">,</span> <span class="s1">&#39;children&#39;</span><span class="p">,</span> <span class="s1">&#39;charges&#39;</span><span class="p">,</span> <span class="s1">&#39;sex_female&#39;</span><span class="p">,</span> <span class="s1">&#39;sex_male&#39;</span><span class="p">,</span>
       <span class="s1">&#39;smoker_no&#39;</span><span class="p">,</span> <span class="s1">&#39;smoker_yes&#39;</span><span class="p">,</span> <span class="s1">&#39;region_northeast&#39;</span><span class="p">,</span> <span class="s1">&#39;region_northwest&#39;</span><span class="p">,</span>
       <span class="s1">&#39;region_southeast&#39;</span><span class="p">,</span> <span class="s1">&#39;region_southwest&#39;</span><span class="p">]</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">insurance</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<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>age</th>
      <th>bmi</th>
      <th>children</th>
      <th>charges</th>
      <th>sex_female</th>
      <th>sex_male</th>
      <th>smoker_no</th>
      <th>smoker_yes</th>
      <th>region_northeast</th>
      <th>region_northwest</th>
      <th>region_southeast</th>
      <th>region_southwest</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-1.438764</td>
      <td>-0.453320</td>
      <td>-0.908614</td>
      <td>0.298584</td>
      <td>1.010519</td>
      <td>-1.010519</td>
      <td>-1.970587</td>
      <td>1.970587</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
      <td>-0.611324</td>
      <td>1.765481</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1.509965</td>
      <td>0.509621</td>
      <td>-0.078767</td>
      <td>-0.953689</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
      <td>1.635795</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-0.797954</td>
      <td>0.383307</td>
      <td>1.580926</td>
      <td>-0.728675</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>-0.566418</td>
      <td>1.635795</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>3</th>
      <td>-0.441948</td>
      <td>-1.305531</td>
      <td>-0.908614</td>
      <td>0.719843</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>1.765481</td>
      <td>-0.611324</td>
      <td>-0.566418</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-0.513149</td>
      <td>-0.292556</td>
      <td>-0.908614</td>
      <td>-0.776802</td>
      <td>-0.989591</td>
      <td>0.989591</td>
      <td>0.507463</td>
      <td>-0.507463</td>
      <td>-0.565267</td>
      <td>1.765481</td>
      <td>-0.611324</td>
      <td>-0.566418</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">insurance</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;charges&quot;</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">y</span> <span class="o">=</span> <span class="n">insurance</span><span class="o">.</span><span class="n">charges</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ins_m</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">ins_p</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="p">)</span>
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<pre>0.7509130345985205
0.7494136397729285
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test of Linearity</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">ins_p</span><span class="p">,</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c36580a20&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Normality</span>
<span class="n">st_res</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
<span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">st_res</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span> <span class="n">plot</span> <span class="o">=</span> <span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Linearity</span>
<span class="n">sts</span><span class="o">.</span><span class="n">linear_rainbow</span><span class="p">(</span><span class="n">ins_m</span><span class="p">)</span> <span class="c1"># Check with p value 5%</span>
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<pre>(1.129521986322035, 0.05840533768725773)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Normality</span>
<span class="n">shapiro</span><span class="p">(</span><span class="n">st_res</span><span class="p">)</span>
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<pre>(0.6728276610374451, 5.605193857299268e-45)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Residual and Package for Heteroscedasticity</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">resid</span> 
<span class="kn">import</span> <span class="nn">statsmodels.stats.api</span> <span class="k">as</span> <span class="nn">sts</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Heteroscedasticity</span>
<span class="n">sts</span><span class="o">.</span><span class="n">het_goldfeldquandt</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">ins_m</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">exog</span><span class="p">)</span>
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<pre>(0.9564376445017754, 0.7162991411316741, &#39;increasing&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Durbin Watson Test - Test of Autocorrelation</span>
<span class="c1"># Null Hypothesis - linear regression residuals are uncorrelated </span>
<span class="c1"># Alternate - Autocorrelation exists.</span>
<span class="n">sts</span><span class="o">.</span><span class="n">stattools</span><span class="o">.</span><span class="n">durbin_watson</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="c1"># No Correlation</span>
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<pre>2.0884229986673084</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scale Location Plot</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">ins_m</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">st_res</span><span class="p">),</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Fitted Values&quot;</span><span class="p">,</span> <span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Sqrt(Standardized Residuals)&quot;</span><span class="p">,</span> <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Scale Location Plot&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in sqrt
  
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cRMOmTIcxPOB5EkyIKgBiUJQlpHsEVdgUyaNAmTJk0CoPpDTpw4gVNPPTXlglEoyaazz04pbSMwQx4AOKMRsiCgfEdBp7nOXP4wDMnP7zL3YVQF8tZbb7XYtnfvXtx6660pEYhCSQX+2WlXiX6htCRdMuS7UgRbVAUS6DD3er347rvvMH78+JQKRaEkm3SYnVLaBs2Qb3+iKpDVq1cHva6srExKMUWn04nZs2fj9ddfx4ABA4LeO3DgAP785z/D5XLhnHPOwfLly8HzUUXttqSbaaYj5E2X2SklcWiGfPsT1YkeSp8+fVBWVtamg/7444/43e9+h+PHj4d9//7778djjz2Gjz/+GIQQbNmypU3H68qkg+MwkI6S19gnF0pI9CCdnXYtrGPHYOjihTBYsyE7nTBYszGUmihTSlw+EEIIioqK0KtXrzYddMuWLXj88cfxwAMPtHivrKwMgiBg9OjRAIDrrrsOL730Em666aY2HbOrkm6mmY6Sl85Ouwddyb+QDsTlAwGAfv36hR3442HVqlUR36uqqkJOTo72OicnB5WVlXHtv6ioKGHZUo3NZkvq/oSSEsBoBON2adsIIRBKStp8rGTJKh85CnnPN1AcDsDpBDIywCiy9n4y5I3lu8qlF6ty2GvBZmWBu2gifgXBr0m+Jq2R7OufSqisqSGdZI1G3D6QVKMoSlDHQ0JI3B0QR44cCUNItmdnwGazYezYsUndZ9HAgS0ch7IgwDBwIEa24VjJktVuK8Sx3Z+D43mw1l4QmpqgNDqhN5rAWyxJkTdmWceOBW6cldAxkkEqrn+q6KqydrS/sLOeV4/Hk9DEO6ICmTNnTqsD98aNG+M+WCz07dsX1dXV2uuamhrk5lI7dSQ6u2km1GSlt1ohVFVDtNvBmc3tIm+0QaOjBxVK+0BDuZNPRCf6Lbfcgptvvhm5ubkwm82YM2cO5s+fj+zsbAwePDhlAvXv3x8Gg0Fb5hUUFODCCy9M2fHSnc7uOBQqq4Jq/3BmCww5vdUs3BjltdsKUbTscexbdAeKlj0el8M9mtM+3YIQKIlDW/Emn4grkMmTJwMA3nzzTbz33ntgWVXXXHzxxbjxxhuTLsiiRYuwZMkSjBo1Cs8++yyWLVsGp9OJM844A3Pnzk368boSndlxGC42n+F49Dj1FIxcuTzq99s6a4zmtE+3IARK4tBQ7uQT1QdSV1cHj8cDk8kEAHC5XKivr0/KwT/99FPt7/Xr12t/n3rqqdi2bVtSjkHpWNpqYmvrAB9t0KCDSveBJhomn6gKZNq0aZg1axYuv/xyEELw0UcfYdasjnNEUtKLtnava+sAH23QoINK96Gz+wvTkagK5O6778bIkSOxd+9eAMBDDz2Eiy66KOWCUboObTGxtXWAjzZo0EGl+0Bb8SafiArk6NGjGDZsGH7++Wf07dsX1157rfbezz//jDPOOKNdBKR0b9o6wEcbNOigkn6ERs3Jo85QQ7RjoDP7C9ORiArk6aefxrp16/DHP/6xxXsMw2D37t0pFYzStvDSrhKamowBPtqgQQeV9CFcUIX44cew5+fTa9gBRFQg69atAxDs6KbET+BALppNsIOJ6UZvS/RRV4t3pwM8xU+4oArQqLkOI2oxxaNHj2Lr1q0ghGDp0qW47LLL8M0337SHbGlPaI6B0uiMOcegLTHrnSXevS35GxRKONwlJfDW1sB94gSE8jLIbheg42nUXAcR1Yn++OOPY9asWfj8889x8uRJrFq1Cs8//zw2b97cHvKlNaGzJUavA8NyMc2W2hJ91N6hqeHMZQCSvgrqKmY5SmKc2LwVYn0DQAjAMFAIgae6BjCbYExhcjMlMlFXIB6PB1dffTW++uorTJkyBePGjYMoiu0hW9oTmoUNxD6Qt6X8OGcyQSgthfvECTSVlUFyuVIWmhopk/vE2+8kdRVEM8a7N3ZbIco2b1WVBwAQAiLLIIoCuN00aq6DiKpAvF4vampq8Pnnn2PChAmoqamBJ2Rgo4SnLUog79oZIJIEWRDUsh+CEFP0kd1WCLG+HoosgwAgkgRPdTUkpzMlD1kkc1lTeXnCyjOe49AyFN2DE2+/AyLLLd8gBNAb6Eq0g4iqQG688UZMmjQJY8eORX5+PmbOnIl58+a1h2xpT6gSIF4x5hDURGtcle8oAGexwJibA06nWihZnoM+OyslD1mkVRaApDZwastqjpL+NJWXA/7irgzT/DchYHNzIn+RklKi+kBuuukmzJ49W6uFtWPHDmRnZ6dcsHShNbt8aAgqm5mBoXNviXkgTyT6yO//YBgGnFktl+4vXJgKIiX6mfrnQW4SkpagF3gc2e2C6HBA9orgjEawR47GnAdASWMYRl1x+M1YPrgJ53eQQJSoKxCXy4WVK1di3rx5cDgcWLNmDVwuV7SvdQtisctbx47ByJXLcc76tdDPvTnlS+32bt0aydQ2eO4tSa0S7D+O6HBAqKqG7BXBMAxYg0HNA6C+kC6NqX+eqkBYtnn1AUCfmwMuf1gHSta9iapAVq5ciczMTNTW1sJgMMDpdOKxxx5rD9k6PZ3RLp+o7yRRWjO1BSrPkSuXt0l5+o8jCwIAgNXpoO/dG/rsbIDjUnrOaThyxzN47i3QZWaC1fEAy4I16KHLysKw23/f0aJ1a6KasA4cOIDVq1fjiy++gMlkwrPPPotp06a1h2ydns5YybUjSnO0V6KfdewY8BYLDH36BDc7a2MeQGtmyK6WlJmuWMeOQf6SO8Nep/ZsSUwJJqoC8fs+/Miy3GJbd6W9K7n6BzpXcQmIJIHV8TAPHNhCQSRzQO+I3IvWjhnunEOUYMzrl/CxWlMQtF9I54FWJOh8RNUE5557Lp555hkIgoAvv/wSf/zjH3Heeee1h2ydnvY0F/kHOndZOWSXC7IgQGx0oqm8ImX5EB2RexHtmOHOOZqaIDY2JmRiimaGpNFflLYSaAL1btzUpUygUVcg9913H9544w1kZmZizZo1+O1vf4s//OEP7SFbpycV5qJIs2//QCfa7YAsa9m4YkMDDLm5KZkRBw6ugZFPh557ASPuvUf7TFt+e+jvFRsaW53xh55zzmRSs5JFKSETUzQzJO0XQmkLLVa49fVdygQaVYHodDrceeeduPPOO7VtR44cQX5+fkoFSxeSbS6KZE4RKqtAGAYIrAJACIgoQnI5QVJQHcA/uMpul1oygmHAsCxkQcDhF19RZ+wWiybrkZdeha5nT8hNTVEVit1WiBMb34G7uAQsz4PPzobHXgehogL6nBxwAZ9tbcbvqakBODZhE1M0BUH7hVDaQlvKGaUDERVIaWkp1qxZg6ysLNx3330wmUxwuVx46aWXsGnTJhQVFbWnnN2C1uztxj65aPzlUNjvSY566H+T/FpA/sFVdDg05UEUBaxOB6WpCWAAfa9e6ocVGWJ9PcT6ejAcB9HhwOEXX8Hwu+/Sfpt/lZE5aiSqd38G0VEHsCwIIRBra6Hv3RusjodUVwddwKogcEBvoWRrawFCIFtcWt5LPCamaAqC9guhtIXOGGiTTCIqkEceeQQjRoxAVVUV1q1bh0suuQRLlixBRkYGNmzY0J4ydhtau9mG3r4IB1aujvxlEvmtRNEGV6+oKQ8QAl1WFrw1NSABCV2emtrmBC+fUpCcThx9/Q0wDBu0qmrYuh26Hj1AZEUNyGAYEEWB6HBAl50NT1V1xAH9xMZ3IDrq1O/qeLAcB8WXH+JXIJFMTBGd81EUBHXeUhKlq5tAIyqQkydPYuPGjRAEAddddx22bNmC+fPnY8GCBeD5qJYvSgL4bzYoMkSHA4oogeFYGPv1g3XsGJgHDYT7+IkW32N0OshNTQkdM5ZM+kPPvaAO6DoddFlZ4C0WiHZ7YD4XiCT5hGHUEFtf1rC3ugbGvLygVRVRFEguJ1gdD0WStc8TSQLD8TAPGghdZmYLmey2QriLS9Q8AJaFIslafSTZK4IQEtHEFC3aiioISioIXeESrwjCky5jAo2oCcxmMwDAaDSivr4eTz/9NCZOnNhugnVH8q6dgSMvvQqxsVEdiAEokgxvnQN2WyEGz70FB1c/DaIoYDhOK+vAZ2bGNaPxKw13SQkklxt8ZiZ0WVlBg6of69gxGHHvPdrgyxoM6sNgMoFhGO3B8K8+GK7Ze0EAtVZRaBSTTgdFFGHMzYGnukZb2TAsq2ax3zY/7IBevqMALM+rKx+/ogJAZFk19zmdEU1MNByX0hG0tZxRZyempYTVaqXKox2wjh2jOqHdblVJ8Dz0WVlgfJnWI1cuR/8brkfZ1u0gsgxWpwNnNoPV6WKe0QTOxGVBUFcDDQ1g9XrwFos2qOLaq4PkCjXzDLltPoBm3wZrNEDxitrKg/iUm98MFbiE58xmkMZGgOWg790LYl0dFFGGqX9/DJ4X+eESKqvAZ2dDrK1VlY6/NhIIRtx7T9D3QldWruKSZn+Nj65ki6Z0XgJXuDabrcsoD6AVBRKY6UtNVu2H3NQE44ABYBgGkksNnSWSBE9VlboKufEGZOYPS9ipGzgTJ5IMluNUJ7bDAd5i0QZVJuR7/v37j1u+owB5187AyJXLAagD9pGXXoXc5IYiyWB5DrwlE32mXonq3Z8F+TRYnQ79b7gejT8VQaisQuaIETH9Br+JT9+7N7x2O4jXq76hC74/w5mrZLcbIseppU98hLNF06ZVFErsRNQMv/zyC8aMUR8cQRC0vwkhYBgGhYVdJxmms2C3FUJyudTIIpYFFEU16wBgWSYpNvtARz3DslBEMSAc2AWG49SBOoxs0XwIkUpNhFN4AND4U+yRfHZbIcTGRggVFc0Ofb+5zGSKmj2u69EDYkMDOJMpYjguLVtCocRHRAWya9eulB30gw8+wNq1ayFJEubNm4ebb7456P1XXnkF27dvR48ePQAAs2bNavGZroZ/8PLb5gPzPRiGgd5qBZIQPx7oqCf+hET1IPBUV4PPyMCQ2+bj15Cwrlh8CJEUW+j2SAN146WTtFVJqPPc/3lDbg48lVWqz0Sng6F3L3iAoNj6cNFsfM+eILIEgzU74uqC+knSh8CVomg2wQ6GXqMOIKIC6d+/f0oOWFlZiTVr1uD999+HXq/H7NmzMW7cuKDExKKiIjz//PM4++yzUyJDZ8Q/ePEZGWD1OggnTwJEXfEZc3PAmS0ghIS12cdjdvFHhYiOOjUxkePUzHaGCWo8FVqgLpnx7OEGatHhQNnW7TDk5raY/Yd+3svVAlAd9pzZArhdMWWPmwcO1Exu4XCXlPjK0chgdTx0WVlgTWbqJ+lkdPXs7nSi3asi7tmzB+effz6ysrJgNpsxefJkfPTRR0GfKSoqwrp16zB9+nSsWLGiW7TQDay5xJktYA1qTSaGZVvNb4inXpVf0ciCoDq8fUmBhr59YRkyBMb+AyC7w4cDJ6vPiN1WiIaDv0A4eRJCeRlkt9pbRnI5QRQlbE2q0HpUrI7X2vWGkyWRGmWq+dCthk77QoQ91TWQ6uu7TMx+VyG0fhmj13V4G4XuSrt7x6uqqpCT09yCMjc3F/v379deu1wunHbaabj//vsxePBgPPTQQ3jttdewdOnSmI/RmbPkbRFKT4tmEzz19WD0OgAAMRmB+gaAZeFyOQFRAmQZ8kUTg/bh3bgJiiSBYRmgya1+V5Lwy8Z3oA8wQ8lHjkL88GN1xWEyAi4XoCggZhO8DOB1u0C8ItjMDG3/gceRR52hfl8QVKe12w243RDq6rD37j+Bm3B+1MY+mgw+s5nsFSFXVYPp6QHxigDPwe1ublZGCIFQUgI2Kyvo3MBkAjz1IAHnRpCFoHOjXHox5D3fQLHXgs3KAnfRRPwKErH0t3fjJhCDAWhqgiLLUGOoCbz1DpDJl0W8bvGSrP20B4nIKh85qp53h0M97zHcF/EilJQARiOYgHtFkCUIJSVpcX7TQcZYaXcFoihKUISX3ynvx2KxYP369drrBQsW4JFHHolLgYwcORKGkNyDzoDNZsPYCK1X7VCd5AzLqU5eloOkEOizsyC7m2DM6xfWNLXv9Q3gevYMPqcmtYVt4LGKdvwLHotFM+tIUH0ejMsFY1a26lTmiRaj3kLWsWNhz89vzh9pEsD36AldVpb63d2fY0h+fqsmBL8MxGiEt0atrUVg0TRjAAAgAElEQVQIAeNyARwHPiMTet9qCwBkQYDBV64+9NzICtHqbomZGTglNLZ+7FjgxlkxXRftPObmqtFavsg3Rq8DazDgvDj20xqtXf/ORiKy2m2FOLb7c3A8D9baK+b7Il6KBg4MMlG63S4YOR6GgQMxspOf3856D3g8noQm3hEVyMMPP9zqF1evbqWsRiv07dsX+/bt015XV1cjN7fZRFBeXo49e/Zg5syZAFQF0x3CiCPlWsQa2soZjVrYryKqvcLttkLt+6E+DN6i+lREu73VBLxQGa1jx6Bo2eNBD3CszubAfu0A1BpbkgSiEPS/4foW4b5+s1PYciMB5yYZsfX+88hbLOAtqhKTBQEGa3aUb1L8tFcQQlfP7k4nIo7Mw4cPBwAUFhaivLwcV199NTiOw4cffoiBAwcmfMAJEybg5Zdfht1uh8lkws6dO/HEE09o7xuNRjzzzDMYN24cBgwYgE2bNuHyyy9P+HjphH+A9vsqjr2+HuUxOsW9dXWQGhq0+lSc0RjkWAznWGZ5Hj1OPaVVx3I4XMUl2gDP8KqzmTNHdzYHyuAfqP2DdLT8llSXG6FVd9tOexUO7OrZ3elERAWyYMECAGo476ZNm2AymQCoIbVz585N+IB9+vTB0qVLMXfuXIiiiJkzZ+LMM8/EokWLsGTJEowaNQorVqzAHXfcAVEUMWbMGNx6660JHy/diDcXIbBeFSEEnF7nG9AtQbO/WAdILaKrpARFYbod2m2FzZnyHAciy/DW1IDv0QO6zEwULXs8YjRYLJVv41WgyYJW3W07Xb1wYGelI5Nfo9qGamtrodfrtdcMw6Curq5NB50+fTqmT58etC3Q7zF58mRMnjy5TcdIVxIxA0TqFR44+4tlgAxUXjAawyqv8h0FalJefb2ai8EwUHyVdKEoUKTIjZ3ilaG9k/loUcW20V6rOBrG20xHJ79GVSDjx4/HwoULMW3aNBBCUFBQgEsuuSTlgnVXEjUDxDL7izZABoVHul1hlZdQWQW+Z0+wep1WMZjV8SCSpDaXiqL44pGhtf1QOh/ttYrr6k2a4qGjn5eoCuTRRx/Fpk2btMz0KVOmYPbs2SkXrLuSqBkgGbO/WJSXJp/ZouWnyIIAT1Vyeoe31Y5Oa1lFJ5XnqD1Wce3dpKkj7qlYj9nRDauiKhCe5zF58mT85je/wQUXXICqqiq1CRAlJbRFEXBGI5rKywEApv55EcuiA+Fv0FiUVyT5TP3zWlTdjcf+7ZfHW1cHpt4BvdUatUFUuH3QWlat0xXOUXv6WhI5X21VOPEcs6P9TlE1weeff47Zs2dj+fLlsNvtuOqqq/DJJ5+0h2zdEuvYMRi6eCEM1mzITicM1mwMjfJw+284RZJgGjgQhtxcyE1C1M+HZq9njhoZNYM7knyD594Sd/Z3OHl0VisUSYZQVQ3J5YxrP6EZyoHZ7BSVrnCOQisNEK+Ysog5fwdMT2UlPBXlgCK3er7iqQwRiXiuUSJVF5JJ1BXIq6++ii1btuD3v/89cnNz8Y9//AMPPvggLrvssvaQr1sSrxkgHjuo3VaodRgMjdhq/KkIQ/027JISLYkvdB8R5UvQ/h0oPwc1UEO02+GttcOUlwfo+Jgisjp6OZ8OdIVz1F5hvOE6YHqqa6Dv3Svi+UqGTyKea9TR0YNRFYgsy0GJfqeddlpQpA+l44n1hvPPjmRBCKr3ZMiBVjTQrxxsNlvcWb2J2r/DJTlyZjO8tbWqrDGaDzp6OZ8OdJVzlMwmTZFMTuE6YBJFgVhXh8wRI8LuKxk+PH9Lh8AJXmvXqCOjB6MqEJPJhPLyck1p7Nu3r1OWCenKRLOpxjoo+GdHrE4HIstaXw3R4YCO5TpsEIkkvz9RMdbZHE0GjE5nPUft5agOPU7mqJGo3v1Z2ElKpA6Yiii36CPj36fkckGRpKiNyyLJdmzdBu26+GvF6bNEMDzf4dcoHFEVyL333osFCxaguroaN954I44fP46XX365PWSjICQ3g2XQeOgQDqxcDfPAgVr711gHBf/sSJeVBW+Nrxc51KKGfAcOIpHkZ3V8XJFdHb2cTwc66hy1piDay7Ef7jgNW7dD16MHGJaBp6JcrcbMsTix8Z2gDpj++miEELA6nWZSDVVARJbhdTgAoLlOXJw+PH1GBli9XitLJAtCi5bNnYWoCmTMmDHYsmULvv/+eyiKgrPOOgtWq7U9ZKOg+aaCIsNbU6supVkWQkVF8EMWw6AQWO9JFgRIDQ2AogAsi5xLJ3XIDeofWBSPAMWprjgsg1TfS/mOgrjNLTQZMDrtfY6iKYj2ymUIdxyiKBAbGtQK0b5nS5EVuItLMGD2LAg+5WDMy4PocECsrwefkRGkgPjMTOh9ZitdVhYANbQ91hpzfgLNX/5SP4SohVE76z0dVYE8+OCDePDBB3HRRRdp2xYvXox169alVDCKiv+m8lSUazc4IUQtJeKLzPAPCNFussC6WbLTqS7JOQ76rCxU7/4MmfnD2u1GtdsKcWLjO3AXl4DlefDZ2dBZ+OACikCnNLd0J5JhWoqmINrLsR/2ODodFEEAo9P5lIesNlgjBBUFH6DfjOlal0zF44E+K0tTEn4FJLvdQIDJCjwPxesFLBbEQzr6p6KG8e7cuRO/+93v8Ouvv2rbKisrUyoUpRl/IydFlJqDFwhRfRlxPmT+EFzF49HqZhlzc6DLymrXUE7/jFSoqAB8ClGsrVX9MgFyxBPSbLcVomjZ49i36A4ULXs8rrBJSniSEZIKBDdL8xPaQTIZzcqiEe44nNms/kEIFEkCJEldjXAcZEFA9e7PkHftDJyzfq26KujZM/h36HgoAe2nJZcL3poaMCwT9znLu3YGZJcLTaUlcB0/jqbSEsguV6eeMEVVIP6mTvPnzw8qw05pH/xx3gzHQlEU1W9BiGZfjfch89fNMg8eDGNefy1Zrz1DOf0zUiIrYFkWDMsCDAPR4Wghh3XsGIxcuRznrF+LkSuXh1Ue8pGjrQ50VLkkRrJyRgIHbtntglBeBveJE5BcLththe2WyxDuOKxOB31OjnoP+nyCWifQkE6H4RQQb8kAw7LaPkW7HQCgt1oTOmeEEICooez+ltadmagmLIZhMGnSJGRnZ2PJkiV48MEHg4orUlKL37/hN/cwPA/ealUr4cbxkMUTKWK3FcK7cRP2vb4hLrNFvOUXWB0PRZLVh4VhQCQpIaUo7/kGXAQTCYC0z7zuKCKZllzFJWGrLp/YvBUVBR9AbmoCZzKh34zpGHzjDZrpVHQ4NAczwzBgDQYcW7cBQxcvbM4/SqFjP5KvEFDvEU9NjapIAG2SFjihCRfswfA8+t9wvWbmIoTAkNNbm5j5z1ksk7PyHQWqf6V3b21bZ68DF1WB+DXg6NGj8fbbb+P3v/897D4tS2kfQsucC5VVMFiz4xrYAwfR1iJFArPauZ49Yx5wEym/oMvKgqe6RltVMSyb0MxTcTjAWnsFbfM/tB1dbC6dCWeTFx0OyG63ttprKq/AwdVPgzAM4PUCLAvGZ/4p3bwVADD4xhu0lgOA6nfQZWVpwRzlOwoiri6TTWtJsJESbP0TmlaDVW68AQC0Zmt+JJcLot0OQgiKlj0OedQZarfMMKRjkmdUBXLXXXdpfw8ZMgTvvvsuXnrppZQKRQlPotEzoYNoa5EiRcse9y3hGW0JHsuAG89ArbWo5Xnoe/eCWFcHRZRh6t9fC02OB9anBMM5H9PxoewshJtxS42N0PXooV5ftwtifb06yZRl9UuKAnAsWJ6HIkmoKPgAg2+8IaaWAx2JdewYjLj3Hu2+ZA2GiKV8Wrs/A8+ZIklq62YAhpze8NjrIH74MewhLX6D6sA5HNBZrVpXzM7uRI+oQPbu3Yvx48dDURTs3Lkz6L2JEyemXLDuSKqSqcINonzPnmA4DuesXxv+s01ubVssD3lbyi9kjhgR9rfGej64CeeD7P48bLRWvKHAtJpvM+Fm3LLLqTmSRYejOTLQr0AYBkSSweg51TfQ1KTtjzOZIJSWahGEuqwsMFxyEliTUcCwfEeBpjRYHQ9zhFI+rRF4zhoO/gKW54IKgyJkUhW4ctf3ssJTXQNPdbWab8LznT7qMKIC+d///V+MHz8ef//731u8xzAMrrjiipQK1t1IZTKVsU8u3GXlaidBf3a32Qxz/7wWn9UecllCk04f80PeWghipIc7mknsyEuvQm5yQ5FkiPUOHHnpVeQvubPF97j8YRiSnx9xAIk1FLgrVKpNNqHXyW+i4YxGNTLQF0UXhO81URRwvk6mdlshxPp6NUyWYQBJgqe6GnxGBobcNr9NMspHjuLY7s8Tvm7Bg3ivoHsk3EohmpLyn7N9i+4Al5ERXPpJxwdNqlr0NmEYeO12iHY7epx6SqefwERUICtXrgSAsAqEknxSaavPHDUS9f89AABqrLsoQqmvR+aVwZOAoIecACSOhzxSNnnmqJEJDconNr4DsbERDMuC5TgQhUBsbMSJje+0+tCG2+6fEbpLStRBLyRU2A/1l0Qn8DozPKeFl7NmMxS3W1MeiiQBAPrNUDuPlu8oUBuOmYzNjch4DvrsrDaf29aCKOIt5hnp+4lMLsJNqiBKMOb1016GW7mDYUBkKZ5T0GFEVCAPP/xwq19cvXp10oXpzqTSVt/4UxH0WVmQ3S4oogROrwNntqDxpyLN+QcEP+RCrR1QlJgf8kgOxkQH5aay5sRJQJ2ZEULU7XESuBLhLKp9O9wAQP0l0Qm8zrLLCSK7wWdmQpeVBaGqCorTqX5QUWDIzUFm/jAAzeeWYRjNnOPPsm4rrQVRRMNuK0TDwV+ac6uMRig+M5anqgp2W2HC2fLhJlWQg+toBSoZ2e1Sg0oIAavXp8UKOKICGT58OACgsLAQ5eXluPrqq8FxHD788EMMHDiw3QTsLoSbrUj19ZAFAfsW3dEme7y/Da3feQ6oD2/oAxb4kDMAzGZLXA95uFXAsdfXJzwoh9Z8bksN6FgGgHTMBO4IAq9zkFmnVy+IPA/OYtEGTP8AGDpQig4HZK8IzmjUBmk/8fozWguiaA2/mZT4kgeJJEERBIBlfSXcmaDCivGENPvPUwsf0kUTg35LoJLx1tWBEAKGYdQosDRYAUdMJFywYAEWLFiA2tpabNq0CfPnz8ecOXPwt7/9DSdOnGhPGbsFoUlO/ph51mBoUxYwEHumbyoyghPdpykvTyvZEvi/Ka+l3yYWomVDAx3fnCcdCUz01PXIVFewYRIP/edWdDggVFVD9opBuSCBSZ/xZr9zE85P6LoFmkmDUBQw8CUD+uQPdx+HhjSHkzU0EZbzrcgC3/dXW1C8oprY2Lu3FoXV2VfAUcN4a2trgxIHGYZBXV1dK99IX+pshTj6+htqNz+Gger78s17Gcb3J+NLfPO9p33O9xkwvs3Nf/u3ezwCCo3Glp/TdkDgra1Vq376ykfLTidkl0t9V1Fw6JnnYRowIOwxgpx1AfuVnE54qqvV930Zt4QQcAYDfv6fJ7TfIDU1wVtd7SsvoqCpzgEQAj7DggNPPhV8Tnz7ZjgWDK9TK+fqdFq5eEanA8vzMPTtC9dxNevYX0YeioKsKy6D6/gJcCYjOJNJHWx0Ou03DJ53Cw6/+AqUpiY1aodlwWVkYPC8WxK6trGsLmg137bRmgnQf27D5YJ46+pw6LkXwFss6n1iMGjFCWOZhUcLooiEZiblOIBlQMRmv4M/GdC/Uh96+6JWQ5pjlTUc/hVdYICCn86+Ao6qQMaPH4+FCxdi2rRpIISgoKAAl1xySXvI1u40HPwFnqrqlB6jKfpHglD84ZE+ZFGE8/DhhI4dWhTBdexY68f2/e/85VBCx2uNE2+9jRbrWJZVFYpRVSq8xQxRkgBRBGc2wTJsGBp+KoLryFFV6fiUj1xahnqjybdN3c7yvJbUxrAs+s2YjmNvvKkeppVoLFrNNzGiNULSwmSbmsDq9ZrykFwuSA0NagZ3nz7w1NZC8XjA6vVxzcITvW7a3I/lQBhZq8rr99P45Y8W0uynLSuGztqrpTWiKpBHH30UmzZtwq5duwAAU6ZMwezZs1MuWEcwcNZM9DjtVDV2nRA1oEQLUSQBtWmIup34tqO5ho3/PRLwt3/kPnHiBAYNGqi+9u2HEMBdXIKaPXvBsKxaI0qSIDU2gjUYwJvNWpgkkSTwZhP6XH5ZwDF9OyckOJySkOBtgZ9t7bu+7ZWVVb5OlMG/lYT+bllWbceiCEWUtL9J6GtJhOIV1TIqIaYADUWB7HJDdrlbvuX1wmErhCOCKaPo3c3h9xkJhgGr1+PwmhdVEwbLgeE4dSXlG7x02dnQW60w9LJCb7VCb82G3vd3UGRNJ8M/WLuKS9qU0xArJzZvRdnW7ZovQfaQoEZIgZF4rF4HRRS1BDvR4dAKezIMA06vg+wVITocKU+mM+XlwV1aqiY/+lfnsgxwamhy6ADeWkizn7bImo4r4KgK5LbbbsPbb7+NefPmtYc8HQqr0yF7zNkp23+ZzYZ+YcoYFC17PGgpDAAsz0P0zcxktxuKKIJhWfSdNhUDZl6XMhn92G02DI2jpW2o47P/zOvC3vhFyx6HUGsHZzCoiktRIAsCdBkZ+M28WyA3Nfn+Cc1/C0Lwa22b+jnJ5WrOhI4V3wARUZlFgbOYoc9WlQqfmQldj0z1/8xM8JkZ4DMzwZvN4Mxm8BYzOJNZa+CVSrRSNKII2eXy+QSApvKKmCJ6tOtYUoKiGJTOic1bUfruZm3m7u/aB0BrhBQYwKDPztYijfwNk4Dm6gisyQS5SYAiy2gqKwNnNoPV6VIyCx887xb88uwaKL4JIxgG0OthzusXUy+PVKwY0m0FHFWBNDY2wu12w+wve5wEPvjgA6xduxaSJGHevHm4+eabg94/cOAA/vznP8PlcuGcc87B8uXLwfNRRU1bImWKy4IAqbERRFHA6njwlox279sRC9Fi5AOVi7euDjqrtTk8l+PA8DwkpxNZo89K6Pg2mw1nn3UWlAClQhQZRFbUFZLi+9/nf9Fea9sVdbvsW0l5RUhOJ7x2O7y1dnjtdfDaayHWN2jHlF1uNLncaCotjUvWvUajqlDMZk3BcGYTeLMFnMWsme44k1/xmMBZLL7PmtQBVa8P9ncF4B+s5fp6dZXFcb6eFS7orL1atc8Hdb80GqOGkdpthSjbuj1oZQtC1Guq48FbLLCOHYNjr68HWAZCeZmWfAhCoHi9mv+LM1sgu12QGhrVlQCIunJtbET/G65P2f3O6fWALEGRZLA8B85kxuC5sZXTibZisNsKceLtd9BUroaem/rnQZ5wfsRaWOlITD3RJ02ahFNOOSVIibz++usJHbCyshJr1qzB+++/D71ej9mzZ2PcuHHIz8/XPnP//fdj5cqVGD16NB555BFs2bIFN910U0LHSwciOXgBwJCbG7Q90EnXWcputBYiCwRXw2XqHWq/BIZJqomC5XmwGRngQ5OykogiqqYVT629WbnU1UFyOiE1NEJsbITU2Ki+bnSqTYVC9yEI8AoCUJt4QVKG54MUTaDiaTxyVJ0Ne70BwRlq22JDFPt8UPl2tyuqU7h8R0FQr3AtA92nrLk+ahY6ZzbBXVKqmmgDGqKZBw3E4Lm3tAxjZVktEkkWhBb5SsnCn/ek79WcQxKvEzzSisFuK8ThF1+B5HSqOUwA3CWlQMH/a1ELK53h/ud//ud/WvsAy7KYOHEiTjvtNOTn52v/TjvttIQOuGvXLrAsi6uuugo6nQ52ux1HjhzBeeedBwAoKyvDtm3b8MADDwAAevbsiU2bNuHaa6+Num9ZllFVVYWffvoJw4cPhyRJuP3228FxHIYPHw5BEPCHP/wBBoMBw4YNg9PpxF133QWLxYIhQ4bA4XBgyZIl6NmzJwYPHoyamhrcc8896NWrFwYOHIiTJ0/iT3/6E3JzczFgwACUlpbinttvh7j7M7j/+QF+/vQzrNr4Nn6Tn4++ffviyJEjeOihhzB06FDk5ubiP//5D55//nmMGDECvXv3xs8//4xly5Zh9KRJIAcP4Ui9A28e+QW/MRhhYRgcamzAxrJijOiZBYtOh//W2fH2scMYBgbZgwbig6efxd8O/hej+vYD1yTgi1278PKO93HRpZfCaDTi008/xerVqzFp0iTo9Xrs3LkTTz31FC6//HLwPI8PP/wQzzzzDKZMmQKWZfHBBx/gueeew/Tp01FRUYFvv/0WL7/8Mq666ioAwNatW7Fu3TpMmTIFAPDuu+/ir3/9K06vqwdnNmNnaQl2lhXj3Jw+YDgOHxz4Lwo++xRjsnuBMxrx/4qP4+sGB87UG0E8HvyvvQZfV5ThrMyeGHTLTXjz/e3YtWsXLrzwQgDACy+8gC+++EKrvfbcc89h7969mDBhAgDgqaeewr59+zB48GDk5eVh1apV2L9/v3YvLV++HAcPHsQ555wDQPXnHTt2DGPGqA/vww8/jNLSUowePRqAOnGprKzEWWepK6GlS5eirq4Oo0aNAgDcvXQpBEIw5qILYR44EA+++ALMp52KC+bOQe+JF+DRze+i35QrMemB+9Dvumvwl88/xZDrr8OEP9yOrIkXYP1XX2LIFZfjtKumwnTKKfji6GHkjDoTeWeNAte7N45UVaFH376w9LKCcDyaBAF8uJWGokDxeiE51XwKT1U1hPJyuI+faM5l8A/oirq6UghBRU21amVyu3Hshx+w9oUX0K9fP1j79sX+n37C6jfWId/aG5l6Pf5rr8XGo4dxirUXdI1OlPTJwYoVK3DuueciIyMDe/bswbNvvYXTzRboARS5XdjsqMUZRhP0LIsfm9x4t7QY44YPh/vnA/i2ohzb7NU425IJjmHwnbMBWyvKMfv++2Ae0B//3v0JtpacwDiTBQzHYa/bie3FxzEhbwDEujp87WkKe+9deeWVqKiowO7du/HOO+9oJZb+9re/YfPmzbjsMtVfuGHDBuzYsUMLAHr99dfx7092Y0y/PDAMg+2/HsW+6iqMzu0Dsa4OW44dieneO//88wGgxb338B134Li9FiNMZjAch401lTgpisjndRBrarFm186W997Jkzhj+AhILjceu+9+2ItLMKhnFoSTFXj+z8vgOHwY2V4vGn85hDdXPIH6/T9BV1aOun027HjqadTuL8KgceOgMEzc495jjz2GM888E7m5uXFZe6J+MnTgJoS0KQ+kqqoKOTk52uvc3Fzs378/4vs5OTlxd0AsLS2FzWaDLMtwOp349ddfYbPZ4PV64XQ6cezYMdhsNjQ1NcHpdOLo0aPo2bMnnE4nnE4njhw5AovFgvr6ejidThw+fBgGgwF2u117zfM8KvfZ4C4tg0si8GRno7GqGu7SMvx396eQJAllZWVwOp04ePAgBEEAADidThw4cABOpxPHjx+H0+lEGQj6XnoxxI8+Vme5PAfl0ouh/PsjKDVVaBLccBMFHo9a4VMyGfHLxncgKjIUQiAITdDr9BAVGe6Ttfjxxx+RkZGBo0ePwul04ocffoDJZMKxY8fgdDrx/fffQ6/X49dff4XT6URhYSE4jtPksdlsAFSnf0NDg/a6uLgY9fX12uuSkhLU19dDzMyCp74eXtELWZLgdrtAvCJkPQ+xrhGCLIFxuyCKohrZ1SMTSkMjRKEJCstCufRi/AqCkydPBh2/srISXq836DUA7XVVVZUWYm6z2VBdXY2mpibt/ZqaGiiKor2ura2FTqfTXtfV1aGsrEx77XA4tHsHAOrr61FSUhL0uri4WHvd0NCAEydOaK/919R/7zW4XDh2sgK5ZWXwer0o51gcJgqyemSiScdjL1Fg7JeLjDFj4HQ68a+ffsBl55yNs846C676emzYsAGTr7gCZ5xyChwnK7Fj83u4ePwEDO7XDw3V1dj7+Rc4+/TT0btHD7jsdfj14AEMzsqG2VEPryxBlGVYWA6AmvCVw/GAuwll23cAACYDqH3yaexlGEgmE6619IC+uhqu+nr0kkSczetA7HZ4MzNR8eVXMNc3oOj/vkRp3z44fPQoCMtC0esAT3NHPg2jEcTThMPvboHB6QRMRsDVCCJLIDo9YDRCaXKjsLAQ+PU4vO4mrcUyURSIzkbIogh3fT3YzIyI957/dWlpKepKy7D37j9BcThwwtkAu8movV9eXo7a2lrtdUVFBRQA7uJigBCIDQ2QWBZuhwNMhgX2snLIXg+++2Q3IEkQThSDJQT/2bYdkGTwx34Fx3H49s2/ApKErCPHYNLp8M1zawBJwihRglmnV0PyAVxszoCJYQFJQv1/D+ACBjAcPYGvP/kMECVMFwRwh47h2+3qqn0qAJSUY/+/1UK2FwFA1dc4/H9fAwDOAYDCH1Ba+AMA4BQA+HE/CrdtA049Je5xz+1uGbgSCwyJ0vLqvffew9NPP42mgKqaVqsVX3/9dUIHXLt2LTweD+655x4AwJYtW1BUVIQVK1YAUAeC5557Dv/4xz8AAMePH8ftt9+Ojz76KOq+PR4PioqKMHLkSBhCksZSQbgoDFkQYLBmY+TK5S0+b7PZMDZOx3RgeWm/k27o4oVahnegLdyfNR5aYTcR4pG1NTnDVcNt7RylWtaOpL3kDIzCUkQRLMdCn50Ny/B8sCyHpooKCBUn4bXbg/0XccL48noYhmmuxuvb7k/aVCMHzZB9zZf8EFkGZzQi58KJqPnyK0hN6qpJdrubTWIsA85gRM+zzoQxN0c1l/l9WIqi+a5qaqqRoRA4Dx9R60gRAuL1AoSANRhgyOmt9sCpq4PiFYOiD9MFRqcDq9eDMxjAGvRqgrHBANb/Wm+AMTcHA2bNBJ+AvzrRsTPqCuSNN97AW2+9hbVr1+Kee+7BZ599hpMnT8YtoJ++ffsGtcatrq72hYs2v19d3ZyLUVNTE/R+ZyLVtZNac9KVd1DZjYh+l1aciekW257uxMmA4rMAACAASURBVBrJo4giPDW18FRWQqiqhqeqCs5jv8J15ChElwucLzFUdruDFIQf4ougCh2GiShCDugT7vWtvkORnU6Uvf/Plm/4B3ZZVSj2vd9E/S0NEbYrHg+aSsuifj8eWL3eN6DrwOr0Qf/7B3rJ5YLr2K9qaHMgPI+cCyci4ze/iagIml8bwBn06vE4Lqm/IVlEVSBZWVk466yzcNppp6G2thZ33HEHpk6dmvABJ0yYgJdffhl2ux0mkwk7d+7EE088ob3fv39/GAwGbbZWUFCg2cQ7G+1ROynSYNARSUfRoq2iVcPtaGc/JRhWp4OpX1+Y+vVt8Z7NZsMQqLWg+B49wOj1apFBUUT/66+FZfAgSE4XZJcLDQcPwv7NfyDW16vNpAwGMGBAFAUZ+cOgy8yAp9YOoeKkmiRo0MPQpw90PXoAigLnkSNQREmNvgooBc/qdMgcnq8mhPr+hfvbXlcH8sshsDodJH9F4ADHvlaqhGG0qgbawO5LNDX07g0CNcjh9GUPq03VeA6sXh3AWZ9iYHg+YgRcKOGisMQJ52PEjbPafO06C1EVCM/zqK+vx+DBg7F//35ccMEFkOONuQ+gT58+WLp0KebOnQtRFDFz5kyceeaZWLRoEZYsWYJRo0bh2WefxbJly+B0OnHGGWdg7ty5CR8vlXRk5mhHDMyJVtZNt9h2ikro9WbNZsiCAPuevcibeiUAdZB0/LBfNacajZDq6qAIHi3CKt5+HKEm0Fi+b7PZYNjxL3jsdWoSMMepZjVfNWl/3S0AmgIgsqyFHENRwPl+m3lAf2SE1KsKlDOe5y3cfe/3wXQVoiqQWbNmYfHixXj99ddxzTXXYNeuXRg6dGibDjp9+nRMnz49aNv69eu1v0899VRs27atTcdoD5I9iCfjBk0ltNx59yKW6x2oZDgAuowMNTE0MzOuUNi2PEfykaMQGxshVFQ0R5/5VjO6rCwQu121s/k7J/q7KPrygFidLmoBxnj7gcT7LHeWkPx4iapAZs6cialTp8JsNmPz5s346aefaEvbAJI1iKe6G14yblBa7rx7Ecv1TtakItHnyG4rhPjhx+AsFhhyc+CpqQEkVUnoe/cCWDU5kPhWG1JDg9bsisvMgNKkWg8M1uxWn4l4Vt+tPcuRfkO6dsKMqED++c8wzi0fH330Ea655pqUCNRdSWU3vGTdoOlY7I2SOLFc70RaGSeT8h0FAMdpx+ctGWq0lccDKASGnGzk3TZf+6zLV7st3vpgrSnK0N8pNjZGTqy99uqwvyFdO2FGVCD+sNnq6mocO3YM559/Pniex7fffovTTjuNKpAkk0rzULgbNLCEdqwPN3WIdy9iud7hlIzkdIJIIg6sXA2W58FnZ6dsVi1UVgG64GFMl5UVNpy9LceNpCg5s6nF5EyoqADfswdEe63aulfHg+/ZE0JlVdimaJGefXdJ5GZVnYWICsRfquT3v/891qxZg0GDBgFQE3IeffTR9pGuG5FK81DoDdqihHYcDzd1iHcvol3vUCXDmUxgGAaiw+HrK0Mg1tZC37u31pwpmfePsU8uPOUVQdv8A3syB99IqzHwfIvJGcOykOocan8bloUiyfBU18A8cADCldOM1I1UcrVsVtXZzFoROxL6qaio0JQHAOTl5bUpD4QSnlR2wwvtptaihHZA5zgKJV7CdSUksgLWH27rUyitrajttkIULXsc+xbdgaJlj7foQBjp/bxrZwCyHPTcyC4XvHWOuLoaxvIb/Z0DZacTBms2hi5eqPY3CUm8I4HJvUFvhN93uGdfbGgAn5kZtrtjZyKqAsnJycFLL72EkpISlJSU4Nlnn6U90VNApBs0GbON0Bs0tIQ2QKOpKMnB3zqY1fHNPWYYRq3RFWFFHa2NbWvvW8eOgW7q5KDnRtezJ/iMjKQPvqHtaa1jx4RtdQtFAXS8FiLMcBz0vXurIcYR9hv67HNmc9DzCXTOZzRqFNZf/vIXrFixAjNmzADDMLjwwgvx5JNPtods3Y5UmYdamBmMRq2Etp9EzWXpGn5ISQ1+c4wuK0vt+yFJWha7p6oKvS/6bYvvRHMil+8oUJusNdRrPgXObNHe5/KHIS+gpa23rg6s2ay1h2b4Zh+En2Tdt+FMWwzLQtejR5AC8JfvidR9JtXNqlJF1BVIbm4uXnnlFRQWFsJms2HNmjWwWq3tIRsliQTOnkbce49aoqKN5rJoM0dK98O/2gXLgcvMaC6BotOBz8xE9e7PWtwf/lVLIIGzbeexX+GtrVX7vMgyFK8Ir8MBd0kJADUPJPA+BCGQ6+vVkva+3A9vTQ04k1pePpn3bbjVQ/8brm/z85VKk3YyiboC+f777/H888+jvr4+qGXqBx98kFLBKKkjWdFU6Rx+SEkNgfeWp6pK7X9utWq9X8LdH60FkJzYvBWyy9V8AEJUpcSyavkTAPKeb8AErFA0pUUIGAS4HnyuicD7VnK5tM6Ih557ASPuvSfuezec5SAzf1jY5+vXGDPR0yXiMaoCeeyxx3Ddddfh9NNPj7kGDKXzkwxzGc1Mp4TDf2/tW3RHi4rR4e6PSBFOmaNGqh0Pw6EoWnVfpboGXkFobljlr3Pla1ylhtFaIbtVH4T/vpVcLrU3uy8zXRaEpEU6JeP5SoeIx5hqYd16663tIQslzaCZ6ZTWiPX+aOGjM5sAnkfZ1u1axd8WMAwsg3zBPLKqMAKLJvrLmZgHD1Y/Iggw5GQDADiTCUJpaXPXRl/tLE6vS0mocVcmqgIZPnw4fvnlF5xyyintIQ8ljaCZ6RQgskM6nvvDP9sOrJoQ1C43BIbnm/fDcmAYufnzLAv4iiUSQoKOa7cVQqyvhxJg5oIkgbAsdL17degKOh0DUqIqkJKSElx//fXIy8sLajRCfSCUVNhp0/Eh6o74r5O7pASSyw0+M1ONvApNeIvz/ggqzqjXQfZ4mxWIX5kwDPrfcL22HzY3B1xDo9q3RJLA6nRgTCbVme50ascFgEPPvaAqNI6DoijavllejeySBSFohRR6P2aOGonGn4rgLimBIqoRXpZBsZdEae18pmM9rKgKZOnSpe0hByVNSaadNl0fou5G4HWSBQFEUSA1NIDV68FbLEGO8njvj0C/mi4rC0p1jeoE95mkGJZF/xuux+Abb9C+w004H+zuz8H16hWxHLxfZtnnKyGBPUMAKF4vRIcjaGUTej82lVeg/r8HwJlMWk4HwzBwl5W3+T5N14CUqArkvPPOg8PhQFNTkxpOJssoLi5uD9koaUhbVhDp+hB1NwKvE5FksBynlixxOMBbLG0yAwX6TTizBYYcwGu3gygEPU49Jez9xOUPw5CAPJBw951fZtbXhheEqAl/AYgNDUErm9D7UXa7fP+7wfgaURFFgex2g+vVK6b7VD5yFEU7/tVCznQNSImqQF588UW88cYbAACO4yCKIvLz86kJi9KCtq4g0vUh6m4EXidWx0PxlU/3Rz+JDgcUjwf/v70zj26qTv//+96bpU1aWgptpYAwUGWU4rQsB1SGiqi1tLQsArLIILgM8kXqggcEZISZgzLICIILo+MgoKMIpYwW9dgDw2gVuqhDPUIFf2KhWFq60KTNdvP5/ZHm0qRZmjRJb+jzOodDc3OT++Rzl+fzedbSh5f4PIlw9puAF6CM9V6VwdtKxy6zMjYWpro6h1aznEJhq9UlCGg+WQG0rW6cr0er2WJTGmbz1f7ubb+7M9epvfS8UavtcH+Ea0CK10TCgoICHDlyBBkZGfjss8+wceNGJCcnh0I2IsxoP2Pzp4SEq7IQ4XAT9TTanydlbKyUm8EpFDA1NMDc1GRbQfiZpCdERsB46RJaq6rAKxQBKeljl1mh1ULVt6/NfAUAPA9V374uV072z4gtehiqL4CJoi0qjOOu5sQxBk6h6NR12r70vPP9ES6Jg854VSBxcXFISEjAkCFDcOrUKUydOhWVlZWhkI0IM7xlFHsjXG+inkb788RHaqCMiQHH85L/QRUbC2VsrM+TiPqycvy4dTsM1bbOghzHwdTYGHCZBY0GvFIJCAIiEhOkJEdnJZA0LReiXg/DpVqI9p7tgKQwrRYLwBgEjaZT16mr0vP2+yOYtfCCSafyQH755RcMGTIEpaWlGD9+PIzOxcMIAl3PCwmX7NuejvN5ikzqh6FL/+iQPNiezk4izu3aA4tOZ8vnaHN0W3Q6nNu1JyBJeQ7XVr9+MDc1AbzQIdS3/WeUMTFt/U0sUs4IYEtq59uUo6Z/UqeuU3el5+33RzgkDjrjVYE8+uijWLt2LV577TVs3boVBw8exB133BEC0YhwIxB5IeF4E/VE3J2nrkwiWqurwbVlhQOQoqRaq6uDInNnAj7sKyBOqbwatcUYBK0WY9/5h0/HT5qWi6ZtO66pvCmvCuTmm2/Grl27ANja3J47dw4879XyRfRAaAVBeJpEdOaBzQCHrn3OrwNJZyYrzGIBYwx828oDHAerxeLghPfleMrJGVCf/P6auT/cKpDGNs378MMPY/fu3ZLTqG/fvpg/f77U8pYg2kMriJ5N+0lE+2S7c7v22JzrLiKQ7NdLZP8ktFSdB7Nawdkd1YwhckD/bvs9vFIB0QBHmdq2+4OQPBQps2c5bAvn5Fm3S4mnnnoK48aNQ2VlJcaOHYtx48Zh3LhxuOOOOzB8+PBQykgQRBhhL2PCqyOgjI2Fqk8fGC5ehLm5GbCKbp3rgxbMhzI6GhzPwSqK4HgOyuhoDFowv9t+i2bgQKhiY8ErBFthRoUAVWwsNAFqqhfuLRHcqtG33noLALBq1Sps3LgxZAIRBBGetJ9JW/R68Go1VG0OdXudKnNjo9TIzNm5HjdqJJIfXxrU2bivs327SU4Z1ycofotwT571uA5jjGH9+vUAAJ1Oh+LiYgwbNgyD2ipcEgRBAC6SSC9fhtVolMqbcAoFYLFIPTwA95V5g/Xg9DfRVYiMQOsFmyM/MikJgxYvDJiM4Z4869aEdebMGUyaNAlffPEFDAYDZs6cib/97W+YP38+vvzyS78PWF1djXnz5uHee+/FkiVLoG/fLKaNCxcuIC0tDbm5ucjNzcXixYv9Ph5BEMGnOr8AVrMZpsuX0frLL1KuhLnNl6qMjQVjDJzAd1uOj6+JrnaFYzVbEDlwINQJCRANhoDKFO7Js25XIJs2bUJeXh4mTpyI/fttTV0+/vhj1NTU4IknnsDtt9/u1wGff/55zJ07F1lZWdixYwdeffVVrFixwmGfiooKTJkyRVr9EAQhb/S/VNk6B9rLqbfVmrIajW2KQ4AiKgqq3rEOFXJDaabxdbbvybxkfz8YPdV9Uazd7YB3uwK5ePEicnJyAADHjx/HpEmTwPM8+vXrB51O59fBzGYzSkpKkJGRAQCYPn26y2iukydPorKyErm5uViwYAFOnz7t1/EIgggN9nBXjufBcRx4hcKmSDhOyqy+Yfn/IW3rFoz++2tI+fPzXX7Q1ZeVo2LNOhi27UDFmnVeHc++zvbdVVZoqaoKak/1zmagy8EB73YF0j7X45tvvsGaNWuk1/5mojc0NCAqKgqKtkJk8fHxqKmp6bCfWq1GTk4O7r//fvz3v//F0qVLUVhYCJVK1anjVFRU+CVfKCjrZE9kORBIWcUzZyEWfw1rYyP42FgIt42DkDw0YN8fLuMaLnICvskqAgBjtvIe7ZtARUaC++NDMAL4f2Cd7gnu9XhnzsJc+KktMzwiAk3VF9G0bQeUkzPcXlfiiOG2zxgMtpIiZgsgihDTx7v8rWZNJIxNTeBUSmkbM5ltnxcU4HgOaG2xbbdYcPqdPVChY/MrZ1yO67QccIBP42R6Zy+sFovfcgQCtwokJiYGp06dgk6nQ21tLcaMGQMAKC8vR2JiotcvPnz4cIforUGDBnXoq+6qz/qyZcukv9PT0/HSSy/hp59+wm9/+1uvxwWAlJQUh+ZXcqGsrAyjRo3qbjE6RSBlrS8rx09FRyEoFODj+tiW6UVH8Zvk5IAst8NlXMNFTsB3WSuG/Aat1RchtuhhNVvAq5QQNFpEJvVDShB+c0X+IVtV24gItLTooYmNhbmxEWLBR+C1WtfmnFGjUN++7HtSP48mn3pwNqc7L1w1LykYRHUEVDExDs8uFmlrXuVtzAJ5DZS+/iYEP+Vwxmg0+jXxdqtAnnzySSxcuBA6nQ5PP/00NBoN3nrrLbz++uvYsWOH1y/OzMxEZmamwzaz2YyxY8dCFEUIgoDa2lokJHRcPu7evRvZ2dno3dvWw5gxJq1armW6254ZLMI9VLEn4HztiSOGAz48hIIV7urunnD2Z4gteqnsiDox0W2ElS9RXu4qK1TnF8ii9LocSsC7fSqnpqbi2LFjMBgM6NWrFwAgLS0N+/btw+DBg/06mFKpxOjRo1FYWIgpU6bg4MGDmDBhQof9SkpKYDAY8PDDD+PEiROwWq0YMmSIX8cMF67lbnz+hipeqwpVbri69syFn6LehxViV8vYuDrXANzeE84PT3u0F69UShFWgZikuFM4Xa35FggCUXuuq7h1ohcXF0OlUknKAwBGjhzpoDy++OILnw+4bt06fPDBB5g8eTJKS0uRl5cHAHjvvfewdetWAMDq1atRXFyM7OxsvPjii3jppZeu+fpbXe2lIWf8CVWUg4Owp+Dq2oMg+HztxY0aiZQ/P++zk9zduT63a4/be6JD6X+TGRzH2fqTtBGsfAq5lF6XgxxuVyDFxcXYs2cPZs+ejdtvv10yIZlMJhw/fhx79uzBkCFDMH78eJ8O2L9/f+zevbvD9jlz5kh/JyYm4u233/bpe8OdcE8o8oQ/MyUye4UOV9celIqQXXvO55qJIswNDTCaTOAjbOVQ7D072vfPkFY8VVUQIiLAq9XSfkBwzTlyqfnW3XK4VSBPP/00vvvuO7zyyivIy8tDQkICrFYr6urqMHr0aDz22GNIS0sLpazXNHKwZwYLf8wb4apQw9Hs5urag9mCiKR+ITl++3Nt0ethqquT3rOazdJrhVbrsn9GWVkZftPm8A6FOSccz3Gw8OiZ/t3vfoc333wTzc3NOHfuHDiOw/XXX4/o6OhQyddjkIM9M5j4OlMKR4Uarn4sV9ceRDFk1177c21ubJTazfIqFZjVCsYYzI2N4ATB7T0RqlYC4XqOg4VXx8Kzzz6L6OhopKSkYPjw4YiOjsbjjz8eCtl6FHKwZ8qJcGxvG65+LFfXnnJyRsiuvaRpubDodGitqoLVYAAzm8GsVqj6xEEd3xe8UgGryeT1nvDXB+ML4XqOg4XbFci6detQU1ODsrIy1NfXS9stFguqqqpCIlxPo7vtmXIiHJtThavZDeh47QUj4dGT6YfjOIfOUfY/BY0WKl6AOq43Uv78fMBl8pVwPsfBwK0Cue+++/Djjz/i9OnTUukRABAEAampqSERjggdXc0DCAbhplDD0ewWKjyZfqrzCyBotVD16SP5QBhjMDU0QMW7N1t1B67OsaWpCaLBgNKHl4TFRCeQuFUgI0aMwIgRI1BeXo5p06aFUiYixLS/uRnHofl0JawV3+Ob4q8xaMH8HnMzdJVr3Y/VFTxF1bWf1dujqMyNjZLZSk4PZOdzbGlqgqmxEcqYmB7pE/Ga3v3dd9+FQg6iG7Hf3EwUYb582ebE5DgYLl7062awr2batzTVXj9QVg+CYBCOZrdQ4cn04zyrV2i14ITuM1t5MrU5n2PRYIAyJgaqtqoZPS3c3KsCGTBgABYtWoSRI0dC2y7G+sEHHwyqYETosN/chupqgOPA8baeDUy0Sg5CX5PCmMUCc7OtajPHcWi5UN0jZmbhZnYLFZ7Me3JauXUmyqr9OS59eEmP9ol4jcKKjY1FYmIiLly4gMrKSukfce1gzxRn9kqqAMAYeKXC55vBvpoRW/TgeN5W1pvjILa09OholZ6Op6g6OUUg+hplFe4NobqK1xUI9UMPH/xNcLLPADmet8XdtykRZWyszzeDfTVjNVvA2cvPcByYxdKjZmaEI97Me3JZufkaZSWn1VN34FWBPPDAAy5Lrtt55513AioQ4Rl3SqIrCU72m/vcrj1oqaoCr1SARUUDXiJgXMliN1XwSgWsFhEcx4GJIsAYWn7+GQBwfMGiHuETIRyRi5LwhK+RdD3d7+VVgaSkpODs2bOYNWsWlEolCgoKYLFYkJWVFQr5iHZ4C4XsSu0o+81tVwpNVVUeI2DcyRI/aSIMRUcApRKs1dCxrQ3HQdTre4xPhAgv/FlRhINiDBZeFUh5eTneffddCIIAAPj973+PWbNmOeSGEKGhs6GQdvwxGbWvL+SpEZA7WZpPViB+0kRc2Lff1tLUanWUqa08hdjSAqFPn4BEq4hnzqIi/1CPnAESgaWnryh8xasCqa+vh9FohEajAQDo9XoYDIagC0Z0xJdQSCC4zjzPCqsC6oQEW7e4c+fA8TysJtNVB70Xn4gvvpz6snKYCz+1dafrQhw+Fcgj7PTkFYWveI3Cys7OxuzZs7Ft2zZs3boVM2fOxNy5c0MhG+GEp4iPUNeO8iSLoeYS+LaWwrxSAWbvj93uf06hcKngfO0DUp1fAAhCl2oTUe8RgvAPrwpk+fLlWLZsGZqammA0GrF+/XpSIN2EnEIhPcnSXrkoY2NtiqMtIstqNNqK5VkssOh0HRScr2GUhppLgNJxIe1v6DEVyCMI3/BowmKMQRRF3HPPPbjttttQXFzssoc5ERrkFArpTRbJERmpgTImBqaGBgcTFifwLqP7fPXlRCQmwFh90WGbv6HHnT0mQRA23CqQM2fO4JFHHsHatWtx6623YubMmQAAnU6HF154AbfffnvIhCSuIif7rDtZnJVLZFI/KKJtuSHtfTTOUWL1ZeWw6PUwXr4MXqmUOtF5UghJ03LRtG2H16gZTz4OKoJIEP7hVoFs2rQJeXl5mDhxIvbv3w/GGAoLC1FTU4MnnniCFAjhEWfl4q3kg90PYY/msneis5pM4JVKt76cuFEjoZycAfXJ7906wN2FHDdPmojmkxXQ/1IFsaUFyl69oIiJ6XHJYAThL24VyMWLF5GTkwMAOH78OO666y7wPI9+/fpBp9OFTEAi/Gm/shBUtpWFoHFcWdj9EIqoKPAqJcyNjRBNZliNRiT/3xKPqy4heShSZs9y+76rkGNzYyMu7NsPdUICVH36wCwIMF+5AiZaoBnoX5IjRXIRPQ23CoTnr/rXv/nmG6xZs0Z6bXSKviHCh1A/5Oyzf7t5STSZIV6qhSrWDE6hkGb57f0QgkYLQaO1+eB0ui7L58rHYdHrwKxWSamoeveGEBnpdwVYanVK9ETcKpCYmBicOnUKOp0OtbW1GDNmDABbYmFiYmLIBAwn5D4D7Y6HnH32r4qKAq9S2fo8mM0QDQbc+FQems+cReVLL0PU64HaWih7x0LVOw5A4PwQLn0cZgt4pdJhv644zrtaCYAgwhG3YbxPPvkkFi5ciIULFyIvLw8ajQZvvfUWHn30UeqJ7oJwyCXojnDV9jkhCq0Wkf37QzNoEBRaLZrPnMX59/dBNBhsYb6MwVzfAGP95YDmsbgKOeZ4HkJbcqydriis9r/TDkVyEdc6blcgqampOHbsGAwGA3r16gUASEtLw759+zB48OBQyRc2hMMM1FBzCYzjYLpwAcxia/SkiIkJ6kPOU4TTxYJ/A4Ct5DsAqygCFgssjU3QDhgQsBWcq5Djvum/R23RkYBVUaVILqIn4jEPRKVSQaVSSa9HjpTHg1COhEMugRAZiZbz521l1nkeTBRhqquDZsCAoB3TU3G6H/68EVxbjTUA4AUBrK16b6A70bkKOY5OHuq3ydHZXBk9IgWGACokgggHvNbCChYvv/wyBEHAsmXLOrxnMpmwevVqVFRUICIiAps3b8bQoUO7QcrO010zUJ/8Lpzjn8zF9kDjKeFQiIyE2NJik4MxKdHQ2bQUTNn8WeG48iUZio4gvi0sWK4+MIIINCFXIM3Nzdi4cSM+/vhjPPTQQy732b17NyIjI3H48GGUlJRg1apV+OCDD0IsqW90R2MZX53iYksr1PF9YWlqanMiK6CIiYPY0ho0GQH3D+rYMaNw+eixqxvaamXFjnFfBVgOeKpE3B09vAmiuwi5AikqKsLgwYM99lQ/evQoli9fDgAYM2YM6uvrUV1djaSkpJDI6E80VXeUgfbV72JfJUUk9Ze2iQYD1PG9gyYj4H48LZfrIfSKhqjT28q+8zyEKC0sl+ulz+h/qbJV7lUq/M7PCDThYK4kiFDAMalUamh55ZVXAMClCSsjIwM7d+7EoEGDAABz5szBihUrvPpgjEYjKioquiSXeOYszIWfAoJgK9JntgCiCOXkDAjJ8jKjGbbtANoiquwwxgCDARGPL+2wv8NvEy2ATg+IIriEeHA3/Rb4+RysjY3gY2Mh3DYuIL/X03iaCz9xLX+zDlAqAYsFaG29WsU3SgvwgnQuxDNnIRZ/HXCZneV3PoZY/DWszTpwqqthwMxkBh8dBdWCeQE9PkGEkpSUFKidogk9EbQVyOHDhzv0Ux8yZAj++c9/ev0sY6zDQ6V9YqM3fB2E9lTkH7L1lnCq2aQ++b3HbOfOUFZWhlEemjT5SsXAgR38LqLBAPXAga6bQY0ahfrkZIfWtcq+fWA1mWH+75dQxcZCEdcHVqMRhsJPkfz40i7P9j2NJ9zIbwag1GphunwZjOelXu282QxlXC+oT36PpORk/FR0FIJCAUNEBFRmC1jRUfwmOTlgK5T6snLpGHzbuLCio0icNBG1RUfA8cJVc6WCYciC+R6PHejzH0xI1uAgV1n9nXx3/qnsI5mZmTh27JjDv84oDwBITEzEpUtXzQF1dXUhqwIsp3j++rJyVKxZh9KHl6BizboOOSX+9ACJGzUSyl7RiOjXD5EDBkKhjYLY0gIAEFv0Un4IBCEg+SGextOdYxjavQAADgdJREFU/JxCAV6tBrNYJMc6x3E2v03bZ0OR0+LuGM0nK0JaOp8g5Eq3RWF5Ij09HQUFBRg9ejRKS0uhVqtD5v+QSzx/Zxzk/vpdnG34zGKxdQ00W67upFQERGl6Gk938lfnF8BY3wBOoQATRVsHQ8bAKxUOTauC7YfwdAw5VUUmiO5CNgrkvffew6VLl7B8+XI88MADeO6555CVlQWVSoVNmzaFTI7uiKZyRWcd5P48yJwf6pxCAavZDKHNpm/R64G6yzBxHCrWrPPaUtaTAms/nlaLBZaGBljbnOL1ZeVu5f/pjTchaDSwXLkCq8Wm2ARNL+lc2JVMMBW9XCYTBCFXgmbC8sayZcscHOhz5syRIq/UajVefPFFfPzxx8jPz8fw4cNDJleoO/u5I5imNGfTkT3vQtBoYdbpYKytBaxWqPrEeSzJ0pnyLfbx5BUKmGprAQ5QJ8TDara4/V77ZzT9kyC0+U+U0VGITOonnYtQtPANdZtgggg3ZLMCkRNyME8Ec/brbDrS9E9C9L33oPlkBa6cOg1eIYBptVBobeYbd6HBvqySqvMLENGvn8eGUs4yejoHDr+hqgrqIIT4dkdoNkGEE6RA2pBbJd1gm9JcPqBnz5QaP7W2tkib3a18fPFDBMNnYf8NZWVlrqPOAoAcJhMEIVe6zYQlJ+RYSTfUpjR7xJepoQGGC+eBdj1f3K18IhITYHXqDROIfQmCCA9oBQL5VtIN1ey3fcSXMi4Opro6oLEJFpUKnKBwu/LxZZUkl+AEgiACB61AIK/cj+6gvQJVRkVBHR8P8DxMl+s9rnx8WSXJJTiBIIjAQSsQyDNcs6s+GV8+7+yfUGi1MIJBZWVeiwP6skoifwJBXFvQCgTyC9fsqk/G18+78k/AbCH/BEEQHiEFAvmZV7papsPXz7tSoBBF8k8QBOERMmG1ISfzSldDXn39vKt8BzF9vGzGgyAIeUIKRIZ01Sfjz+edFWhZWZkfkhME0ZMgE5YM6apPRm4+HYIgrk1IgciQrvpk5ObTIQji2oRMWDKlqz4ZOfl0CIK4NqEVCEEQBOEXpEAIgiAIvyAFQhAEQfgFKRCCIAjCL0iBEARBEH5xTUVhMcYAACaTqZslcY/RueaUjCFZA0+4yAmQrMFCjrLan5n2Z2hn4Zivn5Axzc3NqKys7G4xCIIgwpIbb7wR0dHRnd7/mlIgVqsVer0eSqUSHMd1tzgEQRBhAWMMZrMZWq0WPN95z8Y1pUAIgiCI0EFOdIIgCMIvSIEQBEEQfkEKhCAIgvALUiAEQRCEX5ACIQiCIPyCFAhBEAThF6RACIIgCL8gBRICXn75Zbzyyisu3zOZTFixYgUyMzMxbdo0nD17NsTS2aiursa8efNw7733YsmSJdDr9R32uXDhAtLS0pCbm4vc3FwsXrw4ZPL9+9//xuTJk3HPPfdg7969Hd7/4YcfMH36dGRkZGD16tWwWCwhk80Zb7Ju374dEydOlMbR1T6hRKfTITs7G+fPn+/wnpzG1ZOcchrT7du3IysrC1lZWdi0aVOH9+U0pl2GEUHjypUrbNWqVeyWW25h27Ztc7nPm2++ydauXcsYY+zEiRNs5syZoRRR4pFHHmEfffQRY4yx7du3s02bNnXY55NPPpFkDSW//vormzhxImtoaGB6vZ5NmTKF/fjjjw77ZGVlsW+++YYxxtiqVavY3r17Qy4nY52T9dFHH2Xl5eXdIp8z3377LcvOzmbDhw9nVVVVHd6Xy7h6k1MuY/rll1+y2bNnM6PRyEwmE1uwYAH77LPPHPaRy5gGAlqBBJGioiIMHjwYDz74oNt9jh49ipycHADAmDFjUF9fj+rq6lCJCAAwm80oKSlBRkYGAGD69On45JNPOux38uRJVFZWIjc3FwsWLMDp06dDIl9xcTHGjRuH2NhYaDQaZGRkOMh34cIFGAwGpKamepRfDrICQEVFBd544w1MmTIF69ev79bieh988AHWrVuHhISEDu/JaVw9yQnIZ0zj4+OxcuVKqFQqKJVKDB061OF+ltOYBgJSIEFk6tSpeOSRRyAIgtt9Ll26hPj4eOl1fHw8fv3111CIJ9HQ0ICoqCgoFApJhpqamg77qdVq5OTkID8/H4sXL8bSpUtDUvnYeYwSEhIc5HM1hq7kDwXeZNXr9bjpppuwYsUK5Ofn48qVK3j11Ve7Q1QAwF/+8heMHj3a5XtyGldPcsppTG+44QZJOfz88884fPgw0tPTpfflNKaBgBRIADh8+DAmTJjg8G/hwoWd+ixjzKHwI2PMp2JmvuJK1qeeeqpD8UlXxSiXLVuGuXPngud5pKenQ6PR4KeffgqarHasVmuHMWr/2tv7ocSbLFqtFn//+98xdOhQKBQKLFq0CP/5z3+6Q1SvyGlcPSHHMf3xxx+xaNEiPPPMMxg8eLC0PVzGtLNcU/1AuovMzExkZmb69dnExERcunQJ119/PQCgrq7O7TI9ELiS1Ww2Y+zYsRBFEYIgoLa21qUMu3fvRnZ2Nnr37g3AdvHbVy3B5LrrrkNpaan02lm+6667DrW1tdLrYI+hJ7zJWl1djeLiYtx3330AQjeG/iCncfWE3Ma0rKwMjz/+OJ599llkZWU5vBcuY9pZaAXSzaSnp6OgoAAAUFpaCrVajaSkpJDKoFQqMXr0aBQWFgIADh48iAkTJnTYr6SkBB9++CEA4MSJE7BarRgyZEjQ5bvtttvw1Vdfob6+Hq2trfjss88c5Ovfvz/UajXKysoAAAUFBS7lDwXeZI2IiMBf//pXVFVVgTGGvXv34u677+4WWb0hp3H1hJzG9OLFi1i6dCk2b97cQXkA4TOmnaZ7fPc9i23btjlEYb377rvs5ZdfZowxZjAY2DPPPMMmT57Mpk6dyioqKrpFxvPnz7P58+ezzMxMtmjRItbY2NhB1l9//ZUtXLiQZWVlsenTp7MffvghZPIdOnSIZWVlsXvuuYft3LmTMcbYQw89xP73v/8xxhj74Ycf2IwZM1hGRgZ78sknmdFoDJlsvsr6ySefSO+vXLmyW2W1M3HiRCm6Sa7jyph7OeUyphs2bGCpqaksJydH+vfuu+/Keky7AvUDIQiCIPyCTFgEQRCEX5ACIQiCIPyCFAhBEAThF6RACIIgCL8gBUIQBEH4BSkQokcwbNgwTJkyRarWmpubi9WrVwMAcnNzceXKFTQ3N2PBggXSZ7Zv347PP//c52OlpaV1qBibn5+Pe++9t8O+dXV1SE1NRV1dndvvO378OLKzs32WgyCCjTxTYAkiCOzatQtxcXEdttsTOc+fP4+TJ09K248fP47k5OSAHHvy5Ml44YUXUFZWhlGjRknb9+/fj0mTJqFv374BOQ5BhBJSIESPZ9iwYfjqq6+watUqGAwG5ObmYtasWaioqMCmTZsgCALS09OxefNmlJSUQBRF3HzzzVizZg2ioqJQWlqKDRs2gOM4jBgxAlartcMx1Go1ZsyYgf3790sKhDGGffv2ST0jjhw5gjfeeAMmkwn19fWYOnUq8vLyHL5n5cqVuOGGG6ReLO1f19TUYP369bh48SLMZjOysrLwxz/+ERaLBRs2bEB5eTmUSiUGDBiAjRs3QqvVBnlkiWsdMmERPYY//OEPDiasy5cvO7y/ceNGREREoKCgAPPmzUNKSgqeeeYZ3H333di5cycEQcCBAwdw6NAhJCQkYPPmzTCZTFi+fDlWrlyJgwcPYuzYsTAYDC6PP2fOHHz66adSs67i4mJERUVh5MiRYIzhH//4B1544QUcOHAA77//Pnbu3In6+vpO/74VK1ZgxowZOHDgAD788EMUFxejsLAQ3377LU6cOIFDhw7hwIEDGDhwYMhK8RPXNrQCIXoM7kxYneHo0aNobm5GcXExAFsByj59+qCyshIKhQK33norACA7OxvPPfecy+8YOHAgRo0ahcOHD+O+++7D+++/j3nz5gGwVT9+/fXXcfToUXz00Uc4e/YsGGNobW3tlHwtLS0oKSlBU1MTtm7dKm07deoUxo8fD0EQMHPmTIwfPx4ZGRm45ZZb/BoHgmgPKRCC6ARWqxXPPvus1NtBr9fDaDSiuroaztWAPFWCnTt3Ll5//XXceeedKCsrk8xXLS0tmDZtGu666y6MHj0aM2bMwOeff97huzmOc9hmNpsl+Rhj+Ne//oXIyEgAQH19PdRqNbRaLQoKClBeXo6vv/4aeXl5WLx4saS8CMJfyIRFEG0oFAqIoig9oAVBkPpVjx8/Hnv37oXJZILVasXatWuxZcsWDBs2DIwxqf9EUVERmpqa3B5jwoQJqKurw2uvvYacnBxEREQAAM6dOwedToe8vDzceeedOH78uHSs9vTu3RsVFRUAgJqaGpw4cQIAEBUVhdTUVLz99tsAgCtXrmDOnDkoKirCkSNHsHDhQqSlpWHZsmWYOnWq9B0E0RVoBUIQbcTHx+OWW25BVlYW9u7dizvvvBNbtmyB2WzGY489hhdffBHTpk2DKIq46aabsHLlSiiVSuzYsQN/+tOfsGXLFtx0003o06eP22PwPI/7778fW7ZscWhlOmzYMNxxxx3IzMyESqXCjTfeiOTkZJw7dw4qlUra74EHHsDTTz+NjIwMDBgwAOPGjZPe27x5MzZs2IApU6bAZDIhOzsbOTk5EEURx44dQ3Z2NjQaDWJiYrBhw4bgDCLRo6BqvARBEIRfkAmLIAiC8AtSIARBEIRfkAIhCIIg/IIUCEEQBOEXpEAIgiAIvyAFQhAEQfgFKRCCIAjCL0iBEARBEH7x/wEr7yNW4VbM1QAAAABJRU5ErkJggg==
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Heteroscedasiticy xample</span>
<span class="n">cars</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_rdataset</span><span class="p">(</span><span class="s2">&quot;cars&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">data</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Splitting the Data</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">cars</span><span class="o">.</span><span class="n">speed</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">cars</span><span class="o">.</span><span class="n">speed</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">cars</span><span class="o">.</span><span class="n">dist</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Deploying the Model and Prediction</span>
<span class="n">carmodel</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">pred_car</span> <span class="o">=</span> <span class="n">carmodel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Residual Plot</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">pred_car</span><span class="p">,</span> <span class="n">carmodel</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c1e8df390&gt;</pre>
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<div class="prompt input_prompt">In&nbsp;[19]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scale Location Plot</span>
<span class="n">cars_residuals</span> <span class="o">=</span> <span class="n">carmodel</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">pred_car</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cars_residuals</span><span class="p">)),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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    <div class="prompt output_prompt">Out[19]:</div>


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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c1e8c1ba8&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Heteroscedasticity</span>
<span class="kn">import</span> <span class="nn">statsmodels.stats.api</span> <span class="k">as</span> <span class="nn">sts</span>
<span class="n">sts</span><span class="o">.</span><span class="n">het_goldfeldquandt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cars_residuals</span><span class="p">)),</span> <span class="n">carmodel</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">exog</span><span class="p">)</span>
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<pre>(1.021646905470046, 0.47974318608872085, &#39;increasing&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="mf">0.47974318608872085</span><span class="o">&lt;</span><span class="mf">0.05</span>
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<pre>False</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">carmodel</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<table class="simpletable">
<caption>OLS Regression Results</caption>
<tr>
  <th>Dep. Variable:</th>          <td>dist</td>       <th>  R-squared:         </th> <td>   0.651</td>
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  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.644</td>
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  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   89.57</td>
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  <th>Date:</th>             <td>Wed, 06 Mar 2019</td> <th>  Prob (F-statistic):</th> <td>1.49e-12</td>
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  <th>Time:</th>                 <td>08:48:43</td>     <th>  Log-Likelihood:    </th> <td> -206.58</td>
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  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   417.2</td>
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  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   421.0</td>
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  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   
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  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   
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    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  
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  <th>const</th> <td>  -17.5791</td> <td>    6.758</td> <td>   -2.601</td> <td> 0.012</td> <td>  -31.168</td> <td>   -3.990</td>
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  <th>speed</th> <td>    3.9324</td> <td>    0.416</td> <td>    9.464</td> <td> 0.000</td> <td>    3.097</td> <td>    4.768</td>
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  <th>Omnibus:</th>       <td> 8.975</td> <th>  Durbin-Watson:     </th> <td>   1.676</td>
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  <th>Prob(Omnibus):</th> <td> 0.011</td> <th>  Jarque-Bera (JB):  </th> <td>   8.189</td>
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  <th>Skew:</th>          <td> 0.885</td> <th>  Prob(JB):          </th> <td>  0.0167</td>
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  <th>Kurtosis:</th>      <td> 3.893</td> <th>  Cond. No.          </th> <td>    50.7</td>
</tr>
</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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<h1 id="Advertising-Data">Advertising Data<a class="anchor-link" href="#Advertising-Data">&#182;</a></h1><h3 id="Objective---To-Find-if-the-Advertising-Expenditure-Really-Driving-the-Sales">Objective - To Find if the Advertising Expenditure Really Driving the Sales<a class="anchor-link" href="#Objective---To-Find-if-the-Advertising-Expenditure-Really-Driving-the-Sales">&#182;</a></h3><h4 id="From-ML-Stand-Point">From ML Stand Point<a class="anchor-link" href="#From-ML-Stand-Point">&#182;</a></h4><ol>
<li>Import the Dataset</li>
<li>Try Basic EDA if that is required</li>
<li>Split the Data in Train and Test</li>
<li>Apply Linear Regression from Scikit Learn</li>
<li>Check the Error Metrics</li>
</ol>
<h4 id="Assumptions">Assumptions<a class="anchor-link" href="#Assumptions">&#182;</a></h4><ol>
<li>Import the Dataset</li>
<li>Try Basic EDA if that is required</li>
<li>Create Linear Regression Model from statsmodels.api</li>
<li>Check the Assumptions with Tests and Plots</li>
<li>Conclude the Observations</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">adv</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="s2">&quot;~/Downloads/advertising.csv&quot;</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Unnamed: 0</th>
      <th>TV</th>
      <th>Radio</th>
      <th>Newspaper</th>
      <th>Sales</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>230.1</td>
      <td>37.8</td>
      <td>69.2</td>
      <td>22.1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>44.5</td>
      <td>39.3</td>
      <td>45.1</td>
      <td>10.4</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>17.2</td>
      <td>45.9</td>
      <td>69.3</td>
      <td>9.3</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>151.5</td>
      <td>41.3</td>
      <td>58.5</td>
      <td>18.5</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>180.8</td>
      <td>10.8</td>
      <td>58.4</td>
      <td>12.9</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
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<div class="prompt input_prompt">In&nbsp;[27]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">adv</span><span class="o">.</span><span class="n">columns</span>
</pre></div>

    </div>
</div>
</div>

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<pre>Index([&#39;Unnamed: 0&#39;, &#39;TV&#39;, &#39;Radio&#39;, &#39;Newspaper&#39;, &#39;Sales&#39;], dtype=&#39;object&#39;)</pre>
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<div class="prompt input_prompt">In&nbsp;[29]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">adv</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Unnamed: 0&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[31]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">adv</span><span class="p">))</span>
</pre></div>

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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c1fa0f278&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">mean_absolute_error</span><span class="p">,</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;Sales&quot;</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">y</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">Sales</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xtrain</span><span class="p">,</span><span class="n">xtest</span><span class="p">,</span><span class="n">ytrain</span><span class="p">,</span><span class="n">ytest</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.30</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">123</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">xtrain</span><span class="p">,</span><span class="n">ytrain</span><span class="p">)</span>
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<pre>LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred_lm</span> <span class="o">=</span> <span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">xtest</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># MAE &amp; RMSE - train</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">ytrain</span><span class="p">,</span><span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">xtrain</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">ytrain</span><span class="p">,</span><span class="n">lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">xtrain</span><span class="p">))))</span>
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<pre>1.3319897007579353
1.7613917369368022
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test Error Values</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">ytest</span><span class="p">,</span><span class="n">pred_lm</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">ytest</span><span class="p">,</span> <span class="n">pred_lm</span><span class="p">)))</span>
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<pre>1.15604990178548
1.4472433065929022
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">adv</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;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>TV</th>
      <th>Radio</th>
      <th>Newspaper</th>
      <th>Sales</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>200.000000</td>
      <td>200.000000</td>
      <td>200.000000</td>
      <td>200.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>147.042500</td>
      <td>23.264000</td>
      <td>30.554000</td>
      <td>14.022500</td>
    </tr>
    <tr>
      <th>std</th>
      <td>85.854236</td>
      <td>14.846809</td>
      <td>21.778621</td>
      <td>5.217457</td>
    </tr>
    <tr>
      <th>min</th>
      <td>0.700000</td>
      <td>0.000000</td>
      <td>0.300000</td>
      <td>1.600000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>74.375000</td>
      <td>9.975000</td>
      <td>12.750000</td>
      <td>10.375000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>149.750000</td>
      <td>22.900000</td>
      <td>25.750000</td>
      <td>12.900000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>218.825000</td>
      <td>36.525000</td>
      <td>45.100000</td>
      <td>17.400000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>296.400000</td>
      <td>49.600000</td>
      <td>114.000000</td>
      <td>27.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sms</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;Sales&quot;</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">x</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">Sales</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_adv</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">adv_pred</span><span class="o">=</span> <span class="n">model_adv</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test of Linearity</span>
<span class="kn">from</span> <span class="nn">statsmodels.stats.api</span> <span class="k">import</span> <span class="n">linear_rainbow</span><span class="p">,</span> <span class="n">het_goldfeldquandt</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">linear_rainbow</span><span class="p">(</span><span class="n">model_adv</span><span class="p">)</span> <span class="c1"># It is Linear</span>
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<pre>(0.6576965219996191, 0.980543336515235)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plotting the Residuals</span>
<span class="n">sns</span><span class="o">.</span><span class="n">residplot</span><span class="p">(</span><span class="n">model_adv</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> 
              <span class="n">model_adv</span><span class="o">.</span><span class="n">resid</span><span class="p">,</span> <span class="n">lowess</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c2a7f1a58&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Standard Residuals</span>
<span class="n">st_adv_residual</span> <span class="o">=</span> <span class="n">model_adv</span><span class="o">.</span><span class="n">get_influence</span><span class="p">()</span><span class="o">.</span><span class="n">resid_studentized_internal</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">probplot</span>
<span class="kn">import</span> <span class="nn">pylab</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># QQPLOT</span>
<span class="n">probplot</span><span class="p">(</span><span class="n">st_adv_residual</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;norm&quot;</span><span class="p">,</span> <span class="n">plot</span> <span class="o">=</span> <span class="n">pylab</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h1 id="Shapiro-Wilk-Test">Shapiro Wilk Test<a class="anchor-link" href="#Shapiro-Wilk-Test">&#182;</a></h1><ol>
<li>Null Hypothesis - Residuals are Following the Normal Dist Pattern</li>
<li>Alternate Hyp - Residuals are Not Following the Gaussian Curve</li>
</ol>
<p>Thumb Rule - If test statistic value &lt; 0.05, Reject the Null Hyp &amp; vice versa</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Normality Test</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">shapiro</span><span class="p">,</span> <span class="n">boxcox</span>
<span class="n">shapiro</span><span class="p">(</span><span class="n">model_adv</span><span class="o">.</span><span class="n">resid</span><span class="p">)</span> <span class="c1"># Data is Not Normal</span>
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<pre>(0.9146578311920166, 2.405071697353378e-09)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="mf">3.938041004403203e-09</span><span class="o">&lt;</span><span class="mf">0.05</span>
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<pre>True</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ss</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">newadv</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">ss</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">adv</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">newadv</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">columns</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">newadv</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
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      <th></th>
      <th>TV</th>
      <th>Radio</th>
      <th>Newspaper</th>
      <th>Sales</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.969852</td>
      <td>0.981522</td>
      <td>1.778945</td>
      <td>1.552053</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1.197376</td>
      <td>1.082808</td>
      <td>0.669579</td>
      <td>-0.696046</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-1.516155</td>
      <td>1.528463</td>
      <td>1.783549</td>
      <td>-0.907406</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.052050</td>
      <td>1.217855</td>
      <td>1.286405</td>
      <td>0.860330</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.394182</td>
      <td>-0.841614</td>
      <td>1.281802</td>
      <td>-0.215683</td>
    </tr>
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</table>
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<div class="prompt input_prompt">In&nbsp;[106]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;Sales&quot;</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">x</span> <span class="o">=</span> <span class="n">sms</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">adv</span><span class="o">.</span><span class="n">Sales</span>
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<div class="prompt input_prompt">In&nbsp;[103]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">TV</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">Sales</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;magenta&quot;</span><span class="p">)</span>
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<pre>&lt;matplotlib.collections.PathCollection at 0x1c292687f0&gt;</pre>
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<div class="prompt input_prompt">In&nbsp;[104]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">TV</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">Sales</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
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    <div class="prompt output_prompt">Out[104]:</div>


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<pre>&lt;matplotlib.collections.PathCollection at 0x1c28f431d0&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Transforming the Adv Data</span>
<span class="n">adv</span><span class="o">.</span><span class="n">TV</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">TV</span><span class="p">)</span>
<span class="n">adv</span><span class="o">.</span><span class="n">Radio</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">Radio</span><span class="p">)</span>
<span class="n">adv</span><span class="o">.</span><span class="n">Newspaper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">Newspaper</span><span class="p">)</span>
<span class="n">adv</span><span class="o">.</span><span class="n">Sales</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">adv</span><span class="o">.</span><span class="n">Sales</span><span class="p">)</span>
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<pre>StandardScaler(copy=True, with_mean=True, with_std=True)</pre>
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