bills_of_lading.html

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="Parsing-Shipping-Data">Parsing Shipping Data<a class="anchor-link" href="#Parsing-Shipping-Data">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="nx">javascript</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># I often switch between editing notebooks from my PC to my Mac;</span>
<span class="c1"># the code in this cell is to find the proper local data when I do so.</span>

<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">locale</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">from</span> <span class="nn">geopy</span> <span class="k">import</span> <span class="n">geocoders</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">multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">plotly.plotly</span> <span class="k">as</span> <span class="nn">py</span>
<span class="kn">from</span> <span class="nn">plotly.tools</span> <span class="k">import</span> <span class="n">set_credentials_file</span>
<span class="kn">import</span> <span class="nn">plotly.offline</span> <span class="k">as</span> <span class="nn">offline</span>

<span class="n">o_s</span> <span class="o">=</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span>
<span class="n">paths</span> <span class="o">=</span> <span class="p">{}</span>

<span class="k">if</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;/Users/drew/data/Data_Aggregation&#39;</span>
<span class="k">elif</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;C:\Users\DMacKellar\Documents\Data\Data_Aggregation&#39;</span>

<span class="n">locale</span><span class="o">.</span><span class="n">setlocale</span><span class="p">(</span><span class="n">locale</span><span class="o">.</span><span class="n">LC_ALL</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>

<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">],</span> 
                                <span class="s1">&#39;seattle_shipping_2017.csv&#39;</span><span class="p">)</span>

<span class="n">sea2017_df</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="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017&#39;</span><span class="p">])</span>
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<pre>&#39;en_US.UTF-8&#39;</pre>
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<h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">&#182;</a></h2><p>I was recently playing with Pandas' built-in DataReader module in a project to explore some financial markets' data.  Many online examples involve using it to draw historical stock price and volume data from Yahoo or Google Finance.  Yahoo's financial data is now completely defunct (not accessible via the pandas module; there is still a <a href="https://finance.yahoo.com/">web interface</a>), and a warning has been added that the Google Finance data interface is prone to failure (and, indeed, I haven't been able to recover data with it, either).  So I was perusing <a href="https://pandas-datareader.readthedocs.io/en/latest/">the docs</a> associated with the DataReader module.</p>
<p>While looking over the other data sources listed in the Pandas Datareader docs page, I saw one listed as "the world’s largest repository of structured public data", referring to a site called <a href="https://public.enigma.com/">Enigma</a>.  It does indeed have some interesting and diverse sets of data, and I quickly settled on <a href="https://public.enigma.com/spotlight/bill-of-lading">Bills of Lading</a> from among the links on the front page, a set which claimed to list the contents of tons of shipping from around the world.  The top few rows listed today happened to have Seattle, WA as the port of unlading, and I thought it would be a cool practice to try to categorize all of the arrivals in Seattle for a period, and how they change over time.  <a href="http://docs.enigma.com/public/public_v20_user_api.html">Their API</a> involves installing a program called Postman; I downloaded it and set it up via my Google account.  I also signed up for Enigma using my Gmail account, so it appears to have transferred the credentials automatically, because all I had to do to open the API was copy the URL from Enigma's page (after clicking on the API button within the dataset's window), paste it into the top field in the Postman window, and it returned the basic data.</p>
<p>I'm accessing this on 20180408, and the entire Bills of Lading dataset states that it has 27,788,013 rows.  But the date range is just for 2017.  They also have separate sets for each year going back to 2014.</p>
<p>To be specific, the overall organization of this dataset within Enigma's system is:</p>

<pre><code>International Trade &amp; Shipping
-&gt; United States Import Records

</code></pre>
<p>But within that they have 3 different sections:</p>

<pre><code>   -&gt; United States Import Records - Bill of Lading Summary
      United States Import Records - Cargo
      United States Import Records - Toxic &amp; Hazardous Materials

</code></pre>
<p>The first two have results for each year 2014-2017, but the toxic materials data just has 2015.</p>
<p>Summaries for each of these are offered in the "overview" tab:</p>
<p><em>Bill of Lading Summary</em>:</p>
<p>Bills of lading header information for incoming shipments regulated by U.S. Customs and Border Protection's Automated Manifest System (AMS) for 2017.
Updated a month ago
27,788,013 rows
31 fields</p>
<p><em>Cargo Summary</em>:</p>
<p>Cargo imports in 2017. The Automated Manifest System (AMS) is designed by U.S. Customs to facilitate cargo information between steam ship lines, airlines and rail carriers for shipments destined to or transiting the United States. Recently, the processing of other cargo transportation (electronic truck, rail and sea) manifests has been transitioned to Automated Commercial Environment (ACE). AMS only refers to electronic air manifests.
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Updated 19 days ago
32,861,196 rows
9 fields</p>
<p><em>Toxic &amp; Hazardous Materials</em>:</p>
<p>Information for incoming shipments containing toxic and hazardous materials provided by U.S. Customs and Border Protection's Automated Manifest System (AMS) and Automated Commercial Environment (ACE).
Read less
Updated 9 months ago</p>
<hr>
<p>Actually, after poking around for a bit, using their default browser-based data viewer is pretty handy, too.  Click on the leftmost icon that appears when hovering over any column name, and <a href="http://docs.enigma.com/public/public_v20_user_filtering.html">you can enter specific strings to filter its content</a>.  When I enter "Seattle, Washington" for Port of Unlading, it returns 27,100 of 27,788,013 rows in the Bill of Lading Summary - 2017 dataset.  There's then a button to export the resulting, filtered data set as a csv file, which in this case is ~14MB.  Import below:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">o_s</span> <span class="o">=</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span>
<span class="n">paths</span> <span class="o">=</span> <span class="p">{}</span>

<span class="k">if</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;/Users/drew/data/Enigma&#39;</span>
<span class="k">elif</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;C:\Users\DMacKellar\Documents\Data\Enigma&#39;</span>

<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">],</span> 
                                <span class="s1">&#39;seattle_shipping_2017.csv&#39;</span><span class="p">)</span>

<span class="n">sea2017_df</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="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sea2017_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>identifier</th>
      <th>trade_update_date</th>
      <th>run_date</th>
      <th>vessel_name</th>
      <th>port_of_unlading</th>
      <th>estimated_arrival_date</th>
      <th>foreign_port_of_lading</th>
      <th>record_status_indicator</th>
      <th>place_of_receipt</th>
      <th>port_of_destination</th>
      <th>...</th>
      <th>shipper_comm_number_qualifier</th>
      <th>shipper_comm_number</th>
      <th>container_number</th>
      <th>description_sequence_number</th>
      <th>piece_count</th>
      <th>description_text</th>
      <th>harmonized_number</th>
      <th>harmonized_value</th>
      <th>harmonized_weight</th>
      <th>harmonized_weight_unit</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>TCNU3666378</td>
      <td>1</td>
      <td>127</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>YMLU8500198</td>
      <td>1</td>
      <td>136</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>YMLU8687140</td>
      <td>1</td>
      <td>56</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>201712261237</td>
      <td>2017-11-23T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>YM UNANIMITY</td>
      <td>Seattle, Washington</td>
      <td>2017-12-20T00:00:00</td>
      <td>Tsingtao,China (Mainland)</td>
      <td>New</td>
      <td>QINGDAO</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>UACU5040364</td>
      <td>1</td>
      <td>1263</td>
      <td>PE TARPAULIN THIS SHIPMENT CONTAINS NO WOOD P ...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4</th>
      <td>201712261259</td>
      <td>2017-11-29T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Pusan,South Korea</td>
      <td>New</td>
      <td>DALIAN CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>YMLU8721007</td>
      <td>1</td>
      <td>37</td>
      <td>WOODEN DOORS</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 31 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sea2017_df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<table border="1" class="dataframe">
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      <th></th>
      <th>identifier</th>
      <th>foreign_port_of_destination</th>
      <th>description_sequence_number</th>
      <th>piece_count</th>
      <th>harmonized_number</th>
      <th>harmonized_value</th>
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      <th>count</th>
      <td>2.710000e+04</td>
      <td>0.0</td>
      <td>27100.000000</td>
      <td>27100.000000</td>
      <td>1.597600e+04</td>
      <td>1.643600e+04</td>
      <td>16436.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>1.018439e+12</td>
      <td>NaN</td>
      <td>2.278155</td>
      <td>355.122509</td>
      <td>3.327146e+07</td>
      <td>2.503881e+05</td>
      <td>11218.306461</td>
    </tr>
    <tr>
      <th>std</th>
      <td>9.064852e+11</td>
      <td>NaN</td>
      <td>8.894217</td>
      <td>1087.079547</td>
      <td>4.898259e+08</td>
      <td>7.272369e+05</td>
      <td>28254.145238</td>
    </tr>
    <tr>
      <th>min</th>
      <td>2.017123e+10</td>
      <td>NaN</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>2.013000e+04</td>
      <td>0.000000e+00</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>2.017123e+11</td>
      <td>NaN</td>
      <td>1.000000</td>
      <td>8.000000</td>
      <td>2.931900e+05</td>
      <td>0.000000e+00</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>2.017123e+11</td>
      <td>NaN</td>
      <td>1.000000</td>
      <td>26.000000</td>
      <td>4.409100e+05</td>
      <td>0.000000e+00</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>2.017123e+12</td>
      <td>NaN</td>
      <td>1.000000</td>
      <td>393.250000</td>
      <td>8.422900e+05</td>
      <td>4.243400e+05</td>
      <td>23885.750000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>2.017123e+12</td>
      <td>NaN</td>
      <td>133.000000</td>
      <td>101134.000000</td>
      <td>9.404210e+09</td>
      <td>9.748180e+06</td>
      <td>487409.000000</td>
    </tr>
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<h2 id="Counting-Cities">Counting Cities<a class="anchor-link" href="#Counting-Cities">&#182;</a></h2><p>I'll start by just enumerating the sources of the material imported into our fair city (as of 2017, according to this data set):</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[:</span><span class="mi">15</span><span class="p">]</span>
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<pre>Vancouver, BC ,Canada         13102
Pusan,South Korea              3240
Shanghai ,China (Mainland)     2936
Ningpo ,China (Mainland)       1999
Yantian,China (Mainland)        999
Hong Kong,Hong Kong             940
Xiamen,China (Mainland)         777
57037                           721
Sines,Portugal                  373
Auckland,New Zealand            317
Manzanillo,Mexico               266
Cartagena ,Colombia             181
Puerto Quetzal ,Guatemala       148
Kaohsiung,China (Taiwan)        142
La Spezia,Italy                 121
Name: foreign_port_of_lading, dtype: int64</pre>
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<p>The counts of numbers of entries in the table originating in each city might be interesting (and relatively straightforward) to visualize.  First, I'll need to clean up and reformat the city names.</p>
<p>To begin with, it's apparent from the list above that some of the ports of lading are just numerical codes.  By Googling, I find <a href="https://www.cbp.gov/sites/default/files/assets/documents/2017-Feb/appendix_f_0.pdf">resources</a> that can be used to translate them.  For instance, 57037 is Yangshan, China.  I wonder how many such values there are.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">matches</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;[\d.]</span><span class="si">{5}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">sea2017_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">matches</span><span class="p">,</span> <span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>57037    721
57073      2
35136      1
Name: foreign_port_of_lading, dtype: int64</pre>
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<p>Ok, just 3 unique entries.  That should be quick to fix.  Except that I can't seem to find any city corresponding to 35136.  It's probably an error in the data entry.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">origin_codes_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;57037&#39;</span><span class="p">:</span> <span class="s1">&#39;Yangshan ,China (Mainland)&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;57073&#39;</span><span class="p">:</span> <span class="s1">&#39;Nansha ,China (Mainland)&#39;</span><span class="p">,</span>
                     <span class="s1">&#39;35136&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
                    <span class="p">}</span>

<span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">origin_codes_dict</span><span class="p">,</span> 
                                             <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># No point keeping record with no city listed</span>
<span class="n">sea2017_df</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</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">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
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<pre>count                     27099
unique                       77
top       Vancouver, BC ,Canada
freq                      13102
Name: foreign_port_of_lading, dtype: object
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<p>Now, let's see where we get our imports from.  I'll want to use <a href="https://plot.ly/python/bubble-maps/">plotly</a> to make a bubble map in a global mercator projection.  To get the coordinates for the cities, I'd like to try programmatic access to <a href="https://developers.google.com/maps/documentation/geocoding/start">Google Geocoding</a>, provided via <a href="https://pypi.python.org/pypi/geopy/1.9.1">geopy</a>.  One question is whether the interface is smart enough to handle the messy strings provided in the data set, or whether I'll have to clean them up, first.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">plotly.plotly</span> <span class="k">as</span> <span class="nn">plotly</span>
<span class="kn">from</span> <span class="nn">geopy.geocoders</span> <span class="k">import</span> <span class="n">Nominatim</span>

<span class="n">geolocator</span> <span class="o">=</span> <span class="n">Nominatim</span><span class="p">()</span>
<span class="n">location1</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="s2">&quot;Kaohsiung,China (Taiwan)&quot;</span><span class="p">)</span>
<span class="n">location2</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="s2">&quot;Kaohsiung, China (Taiwan)&quot;</span><span class="p">)</span>
<span class="n">location3</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="s2">&quot;Kaohsiung, China&quot;</span><span class="p">)</span>
<span class="n">locations</span> <span class="o">=</span> <span class="p">[</span><span class="n">location1</span><span class="p">,</span> <span class="n">location2</span><span class="p">,</span> <span class="n">location3</span><span class="p">]</span>
<span class="k">for</span> <span class="n">location</span> <span class="ow">in</span> <span class="n">locations</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">location</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">location</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span> <span class="n">location</span><span class="o">.</span><span class="n">longitude</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="s1">&#39;</span><span class="si">{}</span><span class="s1"> not found&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">location</span><span class="p">))</span>
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<pre>/anaconda3/lib/python3.6/site-packages/requests/__init__.py:80: RequestsDependencyWarning: urllib3 (1.22) or chardet (2.3.0) doesn&#39;t match a supported version!
  RequestsDependencyWarning)
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<pre>None not found
None not found
中鋼, 鳳宮里, 大林蒲, 小港區, 高雄市, 812, 臺灣 22.5447429 120.356598755463
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<p>Ok, yeah; they need to be cleaned up.  We have cities always first, then a country, with a comma in between.  But we have some with no spaces on either side of the comma, with spaces on one side or the other, or on both sides, and none appear to be tolerated.  This definitely requires some regex.  Or maybe just '<code>.split()</code>' and '<code>.strip()</code>'.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>

<span class="n">words</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s1">&#39;[A-z]+&#39;</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">get_words</span><span class="p">(</span><span class="n">source</span><span class="p">):</span>
    <span class="n">stuff</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;\([^)]*\)&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">source</span><span class="p">)</span>
    <span class="n">parts</span> <span class="o">=</span> <span class="n">stuff</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">sep</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
    <span class="n">formatted</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">parts</span><span class="p">:</span>
        <span class="n">new_parts</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
        <span class="n">parts_words</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">part</span><span class="p">)</span>
        <span class="n">new_parts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts_words</span><span class="p">))</span>
        <span class="n">formatted</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">new_parts</span><span class="p">))</span>
    <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">formatted</span><span class="p">))</span>

<span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span>\
<span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">get_words</span><span class="p">)</span>

<span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>Vancouver, BC, Canada    13102
Pusan, South Korea        3240
Shanghai, China           2936
Ningpo, China             1999
Yantian, China             999
Hong Kong, Hong Kong       940
Xiamen, China              777
Yangshan, China            721
Sines, Portugal            373
Auckland, New Zealand      317
Name: lading_formatted, dtype: int64</pre>
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<p>Man, that took forever; I kept trying to exclude the junk in parentheses with a regex exclude clause, and couldn't get it to work.  It turns out quicker to create a new variable using <code>re.sub</code> to just get rid of anything in parentheses.  Anyways, now I want to feed them to geolocator iteratively, and build a new column with any lat/long I get out.</p>
<p>I had some trouble with the default <code>Nominatim</code> geolocator, imported above, when trying to run the cell below.  It's specifically calling upon the <a href="http://geopy.readthedocs.io/en/latest/#geopy.geocoders.Nominatim">Openstreetmap server</a>, which leaves too many cities without coords.  Also, after trying to re-run them, I'm seeing timeout and other server-interaction errors.  So instead I ended up trying a couple others, and <code>ArcGIS</code> seems to work the best.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">geolocator</span> <span class="o">=</span> <span class="n">geocoders</span><span class="o">.</span><span class="n">ArcGIS</span><span class="p">()</span>

<span class="n">cities</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="n">lat_long</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">failures</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="p">:</span>
    <span class="n">location</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">location</span><span class="p">:</span>
        <span class="n">lat</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">latitude</span>
        <span class="n">long</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">longitude</span>
        <span class="n">lat_long</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">long</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">failures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
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<span class="nb">list</span><span class="p">(</span><span class="n">lat_long</span><span class="o">.</span><span class="n">items</span><span class="p">())[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>[(&#39;Hong Kong, Hong Kong&#39;, (22.351958323000076, 114.1193859870001)),
 (&#39;Tsingtao, China&#39;, (36.098610000000065, 120.37194000000011)),
 (&#39;Pusan, South Korea&#39;, (35.10278000000005, 129.04028000000005)),
 (&#39;Yantian, China&#39;, (26.853330000000028, 119.85750000000007)),
 (&#39;Shanghai, China&#39;, (31.22222000000005, 121.45806000000005)),
 (&#39;Ningpo, China&#39;, (29.86569000000003, 121.53916000000004)),
 (&#39;Yangshan, China&#39;, (32.925670000000025, 119.8071900000001)),
 (&#39;Vancouver, BC, Canada&#39;, (49.260380000000055, -123.11335999999994)),
 (&#39;Sines, Portugal&#39;, (37.957250000000045, -8.86934999999994)),
 (&#39;Yentai, China&#39;, (37.52803000000006, 121.3826600000001))]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">lat_long</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="nb">len</span><span class="p">(</span><span class="n">failures</span><span class="p">))</span>
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<pre>77 0
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<p>Using '<code>geolocator2 = geocoders.GoogleV3()</code>' yields:</p>

<pre><code>GeocoderQueryError: HTTP Error 400: Bad Request

</code></pre>
<p>Probably something outdated about the API/geocode interface.  I tried getting an API key, which worked, but it still returns '<code>Bad Request</code>'.</p>
<p>Anyways, I ended up trying <a href="https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm">ArcGIS</a>, and that was sufficient to get the rest.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">lookup</span><span class="p">(</span><span class="n">city</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">lat_long</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">coords</span> <span class="o">=</span> <span class="n">lat_long</span><span class="p">[</span><span class="n">city</span><span class="p">]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
    <span class="k">return</span> <span class="n">coords</span>

<span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cities lacking lat-long: </span><span class="si">{0:.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">sea2017_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
<span class="n">sea2017_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Cities lacking lat-long: 0.00%
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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>identifier</th>
      <th>trade_update_date</th>
      <th>run_date</th>
      <th>vessel_name</th>
      <th>port_of_unlading</th>
      <th>estimated_arrival_date</th>
      <th>foreign_port_of_lading</th>
      <th>record_status_indicator</th>
      <th>place_of_receipt</th>
      <th>port_of_destination</th>
      <th>...</th>
      <th>container_number</th>
      <th>description_sequence_number</th>
      <th>piece_count</th>
      <th>description_text</th>
      <th>harmonized_number</th>
      <th>harmonized_value</th>
      <th>harmonized_weight</th>
      <th>harmonized_weight_unit</th>
      <th>lading_formatted</th>
      <th>lat_long</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>TCNU3666378</td>
      <td>1</td>
      <td>127</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>Hong Kong, Hong Kong</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>YMLU8500198</td>
      <td>1</td>
      <td>136</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>Hong Kong, Hong Kong</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong,Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>YMLU8687140</td>
      <td>1</td>
      <td>56</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>Hong Kong, Hong Kong</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>201712261237</td>
      <td>2017-11-23T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>YM UNANIMITY</td>
      <td>Seattle, Washington</td>
      <td>2017-12-20T00:00:00</td>
      <td>Tsingtao,China (Mainland)</td>
      <td>New</td>
      <td>QINGDAO</td>
      <td>NaN</td>
      <td>...</td>
      <td>UACU5040364</td>
      <td>1</td>
      <td>1263</td>
      <td>PE TARPAULIN THIS SHIPMENT CONTAINS NO WOOD P ...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>Tsingtao, China</td>
      <td>(36.098610000000065, 120.37194000000011)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>201712261259</td>
      <td>2017-11-29T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Pusan,South Korea</td>
      <td>New</td>
      <td>DALIAN CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>YMLU8721007</td>
      <td>1</td>
      <td>37</td>
      <td>WOODEN DOORS</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>Pusan, South Korea</td>
      <td>(35.10278000000005, 129.04028000000005)</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 33 columns</p>
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<p>Ok, even though ArcGIS worked, it is finicky, and requires multiple tries, timing out often.  Since I've gotten the coordinates for the cities in a useable df, let's save this, and reload it in the future.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017_cities_coords_pkl&#39;</span><span class="p">]</span> <span class="o">=</span> \
<span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;sea2017_cities_coords.pkl&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017_cities_coords_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">sea2017_df</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;enigma_dir&#39;</span><span class="p">]</span>
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<pre>&#39;/Users/drew/data/Enigma&#39;</pre>
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<h1 id="If-Restarting-the-Notebook,-Resume-with-Cell-Below-(Outdated)"><del>If Restarting the Notebook, Resume with Cell Below</del> (Outdated)<a class="anchor-link" href="#If-Restarting-the-Notebook,-Resume-with-Cell-Below-(Outdated)">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># I often switch between editing notebooks from my PC to my Mac;</span>
<span class="c1"># the code in this cell is to find the proper local data when I do so.</span>

<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">locale</span>
<span class="kn">import</span> <span class="nn">platform</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">multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">pickle</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="n">o_s</span> <span class="o">=</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span>
<span class="n">paths</span> <span class="o">=</span> <span class="p">{}</span>

<span class="k">if</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;/Users/drew/data/Enigma&#39;</span>
<span class="k">elif</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;C:\Users\DMacKellar\Documents\Data</span><span class="se">\</span>
<span class="s1">\Enigma&#39;</span>

<span class="n">locale</span><span class="o">.</span><span class="n">setlocale</span><span class="p">(</span><span class="n">locale</span><span class="o">.</span><span class="n">LC_ALL</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">root</span><span class="p">,</span> <span class="n">dirs</span><span class="p">,</span> <span class="n">files</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]):</span>
    <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
        <span class="n">file_under</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">file</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;_&#39;</span><span class="p">)</span>
        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
        <span class="n">paths</span><span class="p">[</span><span class="n">file_under</span><span class="p">]</span> <span class="o">=</span> <span class="n">path</span>
        
<span class="c1"># That should populate all data files.  Whenever a loading call fails, do:</span>
<span class="c1"># print(*paths.keys(), sep=&#39;\n&#39;)</span>
<span class="c1"># And look for the key with the proper name (with underscore instead of period)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;sea2017_cities_coords_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pickle_file</span><span class="p">:</span>
    <span class="n">sea2017_df</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
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<p>Now I want to count the total number of containers arriving based on the city of origin, and move them into a new dataframe indexed by city name, and including the coordinates of each.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_coords</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span>
    <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">&#39;coords&#39;</span><span class="p">)</span>

<span class="c1"># The lat/long are trapped in unnecessary lists: bust them out</span>
<span class="n">city_coords</span> <span class="o">=</span> <span class="n">city_coords</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

<span class="n">city_counts</span> <span class="o">=</span> <span class="n">sea2017_df</span><span class="p">[</span><span class="s1">&#39;lading_formatted&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">&#39;containers&#39;</span><span class="p">)</span>

<span class="n">city_records</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">city_coords</span><span class="p">,</span> <span class="n">city_counts</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># Actually, let&#39;s just make lat and long explicit columns</span>
<span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;coords&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;coords&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_records</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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    .dataframe tbody tr th {
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      <th></th>
      <th>coords</th>
      <th>containers</th>
      <th>lat</th>
      <th>long</th>
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      <th>Adelaide, Australia</th>
      <td>(-34.92584999999997, 138.5998300000001)</td>
      <td>13</td>
      <td>-34.925850</td>
      <td>138.599830</td>
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      <th>All Other Oman Ports, Oman</th>
      <td>(42.26966173436233, 26.92581511332239)</td>
      <td>1</td>
      <td>42.269662</td>
      <td>26.925815</td>
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      <th>Anvers, Belgium</th>
      <td>(51.22209000000004, 4.397680000000037)</td>
      <td>6</td>
      <td>51.222090</td>
      <td>4.397680</td>
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      <th>Auckland, New Zealand</th>
      <td>(-36.84841014899996, 174.76437652000004)</td>
      <td>317</td>
      <td>-36.848410</td>
      <td>174.764377</td>
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      <th>Balboa, Panama</th>
      <td>(8.370532224000044, -78.95069304799995)</td>
      <td>90</td>
      <td>8.370532</td>
      <td>-78.950693</td>
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<p>Alright, let's try making the plot.  Since I don't know Plotly that well, I'll start by trying to import <a href="https://plot.ly/python/bubble-maps/">a cell with an example from their docs</a> wholesale, then modify the most likely fields.  But I keep getting errors on the final line, referring to an error loading a URL or signing.  It appears that <a href="https://plot.ly/python/getting-started/#initialization-for-online-plotting">Plotly wants you to get an API key</a>, so I signed up and I'll store the key locally:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">json</span>

<span class="c1"># DCM code for retrieving credentials</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;plotly_api_credentials.json&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">cred</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">plotly_username</span> <span class="o">=</span> <span class="n">cred</span><span class="p">[</span><span class="s1">&#39;username&#39;</span><span class="p">]</span>
<span class="n">plotly_api_key</span> <span class="o">=</span> <span class="n">cred</span><span class="p">[</span><span class="s1">&#39;api_key&#39;</span><span class="p">]</span>
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<p>First, a bare-bones example to simply see if the map will render with the cities in their proper location:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">trace</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
             <span class="n">lon</span><span class="o">=</span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
             <span class="n">lat</span><span class="o">=</span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
             <span class="n">text</span><span class="o">=</span><span class="n">city_records</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
             <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;markers&#39;</span><span class="p">)</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;first_plotly_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;first_plotly.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;first_plotly_html&#39;</span><span class="p">])</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">([</span><span class="n">trace</span><span class="p">])</span>
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<p>Ok, cool; that was easy.  Now to adapt the tutorial's code to get the more elaborate example, where you partition the data into groups of cities based on some variable.  In this case, I'll choose based on the number of shipments they made to Seattle in 2017.  I'd like a dynamic way to do this, so I'll choose the <code>pd.qcut</code> method:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</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">num</span><span class="o">=</span><span class="mi">4</span><span class="p">)))</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

<span class="n">group_labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(((</span><span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">left</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span> 
                      <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">right</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">],</span> <span class="n">q</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">duplicates</span><span class="o">=</span><span class="s1">&#39;drop&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()))</span>

<span class="n">group_labels</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">interval</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">],</span> <span class="n">q</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">interval</span><span class="p">)</span>
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<pre>0 (0.0, 2.0]
1 (2.0, 22.0]
2 (22.0, 13102.0]
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<p>Now, in the process of making the graph, I encountered a lot of problems that I ended up working through piecemeal; they'll be explained in the markdown cell directly after the graph below.  But, after figuring out what I needed to do, I cleaned up this notebook, and just kept what worked.  So, for now, just accept that the next two cells are necessary.  First, I move the city names in the summary dataframe out of the index and into its own column, which gets named 'city'.  Then, I specifically write a new column containing the text that I'll want to show up as hovertext in the plot.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_records</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">city_records</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;index&#39;</span><span class="p">:</span><span class="s1">&#39;city&#39;</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">city_records</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
    <span class="n">city_records</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1"> ship(s)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">set_credentials_file</span><span class="p">(</span><span class="n">username</span><span class="o">=</span><span class="n">plotly_username</span><span class="p">,</span> <span class="n">api_key</span><span class="o">=</span><span class="n">plotly_api_key</span><span class="p">)</span>

<span class="n">q</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;quant&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span>
    <span class="n">x</span><span class="o">=</span><span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">],</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">duplicates</span><span class="o">=</span><span class="s1">&#39;drop&#39;</span><span class="p">)</span>

<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;rgb(0,116,217)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(255,65,54)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(133,20,75)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(255,133,27)&quot;</span><span class="p">,</span><span class="s2">&quot;lightgrey&quot;</span><span class="p">]</span>
<span class="n">cities</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">scale</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">log_scale</span> <span class="o">=</span> <span class="mi">100</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">interval</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">city_records</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;quant&#39;</span><span class="p">)):</span>
    <span class="n">lim</span> <span class="o">=</span> <span class="n">interval</span>
    <span class="n">city</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
        <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
        <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;ISO-3&#39;</span><span class="p">,</span>
        <span class="n">lon</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">],</span>
        <span class="n">lat</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">],</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span>
        <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="c1">#             size = group[&#39;containers&#39;] * scale, </span>
            <span class="n">size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">])</span> <span class="o">*</span> <span class="n">log_scale</span><span class="p">,</span>
            <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
            <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(40,40,40)&#39;</span><span class="p">),</span>
            <span class="n">sizemode</span> <span class="o">=</span> <span class="s1">&#39;area&#39;</span>
        <span class="p">),</span>
        <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:,}</span><span class="s1"> - </span><span class="si">{:,}</span><span class="s1"> ships sent&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">lim</span><span class="o">.</span><span class="n">left</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
            <span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">lim</span><span class="o">.</span><span class="n">right</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span> 
    <span class="p">)</span>
    <span class="n">cities</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>

<span class="n">layout</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
        <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;Ports of Lading for Ships &lt;br /&gt;Arriving in Seattle in 2017&#39;</span><span class="p">,</span>
        <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">geo</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">scope</span><span class="o">=</span><span class="s1">&#39;world&#39;</span><span class="p">,</span>
            <span class="n">projection</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;Mercator&#39;</span> <span class="p">),</span>
            <span class="n">showland</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
            <span class="n">landcolor</span> <span class="o">=</span> <span class="s1">&#39;rgb(217, 217, 217)&#39;</span><span class="p">,</span>
            <span class="n">subunitwidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
            <span class="n">countrywidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
            <span class="n">subunitcolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span><span class="p">,</span>
            <span class="n">countrycolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span>
        <span class="p">),</span>
    <span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span> <span class="n">data</span><span class="o">=</span><span class="n">cities</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span> <span class="p">)</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_2017_plotly1_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;seattle_2017_plotly1.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_2017_plotly1_html&#39;</span><span class="p">])</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span> <span class="n">fig</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;sea2017_ports_of_lading&#39;</span> <span class="p">)</span>
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<h2 id="Lessons-Learned-from-First-Plotly-Experience">Lessons Learned from First Plotly Experience<a class="anchor-link" href="#Lessons-Learned-from-First-Plotly-Experience">&#182;</a></h2><p>Ok, this one took me a long time to figure out.  It was kind of a chain reaction of failures in adapting the code from the <a href="https://plot.ly/python/bubble-maps/#united-states-bubble-map">plotly bubble map example</a> where they varied the size of city markers based on population.  At first, I tried mostly just copying their entire set of code, and substituting my dataframe's lat and lon columns for theirs, but when it came to the legend, I didn't want to just shoehorn my data into their general approach.</p>
<p>The tutorial example used 5 "limit" categories, defining the quantiles of population ranks for US cities.  The thinking was obviously to make a dynamic legend that you could click on to reduce the clutter of the map to focus on specific subsets of cities: the 50th through 3000th largest cities in the US are far more numerous and far less populous than the top 50; you might want to turn off the small cities if all you care about is major metropolitan areas.  Conversely, NYC leaves an obnoxiously large imprint on the map, covering up other areas that you might want to be able to check out, so you can turn off NY and LA and see the rest of the map better, if that's your focus.</p>
<p>But their explicit assignment of those rank intervals was a kind of inflexible approach, so I wanted to redefine the legend to be broken up into cities that send more or less stuff to Seattle on the basis of the <code>df.qcut</code> method, which breaks your rows up into quantiles of some category, then pass those to the dict-building <code>for</code> loop from the tutorial code in order to build my own custom legend.  I tried initializing the <code>for</code> loop with:</p>

<pre><code>for interval, group in city_records.groupby('quant'):

</code></pre>
<p>That worked ok for passing lat and long coords straight from the dataframe, but when I went to adapt the hover text to display the number of ships that each city sent, I ran into trouble.  I tried using:</p>

<pre><code>text = '{}: {} ships'.format(group.index, group['containers'])

</code></pre>
<p>But whereas the <code>lat</code>, <code>lon</code>, and marker.size categories would accept arrays as input and parse them correctly, when I tried to set the text that way, it ended up passing the same array for every single row, breaking the hover text.</p>
<p>So then I got the bright idea to break out the data within the <code>groupby for</code> loop into individual rows, using:</p>

<pre><code>for interval, group in city_records.groupby('quant'):
    for index, row in group.iterrows():

</code></pre>
<p>And then set every lat, lon, and text value individually with each row's unique index.  And when I ran the cell, it would execute without complaint, but the resulting map would be empty: no legend, and no city markers.  I had previously noted that the cities might not be visible if you set the marker scale too small, in which case you have to fiddle with that parameter to make them apparent, but you could still always get hovertext when you moused over the map, meaning that the cities were still being plotted.  This time, the hovertext didn't show; plotly was obviously not successfully reading the cities specified by the <code>for</code> loop.</p>
<p>I tried outputting the <code>cities</code> list of dicts and parsing it by eye to see if it made sense, and compared the formatting to the list of dicts output by the tutorial example (of US cities, sized by population), and couldn't detect any missing fields or other dumb mistakes that would cause it to fail to plot.  I tried a lot of comparisons to no effect; the dict looked ok.  Then, finally, I happened to try checking the last dict in the tutorial example, and noticed that it looked very different from the first.  It turns out that the <code>for</code> loop from the tutorial doesn't write an individual dict for each city; it writes one dict for each legend item, or group of cities: one for each of the five groups of ranked city sizes that the author of the tutorial defined in their example.  So plotly wasn't expecting 77 dicts in the list <code>cities</code>, each with their own neatly-contained and well-defined keys and values, but rather a dict with an array of lat coords, an array of lon coords, and, for the value assigned to key 'marker', a dict containing an array of sizes corresponding to each city.</p>
<p>I tried wrenching the nested <code>for</code> loop using <code>iterrows</code> to fit this output, but couldn't get it to work.  So I abandoned the nested loop and went back to assigning variables from just the <code>group</code>s within <code>groupby</code>, but was still stuck with the problem of not being able to dynamically assign text.  So I reverted to the approach they took in the tutorial example: they explicitly create a new column containing the data from the other columns already in the dataframe.  Then you can pass an array of previously-defined strings to plotly in a way it apparently can understand, rather than making every entry the exact same array, with too much info for each city.</p>
<p>I had a little further trouble assigning the new <code>'text'</code> column dynamically with the iterrows function and a <code>'{}: {} ship(s)'.format()</code> call; it turns out you <a href="https://stackoverflow.com/a/31460668/8637821">can't initialize an entirely new column that way</a>, so you create a new column just with <code>np.nan</code> first, and use the for loop to replace them.  Finally, pandas couldn't find the city names as indices when you use iterrows in that way, and I noticed that the tutorial example just had default integer row indices, so I had to use <code>reset_index</code> to move the city names into their own column, rename the column so that it wouldn't get confused with the actual row index any more, and finally got the plot to render with the city name and number of shipments as hovertext (hence the two cells above the plot, resetting the index and specifying a new text column for each city, prior to setting up the plot itself).</p>
<p><del>The final wrinkle for now was that, even though you could now hide Vancouver by clicking on the proper legend dot, the other cities were still too small to be visible easily on the same scale, so I adapted the size variable by calling <code>np.log(group['containers'])</code> to reduce the variation between the sizes, then, since the markers were now too small, multiplied, rather than dividing, by the scale factor.  That's a bit of a fudge, but we all make compromises when visualizing data.</del>  I go back and forth on this last point; I think that having their sizes manageable to see is useful, but taking the log really makes the range of shipping less intuitive.  You can comment out either of the above <code>size</code> parameters in the <code>markers</code> dict in order to see either approach.</p>
<p>Note, also, that you can dynamically alter the graph by changing the number of quantiles set by the first arg, '<code>q</code>'.  But the <code>qcut</code> method seeks to make all the bins of the same size, and since the most cities send very very few ships, you end up with multiple quantiles that contain just one or two records, and since the intervals must be unique, it can't display them separately, meaning you can't get all that many quantiles to display this way.  You can avoid getting warnings for overlapping quantiles by calling the arg <code>drop_duplicates=True</code>, but it'll still plot fewer than you call for.  There may be a way to skew the quantiles to preferentially break up the groups that contain more ships, but I haven't found a way to do this yet.</p>

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<h2 id="Checking-Against-Other-Cities/Years">Checking Against Other Cities/Years<a class="anchor-link" href="#Checking-Against-Other-Cities/Years">&#182;</a></h2><p>Now, the task is to take what I've learned, and generalize the pipeline so I can easily feed in different time ranges or cities of destination in order to do comparisons with others from the Enigma bills of lading dataset.  I think a good workflow would be to have a few cells that could accept a csv file containing a subset of the Enigma db's shipping data from any given year, focused on the city of destination, and then reformat the city names, retrieve coordinates, and map them by number of containers shipped, as I've done above for Seattle in 2017.  It would be cool to have a more programmatic interface to cut out some of the human-centric downloading and moving files around, and their <a href="http://docs.enigma.com/public/public_v20_user_pandas.html#api_export">API</a> should ultimately be compatible with this, but for now I'll settle with just having the work I did above to map one city's imports extrapolate to others in a matter of minutes.</p>
<p>Let's start with comparing Seattle's imports to that of a few other ports.  According to the frequency rank view of the summary statistics for the <a href="https://public.enigma.com/datasets/0293cd20-8580-4d30-b173-2ac27952b74b">Bill of Lading Summary db in Enigma for 2017</a>, the busiest ports that year were:</p>

<pre><code>Long Beach, California:   118,493 records
Los Angeles, California:  105,329
New York/Newark, NJ:       94,620
Houston, Texas:            39,555
Tacoma, Washington:        37,106

</code></pre>
<p>I had previously heard from a friend that Long Beach was the busiest US port for cargo traffic, but didn't know that LA proper has almost as much total activity; together, SoCal is definitely the hub for import in the US.  Coming in at a close third is no surprise: NYC has been the commercial hub for the country for a long time, although they moved the bulk of traffic from being processed in Manhattan <a href="https://en.wikipedia.org/wiki/New_York_Harbor#Container_shipping_and_air_travel">over to New Jersey in the mid-20th century</a>.  It's interesting that Houston is next; I hadn't heard of them being a particularly big depot, although perhaps it makes sense, as it's geographically closer to the center of the continental states than New York or LA.  Finally, I had also previously heard that Tacoma is larger than Seattle when it comes to cargo traffic.</p>
<p>That's a good cross-section of major commercial traffic in the US, let's try plotting them and seeing whether the city of origin for imports varies based on their destination among US ports.  I'll import the data, reformat the city names, download coords, and try to get them to plot in different subplots with plotly.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Specify a list of files to be opened, processed, and plotted</span>
<span class="n">cities_2017_paths</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Long_Beach&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;longbeach_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Los_Angeles&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;losangeles_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;NYC_Newark&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;nyc_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Houston&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;houston_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Tacoma&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;tacoma_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Seattle&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_shipping_2017_csv&#39;</span><span class="p">]}</span>
<span class="n">cities_2017_dfs</span> <span class="o">=</span> <span class="p">{}</span>

<span class="c1"># Import each into Pandas</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">cities_2017_paths</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">cities_2017_dfs</span><span class="p">[</span><span class="n">city</span><span class="p">]</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="n">path</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check for ports of lading with numeric code, rather than names</span>
<span class="k">def</span> <span class="nf">code_lookup</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="p">):</span>
    <span class="n">matches_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">dict_of_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">matches</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;[\d]+&#39;</span><span class="p">)</span>
        <span class="n">matches_series</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">matches</span><span class="p">,</span> 
                                <span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
        <span class="n">matches_set</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">matches_series</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="c1">#         print(&#39;{}:\n{}&#39;.format(city, matches_series))</span>
    <span class="k">return</span> <span class="n">matches_set</span>

<span class="n">code_matches</span> <span class="o">=</span> <span class="n">code_lookup</span><span class="p">(</span><span class="n">cities_2017_dfs</span><span class="p">)</span>
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<h2 id="Consolidating-Data-Aggregation/Cleanup;-Applying-to-Other-Ports-of-Unlading">Consolidating Data Aggregation/Cleanup; Applying to Other Ports of Unlading<a class="anchor-link" href="#Consolidating-Data-Aggregation/Cleanup;-Applying-to-Other-Ports-of-Unlading">&#182;</a></h2><p>I'm going to try to recapitulate the process I used above to get the lat-long coords for ports of lading to Seattle, and extend it as smoothly as possible to the other cities.  I'll see how succinctly I can achieve that.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Specify a list of files to be opened, processed, and plotted</span>
<span class="n">cities_2017_paths</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Long_Beach&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;longbeach_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Los_Angeles&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;losangeles_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;NYC_Newark&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;nyc_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Houston&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;houston_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Tacoma&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;tacoma_shipping_2017_csv&#39;</span><span class="p">],</span>
                     <span class="s1">&#39;Seattle&#39;</span><span class="p">:</span> <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_shipping_2017_csv&#39;</span><span class="p">]}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># DCM code for retrieving credentials</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;plotly_api_credentials.json&#39;</span><span class="p">)</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;google_cred&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;google_api_cred.json&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">plotly_cred</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;google_cred&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">google_cred</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">plotly_username</span> <span class="o">=</span> <span class="n">plotly_cred</span><span class="p">[</span><span class="s1">&#39;username&#39;</span><span class="p">]</span>
<span class="n">plotly_api_key</span> <span class="o">=</span> <span class="n">plotly_cred</span><span class="p">[</span><span class="s1">&#39;api_key&#39;</span><span class="p">]</span>
<span class="n">google_api_key</span> <span class="o">=</span> <span class="n">google_cred</span><span class="p">[</span><span class="s1">&#39;api_key&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Specify the dict of codes to replace with city names</span>
<span class="n">origin_codes_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;57073&#39;</span><span class="p">:</span> <span class="s1">&#39;Nansha, China (Mainland)&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;53304&#39;</span><span class="p">:</span> <span class="s1">&#39;Pipavav, India&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;30110&#39;</span><span class="p">:</span> <span class="s1">&#39;Covenas, Columbia&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;57037&#39;</span><span class="p">:</span> <span class="s1">&#39;Yangshan, China (Mainland)&#39;</span>
                    <span class="p">}</span>


<span class="k">def</span> <span class="nf">import_shipping_data</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span>
    <span class="n">cities_dfs</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="c1"># Import each port of unlading&#39;s shipping records into Pandas</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">cities_dfs</span><span class="p">[</span><span class="n">city</span><span class="p">]</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="n">path</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">cities_dfs</span>


<span class="k">def</span> <span class="nf">replace_city_codes</span><span class="p">(</span><span class="n">cities_dfs</span><span class="p">,</span> <span class="n">codes_dict</span><span class="p">):</span>
    <span class="c1"># Initialize counters to print how many records were dropped</span>
    <span class="n">records_dropped</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">total_records</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">replaced</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="c1"># First, see if any 5-digit codes present in dfs</span>
    <span class="c1"># were missing from dict; if so, add to dict as NaN</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">matches</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;[\d.]</span><span class="si">{5}</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="n">match_list</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">matches</span><span class="p">,</span> 
                            <span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">match</span> <span class="ow">in</span> <span class="n">match_list</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">match</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">codes_dict</span><span class="p">:</span>
                <span class="n">codes_dict</span><span class="p">[</span><span class="n">match</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&quot;</span><span class="si">{}</span><span class="s1">&quot; not found in input dict; </span><span class="se">\</span>
<span class="s1">adding to dict as np.nan.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">match</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">pass</span>
        <span class="c1"># Replace codes with cities where possible; drop the rest</span>
        <span class="n">new_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">new_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">codes_dict</span><span class="p">,</span> 
                                                       <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="c1"># No point keeping record with no city listed</span>
        <span class="n">records_dropped</span> <span class="o">+=</span> <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
        <span class="n">total_records</span> <span class="o">+=</span> <span class="n">new_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">new_df</span> <span class="o">=</span> <span class="n">new_df</span><span class="p">[</span><span class="n">new_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span>
        <span class="n">replaced</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_df</span>
    <span class="c1"># Add a print line to summarize how many records were discarded</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="si">{}</span><span class="s1"> records were dropped from bills of lading </span><span class="se">\</span>
<span class="s1">because no foreign port was found with the numeric code given; </span><span class="se">\</span>
<span class="s1">amounting to </span><span class="si">{:.8f}% o</span><span class="s1">f all records.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
    <span class="n">records_dropped</span><span class="p">,</span> <span class="n">records_dropped</span><span class="o">*</span><span class="mi">100</span><span class="o">/</span><span class="n">total_records</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">replaced</span>


<span class="k">def</span> <span class="nf">get_words</span><span class="p">(</span><span class="n">source</span><span class="p">):</span>
    <span class="n">words</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s1">&#39;[A-z]+&#39;</span><span class="p">)</span>
    <span class="c1"># Get rid of &#39;(Mainland)&#39; and other parenthetical stuff</span>
    <span class="n">noparens</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;\([^)]*\)&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">source</span><span class="p">)</span>
    <span class="c1"># Separate city from country</span>
    <span class="n">parts</span> <span class="o">=</span> <span class="n">noparens</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">sep</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
    <span class="n">formatted</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="c1"># Process city and country separately</span>
    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">parts</span><span class="p">:</span>
        <span class="n">new_parts</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="c1"># Get rid of leading/trailing whitespace</span>
        <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
        <span class="c1"># Discard punctuation, numbers; any non-words</span>
        <span class="n">parts_words</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="n">words</span><span class="p">,</span> <span class="n">part</span><span class="p">)</span>
        <span class="c1"># Separate individual words by a single space</span>
        <span class="n">new_parts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts_words</span><span class="p">))</span>
        <span class="c1"># Re-join city and country, separated by comma and space</span>
        <span class="n">formatted</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">new_parts</span><span class="p">))</span>
    <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">formatted</span><span class="p">))</span>


<span class="k">def</span> <span class="nf">format_ports</span><span class="p">(</span><span class="n">cities_dfs</span><span class="p">):</span>
    <span class="n">new_port_dfs</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="c1"># Use pandas&#39; vectorized function, </span>
    <span class="c1"># rather than iterating over rows slowly</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span>
            <span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">get_words</span><span class="p">)</span>
        <span class="n">new_port_dfs</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span>
    <span class="k">return</span> <span class="n">new_port_dfs</span>


<span class="k">def</span> <span class="nf">geolocate</span><span class="p">(</span><span class="n">cities_dfs</span><span class="p">):</span>
    <span class="c1"># ArcGIS no longer tolerates me; use Google</span>
    <span class="n">geolocator</span> <span class="o">=</span> <span class="n">geocoders</span><span class="o">.</span><span class="n">GoogleV3</span><span class="p">(</span><span class="n">api_key</span><span class="o">=</span><span class="n">google_api_key</span><span class="p">,</span>
                                    <span class="n">timeout</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
    <span class="n">all_foreign</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
    <span class="n">lat_long</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    <span class="n">failures</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="c1"># Don&#39;t spam the server with redundant calls;</span>
    <span class="c1"># first gather all unique cities together</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">all_foreign</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
            <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">))</span>
    <span class="c1"># Feed all unique cities to server, save output</span>
    <span class="c1"># Either in dict with coords, or else as failures</span>
    <span class="k">for</span> <span class="n">port</span> <span class="ow">in</span> <span class="n">all_foreign</span><span class="p">:</span>
        <span class="n">location</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="n">port</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">location</span><span class="p">:</span>
            <span class="n">lat</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">latitude</span>
            <span class="n">long</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">longitude</span>
            <span class="n">lat_long</span><span class="p">[</span><span class="n">port</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">long</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">failures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">port</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">lat_long</span><span class="p">,</span> <span class="n">failures</span>

            
<span class="k">def</span> <span class="nf">lookup</span><span class="p">(</span><span class="n">port</span><span class="p">,</span> <span class="n">lat_long_dict</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">port</span> <span class="ow">in</span> <span class="n">lat_long_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">coords</span> <span class="o">=</span> <span class="n">lat_long_dict</span><span class="p">[</span><span class="n">port</span><span class="p">]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
    <span class="k">return</span> <span class="n">coords</span>


<span class="k">def</span> <span class="nf">get_coords</span><span class="p">(</span><span class="n">cities_dfs</span><span class="p">,</span> <span class="n">lat_long_dict</span><span class="p">):</span>
    <span class="n">coordinated</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="c1"># Again, use vectorized functions</span>
    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">new_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
            <span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">,</span> <span class="n">lat_long_dict</span><span class="o">=</span><span class="n">lat_long_dict</span><span class="p">)</span>
        <span class="n">lat_dict</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="n">long_dict</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">for</span> <span class="n">port</span><span class="p">,</span> <span class="n">coords</span> <span class="ow">in</span> <span class="n">lat_long_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">lat_dict</span><span class="p">[</span><span class="n">port</span><span class="p">]</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">long_dict</span><span class="p">[</span><span class="n">port</span><span class="p">]</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
            <span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">,</span> <span class="n">lat_long_dict</span><span class="o">=</span><span class="n">lat_dict</span><span class="p">)</span>
        <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_df</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
            <span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">,</span> <span class="n">lat_long_dict</span><span class="o">=</span><span class="n">long_dict</span><span class="p">)</span>
        <span class="c1"># drop any without coords</span>
        <span class="n">empties</span> <span class="o">=</span> <span class="n">new_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;12}</span><span class="se">\n</span><span class="s1">Coordinates couldn</span><span class="se">\&#39;</span><span class="s1">t be found for </span><span class="si">{:&gt;5}</span><span class="s1"> </span><span class="se">\</span>
<span class="s1">ports of lading; </span><span class="si">{:&gt;.2f}% o</span><span class="s1">f all records&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">city</span><span class="p">,</span> <span class="n">empties</span><span class="p">,</span> <span class="n">empties</span><span class="o">/</span><span class="n">new_df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
        <span class="n">new_df</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">coordinated</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_df</span>
    <span class="k">return</span> <span class="n">coordinated</span>
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<p>It took me significantly longer than I expected to refactor the approach that worked above for Seattle into a series of functions that would make for a straightforward pipeline to modify the dataframes for several ports of unlading.  I still don't have a lot of experience writing extended pipelines of functions; I could've left a lot of the code the same, but wanted to be able to invoke the whole process in as few lines as possible.</p>
<p>I ended up having to write two new functions (<code>format_ports</code> and <code>get_coords</code>) to replace the <code>.apply(custom_func)</code> calls that I used in the original case to both reformat the <code>foreign_port_of_lading</code> strings and then to populate the <code>lat_long</code> column from the dict.  I could've left those calls separate but, again, I wanted to simplify.  I could also still write a single wrapper function to reduce the 5 lines in the cell below with a single call, but I found it useful to keep them separate while troubleshooting the various functions.  Now that they work I'll still keep them separate.</p>
<p>I'm aware that defining a single class, with each of these as methods within, is probably the canonical, Pythonic way to go, but I have even less experience at present with proper class coding, so I'll pass on that for now.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">time</span>

cities_2017_dfs = import_shipping_data(cities_2017_paths)
cities_2017_dfs = replace_city_codes(cities_2017_dfs, origin_codes_dict)
cities_2017_dfs = format_ports(cities_2017_dfs)
# lat_long, failures = geolocate(cities_2017_dfs)
cities_2017_dfs = get_coords(cities_2017_dfs, lat_long)
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<pre>&#34;35136&#34; not found in input dict; adding to dict as np.nan.
&#34;57045&#34; not found in input dict; adding to dict as np.nan.
&#34;55225&#34; not found in input dict; adding to dict as np.nan.
&#34;57011&#34; not found in input dict; adding to dict as np.nan.
&#34;35180&#34; not found in input dict; adding to dict as np.nan.

905 records were dropped from bills of lading because no foreign port was found with the numeric code given; amounting to 0.21435186% of all records.
Long_Beach  
Coordinates couldn&#39;t be found for 14260 ports of lading; 12.04% of all records
Los_Angeles 
Coordinates couldn&#39;t be found for 13659 ports of lading; 12.97% of all records
NYC_Newark  
Coordinates couldn&#39;t be found for 26096 ports of lading; 27.70% of all records
Houston     
Coordinates couldn&#39;t be found for 19372 ports of lading; 49.58% of all records
Tacoma      
Coordinates couldn&#39;t be found for 11785 ports of lading; 31.76% of all records
Seattle     
Coordinates couldn&#39;t be found for     0 ports of lading; 0.00% of all records
CPU times: user 18.3 s, sys: 1.69 s, total: 20 s
Wall time: 20.8 s
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
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<pre>dict_keys([&#39;Long_Beach&#39;, &#39;Los_Angeles&#39;, &#39;NYC_Newark&#39;, &#39;Houston&#39;, &#39;Tacoma&#39;, &#39;Seattle&#39;])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cities_2017_dfs</span><span class="p">[</span><span class="s1">&#39;Seattle&#39;</span><span class="p">]</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;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>identifier</th>
      <th>trade_update_date</th>
      <th>run_date</th>
      <th>vessel_name</th>
      <th>port_of_unlading</th>
      <th>estimated_arrival_date</th>
      <th>foreign_port_of_lading</th>
      <th>record_status_indicator</th>
      <th>place_of_receipt</th>
      <th>port_of_destination</th>
      <th>...</th>
      <th>description_sequence_number</th>
      <th>piece_count</th>
      <th>description_text</th>
      <th>harmonized_number</th>
      <th>harmonized_value</th>
      <th>harmonized_weight</th>
      <th>harmonized_weight_unit</th>
      <th>lat</th>
      <th>long</th>
      <th>lat_long</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong, Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>1</td>
      <td>127</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>22.351958</td>
      <td>114.119386</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong, Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>1</td>
      <td>136</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>22.351958</td>
      <td>114.119386</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>201712261102</td>
      <td>2017-11-22T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Hong Kong, Hong Kong</td>
      <td>New</td>
      <td>NANSHA CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>1</td>
      <td>56</td>
      <td>SOFA &amp; UPHOLSTERY PO418287/418285/418967 "SH "</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>22.351958</td>
      <td>114.119386</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>201712261237</td>
      <td>2017-11-23T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>YM UNANIMITY</td>
      <td>Seattle, Washington</td>
      <td>2017-12-20T00:00:00</td>
      <td>Tsingtao, China</td>
      <td>New</td>
      <td>QINGDAO</td>
      <td>NaN</td>
      <td>...</td>
      <td>1</td>
      <td>1263</td>
      <td>PE TARPAULIN THIS SHIPMENT CONTAINS NO WOOD P ...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>36.098610</td>
      <td>120.371940</td>
      <td>(36.098610000000065, 120.37194000000011)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>201712261259</td>
      <td>2017-11-29T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Pusan, South Korea</td>
      <td>New</td>
      <td>DALIAN CHINA</td>
      <td>NaN</td>
      <td>...</td>
      <td>1</td>
      <td>37</td>
      <td>WOODEN DOORS</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>35.102780</td>
      <td>129.040280</td>
      <td>(35.10278000000005, 129.04028000000005)</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 34 columns</p>
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<p>Ok, not too long a time to wait, considering the amount of data being processed.  Most of it was in the lookup time for retrieving the coordinates from Google using <code>geolocate</code>.  Note that, while I used ArcGIS for the Seattle df earlier in this notebook, it repeatedly refused to process so many ports of origin this time around.  It <a href="https://developers.arcgis.com/rest/geocode/api-reference/geocoding-free-vs-paid.htm">sounds like</a> they aren't expecting you to do bulk access of their geolocator db, maybe just a few records at a time.  So I switched to <a href="https://developers.google.com/maps/documentation/geocoding/start">GoogleV3</a>, got an API key, and they were more patient with me (at least once I changed the default <code>timeout=1</code> arg upon initialization).  Let's see how many coords are missing:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;15}{:.6f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">city</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">failure</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">failures</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;4}{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">failure</span><span class="p">))</span>
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<p>So there were significantly more records lost for destinations other than Seattle because Google didn't find the coordinates of the port of lading.  But from looking at the list of the <code>failures</code>, I can't blame Google at all; those are some strange, wonky-sounding origins in several cases.  The phenomenon of "transshipment" <a href="https://www.usitc.gov/publications/332/ec200404b.pdf">throws a considerable monkey wrench into things</a>, and I'm not gonna solve that issue here.</p>
<p>Okay!  Now I'm ready to plot the various ports of lading for these respective US ports of unlading.  I'll drop the rows lacking coord data; I don't expect attempting to cover the exceptions found in <code>failures</code> will prove very edifying, and they still collectively represent around 1% or less for each of the cities (except Tacoma; and it could be interesting to look into why someday).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cities_2017_dfs_clean</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;12}</span><span class="s1"> size before dropna: </span><span class="se">\t</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
    <span class="n">cities_2017_dfs_clean</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat_long&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;12}</span><span class="s1"> size after dropna: </span><span class="se">\t</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">city</span><span class="p">,</span> <span class="n">cities_2017_dfs_clean</span><span class="p">[</span><span class="n">city</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<pre>Houston      size before dropna: 	19699
Houston      size after dropna: 	19699
Long_Beach   size before dropna: 	104223
Long_Beach   size after dropna: 	104223
Los_Angeles  size before dropna: 	91661
Los_Angeles  size after dropna: 	91661
NYC_Newark   size before dropna: 	68123
NYC_Newark   size after dropna: 	68123
Seattle      size before dropna: 	27099
Seattle      size after dropna: 	27099
Tacoma       size before dropna: 	25321
Tacoma       size after dropna: 	25321
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<p>Ok, now I'll just want to pickle these (as well as the lat_long dict), so I can access them later:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cities</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Houston, Texas&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;Long Beach, California&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
          <span class="s1">&#39;Los Angeles, California&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;Newark, New Jersey&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
          <span class="s1">&#39;Seattle, Washington&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;Tacoma, Washington&#39;</span><span class="p">:</span><span class="s1">&#39;&#39;</span><span class="p">}</span>
<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="n">location</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">location</span><span class="p">:</span>
        <span class="n">lat</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">latitude</span>
        <span class="n">long</span> <span class="o">=</span> <span class="n">location</span><span class="o">.</span><span class="n">longitude</span>
        <span class="n">cities</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">lat</span><span class="p">,</span> <span class="n">long</span><span class="p">)</span>
<span class="n">unlading_coords</span> <span class="o">=</span> <span class="n">cities</span>

<span class="k">for</span> <span class="n">city</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_2017_pkl&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
    <span class="n">filename</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_2017.pkl&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
    <span class="n">paths</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="n">filename</span><span class="p">)</span>
    
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs_clean</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">_2017_pkl&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
        
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;lat_long_dict&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> 
                                      <span class="s1">&#39;lat_long_dict.pkl&#39;</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;lat_long_dict&#39;</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">lat_long</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
    
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;unlading_coords&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;top_6_unlading.pkl&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;unlading_coords&#39;</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">unlading_coords</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
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<h1 id="If-Resuming-Notebook,-Start-With-Cell-Below">If Resuming Notebook, Start With Cell Below<a class="anchor-link" href="#If-Resuming-Notebook,-Start-With-Cell-Below">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># I often switch between editing notebooks from my PC to my Mac;</span>
<span class="c1"># the code in this cell is to find the proper local data when I do so.</span>

<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">locale</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">multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">json</span>

<span class="n">o_s</span> <span class="o">=</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span>
<span class="n">paths</span> <span class="o">=</span> <span class="p">{}</span>

<span class="k">if</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;/Users/drew/data/Enigma&#39;</span>
<span class="k">elif</span> <span class="n">o_s</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;C:\Users\DMacKellar\Documents\Data</span><span class="se">\</span>
<span class="s1">\Enigma&#39;</span>

<span class="n">locale</span><span class="o">.</span><span class="n">setlocale</span><span class="p">(</span><span class="n">locale</span><span class="o">.</span><span class="n">LC_ALL</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>

<span class="c1"># DCM code for retrieving credentials</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;plotly_api_credentials.json&#39;</span><span class="p">)</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;google_cred&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;google_api_cred.json&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_cred&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">plotly_cred</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;google_cred&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">google_cred</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">plotly_username</span> <span class="o">=</span> <span class="n">plotly_cred</span><span class="p">[</span><span class="s1">&#39;username&#39;</span><span class="p">]</span>
<span class="n">plotly_api_key</span> <span class="o">=</span> <span class="n">plotly_cred</span><span class="p">[</span><span class="s1">&#39;api_key&#39;</span><span class="p">]</span>
<span class="n">google_api_key</span> <span class="o">=</span> <span class="n">google_cred</span><span class="p">[</span><span class="s1">&#39;api_key&#39;</span><span class="p">]</span>

<span class="k">for</span> <span class="n">root</span><span class="p">,</span> <span class="n">dirs</span><span class="p">,</span> <span class="n">files</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">]):</span>
    <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
        <span class="n">file_under</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">file</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;_&#39;</span><span class="p">)</span>
        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
        <span class="n">paths</span><span class="p">[</span><span class="n">file_under</span><span class="p">]</span> <span class="o">=</span> <span class="n">path</span>
        
<span class="c1"># That should populate all data files.  Whenever a loading call fails, do:</span>
<span class="c1"># print(*paths.keys(), sep=&#39;\n&#39;)</span>
<span class="c1"># And look for the key with the proper name (with underscore instead of period)</span>

<span class="c1"># For now, just load the 6 dfs with lat_long coords</span>
<span class="n">cities_2017_dfs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">path</span><span class="p">[</span><span class="o">-</span><span class="mi">9</span><span class="p">:]</span> <span class="o">==</span> <span class="s1">&#39;_2017_pkl&#39;</span><span class="p">:</span>
        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="n">path</span><span class="p">],</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
            <span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">path</span><span class="p">[:</span><span class="o">-</span><span class="mi">9</span><span class="p">])</span>
            <span class="n">cities_2017_dfs</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

<span class="c1"># Load api credentials</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;credentials_json&#39;</span><span class="p">],</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">credentials</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># For each port of unlading, aggregate ports of lading</span>
<span class="k">def</span> <span class="nf">get_counts</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="p">):</span>
    <span class="n">city_counts_dfs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">dict_of_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="c1"># Getting tired of typing out &#39;foreign_port_of_lading&#39; all the time</span>
        <span class="n">df1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">df1</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">:</span><span class="s1">&#39;city&#39;</span><span class="p">},</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">grouped</span> <span class="o">=</span> <span class="n">df1</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">df1</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">])</span>
        <span class="n">containers</span> <span class="o">=</span> <span class="n">grouped</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
        <span class="n">df1</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">&#39;city&#39;</span>
                                 <span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">containers</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
        <span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">,</span> <span class="s1">&#39;lat_long&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;lat&#39;</span><span class="p">,</span> <span class="s1">&#39;long&#39;</span><span class="p">,</span> <span class="s1">&#39;containers&#39;</span><span class="p">]</span>
        <span class="n">unique</span> <span class="o">=</span> <span class="n">df1</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">])</span>
        <span class="n">city_records</span> <span class="o">=</span> <span class="n">unique</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">cols</span><span class="p">]</span>

        <span class="c1"># Make a column to contain the &#39;text&#39; data you want in hovertext:</span>
        <span class="n">city_records</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">city_records</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
            <span class="n">city_records</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1"> container(s)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="n">row</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">])</span>
        <span class="n">city_counts_dfs</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">city_records</span>
    <span class="k">return</span> <span class="n">city_counts_dfs</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_counts_dfs</span> <span class="o">=</span> <span class="n">get_counts</span><span class="p">(</span><span class="n">cities_2017_dfs</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">city_counts_dfs</span><span class="p">[</span><span class="s1">&#39;Seattle&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="prompt output_prompt">Out[82]:</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>city</th>
      <th>lat_long</th>
      <th>lat</th>
      <th>long</th>
      <th>containers</th>
      <th>text</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>Hong Kong, Hong Kong</td>
      <td>(22.351958323000076, 114.1193859870001)</td>
      <td>22.351958</td>
      <td>114.119386</td>
      <td>940</td>
      <td>Hong Kong, Hong Kong: 940 container(s)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Tsingtao, China</td>
      <td>(36.098610000000065, 120.37194000000011)</td>
      <td>36.098610</td>
      <td>120.371940</td>
      <td>23</td>
      <td>Tsingtao, China: 23 container(s)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Pusan, South Korea</td>
      <td>(35.10278000000005, 129.04028000000005)</td>
      <td>35.102780</td>
      <td>129.040280</td>
      <td>3240</td>
      <td>Pusan, South Korea: 3240 container(s)</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Yantian, China</td>
      <td>(26.853330000000028, 119.85750000000007)</td>
      <td>26.853330</td>
      <td>119.857500</td>
      <td>999</td>
      <td>Yantian, China: 999 container(s)</td>
    </tr>
    <tr>
      <th>11</th>
      <td>Shanghai, China</td>
      <td>(31.22222000000005, 121.45806000000005)</td>
      <td>31.222220</td>
      <td>121.458060</td>
      <td>2936</td>
      <td>Shanghai, China: 2936 container(s)</td>
    </tr>
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<p>It looks like <a href="https://plot.ly/python/map-subplots-and-small-multiples/">this</a> is the right reference for how to make multiple subplots with maps in plotly.  That example, however, does not use hovertext, and furthermore, each subplot only has one trace.  In the previous example from this notebook, where I made one map for Seattle, I grouped the ports of lading into separate traces, and had the city name and ships sent as the mouse-over text.  It'll require <a href="https://community.plot.ly/t/adding-traces-to-subplots/4424">some modification</a> to the hyperlinked example code to get this to work.  As a fall-back, maybe I can abandon having multiple traces of groupings of ports of lading, and just use log scaling to make all markers visible on each subplot (since I wouldn't then be able to turn each group off individually).</p>
<p>Ok, after playing around with the simpler, <code>fig.append_trace()</code> functionality, it's returning errors that "<code>'xaxis' is not allowed in 'scattergeo'</code>".  It looks like the normal way to do this is to specify a unique '<code>geo</code>' arg for each dict in <code>data</code>, then add a dict with that '<code>geo</code>' label as a keyword within the dict <code>layout</code>, and the value for that 'geo' keyword will itself be a dict, which will contain the keyword 'domain', the value of which will be another dict, which will have two keys, 'x' and 'y', each of which will have a list/array with two elements, which will be the limits along the entire figure's x and y axis, as to the outer bounds for that subplot.  <a href="https://plot.ly/python/mixed-subplots/">This example</a> may be more fruitful a source of inspiration.</p>
<p>So, the syntax appears to be:</p>

<pre><code>trace3 = {
    "geo": "geo3", 
    "lon": df['Longitude'],
    "lat": df['Latitude'],
    "hoverinfo": 'text',
    "marker": {
        "size": 4,
        "opacity": 0.8,
        "color": '#CF1020',
        "colorscale": 'Viridis'
    }, 
    "mode": "markers", 
    "type": "scattergeo"
}
trace2 = {
    "geo": "geo4"
    ...
}

data = [{trace1}, {trace2}]

layout = {
    ...
    "geo3": {
        "domain": {
            "x": [0, 0.55], 
            "y": [0, 0.9]
    }, 

}

</code></pre>
<p>It appears that, to get multiple traces in one subplot, you just use the same 'geo' keyword for each, dict appended to data; the layout will then group them all together.  So that's the reason for the somewhat more convoluted way the first example cited above (the one that outputs multiple US maps with WalMart stores over the years): plotly doesn't let you pass a unique axis object associated with each <code>data</code> dict when the object is a map, it instead requires that you specify what part of the [0, 1] space of the entire figure's x and y axes you want each trace to occupy, and you indicate that in the layout dict, not within the dict sent to the <code>data</code> list for each trace.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plotly_subplots</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">rows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">cols</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
    <span class="n">set_credentials_file</span><span class="p">(</span><span class="n">username</span><span class="o">=</span><span class="n">plotly_username</span><span class="p">,</span> 
                               <span class="n">api_key</span><span class="o">=</span><span class="n">plotly_api_key</span><span class="p">)</span>

    <span class="n">q</span> <span class="o">=</span> <span class="mi">4</span>
    <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;rgb(0,116,217)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(255,65,54)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(133,20,75)&quot;</span><span class="p">,</span>
              <span class="s2">&quot;rgb(255,133,27)&quot;</span><span class="p">,</span><span class="s2">&quot;lightgrey&quot;</span><span class="p">]</span>
    <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span>

    <span class="n">layout</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
        <span class="n">title</span> <span class="o">=</span> <span class="n">title</span><span class="p">,</span>
        <span class="n">font</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">family</span><span class="o">=</span><span class="s1">&#39;Arial, sans serif&#39;</span><span class="p">,</span>
                  <span class="n">size</span><span class="o">=</span><span class="mi">22</span><span class="p">,</span>
                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(0, 0, 0)&#39;</span>
                <span class="p">),</span>
        <span class="c1"># showlegend = False,</span>
        <span class="n">autosize</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">width</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
        <span class="n">height</span> <span class="o">=</span> <span class="mi">900</span><span class="p">,</span>
        <span class="n">hovermode</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">legend</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">x</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="mf">0.5</span><span class="p">,</span>
            <span class="n">bgcolor</span><span class="o">=</span><span class="s2">&quot;rgba(255, 255, 255, 0)&quot;</span><span class="p">,</span>
            <span class="n">font</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span> <span class="n">size</span><span class="o">=</span><span class="mi">11</span> <span class="p">),</span>
        <span class="p">)</span>
    <span class="p">)</span>

    <span class="c1"># Want one subplot per port of unlading, but multiple groups</span>
    <span class="c1"># of ports of lading per subplot</span>
    <span class="n">geo_s</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="c1"># Iterate over dict of dfs</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
        <span class="c1"># Split each df up into quantiles of ports of lading</span>
        <span class="c1"># Need a special case for Seattle, to keep # groups right</span>
        <span class="k">if</span> <span class="n">city</span> <span class="o">==</span> <span class="s1">&#39;Seattle&#39;</span><span class="p">:</span>
            <span class="n">q</span> <span class="o">=</span> <span class="mi">5</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">q</span> <span class="o">=</span> <span class="mi">4</span>
        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;quant&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span>
            <span class="n">x</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">],</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">duplicates</span><span class="o">=</span><span class="s1">&#39;drop&#39;</span><span class="p">)</span>
        <span class="c1"># Specify geo_key, which will tell </span>
        <span class="c1"># layout_dict where to place subplot</span>
        <span class="n">geo_key</span> <span class="o">=</span> <span class="s1">&#39;geo&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">&#39;geo&#39;</span>
        <span class="c1"># Go through groups of cities and plot markers</span>
        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="p">(</span><span class="n">interval</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;quant&#39;</span><span class="p">)):</span>
            <span class="n">lim</span> <span class="o">=</span> <span class="n">interval</span>
            <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: &lt;br /&gt;</span><span class="si">{}</span><span class="s1"> - </span><span class="si">{}</span><span class="s1"> containers&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="n">city</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="p">),</span> 
                <span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">lim</span><span class="o">.</span><span class="n">left</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
                <span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">lim</span><span class="o">.</span><span class="n">right</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
            <span class="n">group_data</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
                <span class="n">geo</span> <span class="o">=</span> <span class="n">geo_key</span><span class="p">,</span>
                <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;ISO-3&#39;</span><span class="p">,</span>
                <span class="n">lat</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">],</span>
                <span class="n">lon</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">],</span>
                <span class="n">text</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span>
                <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                    <span class="n">size</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">scale</span><span class="p">,</span>
    <span class="c1">#                 size = np.log(group[&#39;containers&#39;])*50,</span>
                    <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
                    <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(40,40,40)&#39;</span><span class="p">),</span>
                    <span class="n">sizemode</span> <span class="o">=</span> <span class="s1">&#39;area&#39;</span>
                <span class="p">),</span>
                <span class="n">name</span> <span class="o">=</span> <span class="n">label</span><span class="p">,</span>
                <span class="n">legendgroup</span> <span class="o">=</span> <span class="n">label</span>
            <span class="p">)</span>
            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">group_data</span><span class="p">)</span>

        <span class="c1"># Add a clear map to show plot titles</span>
        <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
            <span class="nb">dict</span><span class="p">(</span>
                <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
                <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
                <span class="c1"># Always put the text in the same place;</span>
                <span class="c1">#</span>
                <span class="n">lon</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">115</span><span class="p">],</span>
                <span class="n">lat</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">60</span><span class="p">],</span>
                <span class="n">geo</span> <span class="o">=</span> <span class="n">geo_key</span><span class="p">,</span>
                <span class="n">text</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="p">)),</span>
                <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;text&#39;</span><span class="p">,</span>
                <span class="n">textfont</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
                    <span class="n">family</span><span class="o">=</span><span class="s1">&#39;Arial, sans serif&#39;</span><span class="p">,</span>
                    <span class="n">size</span><span class="o">=</span><span class="mi">22</span><span class="p">,</span>
                    <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(0, 0, 0)&#39;</span>
                <span class="p">)</span>
            <span class="p">)</span>
        <span class="p">)</span>

        <span class="n">layout</span><span class="p">[</span><span class="n">geo_key</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">scope</span><span class="o">=</span><span class="s1">&#39;world&#39;</span><span class="p">,</span>
            <span class="n">projection</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;Mercator&#39;</span> <span class="p">),</span>
            <span class="n">showland</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
            <span class="n">landcolor</span> <span class="o">=</span> <span class="s1">&#39;rgb(217, 217, 217)&#39;</span><span class="p">,</span>
            <span class="n">subunitwidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
            <span class="n">countrywidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
            <span class="n">subunitcolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span><span class="p">,</span>
            <span class="n">countrycolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span><span class="p">,</span>
            <span class="n">domain</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[]</span> <span class="p">),</span>
        <span class="p">)</span>


    <span class="n">z</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">COLS</span> <span class="o">=</span> <span class="n">cols</span>
    <span class="n">ROWS</span> <span class="o">=</span> <span class="n">rows</span>
    <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">ROWS</span><span class="p">)):</span>
        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">COLS</span><span class="p">):</span>
            <span class="n">geo_key</span> <span class="o">=</span> <span class="s1">&#39;geo&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">z</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">z</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">&#39;geo&#39;</span>
            <span class="n">layout</span><span class="p">[</span><span class="n">geo_key</span><span class="p">][</span><span class="s1">&#39;domain&#39;</span><span class="p">][</span><span class="s1">&#39;x&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">COLS</span><span class="p">),</span> 
                                              <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">COLS</span><span class="p">)]</span>
            <span class="n">layout</span><span class="p">[</span><span class="n">geo_key</span><span class="p">][</span><span class="s1">&#39;domain&#39;</span><span class="p">][</span><span class="s1">&#39;y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">ROWS</span><span class="p">),</span> 
                                              <span class="nb">float</span><span class="p">(</span><span class="n">y</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">ROWS</span><span class="p">)]</span>
            <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="o">+</span><span class="mi">1</span>
            <span class="k">if</span> <span class="n">z</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
                <span class="k">break</span>
    <span class="n">subp_data</span> <span class="o">=</span> <span class="n">data</span>
    <span class="n">subp_layout</span> <span class="o">=</span> <span class="n">layout</span>
    
    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">layout</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;&lt;b&gt;Ports of Lading Presented by &lt;br /&gt;</span><span class="se">\</span>
<span class="s1">Ports of Unlading: 2017&lt;/b&gt;&#39;</span>

<span class="n">data</span><span class="p">,</span> <span class="n">layout</span> <span class="o">=</span> <span class="n">plotly_subplots</span><span class="p">(</span><span class="n">city_counts_dfs</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">&#39;data&#39;</span><span class="p">:</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;layout&#39;</span><span class="p">:</span><span class="n">layout</span> <span class="p">}</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;subplots_2017_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;subplots_2017.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;subplots_2017_html&#39;</span><span class="p">])</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span> <span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;Ports of Lading&#39;</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">900</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1000</span> <span class="p">)</span>
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<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless" src="https://plot.ly/~mackellardrew/32.embed" height="900px" width="1000px"></iframe>
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<h2 id="My-First-Plotly-Maps-in-Subplots-Figure">My First Plotly Maps in Subplots Figure<a class="anchor-link" href="#My-First-Plotly-Maps-in-Subplots-Figure">&#182;</a></h2><p>Ok, after a lot of fiddling, I finally got that to look pretty much the way that I want.  With such a specific plotting agenda, it's probably difficult to avoid a lot of fiddling to get the parameters right.</p>
<p>Another word on the learning curve: plotly is based on D3.js, i.e. Javascript, and thus has a different kind of feel than the typical Python library, in my experience.  In particular, it seems to involve a lot more dicts, and nested dicts.  The easy access to maps is pretty cool, but for some reason it makes it incompatible with easy creation/access to subplots, thus the comment in the cell above about having to create text annotation via a bunch of independent traces mapped to the same figure-grid coordinates.  Walking through <a href="https://plot.ly/python/map-subplots-and-small-multiples/">the example code</a> about Walmart stores was very helpful in getting the plot to eventually render for me.  Basically, you feed everything in to one data object (a list of dicts) and one layout object (a dict), and link the two with the <code>geo</code> key arg.</p>
<hr>
<p>So, finally: time to consider some implications of the data.  Yes, Seattle is unique in receiving so much traffic from Vancouver.  Tacoma has far, far less, and in general processes more East Asian traffic, and less from Latin America or Europe.  LA and LBC process the most cargo, overall, and most of it from China: Hong Kong (Yantian) is tops for LA, and Shanghai sends the most to Long Beach.  Both also get a considerable amount from Latin America and Europe; kind of like Seattle's profile, in that regard.</p>
<p><strong>One significant point that I just realized: I've been saying (or at least implying) that individual rows in the data represent unique ships, but now see that they're individual containers, many of them on the same ship.  It's an example of not looking over your data enough in preliminary analysis before jumping into visualization and in-depth analysis.  So I've changed the legend above and will try to fix the rest of the notebook; if I overlook any prior to this point, please disregard.  Such a major oversight should have been fairly obvious, but I'm not really that familiar with the subject matter: 27 million containers a year is a lot more believable than 27 million ships in a year, but either one is still kind of hard to imagine.</strong></p>
<p>The two major ports that aren't on the West Coast are interesting: Houston gets a lot more traffic from the Gulf side of Mexico, and from the Atlantic coast of South America, as well as being a pretty major import hub from Europe.  Newark, however, is of course <em>the</em> entrepot of Atlantic traffic, although they do also get a lot from South Asia and even East Asia.</p>
<p>Some anomalies are interesting, however.  NYC receives a lot of cargo from a place called South Riding Point, Bahamas.  2,166 containers in 2017, to be exact.  There's no way that the Bahamas are a major exporter of just about anything on that scale, so this is probably more evidence of some kind of economic shenanigans wherein the place where goods are loaded for export isn't necessarily the same country where they're produced.  Other points: Africa and the Middle East aren't huge hotspots for exporting to the major US hubs, and, perhaps more surprisingly, neither is Australia.  This could be evidence that extremes of development in nations decrease the likelihood that it will be a big exporter to the US, but the amount of goods we get from Europe would seem to weaken such a conclusion.</p>
<p>Then again, this is just one look at some of the biggest ports in the US, and visual analysis with a map, while intuitive and immediate, is less thorough, and the largest markers easily dominate the visual field when doing a quick survey.  The 6 cities shown above account for just over 400,000 rows of shipping records from 2017, out of a total 27 million rows in the entire dataset.  It would be interesting to create a heatmap of all lat-long origins of shipping bound to the US, either from the entire dataset (if my laptop can handle it), or just from these cities.</p>

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<h2 id="Average-Lat-Lon-for-Ports-of-Lading">Average Lat-Lon for Ports of Lading<a class="anchor-link" href="#Average-Lat-Lon-for-Ports-of-Lading">&#182;</a></h2><p>First, though, let's just try something extremely simple: just find the 'center of mass' for each port of unlading:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># # Note: Already run; load from paths[&#39;top_6_unlading_pkl&#39;]</span>

<span class="c1"># from geopy import geocoders</span>

<span class="c1"># unlading_ports = [&#39;Long Beach, California&#39;, &#39;Los Angeles, California&#39;, &#39;Tacoma, Washington&#39;,</span>
<span class="c1">#                   &#39;Seattle, Washington&#39;, &#39;Houston, Texas&#39;, &#39;Newark, New Jersey&#39;]</span>
<span class="c1"># unlading_coords = dict()</span>
<span class="c1"># failures = []</span>
<span class="c1"># geolocator = geocoders.GoogleV3(api_key=google_api_key,</span>
<span class="c1">#                                     timeout=100)</span>
<span class="c1"># for port in unlading_ports:</span>
<span class="c1">#         location = geolocator.geocode(port)</span>
<span class="c1">#         if location:</span>
<span class="c1">#             lat = location.latitude</span>
<span class="c1">#             long = location.longitude</span>
<span class="c1">#             unlading_coords[port] = (lat, long)</span>
<span class="c1">#         else:</span>
<span class="c1">#             failures.append(port)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;top_6_unlading_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">unlading_coords</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">name_translator</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">city</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">unlading_coords</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">keys</span><span class="p">())):</span>
    <span class="n">name_translator</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">key</span>
<span class="nb">print</span><span class="p">(</span><span class="n">unlading_coords</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">name_translator</span><span class="p">)</span>
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<pre>{&#39;Houston, Texas&#39;: (29.76058000000006, -95.36967999999996), &#39;Long Beach, California&#39;: (33.76672000000008, -118.19239999999996), &#39;Los Angeles, California&#39;: (34.05349000000007, -118.24531999999999), &#39;Newark, New Jersey&#39;: (40.73197000000005, -74.17420999999996), &#39;Seattle, Washington&#39;: (47.60357000000005, -122.32944999999995), &#39;Tacoma, Washington&#39;: (47.255130000000065, -122.44163999999995)} 

 {&#39;Houston&#39;: &#39;Houston, Texas&#39;, &#39;Long_Beach&#39;: &#39;Long Beach, California&#39;, &#39;Los_Angeles&#39;: &#39;Los Angeles, California&#39;, &#39;NYC_Newark&#39;: &#39;Newark, New Jersey&#39;, &#39;Seattle&#39;: &#39;Seattle, Washington&#39;, &#39;Tacoma&#39;: &#39;Tacoma, Washington&#39;}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plotly_avg_coords</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1E3</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">600</span><span class="p">):</span>
    <span class="n">set_credentials_file</span><span class="p">(</span><span class="n">username</span><span class="o">=</span><span class="n">plotly_username</span><span class="p">,</span> 
                               <span class="n">api_key</span><span class="o">=</span><span class="n">plotly_api_key</span><span class="p">)</span>

    <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;rgb(0,116,217)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(255,65,54)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(133,20,75)&quot;</span><span class="p">,</span>
              <span class="s2">&quot;rgb(255,133,27)&quot;</span><span class="p">,</span><span class="s2">&quot;rgb(40,40,40)&quot;</span><span class="p">,</span><span class="s1">&#39;rgb(100,200,50)&#39;</span><span class="p">]</span>
    <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">scale</span> <span class="o">=</span> <span class="n">scale</span>
    <span class="n">log_scale</span> <span class="o">=</span> <span class="mi">3</span>

    <span class="n">layout</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
        <span class="n">title</span> <span class="o">=</span> <span class="n">title</span><span class="p">,</span>
        <span class="n">font</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">family</span><span class="o">=</span><span class="s1">&#39;Arial, sans serif&#39;</span><span class="p">,</span>
                  <span class="n">size</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span>
                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(0, 0, 0)&#39;</span>
                <span class="p">),</span>
        <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">autosize</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">width</span> <span class="o">=</span> <span class="n">width</span><span class="p">,</span>
        <span class="n">height</span> <span class="o">=</span> <span class="n">height</span><span class="p">,</span>
        <span class="n">hovermode</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
        <span class="n">legend</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">x</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="mf">0.5</span><span class="p">,</span>
            <span class="n">bgcolor</span><span class="o">=</span><span class="s2">&quot;rgba(255, 255, 255, 0)&quot;</span><span class="p">,</span>
            <span class="n">font</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span> <span class="n">size</span><span class="o">=</span><span class="mi">11</span> <span class="p">),</span>
        <span class="p">),</span>
        <span class="n">geo</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="n">scope</span><span class="o">=</span><span class="s1">&#39;world&#39;</span><span class="p">,</span>
                <span class="n">projection</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;Mercator&#39;</span> <span class="p">),</span>
                <span class="n">showland</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                <span class="n">landcolor</span> <span class="o">=</span> <span class="s1">&#39;rgb(217, 217, 217)&#39;</span><span class="p">,</span>
                <span class="n">subunitwidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                <span class="n">countrywidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                <span class="n">subunitcolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span><span class="p">,</span>
                <span class="n">countrycolor</span><span class="o">=</span><span class="s2">&quot;rgb(255, 255, 255)&quot;</span>
            <span class="p">)</span>
    <span class="p">)</span>

    <span class="c1"># Iterate over dict of dfs</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">city</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dict_of_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
        <span class="n">city_read</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="p">))</span>
        <span class="n">total_weights</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
        <span class="n">lat_weight</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span>
        <span class="n">lat_avg</span> <span class="o">=</span> <span class="n">lat_weight</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">total_weights</span>
        <span class="n">lon_weight</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span>
        <span class="n">lon_avg</span> <span class="o">=</span> <span class="n">lon_weight</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">total_weights</span>
        <span class="n">un_lat</span> <span class="o">=</span> <span class="n">unlading_coords</span><span class="p">[</span><span class="n">name_translator</span><span class="p">[</span><span class="n">city</span><span class="p">]][</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">un_lon</span> <span class="o">=</span> <span class="n">unlading_coords</span><span class="p">[</span><span class="n">name_translator</span><span class="p">[</span><span class="n">city</span><span class="p">]][</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">geo_key</span> <span class="o">=</span> <span class="s1">&#39;geo&#39;</span>

        <span class="c1"># Go through cities and plot markers to &#39;center of mass&#39;    </span>
        <span class="n">center_of_mass</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
            <span class="n">geo</span> <span class="o">=</span> <span class="n">geo_key</span><span class="p">,</span>
            <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;world&#39;</span><span class="p">,</span>
            <span class="n">lat</span> <span class="o">=</span> <span class="p">[</span><span class="n">lat_avg</span><span class="p">],</span>
            <span class="n">lon</span> <span class="o">=</span> <span class="p">[</span><span class="n">lon_avg</span><span class="p">],</span>
            <span class="n">text</span> <span class="o">=</span> <span class="s1">&#39;&quot;Center of mass&quot; of ports &lt;br /&gt;</span><span class="se">\</span>
<span class="s1">    exporting to </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">),</span>
            <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
            <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="n">size</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;containers&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">scale</span><span class="p">,</span>
    <span class="c1">#             size = np.log(df[&#39;containers&#39;].sum())*log_scale,</span>
                <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(40,40,40)&#39;</span><span class="p">),</span>
                <span class="n">sizemode</span> <span class="o">=</span> <span class="s1">&#39;area&#39;</span><span class="p">,</span>
                <span class="n">opacity</span> <span class="o">=</span> <span class="mi">1</span>
            <span class="p">),</span>
            <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">),</span>
            <span class="n">legendgroup</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">)</span>
        <span class="p">)</span>
        <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">center_of_mass</span><span class="p">)</span>

        <span class="c1"># Add a clear map to show port of unlading</span>
        <span class="n">un_port</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
            <span class="n">geo</span> <span class="o">=</span> <span class="n">geo_key</span><span class="p">,</span>
            <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;world&#39;</span><span class="p">,</span>
            <span class="n">lat</span> <span class="o">=</span> <span class="p">[</span><span class="n">un_lat</span><span class="p">],</span>
            <span class="n">lon</span> <span class="o">=</span> <span class="p">[</span><span class="n">un_lon</span><span class="p">],</span>
            <span class="n">text</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">),</span>
            <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="n">size</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
                <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
                <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(40,40,40)&#39;</span><span class="p">),</span>
                <span class="n">sizemode</span> <span class="o">=</span> <span class="s1">&#39;area&#39;</span>
            <span class="p">),</span>
            <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">),</span>
            <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1">#             legendgroup = &#39;{}&#39;.format(city_read)</span>
        <span class="p">)</span>
        <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">un_port</span><span class="p">)</span>

        <span class="c1"># One more: add lines connecting ports of unlading</span>
        <span class="c1"># with their respective centers of mass</span>
        <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">geo</span> <span class="o">=</span> <span class="s1">&#39;geo&#39;</span><span class="p">,</span>
            <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;scattergeo&#39;</span><span class="p">,</span>
            <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;world&#39;</span><span class="p">,</span>
            <span class="n">lon</span> <span class="o">=</span> <span class="p">[</span><span class="n">un_lon</span><span class="p">,</span> <span class="n">lon_avg</span><span class="p">],</span>
            <span class="n">lat</span> <span class="o">=</span> <span class="p">[</span><span class="n">un_lat</span><span class="p">,</span> <span class="n">lat_avg</span><span class="p">],</span>
            <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;lines&#39;</span><span class="p">,</span>
            <span class="n">name</span> <span class="o">=</span> <span class="n">city_read</span><span class="p">,</span>
            <span class="n">showlegend</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
            <span class="n">legendgroup</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">city_read</span><span class="p">),</span>
            <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="n">width</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
                <span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
            <span class="p">)</span>
        <span class="p">)</span>
        <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
        
    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">layout</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;&lt;b&gt;Average Lat-Lon of Ports of Lading &lt;br /&gt;</span><span class="se">\</span>
<span class="s1">Presented by Ports of Unlading: 2017&lt;/b&gt;&#39;</span>

<span class="n">data</span><span class="p">,</span> <span class="n">layout</span> <span class="o">=</span> <span class="n">plotly_avg_coords</span><span class="p">(</span><span class="n">city_counts_dfs</span><span class="p">,</span> <span class="n">title</span><span class="p">)</span>
        
<span class="n">fig</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">&#39;data&#39;</span><span class="p">:</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;layout&#39;</span><span class="p">:</span><span class="n">layout</span> <span class="p">}</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;avg_plotly_2017_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;avg_plotly_2017.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;avg_plotly_2017_html&#39;</span><span class="p">])</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span> <span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;CofM Ports of Lading&#39;</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1200</span> <span class="p">)</span>
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<p>Ok, that took longer than I intended to format a new map, but it looks like a useful summary of the places from which different cities receive imports.  Tacoma, Long Beach, and LA all pretty solidly receive East Asian imports, overwhelming everything else.  But Seattle and Houston have as much stuff coming from East and West that the average longitude of a container coming in is pretty close to zero (Greenwich).  Just to see how stable this pattern is, let's compare to the earliest year for which Enigma has these data: 2014.</p>
<p>When I went back to the relevant page, the difference is striking; while there are far fewer records for that year (the whole db is 11 million, as opposed to the ~28 million rows for the 2017 database), the top 6 ports of unlading account for a much larger proportion of overall traffic:</p>

<pre><code>Frequency Rank for Port of Unlading in 2014:
Los Angeles -  2,025,070
Long Beach  -  1,670,075
NYC/Newark  -  1,178,130
Tacoma      -    908,218
Houston     -    676,386
Total rows  - 11,025,607

</code></pre>
<p>That means that Los Angeles handled 5% of the cargo in 2017 compared to what it received in 2014?  That doesn't seem right.  <a href="https://www.portoflosangeles.org/maritime/stats.asp">This page</a> of annual traffic to the Port of Los Angeles contradicts Enigma's data, showing a gradual but consistent <em>increase</em> in containers (twenty-foot-equivalent units) handled by that port over the last decade, and indeed going back to the 80s.  Their monthly breakdown shows over 4 million inbound loaded TEUs for the last few years.  <a href="http://www.polb.com/economics/stats/yearly_teus.asp">Long Beach's site</a> similarly lists pretty consistent volumes, between 3 and 4 million every year.  Obviously Enigma's data, while extensive, aren't comprehensive or authoritative.  Let's check the rest of the years for Enigma's data sets:</p>

<pre><code>Frequency Rank for Port of Unlading in 2015:
Los Angeles -    291,567
Long Beach  -    237,690
NYC/Newark  -    211,192
Seattle     -    149,245
Tacoma      -    149,245
Total rows  -  1,914,262

Frequency Rank for Port of Unlading in 2016:
Los Angeles -  5,024,788
NYC/Newark  -  4,468,536
Long Beach  -  3,979,732
Tacoma      -  2,272,418
Savannah    -  1,827,115
Total rows  - 29,209,522

</code></pre>
<p>Obviously, the datasets are highly variable.  The top ports of unlading across the years are pretty consistent, but the total data present are highly variable, and the number listed for each US port similarly fluctuate.  For a less biased look, <a href="https://www.census.gov/foreign-trade/index.html">the Census Bureau</a> claims to be the official source of statistics on imports and exports in the US.  The <a href="https://www.trade.gov/mas/ian/tradestatistics/index.asp">International Trade Administration</a> also keeps some data.  Their <a href="https://www.trade.gov/mas/ian/build/groups/public/@tg_ian/documents/webcontent/tg_ian_005537.pdf">latest summary</a>, for 2016, lists total imports for the US at 2.7 trillion USD that year.</p>
<p>BTW, while looking into getting more complete access to summary statistics for Enigma's datasets, I came across <a href="https://docs.enigma.com/public/public_v20_user_python.html">this page</a>, which looks like the most accessible way to get programmatic access to the data.</p>

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<h2 id="Seattle-Imports-Across-the-Years">Seattle Imports Across the Years<a class="anchor-link" href="#Seattle-Imports-Across-the-Years">&#182;</a></h2><p>The respective number of rows matching Seattle as the port of unlading for each year are:</p>

<pre><code>2014 - 492,215
2015 - 149,267
2016 - 923,437
2017 -  27,100

</code></pre>
<p>Their csvs, once downloaded, occupy [302, 95, 523, 15] MBs, respectively, on the local disk.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seattle_csvs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
    <span class="n">seattle_csvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;seattle_shipping_201</span><span class="si">{}</span><span class="s1">_csv&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
    
<span class="n">seattle_lading_years</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">seattle_csvs</span><span class="p">:</span>
    <span class="n">year</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">sep</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
    <span class="n">seattle_lading_years</span><span class="p">[</span><span class="n">year</span><span class="p">]</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="n">paths</span><span class="p">[</span><span class="n">file</span><span class="p">],</span> 
                                             <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;foreign_port_of_lading&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seattle_lading_years</span><span class="p">[</span><span class="s1">&#39;2014&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <td>Shanghai ,China (Mainland)</td>
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      <td>Shanghai ,China (Mainland)</td>
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<p>First, some preliminary cleaning, before I can grab coords for each city.  Need to drop any NaN, and identify any 5-digit codes in place of city names.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">year</span><span class="p">,</span> <span class="n">series</span> <span class="ow">in</span> <span class="n">seattle_lading_years</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">series</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
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<pre>foreign_port_of_lading    1492
dtype: int64
foreign_port_of_lading    0
dtype: int64
foreign_port_of_lading    0
dtype: int64
foreign_port_of_lading    0
dtype: int64
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seattle_lading_years</span><span class="p">[</span><span class="s1">&#39;2014&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>{&#39;57045&#39;, &#39;53304&#39;, &#39;99999&#39;, &#39;57073&#39;, &#39;55225&#39;, &#39;35180&#39;, &#39;35136&#39;, &#39;57037&#39;, &#39;57019&#39;}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">origin_codes_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;57073&#39;</span><span class="p">:</span> <span class="s1">&#39;Nansha, China (Mainland)&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;53304&#39;</span><span class="p">:</span> <span class="s1">&#39;Pipavav, India&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;30110&#39;</span><span class="p">:</span> <span class="s1">&#39;Covenas, Columbia&#39;</span><span class="p">,</span> 
                     <span class="s1">&#39;57037&#39;</span><span class="p">:</span> <span class="s1">&#39;Yangshan, China (Mainland)&#39;</span>
                    <span class="p">}</span>
<span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <span class="n">code_matches</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">code</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">origin_codes_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">code</span><span class="p">)</span>
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<pre>57045
99999
55225
35180
35136
57019
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<p>That yielded just one new hit, when comparing to <a href="https://www.cbp.gov/sites/default/files/assets/documents/2017-Feb/appendix_f_0.pdf">these</a> <a href="http://customscodes.com/foreign_port_code.php">two</a> resources:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">origin_codes_dict</span><span class="p">[</span><span class="mi">57019</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;Zhenjiang, China&#39;</span>
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<p>Let's try both of the graph types above: I'll start with the averaged lat/lon approach.  First I need to clean up the port names and retrieve lat/lon for them, however:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">time</span>

cities_2017_dfs = import_shipping_data(cities_2017_paths)
seattle_lading_coded = replace_city_codes(seattle_lading_years, origin_codes_dict)
seattle_lading_formatted = format_ports(seattle_lading_coded)

lat_long, failures = geolocate(seattle_lading_formatted)

seattle_lading_coords = get_coords(seattle_lading_formatted, lat_long)
seattle_lading_counts = get_counts(seattle_lading_coords)
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<pre>2014        
Coordinates couldn&#39;t be found for  1317 ports of lading; 0.27% of all records
2015        
Coordinates couldn&#39;t be found for   903 ports of lading; 0.61% of all records
2016        
Coordinates couldn&#39;t be found for  1615 ports of lading; 0.17% of all records
2017        
Coordinates couldn&#39;t be found for     1 ports of lading; 0.00% of all records
CPU times: user 10.4 s, sys: 609 ms, total: 11 s
Wall time: 11.9 s
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seattle_lading_counts</span><span class="p">[</span><span class="s1">&#39;2017&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>text</th>
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      <th>0</th>
      <td>Hong Kong, Hong Kong</td>
      <td>(22.396428, 114.109497)</td>
      <td>22.396428</td>
      <td>114.109497</td>
      <td>940</td>
      <td>Hong Kong, Hong Kong: 940 container(s)</td>
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      <th>3</th>
      <td>Tsingtao, China</td>
      <td>(36.067108, 120.382609)</td>
      <td>36.067108</td>
      <td>120.382609</td>
      <td>23</td>
      <td>Tsingtao, China: 23 container(s)</td>
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      <th>4</th>
      <td>Pusan, South Korea</td>
      <td>(35.1795543, 129.0756416)</td>
      <td>35.179554</td>
      <td>129.075642</td>
      <td>3240</td>
      <td>Pusan, South Korea: 3240 container(s)</td>
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      <th>5</th>
      <td>Yantian, China</td>
      <td>(22.556499, 114.236875)</td>
      <td>22.556499</td>
      <td>114.236875</td>
      <td>999</td>
      <td>Yantian, China: 999 container(s)</td>
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      <th>11</th>
      <td>Shanghai, China</td>
      <td>(31.2303904, 121.4737021)</td>
      <td>31.230390</td>
      <td>121.473702</td>
      <td>2936</td>
      <td>Shanghai, China: 2936 container(s)</td>
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<pre>[&#39;High Seas, United States Off Shore Tanker Transshipment Points&#39;, &#39;All Other Southern Asia, N E C Ports, Southern Asia, N E C&#39;, &#39;All Other Oman Ports, Oman&#39;, &#39;Pursan, Turkey&#39;, &#39;Nagoya Ko, Japan&#39;, &#39;Port Redon, Vietnam&#39;, &#39;Puduchcheri, India&#39;]
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<p>Ok, that looks good.  I'll save the counts, and the dict.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;lat_long_dict_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">old_dict</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">old_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lat_long</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="n">new_dict</span> <span class="o">=</span> <span class="n">old_dict</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">coords</span> <span class="ow">in</span> <span class="n">lat_long</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="k">if</span> <span class="n">city</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">old_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">new_dict</span><span class="p">[</span><span class="n">city</span><span class="p">]</span> <span class="o">=</span> <span class="n">coords</span>
        
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_dict</span><span class="p">))</span>
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<pre>246 245
246
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_annual_pkl&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;seattle_annual.pkl&#39;</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;lat_long_dict_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">new_dict</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_annual_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">seattle_lading_counts</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;seattle_annual_pkl&#39;</span><span class="p">],</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">seattle_lading_counts</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<p>Now, to check the plots for multiple years of Seattle bills of lading.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">year</span> <span class="ow">in</span> <span class="n">seattle_lading_counts</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="n">name_translator</span><span class="p">[</span><span class="n">year</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;Seattle, Washington&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;&lt;b&gt;Average Lat-Lon of Ports of Lading &lt;br /&gt;</span><span class="se">\</span>
<span class="s1">Bound for Seattle: 2014-2017&lt;/b&gt;&#39;</span>

<span class="n">data</span><span class="p">,</span> <span class="n">layout</span> <span class="o">=</span> <span class="n">plotly_avg_coords</span><span class="p">(</span><span class="n">seattle_lading_counts</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">5E3</span><span class="p">)</span>
        
<span class="n">fig</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">&#39;data&#39;</span><span class="p">:</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;layout&#39;</span><span class="p">:</span><span class="n">layout</span> <span class="p">}</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;avg_plotly_seattle_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;avg_plotly_seattle.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;avg_plotly_seattle_html&#39;</span><span class="p">])</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span> <span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;CofM Ports of Lading&#39;</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1200</span> <span class="p">)</span>
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<p>Ok, so, for Seattle at least, the average number of containers coming from ports didn't shift very far East or West for the years 2014-2016.  The data are highly variable for those years; many more records from 2016 than 2014 (and note that I had to change the scale to make the markers more visible, compared to the 2017 data mapped earlier).  2017 has far fewer records for shipments to Seattle, and therefore it's less surprising that the geographic center of their distribution also shifted.  This almost certainly represents bias in sampling in the data, rather than an actual shift in the number and origin points of cargo received; as indicated above, Enigma's data are highly variable for other ports, and in ways that don't line up with the largest ports' own records.</p>
<p>I found <a href="https://www.portseattle.org/About/Publications/Statistics/Seaport/Pages/10-Year-History.aspx">some historical data</a> for the Port of Seattle, and there is indeed significant variety over the years indicated, but container imports between 500K and 1 million are actually believable.  For some reason the data on that page don't cover more recent years; the <a href="https://www.nwseaportalliance.com/sites/default/files/nwsa_annualreport_2016_full_0.pdf">most recent complete report</a> I could find lists 3.6 million total TEUs, but doesn't break them down by import full/empty vs export full/empty.</p>
<p>For the hell of it, let's look at the city-by-city breakdown of ports of lading for Seattle by year, too:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;&lt;b&gt;Ports of Lading for Containers Received in Seattle, &lt;br /&gt;</span><span class="se">\</span>
<span class="s1">Presented by Year: 2014-2017&lt;/b&gt;&#39;</span>

<span class="n">data</span><span class="p">,</span> <span class="n">layout</span> <span class="o">=</span> <span class="n">plotly_subplots</span><span class="p">(</span><span class="n">seattle_lading_counts</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">,</span> <span class="n">rows</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">&#39;data&#39;</span><span class="p">:</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;layout&#39;</span><span class="p">:</span><span class="n">layout</span> <span class="p">}</span>
<span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_subplots_seattle_html&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;data_dir&#39;</span><span class="p">],</span> <span class="s1">&#39;plotly_subplots_seattle.html&#39;</span><span class="p">)</span>
<span class="n">offline</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">paths</span><span class="p">[</span><span class="s1">&#39;plotly_subplots_seattle_html&#39;</span><span class="p">])</span>
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<p>Ok, not too surprising; the breakdown looks pretty much the same for most years; mostly East Asia, but also good representation of the E.U. and a smattering of Latin America.  Importantly, the phenomenon of a ton of cargo coming via British Columbia has existed for all of those years.</p>
<p>I'd say that's enough for enumerating ports of lading for containers.  Let's move on.</p>

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<h2 id="Part-2:-Checking-Weights">Part 2: Checking Weights<a class="anchor-link" href="#Part-2:-Checking-Weights">&#182;</a></h2><p>Visualizing the number of records associated with different cities shipping to Seattle is cool, but the quantity and quality of goods might not be well represented just by number of containers.</p>
<p>A more nuanced look would involve checking the total volume of goods coming from these places, in which case you'd probably want raw tonnage, which might be extractable from the data set.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Missing data in harmonized_weight_unit&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">missing_rows</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;harmonized_weight_unit&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    <span class="n">total_rows</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;12}</span><span class="s1">: </span><span class="si">{:&gt;7,}</span><span class="s1"> missing fields in </span><span class="si">{:&gt;7,}</span><span class="s1"> rows (</span><span class="si">{:&gt;4}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">city</span><span class="p">,</span> <span class="n">missing_rows</span><span class="p">,</span> <span class="n">total_rows</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">missing_rows</span><span class="o">/</span><span class="n">total_rows</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
    
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Missing data in harmonized_value&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">missing_rows</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;harmonized_weight&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    <span class="n">total_rows</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;12}</span><span class="s1">: </span><span class="si">{:&gt;7,}</span><span class="s1"> missing fields in </span><span class="si">{:&gt;7,}</span><span class="s1"> rows (</span><span class="si">{:&gt;4}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">city</span><span class="p">,</span> <span class="n">missing_rows</span><span class="p">,</span> <span class="n">total_rows</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">missing_rows</span><span class="o">/</span><span class="n">total_rows</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Missing data in description_text&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">city</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
    <span class="n">missing_rows</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;description_text&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    <span class="n">total_rows</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;12}</span><span class="s1">: </span><span class="si">{:&gt;7,}</span><span class="s1"> missing fields in </span><span class="si">{:&gt;7,}</span><span class="s1"> rows (</span><span class="si">{:&gt;4}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">city</span><span class="p">,</span> <span class="n">missing_rows</span><span class="p">,</span> <span class="n">total_rows</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">missing_rows</span><span class="o">/</span><span class="n">total_rows</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
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<pre>Missing data in harmonized_weight_unit
  Long_Beach: 101,948 missing fields in 118,493 rows (86.0%).
 Los_Angeles:  92,615 missing fields in 105,329 rows (87.9%).
  NYC_Newark:  73,543 missing fields in  94,620 rows (77.7%).
     Houston:  29,955 missing fields in  39,555 rows (75.7%).
      Tacoma:  30,343 missing fields in  37,106 rows (81.8%).
     Seattle:  19,174 missing fields in  27,100 rows (70.8%).

Missing data in harmonized_value
  Long_Beach:  90,674 missing fields in 118,493 rows (76.5%).
 Los_Angeles:  69,553 missing fields in 105,329 rows (66.0%).
  NYC_Newark:  65,123 missing fields in  94,620 rows (68.8%).
     Houston:  22,015 missing fields in  39,555 rows (55.7%).
      Tacoma:  14,121 missing fields in  37,106 rows (38.1%).
     Seattle:  10,664 missing fields in  27,100 rows (39.4%).

Missing data in description_text
  Long_Beach:       0 missing fields in 118,493 rows ( 0.0%).
 Los_Angeles:       0 missing fields in 105,329 rows ( 0.0%).
  NYC_Newark:       0 missing fields in  94,620 rows ( 0.0%).
     Houston:       0 missing fields in  39,555 rows ( 0.0%).
      Tacoma:       0 missing fields in  37,106 rows ( 0.0%).
     Seattle:       0 missing fields in  27,100 rows ( 0.0%).
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<p>Or they might not.  There are a lot of missing data for weight of containers; many others have a weight of zero listed.  Obviously the data entry is not standardized and strictly enforced.  But some of the extended text descriptions include some accounting of weight:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">desc</span> <span class="ow">in</span> <span class="n">cities_2017_dfs</span><span class="p">[</span><span class="s1">&#39;Seattle&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[::</span><span class="mi">1000</span><span class="p">,</span> <span class="s1">&#39;description_text&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iteritems</span><span class="p">():</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">desc</span><span class="p">,</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>

<span class="c1"># sea2017_df.loc[::10, &#39;description_text&#39;]</span>
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<pre>(0, &#39;SOFA &amp; UPHOLSTERY PO418287/418285/418967 &#34;SH &#34;&#39;) 

(1000, &#39;WELDING WIRE&#39;) 

(2000, &#39;55  CTN GDSM SOFA___TEL_ 1-650-685 8808 FAX_ 1-650-685 8898 S/C_US0002D2X CY TO CY 1X40_HQFCL&#39;) 

(3000, &#34;LADIES&#39; 100% LINEN KNITTED  CA RDIGAN&#34;) 

(4000, &#34;CHEMICAL WOOD PULP, SODA OR SULPHATE, OTHER T HAN D - &#39;THUNDER BAY&#39; FULLY BLEACHED HARDWOOD KRAFT PULP&#34;) 

(5000, &#39;ROLLS&#39;) 

(6000, &#39;TABLEWARE AND KITCHENWARE OF PLASTICS PLASTIC KITCHEN WARE 669317 GROUP PN3 +1 817-442-818 7&#39;) 

(7000, &#39;PLASTIC SWITCHS&#39;) 

(8000, &#39;ORDER  0085910 ORIENTED STRAND BOARD SUMMARY ID  SUM2092 FREIGHT PREPAID HARMONIZATION COD ES  4410.12.00 NET UNIT  196.458 MSFT&#39;) 

(9000, &#39;CALCIUM STEARATE   HS CODE : 3 812.30   8255556   UDAB   SEA WAYBILL   FREIGHT COLLECT&#39;) 

(10000, &#39;DRIED LEGUMINOUS VEGETABLES, SHELLED, WHETHER OR N - X 100 LB BAGS CROP YEAR 2017: US CHIC KPEAS 2 X20 FCLCROP YEAR 2017: US CHICKPEAS 1 090 X 100 LB BAGS HS CODE: 0713202000 GROSS P OUNDS: 109,218.00 GROSS METRIC TON: 49.540 GR OSS KG: 49,540.00 NET POUNDS: 109,000.00 NET METRIC TON: 49.442 NET KG: 49,442.00 FREIGHTPREPAID DTHC PREPAID 14 FREE DEMURRAGE DAYS A T DESTINATION CAED: 01P233TCE45920171206801 A GENT AT DESTINATION DETAILS: MSC LANKA (PRIVA TE) LTD. 7TH FLOOR 193, DR DANISTER SILVA MAW ATHA COLOMBO 08 SRI LANKA PHONE+94 11 452 900 0 EMAILGENERAL=MSCLANKA.LK FAX+94 11 474 1771&#39;) 

(11000, &#39;WOOD SAWN OR CHIPPED LENGTHWISE, SLICED OR PE ELED, - DOUGLAS FIR KILN DRIED H.T. LUMBER (P SEUDOTSUGA MENZIESII) HEMLOCK KILN DRIED H.T. LUMBER (TSUGA HETEROPHYLLA) 45.8281 M3 19,42 1 FBM&#39;) 

(12000, &#39;HS CODE  020220 FROZEN BEEF CHUCK BONE IN CHU CK SHORT RIB   RIB BONE IN 123A SHORT RIB NET WT 50470.53 LBS NET WT 22893.28 KGS GRS WT 5 3375.09 LBS FREIGHT PREPAID MAINTAIN -18 DEG. CELSIUS   VENTS CLOSED CAED  01R054MC6323201 71202163&#39;) 

(13000, &#39;CHEMICAL NON-HAZ (N-(PHOSPHONOMETHYL)IMINODIA CETIC ACID(PMIDA),98 MIN) S C  RIC5081103 SCA C CODE OERT GROUP A1 NAC IDX THIS SHIPMENT CO NTAINS WOOD PACKAGING MATERIAL WHICH HAS BEEN PROPERLY TREATED&#39;) 

(14000, &#39;30  PKG PRAIRIE MALT 2-ROW BARLEY MALT LOT_36 HSCODE - 1107.10.0000 CAED_ _ 02U356SC433920171200121&#39;) 

(15000, &#39;6,300 CTNS = 9X1000ML INDO NESIAN COCONUT WAT ER &#34;FREI GHT PAYABLE/PREPAID BY ALL MARKET SI NGAPORE&#34;&#39;) 

(16000, &#39;GROSS WEIGHT PER LOT  19,129.00 PERMISO DE EMBARQUE ANACAFE  876ALDABRA REF  17082 SERVICE CONTRACT  S16ECE017 09.01.11 FCL/FCL. SHIPPER FDA  11616749222 FREIGHT PAYABLE AT BASEL, SWITERLAND BY COMMODITY SUPPLIES AG NET WEIGHT PER LOT  18,975.00GREEN COFFEE OF 69 KGS EACH ONE. GUATEMALA HARD BEAN EUROPEAN PREPARATION ARABICA GREEN COFFEE BEANS. CROP 2017/2018, BUYER CONTRACT  P29522&#39;) 

(17000, &#39;NUTS FH-171008 . . .&#39;) 

(18000, &#39;FURNITURE-UPHOLSTERY:UPHOLSTER Y   FREIGHT COLLECT   -222000 CHINA&#39;) 

(19000, &#39;BREADSTICKS,COOKIES,CORN FLOUR&#39;) 

(20000, &#39;OLIVE OIL EXTRA VIRGIN OLIVE OIL&#39;) 

(21000, &#39;ONE EMPTY CONTAINER CLEARED AGAINST CUSTOMS&#39;) 

(22000, &#39;HS CODE  071080 DAE  028-2017-40-00803547 &#34;FREIGHT AS PER AGREEMENT&#34;FROZEN BROCCOLI 40 BOXES BROCCOLI IQF NS3-2025-6 LOT072970 INVOICE  001-011-000033128 KN 17.200 KG; PA 0710802000 RYAN  76678823&#39;) 

(23000, &#39;184 CTNS.MENS T-SHIRTS  67% POLYESTER-33% COTTON 629 CTNS. WOMENS T-SHIRTS  67% POLYESTER-33% COTTON 143 CTNS.MENS POLO SHIRTS  94% POLYESTER-6% LYCRAELASTA 86 CTNS.  WOMENS PULLOVERS  100%POLYESTER&#39;) 

(24000, &#39;PO NO. 209052-7 FG NO. 21-064 801 21-064 801A GREAT VAL 64CT DRY FLOOR WIPE PO NO. 209855-5 FG NO. 21-016 442A HEB 16CT DRY FLOOR WIPE FG NO. 21-016 705BGOOD &amp; CLEAN 16CT DRY FLOOR WIPE FG NO. 21-616 465 HANNAFORD 16CT DRY FLOOR WIPE FG NO. 21-016L151 PUBLIX 16CT DRY FLOOR WIPE FG NO. 21-016 801A GREAT VAL 16CT DRY FLOOR WIPE&#39;) 

(25000, &#39;AUTO PARTS NON HAZ AUTO PARTS HS-CODE(S) 870829 CPO  375092 MOPAR BILL TO  03190 ORIGIN CODE  03173 ENTRY PORT CODE  58201DEALER CODE  33668&#39;) 

(26000, &#39;PU NYLON RAINWEARPVC COTTON PANTPU POLYESTER RAINWEAR&#39;) 

(27000, &#39;WOOD SAWN OR CHIPPED LENGTHWISE, SLICED OR PE ELED, - WOOD, KILN DRIED, PRE-CUT&#39;) 

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<p>I count about 5 rows with weight info among those 28 that I sampled.  They're pretty variable, but sense could be made of them.  I wonder whether the rows that lack values in the dedicated weight column are more likely to contain weight information in the text description.</p>
<p>But identifying the weight data from such a nonstandard format is tricky.  I might just need to pass a series of key terms like 'WT' and 'WEIGHT' with OR operators in between to a re pattern, or I may want to try sending the strings through some NLTK function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>

<span class="n">prefixes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;NET&#39;</span><span class="p">,</span> <span class="s1">&#39;GROSS&#39;</span><span class="p">,</span> <span class="s1">&#39;METRIC&#39;</span><span class="p">]</span>
<span class="n">triggers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;WT&#39;</span><span class="p">,</span> <span class="s1">&#39;WEIGHT&#39;</span><span class="p">]</span>
<span class="n">units</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;POUNDS&#39;</span><span class="p">,</span> <span class="s1">&#39;LBS&#39;</span><span class="p">,</span> <span class="s1">&#39;TONS&#39;</span><span class="p">,</span> <span class="s1">&#39;KG&#39;</span><span class="p">,</span> <span class="s1">&#39;KGS&#39;</span><span class="p">]</span>
<span class="n">strings</span> <span class="o">=</span> <span class="p">[</span><span class="o">*</span><span class="n">prefixes</span><span class="p">,</span> <span class="o">*</span><span class="n">triggers</span><span class="p">,</span> <span class="o">*</span><span class="n">units</span><span class="p">]</span>

<span class="n">weight_words</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;NET&#39;</span><span class="p">,</span> <span class="s1">&#39;GROSS&#39;</span><span class="p">,</span> <span class="s1">&#39;METRIC&#39;</span><span class="p">,</span> <span class="s1">&#39;WT&#39;</span><span class="p">,</span> <span class="s1">&#39;WEIGHT&#39;</span><span class="p">,</span> <span class="s1">&#39;POUNDS&#39;</span><span class="p">,</span>
               <span class="s1">&#39;POUND&#39;</span><span class="p">,</span> <span class="s1">&#39;LBS&#39;</span><span class="p">,</span> <span class="s1">&#39;LB&#39;</span><span class="p">,</span> <span class="s1">&#39;TONS&#39;</span><span class="p">,</span> <span class="s1">&#39;TONNES&#39;</span><span class="p">,</span> <span class="s1">&#39;TON&#39;</span><span class="p">,</span> <span class="s1">&#39;KG&#39;</span><span class="p">,</span> <span class="s1">&#39;KGS&#39;</span><span class="p">]</span>

<span class="n">weight_words_string</span> <span class="o">=</span> <span class="s1">&#39;|&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">weight_words</span><span class="p">)</span>
<span class="c1"># pattern = &#39;(&#39;+weight_words_string+&#39;+[:?]\s{1,2}\d+)&#39;</span>
<span class="c1"># pattern = &#39;((&#39;+weight_words_string+&#39;)[:?]\s{1,2}[\d.,]+\s{1,2}(&#39;+weight_words_string+&#39;))&#39;</span>
<span class="n">pattern</span> <span class="o">=</span> <span class="s1">&#39;(&#39;</span><span class="o">+</span><span class="n">weight_words_string</span><span class="o">+</span><span class="s1">&#39;)&#39;</span>
<span class="n">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">pattern</span><span class="p">)</span>
<span class="n">pattern2</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s1">&#39;\w+\s\d+&#39;</span><span class="p">)</span>

<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">cities_2017_dfs</span><span class="p">[</span><span class="s1">&#39;Seattle&#39;</span><span class="p">]</span>
<span class="n">df2</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;harmonized_weight&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()]</span><span class="c1"># | df[&#39;harmonized_weight&#39;]==0]</span>
<span class="n">df3</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;harmonized_weight&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">df4</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df2</span><span class="p">,</span> <span class="n">df3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">df5</span> <span class="o">=</span> <span class="n">df4</span><span class="p">[</span><span class="o">~</span><span class="n">df4</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">duplicated</span><span class="p">(</span><span class="n">keep</span><span class="o">=</span><span class="s1">&#39;first&#39;</span><span class="p">)]</span>
<span class="n">weight_matches</span> <span class="o">=</span> <span class="n">df5</span><span class="p">[</span><span class="n">df5</span><span class="p">[</span><span class="s1">&#39;description_text&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">pattern2</span><span class="p">)]</span>
<span class="c1"># for i, v in df2.iteritems():</span>
<span class="c1">#     if re.search(pattern, v):</span>
<span class="c1">#         matches = re.search(pattern, v).groups()#[0]</span>
<span class="c1">#         results[i] = matches</span>
<span class="c1"># weight_matches = pd.Series(data=results)</span>
<span class="n">df2</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">df3</span><span class="o">.</span><span class="n">shape</span>
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<pre>((10664, 31), (8757, 31))</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># weight_matches[12000]</span>
<span class="c1"># re.findall(r&#39;[\d,]+[.]?[\d,]+&#39;, sea2017_df.loc[12000, &#39;description_text&#39;])</span>
<span class="c1"># sea2017_df.loc[12000, &#39;description_text&#39;]</span>
<span class="c1"># weight_matches.head()</span>
<span class="n">weight_matches</span><span class="o">.</span><span class="n">shape</span>
<span class="c1"># df5.shape</span>
<span class="n">df5</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="c1"># cities_2017_dfs[&#39;Seattle&#39;].head()</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">cities_2017_dfs</span><span class="p">[</span><span class="s1">&#39;Seattle&#39;</span><span class="p">]</span>
<span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">&#39;harmonized_value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
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    .dataframe thead th {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>identifier</th>
      <th>trade_update_date</th>
      <th>run_date</th>
      <th>vessel_name</th>
      <th>port_of_unlading</th>
      <th>estimated_arrival_date</th>
      <th>foreign_port_of_lading</th>
      <th>record_status_indicator</th>
      <th>place_of_receipt</th>
      <th>port_of_destination</th>
      <th>...</th>
      <th>shipper_comm_number_qualifier</th>
      <th>shipper_comm_number</th>
      <th>container_number</th>
      <th>description_sequence_number</th>
      <th>piece_count</th>
      <th>description_text</th>
      <th>harmonized_number</th>
      <th>harmonized_value</th>
      <th>harmonized_weight</th>
      <th>harmonized_weight_unit</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>13</th>
      <td>201712262236</td>
      <td>2017-11-28T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Yantian,China (Mainland)</td>
      <td>New</td>
      <td>HAIPHONG</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>SEGU5919545</td>
      <td>1</td>
      <td>1243</td>
      <td>APPAREL, MEN S WOVEN SHIRT, LADIES STRIPES SH</td>
      <td>620520.0</td>
      <td>241660.0</td>
      <td>12083.0</td>
      <td>Kilograms</td>
    </tr>
    <tr>
      <th>29</th>
      <td>201712263368</td>
      <td>2017-12-01T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Pusan,South Korea</td>
      <td>New</td>
      <td>BUSAN</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>FCIU2664040</td>
      <td>1</td>
      <td>19</td>
      <td>EDTA-ACID</td>
      <td>NaN</td>
      <td>0.0</td>
      <td>19057.0</td>
      <td>Kilograms</td>
    </tr>
    <tr>
      <th>30</th>
      <td>201712263445</td>
      <td>2017-12-01T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>57037</td>
      <td>New</td>
      <td>SHANGHAI, SHANGHA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NYKU0728968</td>
      <td>1</td>
      <td>640</td>
      <td>OFFICE CHAIR</td>
      <td>940130.0</td>
      <td>215008.0</td>
      <td>10750.0</td>
      <td>Kilograms</td>
    </tr>
    <tr>
      <th>31</th>
      <td>201712263500</td>
      <td>2017-12-01T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>57037</td>
      <td>New</td>
      <td>SHANGHAI, SHANGHA</td>
      <td>NaN</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>TCLU6640359</td>
      <td>1</td>
      <td>54</td>
      <td>NAILHEADS HTS CODE 940161 PO 12030814562 IF A</td>
      <td>940161.0</td>
      <td>57708.0</td>
      <td>2885.0</td>
      <td>Kilograms</td>
    </tr>
    <tr>
      <th>32</th>
      <td>201712263681</td>
      <td>2017-12-15T00:00:00</td>
      <td>2017-12-26T00:00:00</td>
      <td>NEW YORK EXPRESS</td>
      <td>Seattle, Washington</td>
      <td>2017-12-21T00:00:00</td>
      <td>Pusan,South Korea</td>
      <td>New</td>
      <td>TIANJIN XINGANG,</td>
      <td>3512</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>HLXU3272262</td>
      <td>1</td>
      <td>19</td>
      <td>EDTA-ACID S/C S17ASC112 MR CODE BLUBRE1 XFAX +...</td>
      <td>292249.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 31 columns</p>
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<p>Ok, so that list of words for capturing descriptions that might mention the weight of the cargo yields results that are a little underwhelming: only ~7,000 of the 19,000 rows that have no weight listed.</p>

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<h2 id="Analysis">Analysis<a class="anchor-link" href="#Analysis">&#182;</a></h2><p>One question that arises, in the case of Seattle, is why the overwhelming majority of shipments are coming from Vancouver, even though probably most of the ultimate origin of many imported goods is still China.  Vancouver is very close by, and may have a better port or otherwise have a different set of tariff/duties rules than the US, which would make it likely that foreign imports might shuttle through there.  It's a few years old, but <a href="https://www.joc.com/port-news/us-ports/us-importers-moving-more-containers-through-vancouver_20150618.html">this article</a> states that many other US cities receive more imports lately from Vancouver, rather than Seattle or Tacoma, lending credence to the idea that Vancouver is a popular import point for foreign goods from overseas.  Actually, the article sites a labor dispute between longshoreman unions and ports in the PacNW as a driver for a temporary shift to Canada, which may have initiated a shift in traffic up North.  Still, the question as to why goods would pass through there on the way here seems a little puzzling.  A deeper dive into this issue could be interesting, or just too obscure to address adequately in a reasonable amount of time.</p>

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