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Updated: 38 min 7 sec ago

Please inspect your dplyr+database code

Sat, 12/02/2017 - 17:32

(This article was first published on R – Win-Vector Blog, and kindly contributed to R-bloggers)

A note to dplyr with database users: you may benefit from inspecting/re-factoring your code to eliminate value re-use inside dplyr::mutate() statements.

If you are using the R dplyr package with a database or with Apache Spark: I respectfully advise you inspect your code to ensure you are not using any values created inside a dplyr::mutate() statement inside the same dplyr::mutate() statement. This has been my coding advice for some time, and it is a simple and safe re-factoring to break up such statements into safer sequences (simply by introducing more dplyr::mutate()s).

I have since encountered a non-signaling (or silent) result corruption version of the issue. We are now advising code inspection as we now have confirmation that not seeing a thrown error is not a reliable indication of correct execution and correct results.

To keep things in proportion: if you are not writing multi-assignment mutates on a dplyr database-backed system you can’t run into the problem (though, for performance, multi-statement mutates are preferred over database sources such as Apache Spark).

The issue has been reported to the dplyr team. And I presume a fix is in the works. However, one does not want to be distributing incorrect results in the interim. This is the advice I have been giving private clients. After some thought I have come to feel it would be unfair to withhold such advice from the larger R community. This is not meant to make dplyr look bad, but to try and help prevent both dplyr and dplyr users from unnecessarily looking bad.

To be clear: I am a proponent of dplyr plus database development (which is why I ran into this). Also, I am not affiliated with RStudio or affiliated with the dplyr development team.

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Gold-Mining – Week 13 (2017)

Sat, 12/02/2017 - 16:48

(This article was first published on R – Fantasy Football Analytics, and kindly contributed to R-bloggers)

Week 13 Gold Mining and Fantasy Football Projection Roundup now available. Go get that free agent gold!

The post Gold-Mining – Week 13 (2017) appeared first on Fantasy Football Analytics.

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To leave a comment for the author, please follow the link and comment on their blog: R – Fantasy Football Analytics. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

Getting Started with R

Sat, 12/02/2017 - 15:17

(This article was first published on Data Perspective, and kindly contributed to R-bloggers)

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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: 600px { .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: 600px { .container { width: 500px; } } @media (min-width: 600px { .container { width: 500px; } } @media (min-width: 600px { .container { width: 500px; } } .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: 600px { .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: 600px { .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: 600px { .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: 500px) { .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: 600px { .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: 600px { .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: 600px { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 600px { .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: 600px { .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: 600px { .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: 600px { .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: 600px { .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: 600px { .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: 600px { .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: 600px { .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: 500px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 600px 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: 600px and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 600px and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 600px and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 600px 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: 600px and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 600px and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 600px and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 600px { .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: 600px { .visible-lg-block { display: block !important; } } @media (min-width: 600px { .visible-lg-inline { display: inline !important; } } @media (min-width: 600px { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 600px and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 600px and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 600px { .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: 600px { .modal .modal-dialog { width: 700px; } } @media (min-width: 600px { 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 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 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: 80.1%; 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: 600px 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; padding-top: 11px; padding-bottom: 3px; } #maintoolbar .navbar-text { float: none; vertical-align: middle; text-align: right; margin-left: 5px; margin-right: 0px; margin-top: 0px; } .select-xs { height: 24px; } .pulse, .dropdown-menu > li > a.pulse, li.pulse > a.dropdown-toggle, li.pulse.open > a.dropdown-toggle { background-color: #F37626; color: white; } /** * Primary styles * * Author: Jupyter Development Team */ /** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot * of chance of beeing generated from the ../less/[samename].less file, you can * try to get back the less file by reverting somme commit in history **/ /* * We'll try to get something pretty, so we * have some strange css to have the scroll bar on * the left with fix button on the top right of the tooltip */ @-moz-keyframes fadeOut { from { opacity: 1; } to { opacity: 0; } } @-webkit-keyframes fadeOut { from { opacity: 1; } to { opacity: 0; } } @-moz-keyframes fadeIn { from { opacity: 0; } to { opacity: 1; } } @-webkit-keyframes fadeIn { from { opacity: 0; } to { opacity: 1; } } /*properties of tooltip after "expand"*/ .bigtooltip { overflow: auto; height: 200px; -webkit-transition-property: height; -webkit-transition-duration: 500ms; -moz-transition-property: height; -moz-transition-duration: 500ms; transition-property: height; transition-duration: 500ms; 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MathJax.Hub.Config({ tex2jax: { inlineMath: [ ['$','$'], ["\\(","\\)"] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ], processEscapes: true, processEnvironments: true }, // Center justify equations in code and markdown cells. Elsewhere // we use CSS to left justify single line equations in code cells. displayAlign: 'center', "HTML-CSS": { styles: {'.MathJax_Display': {"margin": 0}}, linebreaks: { automatic: true } } });

Rstudio OverView we have 4 panes
1) script pan – to write and save the programing script
2) Console pane – where all the code will get executed
3) Environment/history pane – displays all the variables created,functions
used with in the current session
4) Helper pane – contains multiple tabs to install/display pacakges,
view visualization plots,
locate files within the workspace

In [1]: help(mean)
getting and setting workspace In [2]: # to display current working directory use getwd() function
getwd()
‘C:/Users/Suresh/mlclassscripts’

In [ ]: # to set up workspace or working directory use setwd() function
#syntax is shown below
setwd("path")
In [6]: setwd("C:\\Suresh\\R&D\\Projects\\ML classroom training\\sessions")
setwd("C:/Suresh/R&D/Projects/ML classroom training/sessions")
getting help in R

To get help within R environment, we use help() function to get the
documentation
for any of the functions/packages available within R environment.
To see the arguments required for a function, we use args() function.
to see the example of a function, example() function is used.

In [ ]: help("stats")
help("mean")
args("mean")
example("mean")

#getting help documentation for a package
help(package="caret")
online help for R programming

We can get online help on available packages in R from official website of R-Cran
https://cran.r-project.org/web/views/

We can also get online support for our day to day activities from below websites:
https://stackoverflow.com/
https://stats.stackexchange.com

Installing Packages In [ ]: #install pacakges in R can be done in two ways,
#1) using install.packages() function and from the bottom right pane of Rstudio
install.packages("randomForest")

#loading of installed or downloaded packages can be done using library() function.
#Note that we can only load the package if
# we have installed the package already within our R environment
library(cluster)
In [ ]: #below code to first verify if the library is installed in the R environment,
#if it is not available
# then the package will get installed.
if(!library(cluster)){
install.pacakges("cluster")
}
basic operations in R In [ ]: # Adding two numericals
1+1

#multiplying two numericals
10*2

#dividing two numericals
10/2

#applying modulus operation on two numericals
10%%2
printing results to R console In [ ]: #printing the data on the console
print(10*2)

print("data science")

print(pi^2)
Variable declaration and assignment in R

variable assignment: In the below example, we are creating variable named z:

In [8]: z <- 100

we use left arrow or = symbol for variable assignment. Its always good
practice to use left arrow for assignment.

In [9]: z = 10.009
z <- 10.009
Loading existing or default datasets available in R environment

we can access default datasets avaiable in R using data() function.
data() function will displays all the avaiable datasets within R.

In [ ]: data()

In order to load a specific dataset into R, we need to give the dataset name as argument to the data() function

In [ ]: data(AirPassengers)
Viewing data of R objects

To view first 5 records of a R object (ex:dataframe), we use head() function.
head() function expects the data object as argument and prints the first 5 records on the R console.

In [ ]: head(AirPassengers)

to view all the records in a nice tabular view

In [ ]: View(AirPassengers)
Getting the decription and structure of R object

use str function to see the descriptions of the data object,

In [ ]: str(AirPassengers)

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Correlated log-normal chain-ladder model

Sat, 12/02/2017 - 01:00

(This article was first published on R on mages' blog, and kindly contributed to R-bloggers)

On 23 November Glenn Meyers gave a fascinating talk about The Bayesian Revolution in Stochastic Loss Reserving at the 10th Bayesian Mixer Meetup in London.

Glenn Meyers speaking at the Bayesian Mixer

Glenn worked for many years as a research actuary at Verisk/ ISO, he helped to set up the CAS Loss Reserve Database and published a monograph on Stochastic loss reserving using Bayesian MCMC models.

In this blog post I will go through the Correlated Log-normal Chain-Ladder Model from his presentation. It is discussed in more detailed in his monograph.

Glenn kindly shared his code as well, which I have used as a basis for this post.

Getting the data

The CAS Loss Reserve Database is an excellent data source to test reserving models. It is hosted on the CAS website and contains historical regulatory filings of US insurance companies.

The following code allows me to download the data and extract the information for one company, here company 353, which was the example company Glenn used as well.

library(data.table) CASdata <- fread("http://www.casact.org/research/reserve_data/comauto_pos.csv") createLossData <- function(CASdata, company_code){ compData <- CASdata[GRCODE==company_code, c("EarnedPremNet_C", "AccidentYear", "DevelopmentLag", "IncurLoss_C", "CumPaidLoss_C", "BulkLoss_C")] # rename column names setnames(compData, names(compData), c("premium", "accident_year", "dev", "incurred_loss", "paid_loss", "bulk_loss")) compData <- compData[, `:=`( origin = accident_year - min(accident_year) + 1, # origin period starting from 1, id to identify first origin period origin1id = ifelse(accident_year == min(accident_year), 0, 1), # Reported incurred loss # set losses < 0 to 1, as we will take the log later reported_incurred_loss=pmax(incurred_loss - bulk_loss, 1))] # add calendar period and sort data by dev and then origin compData <- compData[, cal := origin + dev - 1][order(dev, origin)] compData <- compData[, `:=`( reported_incurred_loss_train = ifelse(cal <= max(origin), reported_incurred_loss, NA), reported_incurred_loss_test = ifelse(cal > max(origin), reported_incurred_loss, NA))] traintest <- rbindlist(list(compData[cal <= max(origin)], compData[cal > max(origin)])) return(traintest) } lossData <- createLossData(CASdata, company_code = 353)

The data shows the historical annual developments of incurred claims for accident years 1988 to 1997. I separated the data into a training and test data set. The training data will have incurred claims reported up to calendar year 1997, while data from 1998 to 2006 will be used to test the model.

Let’s take a look at the data for Commercial Auto of company 353.

devPlot <- function(x, data, company_code, ...){ library(lattice) key <- list(rep=FALSE, columns=2, lines=list(col=c("#00526D", "#00526D"), type=c("p", "p"), pch=c(19, 1)), text=list(lab=c("Observation", "Hold out observation"))) xyplot(x, data, t=c("b", "b"), pch=c(19, 1), col=c("#00526D", "#00526D"), layout=c(5,2), par.settings = list(strip.background=list(col="#CBDDE6")), par.strip.text = list(font = 2), key = key, as.table=TRUE, scales=list(alternating=1), ylab = "Reported incurred loss ($k)", xlab="Development year", main="Reported incurred loss development by accident year", sub=paste("Data source: CAS Loss Reserving Database, Comm. Auto, Comp.", company_code), ...) } devPlot(reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=lossData, company_code = 353)

The data appears to be reasonably well behaved. Although the years converge to different levels of ultimate incurred claims, the shape of the curves look similar for most years. The accident years 1988 and 1993 show a distinctly different pattern.

The correlated log-normal chain-ladder model

The correlated log-normal chain-ladder model combines ideas of the classical chain-ladder method, the growth curve model and Bayesian inference.

\[
\begin{align}
C_{i,j} & \sim \log\mathcal{N}(\mu_{i,j}, \sigma^2_j)\\
L_i & = P_i \cdot \ell\\
\mu_{1,j} & = \log(L_1) + \beta_j \\
\mu_{i,j} & = \log(L_i) + \alpha_i + \beta_j + \rho \cdot \left(\log(C_{i-1,j}) – \mu_{i-1,j}\right) \\
& \mbox{for } i = 2, \dots, M \mbox{ and } j = 1, \dots, N+1-i \\
& \mbox{and } \alpha_1 := 0,\, \beta_N := 0\\
\sigma^2_j & = \sum_{k=j}^N a_k \\
& \mbox{with priors}\\
\alpha_i & \sim \mathcal{N}(0, 10) \\
\beta_i & \sim \mathcal{N}(0, 10) \\
\log(\ell) & \sim \mathcal{N}(0, 1) \\
\rho & \sim \mbox{Beta}(2, 2)\\
a_k & \sim \mbox{Uniform}(0,1)\\
&\mbox{and}\\
C_{i,j} &:=\mbox{cumulative incurred claims of origin } i\mbox{, development }j \\
P_{i} & := \mbox{premium of origin } i\\
\ell & := \mbox{'population' loss ratio across all origin periods}\\
L_i & := \mbox{initial expected ultimate loss for origin } i\\
M & := \mbox{number of origin periods} \\
N & := \mbox{number of development periods}
\end{align}
\]

Given that we assume the losses follow a log-normal distribution we have:

\[
\begin{align}
\mbox{Median}(C_{i,j}) & = L_i \cdot \exp(\mu_{i,j})\\
\mathbb{E}(C_{i,j}) & = L_i \cdot \exp\left(\mu_{i,j} + \frac{1}{2} \sigma_j^2 \right)
\end{align}
\] Similar to the chain-ladder method we have one parameter for each development period \(\beta_j\) and one parameter for each origin period \(\alpha_i\).

But unlike the Mack-chain-ladder model, Glenn allows for correlation between origin periods \(i\) and \(i-1\) in his model: \(\rho \cdot \left(\log(C_{i-1,j}) – \mu_{i-1,j}\right)\).

Interestingly as well, Glenn assumes that the volatility shrinks as claims mature over development periods, modelling \(\sigma_j^2\) as the sum of uniform variances.

This model is certainly not short on parameters. I wonder, if it is over-parametrised? Does it provide good in sample prediction, but perform poorly out of sample?

Stan model

So let’s put this model into Stan. Again, I follow Glenn’s implementation, but I have changed some of the variable names and added some lines in the generated quantities block to predict incurred claims for the test data period.

library(rstan) rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) data{ int len_data; // number of rows with data int len_pred; // number of rows to predict // indicator of first origin period int origin1id[len_data + len_pred]; real logprem[len_data + len_pred]; real logloss[len_data]; int origin[len_data + len_pred]; // origin period int dev[len_data + len_pred]; // development period } transformed data{ int n_origin = max(origin); int n_dev = max(dev); int len_total = len_data + len_pred; } parameters{ real r_alpha[n_origin - 1]; real r_beta[n_dev - 1]; real log_elr; real a_ig[n_dev]; real r_rho; real logloss_pred[len_pred]; } transformed parameters{ real alpha[n_origin]; real beta[n_dev]; real sig2[n_dev]; real sig[n_dev]; real mu[len_data]; real mu_pred[len_pred]; real rho; rho = -2*r_rho + 1; for (i in 1:(n_dev - 1)){ beta[i] = r_beta[i]; } beta[n_dev] = 0; alpha[1] = 0; for (i in 2:n_origin){ alpha[i] = r_alpha[i-1]; } // Create ascending set of sig2 sig2[n_dev] = gamma_cdf(1/a_ig[n_dev],1,1); // map into [0,1] for (i in 1:(n_dev-1)){ sig2[n_dev - i] = sig2[n_dev + 1 - i] + gamma_cdf(1/a_ig[i],1,1); } for (i in 1:n_dev){ sig[i] = sqrt(sig2[i]); } // first origin and dev period (top left corner of triangle) mu[1] = logprem[1] + log_elr + beta[dev[1]]; for (i in 2:len_data){ mu[i] = logprem[i] + log_elr + alpha[origin[i]] + beta[dev[i]] + rho*(logloss[i-1] - mu[i-1]) * origin1id[i]; } mu_pred[1] = logprem[(len_data) + 1] + alpha[origin[len_data + 1]] + log_elr + beta[dev[len_data + 1]] + rho*(logloss[len_data] - mu[len_data]) * origin1id[len_data + 1]; for (i in 2:len_pred){ mu_pred[i] = logprem[len_data + i] + alpha[origin[len_data + i]] + log_elr + beta[dev[len_data + i]] + rho*(logloss_pred[i-1] - mu_pred[i-1]) * origin1id[len_data + i]; } } model { log_elr ~ normal(0, 1); r_alpha ~ normal(0, sqrt(10/1.0)); r_beta ~ normal(0, sqrt(10/1.0)); a_ig ~ inv_gamma(1,1); // inverse gamma for numerical resaons r_rho ~ beta(2,2); // model where we have data for (i in 1:(len_data)){ logloss[i] ~ normal(mu[i], sig[dev[i]]); } // model where data is missing, the prediction period for (i in 1:(len_pred)){ logloss_pred[i] ~ normal(mu_pred[i], sig[dev[len_data + i]]); } } generated quantities{ vector[len_data] log_lik; vector[len_total] ppc_loss; for (i in 1:len_data){ log_lik[i] = normal_lpdf(logloss[i] | mu[i], sig[dev[i]]); } // simulate posterior predicted losses for (i in 1:len_data){ ppc_loss[i] = exp(normal_rng(mu[i], sig[dev[i]])); } for (i in 1:len_pred){ ppc_loss[len_data + i] = exp(normal_rng(mu_pred[i], sig[dev[len_data + i]])); } }

The next code block prepares the data as an input for Stan.

createStanDataList <- function(lossData){ with(lossData, { train_idx <- !is.na(reported_incurred_loss_train) list( len_data = sum(train_idx), len_pred = sum(!train_idx), logprem = log(premium), logloss = log(reported_incurred_loss_train[train_idx]), origin = origin, dev = dev, origin1id = origin1id) }) } stan_data <- createStanDataList(lossData) Model run for company 353

Finally, we can run our Stan model for company 353.

fitCCL353 <- sampling(CCLmodel, data=stan_data, seed = 1234, iter = 4000, control=list(adapt_delta = 0.99, max_treedepth = 10)) ## Warning: There were 4824 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See ## http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded ## Warning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See ## http://mc-stan.org/misc/warnings.html#bfmi-low ## Warning: Examine the pairs() plot to diagnose sampling problems

Stan comes back with a couple of warnings and messages to investigate the model in more detail.

I get pretty much the same output as Glenn (see slides 19, 20):

print(fitCCL353, pars=c("alpha", "beta", "rho", "log_elr", "sig"), probs=c(0.025, 0.975)) ## Inference for Stan model: 05311aae980b7925c9bb0a91cd33e3a7. ## 4 chains, each with iter=4000; warmup=2000; thin=1; ## post-warmup draws per chain=2000, total post-warmup draws=8000. ## ## mean se_mean sd 2.5% 97.5% n_eff Rhat ## alpha[1] 0.00 0.00 0.00 0.00 0.00 8000 NaN ## alpha[2] -0.26 0.00 0.02 -0.29 -0.23 2770 1.00 ## alpha[3] 0.11 0.00 0.02 0.06 0.15 1279 1.00 ## alpha[4] 0.21 0.00 0.03 0.16 0.26 1314 1.00 ## alpha[5] 0.01 0.00 0.03 -0.06 0.07 699 1.00 ## alpha[6] -0.06 0.00 0.04 -0.13 0.03 1694 1.00 ## alpha[7] 0.45 0.00 0.06 0.33 0.56 1432 1.00 ## alpha[8] 0.03 0.00 0.08 -0.13 0.20 1790 1.00 ## alpha[9] 0.15 0.00 0.14 -0.14 0.43 1341 1.00 ## alpha[10] 0.18 0.01 0.29 -0.36 0.77 1300 1.01 ## beta[1] -0.59 0.00 0.11 -0.83 -0.38 4196 1.00 ## beta[2] -0.19 0.00 0.07 -0.32 -0.05 2987 1.00 ## beta[3] -0.10 0.00 0.05 -0.20 0.00 1821 1.00 ## beta[4] -0.03 0.00 0.04 -0.10 0.05 1352 1.00 ## beta[5] -0.01 0.00 0.03 -0.07 0.05 1174 1.00 ## beta[6] 0.00 0.00 0.03 -0.06 0.06 1169 1.00 ## beta[7] 0.00 0.00 0.03 -0.05 0.06 1010 1.00 ## beta[8] 0.01 0.00 0.02 -0.05 0.06 1257 1.00 ## beta[9] 0.00 0.00 0.02 -0.05 0.05 1662 1.00 ## beta[10] 0.00 0.00 0.00 0.00 0.00 8000 NaN ## rho 0.17 0.00 0.21 -0.27 0.55 2523 1.00 ## log_elr -0.39 0.00 0.01 -0.43 -0.37 1207 1.00 ## sig[1] 0.26 0.00 0.09 0.14 0.51 3428 1.00 ## sig[2] 0.15 0.00 0.04 0.09 0.26 3884 1.00 ## sig[3] 0.09 0.00 0.03 0.05 0.16 1856 1.00 ## sig[4] 0.06 0.00 0.02 0.03 0.11 467 1.01 ## sig[5] 0.04 0.00 0.02 0.02 0.09 328 1.01 ## sig[6] 0.04 0.00 0.02 0.02 0.07 290 1.01 ## sig[7] 0.03 0.00 0.01 0.01 0.06 291 1.01 ## sig[8] 0.02 0.00 0.01 0.01 0.05 289 1.01 ## sig[9] 0.02 0.00 0.01 0.01 0.04 294 1.01 ## sig[10] 0.01 0.00 0.01 0.00 0.03 330 1.01 ## ## Samples were drawn using NUTS(diag_e) at Sun Dec 3 21:26:56 2017. ## For each parameter, n_eff is a crude measure of effective sample size, ## and Rhat is the potential scale reduction factor on split chains (at ## convergence, Rhat=1).

Some of the parameters don’t appear significant, such as \(\beta_5\) to \(\beta_9\), but then again they shouldn’t have done any harm here either, as they will have had little impact.

However, I am most interested in comparing the posterior predictive distribution with the training and test data. How does the model perform out of sample?

The next code chunk extracts the 95% credible interval from the posterior predictive simulated losses and plots the output.

createPlotData <- function(stanfit, data, probs=c(0.25, 0.975)){ ppc_loss <- as.matrix(extract(stanfit, "ppc_loss")$ppc_loss) ppc_loss_summary <- cbind( mean=apply(ppc_loss, 2, mean), t(apply(ppc_loss, 2, quantile, probs=probs)) ) colnames(ppc_loss_summary) = c( "Y_pred_mean", paste0("Y_pred_cred", gsub("\\.", "", probs[1])), paste0("Y_pred_cred", gsub("\\.", "", probs[2]))) return(cbind(ppc_loss_summary, data)) } plotDevBananas <- function(x, data, company_code, xlab="Development year", ylab="Reported incurred loss ($k)", main="Correlated Log-normal Chain Ladder Model", ...){ key <- list( rep=FALSE, lines=list(col=c("#00526D", "#00526D", "purple"), type=c("p", "p", "l"), pch=c(19, 1, NA)), text=list(lab=c("Observation", "Hold out observation", "Mean estimate")), rectangles = list(col=adjustcolor("yellow", alpha.f=0.5), border="grey"), text=list(lab="95% Prediction credible interval")) xyplot(x, data=data,as.table=TRUE,xlab=xlab, ylab=ylab, main=main, sub=paste("Data source: CAS Loss Reserving Database,", "Comm. Auto, Comp.", company_code), scales=list(alternating=1), layout=c(5,2), key=key, par.settings = list(strip.background=list(col="#CBDDE6")), par.strip.text = list(font = 2), panel=function(x, y){ n <- length(x) divisor <- 5 cn <- c(1:(n/divisor)) upper <- y[cn+n/divisor*0] lower <- y[cn+n/divisor*1] x <- x[cn] panel.polygon(c(x, rev(x)), c(upper, rev(lower)), col = adjustcolor("yellow", alpha.f = 0.5), border = "grey") panel.lines(x, y[cn+n/divisor*2], col="purple") panel.points(x, y[cn+n/divisor*4], lwd=1, col="#00526D") panel.points(x, y[cn+n/divisor*3], lwd=1, pch=19, col="#00526D") }, ...) } plotData <- createPlotData(fitCCL353, data=lossData) plotDevBananas(Y_pred_cred025 + Y_pred_cred0975 + Y_pred_mean + reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=plotData, company_code = 353)

This looks pretty good!

We can see for the older years that the credible interval shrinks as the development years progress. The skewness of the log-normal distribution is clearly visible for the most recent accident years. In all cases the hold out observations are within the 95% prediction credible interval, often very close to the mean, apart from the years 1994 and 1995, where we observe a more unusual pattern in the data.

Finally, here is the distribution of the ‘population’ loss ratio \(\ell\) across all accident years and correlation parameter \(\rho\).

summary(elr <- exp(extract(fitCCL353, "log_elr")$log_elr)) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.6033 0.6695 0.6740 0.6739 0.6786 0.7380 summary(rho <- extract(fitCCL353, "rho")$rho) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -0.86328 0.03551 0.18278 0.17077 0.31764 0.87749

The distribution of \(\rho\) is very interesting. Many of the traditional reserving methods, including the Mack-chain-ladder model assume that the accident years are independent. However, for this data set it is unlikely to be valid. Indeed, the probability of a positive correlation is 80%.

library(MASS); library(latex2exp) truehist(rho, col="skyblue", xlab=TeX("$\\\\rho$")); abline(v=0, col=2)

Company 833

Let’s look at another company. Here is the data for company 833.

lossData <- createLossData(CASdata, company_code = 833) devPlot(reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=lossData, company_code = 833)

This data is not as well behaved as for company 353. The shape of the curves do vary quite a bit from one accident year to the next. Let’s find out how well our model predicts the hold out sample in this case.

stan_data <- createStanDataList(lossData) fitCCL833 <- sampling(CCLmodel, data=stan_data, seed = 1234, iter = 4000, control=list(adapt_delta = 0.99, max_treedepth = 10)) ## Warning: There were 28 divergent transitions after warmup. Increasing adapt_delta above 0.99 may help. See ## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup ## Warning: There were 1443 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See ## http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded ## Warning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See ## http://mc-stan.org/misc/warnings.html#bfmi-low ## Warning: Examine the pairs() plot to diagnose sampling problems

Stan gives messages back, which I shall ignore for the time being and instead look at my ‘banana’ plot again.

plotData <- createPlotData(fitCCL833, lossData) plotDevBananas(Y_pred_cred025 + Y_pred_cred0975 + Y_pred_mean + reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=plotData, company_code = 833)

Interesting! Not too bad either.

Company 25275

Here is another company, whose data show some unusual patterns.

## Other companies lossData <- createLossData(CASdata, company_code = 25275) devPlot(reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=lossData, company_code = 25275)

Let’s fit this data with the same model as well.

stan_data <- createStanDataList(lossData) fitCCL25275 <- sampling(CCLmodel, data=stan_data, seed = 1234, iter = 4000, control=list(adapt_delta = 0.99, max_treedepth = 10)) ## Warning: There were 132 divergent transitions after warmup. Increasing adapt_delta above 0.99 may help. See ## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup ## Warning: There were 1738 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See ## http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded ## Warning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See ## http://mc-stan.org/misc/warnings.html#bfmi-low ## Warning: Examine the pairs() plot to diagnose sampling problems plotData <- createPlotData(fitCCL25275, lossData) plotDevBananas(Y_pred_cred025 + Y_pred_cred0975 + Y_pred_mean + reported_incurred_loss_train + reported_incurred_loss_test ~ dev | factor(accident_year), data=plotData, company_code = 25275, ylim=c(0, 500))

Well, what do you think about that?

References

Stochastic Loss Reserving Using Bayesian McMc Models. Glenn Meyers. CAS monograph series. Number 1. Casualty Actuarial Society. 2015

Session Info sessionInfo() ## R version 3.4.2 (2017-09-28) ## Platform: x86_64-apple-darwin15.6.0 (64-bit) ## Running under: macOS High Sierra 10.13.1 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8 ## ## attached base packages: ## [1] methods stats graphics grDevices utils datasets base ## ## other attached packages: ## [1] latex2exp_0.4.0 MASS_7.3-47 rstan_2.16.2 ## [4] StanHeaders_2.16.0-1 ggplot2_2.2.1 lattice_0.20-35 ## [7] data.table_1.10.4-3 ## ## loaded via a namespace (and not attached): ## [1] Rcpp_0.12.14 knitr_1.17 magrittr_1.5 munsell_0.4.3 ## [5] colorspace_1.3-2 rlang_0.1.4 stringr_1.2.0 plyr_1.8.4 ## [9] tools_3.4.2 parallel_3.4.2 grid_3.4.2 gtable_0.2.0 ## [13] htmltools_0.3.6 yaml_2.1.14 lazyeval_0.2.1 rprojroot_1.2 ## [17] digest_0.6.12 tibble_1.3.4 bookdown_0.5 gridExtra_2.3 ## [21] codetools_0.2-15 inline_0.3.14 evaluate_0.10.1 rmarkdown_1.8 ## [25] blogdown_0.3 stringi_1.1.6 compiler_3.4.2 scales_0.5.0 ## [29] backports_1.1.1 stats4_3.4.2 var vglnk = { key: '949efb41171ac6ec1bf7f206d57e90b8' }; (function(d, t) { var s = d.createElement(t); s.type = 'text/javascript'; s.async = true; s.src = '//cdn.viglink.com/api/vglnk.js'; var r = d.getElementsByTagName(t)[0]; r.parentNode.insertBefore(s, r); }(document, 'script'));

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Hosting RStudio Server on Azure

Sat, 12/02/2017 - 01:00

(This article was first published on R on The Jumping Rivers Blog, and kindly contributed to R-bloggers)

Can’t be bothered reading, tell me now

Host RStudio server on an azure instance. Configure the instance to access RStudio with a nice url

Getting started

Azure is cloud computing framework provided by Microsoft, the same idea as AWS by Amazon. In this post, we’ll describe how to use Azure to run RStudio Server in the cloud.

Unfortunately, things don’t start well – Microsoft have made an endurance test of getting started with Azure. The first stop is the Azure web-page. On this page

click on Free Account and follow the instructions. This is a bit of painful process that will require

  • Email confirmation
  • Text confirmation
  • Credit Card confirmation

Eventually you should get to the dashboard page!

Clicking on Create Resources will take you to the marketplace

Selecting Ubuntu Server will launch a dialogue box with four steps:

  • Step 1: Basics: configuration settings
    • Name: A name for the virtual machine, e.g. rstudio
    • User name: The master user who will have sudo access, e.g. userX
    • Authentication type: Either choose ssh or enter a password
    • Resource group: Since this your first instance, create a new one, say rstudio-group
    • Location: where will your machine be located
  • Step 2: Virtual machine size
    • Select the machine you want. Choose the smallest for the purposes of this exercise
  • Step 3: Settings
    • Nothing to change here
  • Step 4: Summary
    • Click create and we’re good to go!

After around a minute or so, your virtual machine will be ready.

Setting up R

The next step is to ssh into your instance. On the dashboard screen, click on the new box that shows your virtual machine. Select Networking. Near the top of the screen will be a Public IP address, of the form: XXX.XXX.XXX.XXX. In my instance, the IP address is 52.233.194.195

Make a note of your address. Next ssh into your instance via

ssh userX@XXX.XXX.XXX.XXX

To ensure that ubuntu is up-to-date on our virtual machine, we invoke super sudo powers. First we update the list of ubuntu packages

sudo apt-get update

Then we upgrade as necessary

sudo apt-get upgrade

Now we get on with the business of installing R. To use the latest version we need to add a new repository

sudo add-apt-repository ppa:marutter/rrutter

Then update again and install base R

sudo apt update sudo apt-get install r-base

Depending on what R packages you want to install it’s worth installing a couple of other things at this point

sudo apt-get install libxml2 libxml2-dev # igraph sudo apt-get install libcairo2-dev # Graphics packages sudo apt-get install libssl-dev libcurl4-openssl-dev #httr

With an eye to the future it’s also worth installing apache2 to help with redirects

sudo apt-get install apache2 Opening ports ready for RStudio

Whenever you access a web-page, the browser specifies a port. For standard http pages, we use port 80, for secure https pages, we use port 443. For example, when we type

https://www.jumpingrivers.com

in the browser, this is converted to

https://www.jumpingrivers.com:443

By default our azure instance only has port 22 open (the port used for ssh communication). To access RStudio, we’ll need to open the following ports

  • 80 (for http)
  • 443 (for https); only required if we implement SSL
  • 8787 – the default RStudio port. In the last section, we’ll remove this, but just now it’s handy to have it open for testing.

Under Networking, click Add inbound port rule and add the three ports (80, 443, 8787):

If everything is working, you should be able to enter XXX.XXX.XXX.XXX in your browser and you’ll see the Apache2 Ubuntu Default Page with the title. It works!

Installing RStudio

Installing RStudio server is now relatively easy:

# Check the above link for updates to the version sudo apt-get install gdebi-core wget https://download2.rstudio.org/rstudio-server-1.1.383-amd64.deb sudo gdebi rstudio-server-1.1.383-amd64.deb

If everything works correctly, you should be able to view rstudio server via

XXX.XXX.XXX.XXX:8787

If the page hangs, double check you have opened port 8787 under the network settings.

Nicer URLs

The first step is to access the page via a standard URL and not an IP address. In the main dashboard screen, under all resources, click on

rstudio-ip Public IP address

Then select configuration. In the text box under DNS Label, enter text, e.g. rstudio-myname. So in my case, I have used rstudio-jumpingrivers

This means we can now access RStudio via

rstudio-jumpingrivers.westeurope.cloudapp.azure.com:8787

Getting users to type the port number isn’t ideal. What we would like is for users to type

rstudio-jumpingrivers.westeurope.cloudapp.azure.com/rstudio

This involves configuring Apache. First navigate to /etc/apache2/sites-available, e.g.

cd /etc/apache2/sites-available

Next create a file called rstudio.conf. Using your favourite text editor, e.g. vim or nano. Note that this file is very much space sensitive, so check it carefully.

ServerAdmin info@jumpingrivers.com ServerName rstudio-jumpingrivers.westeurope.cloudapp.azure.com ServerAlias www.rstudio-jumpingrivers.westeurope.cloudapp.azure.com Allow from localhost # Specify path for Logs ErrorLog ${APACHE_LOG_DIR}/error.log CustomLog ${APACHE_LOG_DIR}/access.log combined RewriteEngine on # Following lines should open rstudio directly from the url # Map rstudio to rstudio/ RedirectMatch ^/rstudio$ /rstudio/ RewriteCond %{HTTP:Upgrade} =websocket RewriteRule /rstudio/(.*) ws://localhost:8787/$1 [P,L] RewriteCond %{HTTP:Upgrade} !=websocket RewriteRule /rstudio/(.*) http://localhost:8787/$1 [P,L] ProxyPass /rstudio/ http://localhost:8787/ ProxyPassReverse /rstudio/ http://localhost:8787/ ProxyRequests off

Then enable the necessary Apache modules

sudo a2enmod proxy sudo a2enmod proxy_http sudo a2enmod proxy_html sudo a2enmod proxy_wstunnel sudo a2enmod rewrite

Finally, restart Apache

sudo a2ensite rstudio.conf sudo service apache2 restart

You should now be able to access RStudio via

rstudio-jumpingrivers.westeurope.cloudapp.azure.com/rstudio/ Adding SSL

In theory it should be straightforward to add SSL support using Let’s Encrypt. However, I’ve found that you hit rate limiters since the domain is azure.com. However, if we register our own domain, we can easily add SSL support. This will be the subject of our next blog post.

References

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A Public Apology to Tal/R-Bloggers + The Trouble With Tibbles

Fri, 12/01/2017 - 23:18

(This article was first published on R – rud.is, and kindly contributed to R-bloggers)

Over the past few weeks, I had been noticing that some posts in the R-bloggers feed were getting truncated in Feedly. I don’t remember when I noticed that since I usually click through immediately from the headline entry to the R-bloggers page vs read in Feedly since ultimately I want to get to the author’s site to see it formatted they way they intended it to be.

I let frustration get the better of me and — without verifying with Tal first — tweeted in error in said frustration. I’m not going to perform an extensive validation on the R-Bloggers feed always pushing out full content as Tal say they do.

Tal (and any other folks who work with Tal on R-Bloggers): I apologize for the tweet content and the tone of the tweet, but more apologize for not reaching out directly as if I had the following would have likely emerged after dual investigations. So, I’m really daft on at least two accounts. I will also apologize again, in-person, if we manage to cross paths in 2018. Hopefully said apology will be over a delightful beverage (on me — well, hopefully you won’t actually dump said beverage on me, but you’d be right to do so).

The truncated posts (anyone with Feedly can likely validate my experience) seems to be a combination of issues with a common thread: the tibble. I’m going to use the tibble 1.2.0 post [R-Bloggers link] from RStudio as an example. I have to use pictures (apologies), but you’ll see why in a bit.

The Trouble With Tibbles

This is a snap from the early part of the aforementioned post:

Here’s that content on R-Bloggers:

Now you get to play that favorite childhood game of yours: spot the difference.

You should notice the angle-bracket type headers are missing on R-Bloggers version of the post.

While they are visually missing, they are not — in fact — missing. They are there:

But, HTML wonks have likely already figured out the issue.

Here’s what the source view from RStudio’s blog looks like:

One more opportunity to play “spot the difference”.

That difference can wreak havoc with further HTML/XML post-processors (inspect the elements in different browsers or via rvest/xml2) and it seems Feedly’s ingestion process is doing the truncation when it hits invalid HTML.

This means that tibble output will likely cause more posts to be truncated in feed viewers pulling from R-Bloggers (I verified this with a sample of other, recent posts that I knew used tibble output).

Both R-Bloggers and Feedly should work on said issues. I’ll be pinging Feedly and I’m sure after I tweet this post out Tal will see it.

FIN

Tal: I promise to bring up any further issues with you directly and re-iterate my apology one more time.

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A case study in messy data analysis: the Australian same-sex marriage survey

Fri, 12/01/2017 - 20:23

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Last month the Australian people signaled their approval of legalizing same-sex marriage by a 62%:38% margin in a national survey. (On a personal note, I was elated and relieved by the result: my husband and I have discussed eventually retiring to Australia, and with this decision our marriage would be recognized there.) While fears of a surprise Brexit-like electoral backlash proved unfounded, researchers including R user Miles McBain explored the results for correlations to demographic variables. This process wasn't as simple as it might have been though: the Australian Bureau of Statistics released the results as a pair of Excel files that violate just about every good practice for sharing data in spreadsheets:

Miles shares the R code he used to extract useful data from this spreadsheet as a blog post that makes a great case study in dealing with messy data using R. The post demonstrates how he used the read_excel function (from readxl package) to extract specific sub-tables from the spreadsheet by specifying row and column ranges, and then use the dplyr package to clean up and merge the data. If you want to explore the data yourself, you can find the R code and the source data in this Github repository.

In a follow-up post, Miles combines the same-sex marriage survey data with Australian Census data to explore various demographic relationships. Unlike the US Census data (which is easily accessible in R thanks to the tidycensus package), there's no interface package for Australian Census data. (Selected tables are available in the Census2016 package, however.) Instead, Miles demonstrates how to use R to download and extract data from the the "Census DataPacks" (CSV data files and Excel data dictionaries) provided by the Australian Bureau of Statistics.  Yet more data wrangling allows Miles to create summary charts of the responses, such as this chart of proportion voting No by percent of the district population declaring a religious affiliation, broken down by state. As you may expect, those districts with more religious populations voted No at greater rates.

Both of these post provide great examples of working with government data, which is often provided in inconvenient formats with messy structures. Follow the links below for step-by-step guides, including the R code used to extract the data, structure it for analysis, and create useful charts.

Medium (Miles McBain): Tidying the Australian Same Sex Marriage Postal Survey Data with RCombining Australian Census data with the Same Sex Marriage Postal Survey in R

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Note On My Emerging Workflow for Working With Binderhub

Fri, 12/01/2017 - 15:23

(This article was first published on Rstats – OUseful.Info, the blog…, and kindly contributed to R-bloggers)

Yesterday saw the public reboot of Binder / MyBinder (which I first wrote about a couple of years ago here), as reported in The Jupyter project blog post Binder 2.0, a Tech Guide and this practical guide: Introducing Binder 2.0 — share your interactive research environment.

For anyone not familiar with Binder / MyBinder, it’s a service that will launch a fully running Jupyter notebook server and computing environment based the contents of a Github repository (config files as well as notebooks).  What this means is that if you put your Jupyter notebooks into a Github repository, along with one or two simple files that least any Linux or Python packages you need to install in order to run the code in the notebooks (or R packages and perhaps Rmd files if you also install an R kernel/RStudio), you can get a browser access to that running environment at just the click of a link. And the generosity of whoever is paying for the servers the notebook server runs on.

The system has been rebuilt to use Jupyterhub, with a renaming as far as the codebase goes to Binderhub. There are also several utility tools associated with the project, including the really handy repo2docker that builds a Docker image from the contents of a local folder or Github repository.

One of the things that particularly interested me in the announcement blog posts was the following aspirational remark:

We would love to see others deploy their own BinderHub servers, either for their own communities, or as part of a federated public service of BinderHubs.

I’d love to see the OU get behind this, either directly or under the banner of OpenLearn, as part of an effort to help make Jupyter powered interactive open educational materials available without the need to install any software.

(I tried to pitch it to FutureLearn to help support the OU/FutureLearn Learn to Code for Data Analysis MOOC when we were writing that course, but they weren’t interested…)

One disadvantage is Binderhub is a stateless service, which means you need to download any notebooks you’re working on and them upload them again yourself if you stop an interactive session: the environment you were working in is personal to you, but it’s also destroyed whenever you close the session (or after a particular amount of time? So other solutions are required for persisting state (i.e. having a personal file storage area). Jupyterhub is one way to do that (and one of the things we’re starting to explore in the OU at the moment).

Through playing with Binderhub over the last couple of weeks as part of an attempt to put together some demos for how to use Jupyter notebooks to support the creation of educational content that contains rich content (images, interactives) from specifications contained within the notebook document (think: writing diagrams) I’ve come to the following workflow:

  • create a Github repository to host different builds (example). In my case, these are for different topic areas; but they could be different research projects, courses, data journalism investigations, etc.
  • put each build in a branch (example);
  • work up the build instructions for the environment either using Github/Binder or locally; I was having to use Github/Binder because I was working on a slow network connection that made building my evolving image difficult. But it meant that every time I made a change to the build, it used up Binder resources to do so.
  • if the build is a big one, it can take time to complete. I think that Binder will rebuild the Docker image each time you update the repo, so even if you only update notebook files, then *I think* that that package installation steps are also run even if those files *haven’t* changed? To simplify this process, we can instead create a Docker image from out build files and push that to Dockerhub (example).
  • We can then then create a new build process for our working repository that pulls the pre-built image (containing all the required packages) and adds in the working notebooks (example).
  • We can also share a minimum viable repository that can be forked to allow other people to use the same environment (example).

One advantage of this route is that it separates “sys admin” concerns – building and installing the required packages – from “working” concerns relating to developing the contents of the notebooks. (I think the working repository that uses the Dockerfile build can also draw on the postbuild file to add in any additional or missing packages, which can then be added to the container build as part of a maintenance step.)

PS picking up on a recent related Downes presentation – Applications, Algorithms and Data: Open Educational Resources and the Next Generation of Virtual Learning – and a response from @jimgroom that I really need to comment back on – Containing the Future of OER – this phrase comes to mind: “syndicated runtime” eg if you syndicate the HTML version of a notebook via an RSS feed with a link back to the Binder runnable version of it…

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Analysing iOS App Store iTunes Reviews in R

Fri, 12/01/2017 - 14:00

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers)

Unlike Google PlayStore Developer Console for Android App, iOS App Store’s iTunes Connect does not help developers with the bulk download of App Store iTunes Reviews. So if you are an iOS App Developer or a Mobile App Product Manager, You are left with no option but to get paid subscription like App Annie to access and analyze iOS App Store iTunes Reviews of your App.

itunesr an R package to access and analyse iOS App Store iTunes Reviews and We will see how to use itunesr to analyse iTunes Reviews of any iOS App for free.

Installation & Loading:

itunesr can be directly installed from CRAN or the development version from Github.

# install itunesr directly from CRAN: install.packages("itunesr") # the development version from GitHub: # install.packages("devtools") devtools::install_github("amrrs/itunesr") library(itunesr) Reviews Extraction:

To access reviews of any iOS App, We need to have the App Id. The App ID of an iOS App ID could be found in its iTunes URL (Link). Below is the screenshot showing how to identify the App ID of Uber iOS App.

Now we have got the App ID – 368677368 for which we are going to access iTunes Reviews and analyse it.

We can extract iTunes Reviews of any app with `getReviews()` function which takes 3 arguments – 1. App ID, 2. Country Code, 3. Recency / Reviews Page Data . Let us extract the reviews of Uber iOS App.

#Latest (Page 1) Uber Reviews for the Country: US uber_reviews <- getReviews(368677368,'us',1)

As mentioned above, getReviews extracts most recent 50 reviews ~ 1st page from US App Store for Uber App and stores the information in uber_reviews as a dataframe.

Following are the attributes of Reviews that are extracted using getReviews:

#Displaying the column names names(uber_reviews) [1] "Title" "Author_URL" "Author_Name" "App_Version" "Rating" "Review" "Date" Ratings Trend:

Let us trend the Average Ratings for the Reviews received for each day. We will use highcharter package for an interactive plot of this Average Rating Trend.

#Ratings Trend library(highcharter) library(dplyr) library(lubridate) dt <- uber_reviews dt$Date <- as.Date(dt$Date) dt$Rating <- as.numeric(dt$Rating) dt <- dt %>% select(Date,Rating) %>% group_by(Date) %>% summarise(Rating = round(mean(Rating),2)) highchart() %>% hc_add_series_times_values(dt$Date,dt$Rating, name = 'Average Rating')

Gives this interactive plot:

Sentiment Analysis of Reviews:

Having trended Average Rating over Days, Let us implement the most obvious Text Analytics implementation – Sentiment Analysis.

#Sentiment Analysis library(sentimentr) reviews_only <- as.character(uber_reviews$Review) sentiment_scores <- reviews_only %>% sentiment_by(by=NULL) highchart() %>% hc_xAxis(sentiment_scores$element_id) %>% hc_add_series(sentiment_scores$ave_sentiment, name = 'Reviews Sentiment Scores')

Gives this plot:

With that, We have accessed iTunes Reviews of Uber iOS App, performed a basic exploratory analysis and sentiment analysis of the reviews verbatim. This is more than what the paid version of App Annie or similar tool can offer, but can be done for free with R using itunesr package. The code and plots used in this post are available on my github.

References:

    Related Post

    1. Handling ‘Happy’ vs ‘Not Happy’: Better sentiment analysis with sentimentr in R
    2. Creating Reporting Template with Glue in R
    3. Predict Employee Turnover With Python
    4. Making a Shiny dashboard using ‘highcharter’ – Analyzing Inflation Rates
    5. Time Series Analysis in R Part 2: Time Series Transformations
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    RStudio Connect v1.5.10

    Fri, 12/01/2017 - 01:00

    (This article was first published on RStudio Blog, and kindly contributed to R-bloggers)

    We’re pleased to announce version 1.5.10 of RStudio Connect and the general availability of RStudio Connect Execution Servers. Execution Servers enable horizontal scaling and high availability for all the content you develop in R. The 1.5.10 release also includes important security improvements and bug fixes.

    RStudio Connect Execution Servers

    Support for high availability and horizontal scaling is now generally available through RStudio Connect Execution Servers. Execution Servers enable RStudio Connect to run across a multi-node cluster.

    Today, Execution Servers act as identically configured Connect instances. Requests for Shiny applications and Plumber APIs are split across nodes by a load balancer. Scheduled R Markdown execution is distributed across the cluster through an internal job scheduler that distributes work evenly across nodes. Over time, more of Connect’s work will be handled by the internal scheduler, giving admins control over what nodes accomplish certain tasks.

    The admin guide includes configuration instructions. Contact sales for licensing information.

    Other Improvements

    • For configurations using SQLite, the SQLite database is automatically backed up while Connect is running. By default, three backups are retained and a new backup is taken every 24 hours. To disable, setup [Sqlite].Backup to false in the server configuration file.

    • RStudio Connect has always isolated user code from the file system. For example, application A can not access data uploaded with application B. In 1.5.10, R processes can now read from the /tmp and /var/tmp directories. This change enables shared files to be stored in /tmp and /var/tmp and helps facilitate Kerberos configurations. R processes still have isolated temporary directories provided at runtime and accessible with the tempdir function and TMPDIR environment variable. See section 12 of the admin guide for more details on process sandboxing.

    • Improvements have been made in RStudio Connect and the rsconnect package to support deployments using proxied authentication. See the admin guide for details on setting up the proxy. Anonymous viewers and requests authenticated with API keys are also now supported with proxied auth.

    • Scheduled reports are now re-run if execution is interrupted by a server restart. In a cluster, reports are automatically re-run if a node goes down, assuring high availability for scheduled renderings.

    • AdminEditableUsernames is disabled by default for compatibility with the RequireExternalUsernames flag introduced in 1.5.8. These changes increase security by preventing changes to data supplied by authentication providers.

    • User session expiration is better enforced. All user browser sessions will need to login after the 1.5.10 upgrade.

    • Runtime environments for Shiny R Markdown Documents have changed to support rmarkdown versions 1.7+.

    You can see the full release notes for RStudio Connect 1.5.10 here.

    Upgrade Planning There are no special precautions to be aware of when upgrading from 1.5.8 to 1.5.10. Installation and startup should take less than a minute.

    If you haven’t yet had a chance to download and try RStudio Connect we encourage you to do so. RStudio Connect is the best way to share all the work that you do in R (Shiny apps, R Markdown documents, plots, dashboards, Plumber APIs, etc.) with collaborators, colleagues, or customers.

    You can find more details or download a 45 day evaluation of the product at https://www.rstudio.com/products/connect/. Additional resources can be found below.

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    Birds of a Feather sessions at rstudio::conf 2018 and the rstudio::conf app!

    Fri, 12/01/2017 - 01:00

    (This article was first published on RStudio Blog, and kindly contributed to R-bloggers)

    RStudio appreciates the hundreds of smart, passionate, data science enthusiasts who have already registered for rstudio::conf 2018. We’re looking forward to a fantastic conference, immersing in all things R & RStudio.

    If you haven’t registered yet, please do! Some workshops are now full. We are also over 90% of our registration target – with more than 2 months to go. It’s safe to say we will sell out. The sooner you are able to register, the better. It’s going to be an amazing time!

    REGISTER TODAY

    For those who have registered, we’d like to help you connect with others doing similar work.

    Attendees include many kinds of professionals in physical, natural, social and data sciences, statistics, education, engineering, research, BI, IT data infrastructure, finance, marketing, customer support, operations, human resources…and many more. They are sole proprietors and work for the world’s largest companies. They live in developing and developed countries. They use R and RStudio to explore data, develop polished reports, publish interactive visualizations, or create production code central to the success of their company. They share a common bond – a commitment to R, enthusiasm for RStudio products, and a desire to become better data scientists.

    To foster relationships among people doing similar work we’ve made time and arranged spaces for 9 total Birds of a Feather (BoF) sessions.They will be held during breakfast and lunch, so you can grab a meal and head to your preferred BoF room!

    Some topics seem obvious to us. For example, we will definitely set aside rooms for Life Sciences, Financial Services, Education, and Training & Consulting Partner BoFs. As we see it, a BoF is just a short unconference session within a conference, organized or left un-organized (mostly) by participants! Topics may be narrow or broad. Some may have agendas and others may be purely for networking. At a minimum, each room will have a friendly RStudio proctor, chairs, a screen to present, and flipcharts for those who are inspired to create discussion sub-groups, share material broadly, or collaborate.

    What Birds of a Feather sessions would you like to attend?

    In addition to the 4 BoFs we will set aside rooms for, we’re going to use the new community.rstudio.com as a place to discuss which 5 additional BoFs should be allocated time and space. If you are registered for rstudio::conf or planning to register, head on over and look for the rstudio::conf category and the “your ideas for birds of a feather sessions” topic to start proposing and upvoting!

    Once the BoF session topics are decided, we’ll load them into our mobile app for the conference. This, along with community.rstudio.com, will allow for Pre-BoF discussions so you can hit the ground running in San Diego!

    Download the Conference App Now

    rstudio::conf 2018 is the conference for all things R & RStudio. Training Days are on January 31 and February 1. The conference is February 2 and 3.

    Interested in having your company sponsor rstudio::conf 2018? It’s a unique opportunity to share your support for R users. Contact anne@rstudio.com for more information.

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    R in Africa

    Fri, 12/01/2017 - 01:00

    (This article was first published on Blog on FORWARDS, and kindly contributed to R-bloggers)

    R is used around the world and yet, in English-speaking media at least, we tend
    to hear most about what is happening in North America, Europe, and Australasia.
    So we would like to highlight some recent initiatives in the R
    community from other regions of the world, with this first post in the series
    focusing on R in Africa.

    satRdays in Cape Town

    Following the first successful
    satRday in Budapest on 3 September 2016, a
    satRday in Cape Town was held on
    18 February 2017. The event was hosted at
    Workshop 17 in the Victoria & Alfred Waterfront.
    There were three great keynote speakers
    (Jenny Bryan,
    Julia Silge and
    Steph Locke). In addition to the invited
    speakers the organizers received 41 contributed proposals, of which 23 were
    accepted: 3 tutorials, 8 standard talks and 12 lightning talks. Just over 200
    tickets were issued for the event and the venue was literally packed. On two
    days prior to the conference there were workshops by the keynote speakers.
    These were also very well attended.



    satRday was an excellent networking opportunity!

    A second satRday in Cape Town is now
    planned for 17 March 2018, with two exciting keynote speakers: Maëlle Salmon and
    Stephanie Kovalchik.
    They will be presenting workshops on R package development and sports analytics
    with R on the day prior to the conference.

    Next year’s conference will be held at a bigger venue on the campus of the
    University of Cape Town, which will be able to accommodate even more
    enthusiastic R users. Tickets are on sale and the
    Call for Papers is open.

    Software/Data Carpentry

    Since 2015 Software Carpentry and
    Data Carpentry have been enabling R capacity
    development in Africa. Software and Data Carpentry host two-day workshops
    teaching researchers and postgraduate students foundational skills to assist
    with better research software development and data analysis respectively.
    The Carpentries are global non-profit volunteer organisations that not only
    teach tools like R, Python, SQL, and git/Github, but also run a two-day
    instructor training workshop
    to help technical experts teach more effectively.



    Highlights from Carpentry workshops

    Over the past three years more than 30 workshops were run in countries like
    South Africa,
    Mauritius, Ghana,
    Gabon,
    Namibia, Botswana,
    Kenya, and
    Ethiopia. These
    workshops have reached hundreds of researchers and students who mostly had
    limited or no prior exposure to programming.

    More information about the lessons we teach is available at the websites:
    Software Carpentry and
    Data Carpentry.

    If you’d like to get involved in Carpentries in Africa, please
    join our Google Group or
    request a workshop by completing the form.

    R-Ladies in Africa

    The set-up of a Cape Town chapter of R-Ladies
    was inspired by the first satRday
    conference in Cape Town. All three plenary speakers at that conference
    were part of the R-Ladies community and it was extremely refreshing to go to a
    conference largely about science and technology where women took the centre
    stage. Cape Town is a multicultural, multilingual and multiracial city, but this
    diversity is not represented in the R user community. The aim of the Cape Town
    chapter of R-Ladies is to grow the female R user community in Cape Town and
    bring to it the diversity of cultures, ethnicities and backgrounds present in
    the city. By the end of 2017 the group will have had five meetups since their
    launch in July this year. They are still quite a small group, but they are
    confident they will grow in time. One might expect that there would be
    many more people using R in a big city, but issues with congestion and public
    transport may form a barrier to people attending. The answer might be to form
    even more R-Ladies chapters around different parts of the city so that people
    don’t have to travel far! R-Ladies Cape Town can be reached at
    capetown@rladies.org and on
    Twitter @RLadiesCapeTown.

    R Ladies Addis was founded on October 30, 2017 in the course of the 2nd Data
    Carpentry Workshop in Addis Ababa, with 7 founding members and 2 honorary
    members. Thanks to the support provided by Ms. Anelda van der Walt from
    South Africa, an honorary member of R Ladies Addis as well as the R Ladies
    community, R Ladies Addis got ready for its first meeting on November 14, 2017.
    The agenda of this meeting was to decide the framework of R
    Ladies Addis activities. The group has set its special focus on working
    with women students and women researchers by offering R training and R
    competitions. R Ladies Addis has been invited to reach out to the different
    corners of Ethiopia. For the time being R Ladies is present at
    https://r-ladies-addis.github.io/studyGroup and can be reached by e-mail
    addisababa@rladies.org.



    1st Meeting of R Ladies Addis on Nov. 14, 2017

    A new chapter of R-Ladies will soon be starting in Cotonou, Benin. The
    organizers plan to have their first meeting in January to conincide with the
    start of the new academic year. Follow the group on
    Twitter, @RLadiesCtn or email
    cotonou@rladies.org to find out more! Local R-Ladies also hope to
    establish groups in Fez, Morocco and Johannesburg, South Africa; if you are
    interested in these groups, or interested in starting a chapter in your own area
    (in Africa or beyond!) you can contact info@rladies.org.

    >eR-Biostat: making R based education materials accessible for all

    One of the main problems in education at both undergraduate and master’s
    levels in developing countries is the lack of high quality materials
    for courses in education programs. The >eR-Biostat initiative is focused on
    providing R-based materials for higher education programs in
    Biostatistics/Statistics, as well as introductory materials for
    non-statisticians and in future, an E-learning system for courses at different
    education levels.

    Courses are organized in four clusters and are ready to be delivered in class.
    The Introductory courses aim to train students in data analysis using R at a
    basic level. These courses were developed for undergraduate students, both
    non-statisticians and statisticians. The courses within this cluster can also
    be used as courses to support R usage in undergraduate programs in
    biostatistics/statistics. The Basic courses are designed for an
    intermediate level and a basic knowledge in statistics is required in order to
    follow the courses. The courses within this cluster can be used as training
    courses for non-statisticians and as courses to support R usage in
    undergraduate programs in biostatistics/statistics. The
    Statistical modelling (I) and Statistical modelling (II) clusters
    consist of basic and advanced courses, respectively, at master’s level in
    biostatistics/statistics.



    The first >eR-Biostat workshop in Gondar University, Ethiopia

    Course materials and updated information about our activities are available
    online (See >eR-BioStat).

    Follow us on Facebook
    and Twitter (@erbiostat). Interested to
    contribute a course and/or an >eR-Biostat event? Send an email to
    erbiostat@gmail.com or ziv.shkedy@uhasselt.be.

    Other activities

    Here we have focused on some of the more recent initiatives, but of
    course there are many other groups encouraging the use of R in Africa. For
    example, several general R User Groups have been established; many are listed
    on the Data Science Africa web site and a few
    more are listed on the R User Groups
    listing hosted by jumping rivers. On the education side, the centres of the
    African Institute of Mathematical Sciences (AIMS)
    often use R in their courses and workshops. In particular AIMS Tanzania is a
    partner in the
    Africa Data Initiative
    that developed R-Instat a front-end to R
    designed to make R more accessible for students. Going beyond
    the classroom,
    BeST (Bespoke eStyle Statistical Training for Africa and South Asia) assist in upskilling scientists working in
    agricultural development via online courses in R. If you are aware of other
    such initiatives, feel free to share them in the comments!

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    The Value of Welcome, part 2: How to prepare 40 new community members for an unconference

    Fri, 12/01/2017 - 01:00

    (This article was first published on rOpenSci - open tools for open science, and kindly contributed to R-bloggers)

    I’ve raved about the value of extending a personalized welcome to new community members and I recently shared six tips for running a successful hackathon-flavoured unconference. Building on these, I’d like to share the specific approach and (free!) tools I used to help prepare new rOpenSci community members to be productive at our unconference. My approach was inspired directly by my AAAS Community Engagement Fellowship Program (AAAS-CEFP) training. Specifically, 1) one mentor said that the most successful conference they ever ran involved having one-to-one meetings with all participants prior to the event, and 2) prior to our in-person AAAS-CEFP training, we completed an intake questionnaire that forced us to consider things like “what do you hope to get out of this” and “what do you hope to contribute”.

    A challenge of this year’s unconference was the fact that we were inviting 70 people to participate. As a rule, one third of the crowd will have participated in one of our previous unconferences and two-thirds would be first-time participants. With only two days together, these people need to quickly self-sort into project groups and get working.

    So I sent this email to 45 first-time participants:

    Arranging meetings is one of my least favorite activities, but the free Calendly tool made this process relatively painless. When a person clicks on the calendar link in the email above, it reveals only times that I am available in my Google Calendar, the time slot they choose shows up in my calendar, and I receive a confirmation email indicating who booked a meeting with me. In my busiest week, I had 19 meetings, but that meant the bulk of them were done!

    To make the meeting time most effective I followed AAAS-CEFP program director Lou Woodley’s model for onboarding our AAAS CEFP cohort by sending a set of questions to be answered in advance. I took the model to the next level by creating a free Google Form questionnaire so that all answers were automatically collected and could be viewed per individual or collectively, and automatically exported to a spreadsheet.

    Questions included:

    1. List three things you hope to get from the unconference

      • Examples: connect with people working in a similar domain, learn about best practices in data science with R, or develop a new package that does X
    2. List three things you hope to contribute to the unconference

      • Examples: expertise or experience in X, mentoring skills, write all the docs!
    3. Have you had any previous interactions with the rOpenSci community?

      • Examples: I read the rOpenSci blog, or I submitted software for rOpenSci open peer review
    4. Do you have any concerns about your readiness to participate?

      • Examples: I’ve never developed an R package or How do I decide what project to work on?
    5. Would you be interested in writing a blog post about your unconference project or your unconference experience?

    6. Do you have a preferred working style or anything you would like to let us know about how you work best?

      • Examples: I’m an introvert who likes to take my lunch alone sometimes to recover from group activities – it doesn’t mean I’m not having fun. or I know I have a tendency to dominate in group discussions – it’s totally fine to ask me to step back and let others contribute. I won’t be offended.

    These questions encouraged participants to reflect in advance. The example answer snippets we provided gave them ideas from which to seed their answers and in some cases gave them permission to show some vulnerability. Individual’s answers gave me cues for things to address in our chat and freed both of us to spend our time talking about the most important issues.

    The answers to the question “List three things you hope to get from the unconf” were so heartening:

    Beautiful, but in a different way, were answers to the question, “Do you have any concerns about your readiness to participate?”. People expressed real concerns about impostor syndrome, their perceived ability to contribute “as much or as well” as others, and feeling “outclassed by all the geniuses present”. These responses prompted me to reassure people that they were 100% qualified to participate, and opened an opportunity to listen to and address specific concerns.

    To conduct the pre-unconference chats, I used video conferencing via appear.in, a free, browser-based application that does not require plugins or user accounts. Rather than being exhausted from these calls, I felt energized and optimistic and I experienced many direct positive outcomes. These conversations enabled me to prime people to connect on day-one of the unconference with others with similar interests or from related work sectors. Frequently, I noticed that immediately after our conversation, first-time participants would join the online discussion of existing project ideas, or they themselves proposed new ideas. My conversations with two first-time participants led directly to their proposing community-focussed projects – a group discussion and a new blog series of interviews!

    An unexpected benefit was that questions people asked me during the video chats led to actions I could take to improve the unconference. For example, when someone wanted to know what previous participants wished they knew beforehand, I asked for and shared example resources. One wise person asked me what my plan was for having project teams report out at the end of the unconference and this led directly to a streamlined plan (See Six tips for running a successful unconference).

    Big thanks to AAAS CEFP training for giving me the confidence to try this community experiment! Arranging and carrying out these pre-unconference questionnaires and video chats took a big investment of my time and energy and yet, I consider this effort to be one of the biggest contributors to participants’ satisfaction with the unconference. Will 100% do this again!

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    Visualising SSH attacks with R

    Thu, 11/30/2017 - 20:28

    (This article was first published on R – Enchufa2, and kindly contributed to R-bloggers)

    If you have any machine with an SSH server open to the world and you take a look at your logs, you may be alarmed to see so many login attempts from so many unknown IP addresses. DenyHosts is a pretty neat service for Unix-based systems which works in the background reviewing such logs and appending the offending addresses into the hosts.deny file, thus avoiding brute-force attacks.

    The following R snippet may be useful to quickly visualise a hosts.deny file with logs from DenyHosts. Such file may have comments (lines starting with #), and actual records are stored in the form : . Therefore, read.table is more than enough to load it into R. The rgeolocate package is used to geolocate the IPs, and the counts per country are represented in a world map using rworldmap:

    library(dplyr) library(rgeolocate) library(rworldmap) hosts.deny <- "/etc/hosts.deny" db <- system.file("extdata", "GeoLite2-Country.mmdb", package="rgeolocate") read.table(hosts.deny, col.names=c("service", "IP")) %>% pull(IP) %>% maxmind(db, fields="country_code") %>% count(country_code) %>% as.data.frame() %>% joinCountryData2Map(joinCode="ISO2", nameJoinColumn="country_code") %>% mapCountryData(nameColumnToPlot="n", catMethod="pretty", mapTitle="Attacks per country") ## 74 codes from your data successfully matched countries in the map ## 2 codes from your data failed to match with a country code in the map ## 168 codes from the map weren't represented in your data

    Then, you may consider more specific access restrictions based on IP prefixes…

    Article originally published in Enchufa2.es: Visualising SSH attacks with R.

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    R 3.4.3 released

    Thu, 11/30/2017 - 19:18

    (This article was first published on Revolutions, and kindly contributed to R-bloggers)

    R 3.4.3 has been released, as announced by the R Core team today. As of this writing, only the source distribution (for those that build R themselves) is available, but binaries for Windows, Mac and Linux should appear on your local CRAN mirror within the next day or so.

    This is primarily a bug-fix release. It fixes an issue with incorrect time zones on MacOS High Sierra, and some issues with handling Unicode characters. (Incidentally, representing international and special characters is something that R takes great care in handling properly. It's not an easy task: a 2003 essay by Joel Spolsky describes the minefield that is character representation, and not much has changed since then.) You can check out the complete list of changes here. Whatever your platform, R 3.4.3 should be backwards-compatible will other R versions in the R 3.4.x series, and so your scripts and packages should continue to function as they did before.

    The codename for this release is "Kite-Eating Tree", and as with all R codenames this is a references to a classic Peanuts episode. If you're interested in the source of other R release names, Lucy D'Agostino McGowan provides the Peanuts references for R release names back to R 2.14.0.

    r-devel mailing list: R 3.4.3 is released

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    A quick introduction to using color in density plots

    Thu, 11/30/2017 - 15:00

    (This article was first published on r-bloggers – SHARP SIGHT LABS, and kindly contributed to R-bloggers)

    Right now, many people are pursuing data science because they want to learn artificial intelligence and machine learning. And for good reason. Machine learning is white hot right now, and it will probably reshape almost every sector of the world economy.

    Having said that, if you want to be a great machine learning expert, and a great data scientist in general, you need to master data visualization too.

    This is because data visualization is a critical prerequisite for advanced topics (like machine learning), and also because visualization is very useful for getting things done in its own right.

    So let’s talk a little more about data visualization. As you begin learning data visualization in R, you should master the basics: the how to use ggplot2, how to think about data visualization, how to make basic plots (like the bar chart, line chart, histogram, and scatterplot).

    And I really mean that you need to master these basics. To be a great data scientist, you need to be “fluent” in these basics. You should be able to write the code for basic charts and plots without even thinking about it. If you can’t, you should go back and practice them. Don’t get shiny object syndrome and try to move on to advanced topics before you do. Show some discipline. Master the foundations.

    After you do master the foundations though, you’ll need to learn some intermediate tools.

    One such thing that you’ll need to learn is how to work with color. Specifically, you’ll need to learn how to manipulate the “fill” color of things like density plots (as well as heatmaps).

    With that in mind, let’s take a look at using color in density plots.

    As always, we’re going to use the meta-learning strategy of learning the tools with basic cases. Essentially, we’ll learn and practice how to modify the fill aesthetic for very simple plots. Later, once you get the hang of it, you can move on to more advanced applications.

    As always, first we will load the packages we will need.

    #-------------- # LOAD PACKAGES #-------------- library(tidyverse) library(viridis) library(RColorBrewer)

    Next, we will set a “seed” that will make the dataset exactly reproducible when we create our data. Without this, running rnorm() and runif() would produce data that is similar, but not exactly the same as the data you see here. To be clear, it won’t make too much difference for the purposes of this blog post, but it’s a best practice when you want to show reproducible results, so we will do this anyway.

    #--------- # SET SEED #--------- set.seed(19031025)

    Now, we will create our data frame.

    We will use runif() to create 20,000 uniformly distributed values for our x variable, and rnorm() to create 20,000 random normal values for our y variables. We’re also using the tibble() function to ultimately create a tibble/data.frame out of these two new variables.

    #------------------ # CREATE DATA FRAME #------------------ df.data <- tibble(x = runif(20000) ,y = rnorm(20000) )

    Now that we have a dataset created, let’s create a simple plot of the data. Ultimately, we will be working with density plots, but it will be useful to first plot the data points as a simple scatter plot.

    Here, we’re using the typical ggplot syntax: we’re specifying the data frame inside of ggplot() and specifying our variable mappings inside of aes().

    #-------------------- # CREATE SCATTER PLOT #-------------------- ggplot(df.data, aes(x = x, y = y)) + geom_point()



    Ok. We can at least see the data points and the general structure of the data (i.e., the horizontal band).

    Having said that, these data are very heavily overplotted. There are a few ways to mitigate this overplotting (e.g., manipulating the alpha aesthetic), but a great way is to create a density plot.

    To create the density plot, we’re using stat_density2d(). I won’t explain this in detail here, but essentially in this application, stat_density2d() calculates the density of observations in each region of the plot, and then fills in that region with a color that corresponds to the density.

    #------------------------------ # DENSITY PLOT, w/ DEFAULT FILL #------------------------------ ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F)



    This isn’t bad. It gives us a sense of the density of the data (you can see the thick band across the middle). However, there are two issues.

    First, the differences in density are not completely obvious, because of the color scale. The default light blue/dark blue color scheme doesn’t illuminate the differences in data density.

    Second, this is just not very aesthetically appealing. It just doesn’t look that good.

    To fix these issues, let’s modify the color scheme. I’ll show you a few options. Some will work better than others, and after you see these, I’ll encourage you to experiment with other color palettes.

    Let’s first take a look at the color palette options.

    You can examine a large number of ready-made color palettes from the RColorBrewer package by using display.brewer.all().

    #----------------------- # DISPLAY COLOR PALETTES #----------------------- display.brewer.all()



    As you can see, there are quite a few palettes from RColorBrewer. We’ll use a few of these to change the fill color of our plot.

    #-------------------------------- # FILL WITH COLOR PALETTE: Greens #-------------------------------- ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F) + scale_fill_distiller(palette = 'Greens')



    Now let’s use a few more color palettes.

    #------------------------------------------------------ # FILL WITH COLOR PALETTE: Reds, Greys, Red/Yellow/Blue #------------------------------------------------------ ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F) + scale_fill_distiller(palette = 'Reds')



    … grey

    ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F) + scale_fill_distiller(palette = 'Greys')



    … and a scale from red, to yellow, to blue.

    ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F) + scale_fill_distiller(palette = 'RdYlBu')



    These aren’t bad.

    I think they work a little better than the default color scheme, but I think we can do better, so let’s try one more.

    The following plot uses a custom color palette from the viridis package.

    #--------------------------------- # FILL WITH COLOR PALETTE: Viridis #--------------------------------- ggplot(df.data, aes(x = x, y = y)) + stat_density2d(aes(fill = ..density..), geom = 'tile', contour = F) + scale_fill_viridis()



    I should have called this blog post “ggplot for people who love Mark Rothko.”

    Ok, the viridis color palette (and a related set of palettes in the viridis package) is probably my favorite option. Not only do I think this color palette is one of the most aesthetically attractive, it’s also more functional. As noted in the documentation for the package, the viridis color palette is “designed in such a way that it will analytically be perfectly perceptually-uniform … It is also designed to be perceived by readers with the most common form of color blindness.”

    Master these techniques with simple cases

    Admittedly, when you move on to more complex datasets later, it will take a bit of finesse to properly apply these color palettes.

    But as I noted earlier, when you’re trying to master R (or any programming language), you should first master the syntax and techniques on basic cases, and then increase the complexity as you attain basic competence.

    With that in mind, if you want to master these color and fill techniques, learn and practice these tools with simple cases like the ones shown here, and you can attempt more advanced applications later.

    Sign up now, and discover how to rapidly master data science

    To master data visualization and data science, you need to master the essential tools.

    Moreover, to make rapid progress, you need to know what to learn, what not to learn, and you need to know how to practice what you learn.

    Sharp Sight is dedicated to teaching you how to master the tools of data science as quickly as possible.

    Sign up now for our email list, and you’ll receive regular tutorials and lessons.

    You’ll learn:

    • How to do data visualization in R
    • How to practice data science
    • How to apply data visualization to more advanced topics (like machine learning)
    • … and more

    If you sign up for our email list right now, you’ll also get access to our “Data Science Crash Course” for free.

    SIGN UP NOW

    The post A quick introduction to using color in density plots appeared first on SHARP SIGHT LABS.

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    Introducing RITCH: Parsing ITCH Files in R (Finance & Market Microstructure)

    Thu, 11/30/2017 - 12:57

    (This article was first published on R – Data Shenanigans, and kindly contributed to R-bloggers)

    Recently I was faced with a file compressed in NASDAQ’s ITCH-protocol, as I wasn’t able to find an R-package that parses and loads the file to R for me, I spent (probably) way to much time to write one, so here it is.

    But you might wonder, what exactly is ITCH and why should I care?

    Well, ITCH is the outbound protocol NASDAQ uses to communicate market data to its clients, that is, all information including market status, orders, trades, circuit breakers, etc. with nanosecond timestamps for each day and each exchange. Kinda a must-have if you are looking into market microstructure, a good-to-have-looked-into-it if you are interested in general finance and/or if you are interested in data analytics and large structured datasets.

    If you are wondering where you might get some of these fancy datasets in the first place, I have good news for you. NASDAQ provides some sample datasets (6 days for 3 exchanges (NASDAQ, PSX, and BX), together about 25GBs gzipped) on its FTP server: ftp://emi.nasdaq.com/ITCH/

    The RITCH Package

    Now that I (hopefully) have your attention, let me represent to you RITCH an R package that parses ITCH-files (version 5.0).

    Currently the package only lives on GitHub (https://github.com/DavZim/RITCH), but it should find its way into CRAN eventually. Until then, you have to use devtools to install it

    # install.packages("devtools") devtools::install_github("DavZim/RITCH")

    And you should be good to go (if not, please let me know! If you get a message related to make, you can safely ignore it for now).

    RITCH so far has a very limited scope: extracting the messages from the ITCH-file plus some functions to count messages. The package leverages C++ and the excellent Rcpp library to optimise parsing. RITCH itself does not contain any data as the datasets are too large for any repos and I have no copyright on the datasets in any way.

    For the following code I will use the 20170130.BX_ITCH_50-file from NASDAQ’s FTP-server, as its not too large at 714MB gzipped (1.6GB gunzipped), but still has almost 55 million messages. All functions can take the gzipped or unzipped files, but if you use the file more than once and hard-drive space is not of utmost concern, I suggest you gunzip the file by hand (i.e., use R.utils::gunzip(file, new_file, remove = FALSE) in R or gunzip -k YYYYMMDD.XXX_ITCH_50.gz in the terminal) and call the functions on the “plain”-file. I will address some concerns to size and speed later on.

    To download and prepare the data in R, we can use the following code

    # might take some time as it downloads 714MB download.file("ftp://emi.nasdaq.com/ITCH/20170130.BX_ITCH_50.gz", "20170130.BX_ITCH_50.gz", mode = "wb") # gunzip the file, but keep the original file R.utils::gunzip("20170130.BX_ITCH_50.gz", "20170130.BX_ITCH_50", remove = FALSE)

    First, we want to get a general overview of the file, which we can do with count_messages()

    library(RITCH) file <- "20170130.BX_ITCH_50" msg_count <- count_messages(file, add_meta_data = TRUE) #> [Counting] 54473386 messages found #> [Converting] to data.table msg_count #> msg_type count msg_name msg_group doc_nr #> 1: S 6 System Event Message System Event Message 4.1 #> 2: R 8371 Stock Directory Stock Related Messages 4.2.1 #> 3: H 8401 Stock Trading Action Stock Related Messages 4.2.2 #> 4: Y 8502 Reg SHO Restriction Stock Related Messages 4.2.3 #> 5: L 6011 Market Participant Position Stock Related Messages 4.2.4 #> 6: V 2 MWCB Decline Level Message Stock Related Messages 4.2.5.1 #> 7: W 0 MWCB Status Message Stock Related Messages 4.2.5.2 #> 8: K 0 IPO Quoting Period Update Stock Related Messages 4.2.6 #> 9: J 0 LULD Auction Collar Stock Related Messages 4.2.7 #> 10: A 21142017 Add Order Message Add Order Message 4.3.1 #> 11: F 20648 Add Order - MPID Attribution Message Add Order Message 4.3.2 #> 12: E 1203625 Order Executed Message Modify Order Messages 4.4.1 #> 13: C 8467 Order Executed Message With Price Message Modify Order Messages 4.4.2 #> 14: X 1498904 Order Cancel Message Modify Order Messages 4.4.3 #> 15: D 20282644 Order Delete Message Modify Order Messages 4.4.4 #> 16: U 3020278 Order Replace Message Modify Order Messages 4.4.5 #> 17: P 330023 Trade Message (Non-Cross) Trade Messages 4.5.1 #> 18: Q 0 Cross Trade Message Trade Messages 4.5.2 #> 19: B 0 Broken Trade Message Trade Messages 4.5.3 #> 20: I 0 NOII Message Net Order Imbalance Indicator (NOII) Message 4.6 #> 21: N 6935487 Retail Interest Message Retail Price Improvement Indicator (RPII) 4.7 #> msg_type count msg_name msg_group doc_nr

    As you can see, there are a lot of different message types. Currently this package parses only messages from the group “Add Order Messages” (type ‘A’ and ‘F’), “Modify Order Messages” (type ‘E’, ‘C’, ‘X’, ‘D’, and ‘U’), and “Trade Messages” (type ‘P’, ‘Q’, and ‘B’). You can extract the different message-types by using the functions get_orders, get_modifications, and get_trades, respectively. The doc-number refers to the section in the official documentation (which also contains more detailed description what each type contains).

    If you are annoyed by the feedback the function gives you ([Counting] ... [Converting]...), you can always turn the feedback off with the quiet = TRUE option (this applies to all functions).

    Lets try to parse the first 10 orders

    orders <- get_orders(file, 1, 10) #> 10 messages found #> [Loading] . #> [Converting] to data.table #> [Formatting] orders #> msg_type locate_code tracking_number timestamp order_ref buy shares stock price mpid date datetime #> 1: A 7584 0 2.520001e+13 36132 TRUE 500000 UAMY 0.0001 NA 2017-01-30 2017-01-30 07:00:00 #> 2: A 3223 0 2.520001e+13 36133 TRUE 500000 GLOW 0.0001 NA 2017-01-30 2017-01-30 07:00:00 #> 3: A 2937 0 2.520001e+13 36136 FALSE 200 FRP 18.6500 NA 2017-01-30 2017-01-30 07:00:00 #> 4: A 5907 0 2.520001e+13 36137 TRUE 1500 PIP 3.1500 NA 2017-01-30 2017-01-30 07:00:00 #> 5: A 5907 0 2.520001e+13 36138 FALSE 2000 PIP 3.2500 NA 2017-01-30 2017-01-30 07:00:00 #> 6: A 5907 0 2.520001e+13 36139 TRUE 3000 PIP 3.1000 NA 2017-01-30 2017-01-30 07:00:00 #> 7: A 5398 0 2.520001e+13 36140 TRUE 200 NSR 33.0000 NA 2017-01-30 2017-01-30 07:00:00 #> 8: A 5907 0 2.520001e+13 36141 FALSE 500 PIP 3.2500 NA 2017-01-30 2017-01-30 07:00:00 #> 9: A 2061 0 2.520001e+13 36142 FALSE 1300 DSCI 7.0000 NA 2017-01-30 2017-01-30 07:00:00 #> 10: A 1582 0 2.520001e+13 36143 TRUE 500 CPPL 17.1500 NA 2017-01-30 2017-01-30 07:00:00

    The same works for trades using the get_trades() function and for order modifications using the get_modifications() function.

    To speed up the get_* functions, we can use the message-count information from earlier. For example the following code yields the same results as above, but saves time.

    orders <- get_orders(file, 1, count_orders(msg_count)) trades <- get_trades(file, 1, count_trades(msg_count))

    If you want to get more information about each field, you can have a look at the
    official ITCH-protocol specification manual or you can get a small data.table about each message type by calling get_meta_data().

    Having a Look at some the most traded ETFs

    To have at least one piece of eye-candy in this post, lets have a quick go at the orders and trades of SPY (an S&P 500 ETF and one of the most traded assets, in case you didn’t know), IWO (Russel 2000 Growth ETF), IWM (Russel 2000 Index ETF), and VXX (S&P 500 VIX ETF) on the BX-exchange.

    In case you are wondering, I got these four tickers with

    library(magrittr) get_orders(file, 1, count_orders(msg_count), quiet = T) %>% .$stock %>% table %>% sort(decreasing = T) %>% head(4) #> . #> SPY IWO IWM VXX #> 135119 135016 118123 117395

    First we load the data (orders and trades) from the file, then we do some data munging, and finally plot the data using ggplot2.

    library(ggplot2) # 0. load the data orders <- get_orders(file, 1, count_orders(msg_count)) #> 21162665 messages found #> [Loading] ................ #> [Converting] to data.table #> [Formatting] trades <- get_trades(file, 1, count_trades(msg_count)) #> 330023 messages found #> [Loading] ................ #> [Converting] to data.table #> [Formatting] # 1. data munging tickers <- c("SPY", "IWO", "IWM", "VXX") dt_orders <- orders[stock %in% tickers] dt_trades <- trades[stock %in% tickers] # for each ticker, use only orders that are within 1% of the range of traded prices ranges <- dt_trades[, .(min_price = min(price), max_price = max(price)), by = stock] # filter the orders dt_orders <- dt_orders[ranges, on = "stock"][price >= 0.99 * min_price & price <= 1.01 * max_price] # replace the buy-factor with something more useful dt_orders[, buy := ifelse(buy, "Bid", "Ask")] dt_orders[, stock := factor(stock, levels = tickers)] # 2. data visualization ggplot() + # add the orders to the plot geom_point(data = dt_orders, aes(x = datetime, y = price, color = buy), size = 0.5) + # add the trades as a black line to the plot geom_step(data = dt_trades, aes(x = datetime, y = price)) + # add a facet for each ETF facet_wrap(~stock, scales = "free_y") + # some Aesthetics theme_light() + labs(title = "Orders and Trades of the largest ETFs", subtitle = "Date: 2017-01-30 | Exchange: BX", caption = "Source: NASDAQ", x = "Time", y = "Price", color = "Side") + scale_y_continuous(labels = scales::dollar) + scale_color_brewer(palette = "Set1")

    Now its up to you to do something interesting with the data, I hope RITCH can help you with it.

    Speed & RAM concerns

    If your machine struggles with some files, you can always load only parts of a file as shown above. And of course, make sure that you have only necessary datasets in your R-environment and that no unused programs are open (Chrome with some open tabs in the background happily eats your RAM).

    If your machine is able to handle larger datasets, you can increase the buffersize of each function call to 1GB or more using the buffer_size = 1e9 argument, increasing the speed with which a file is parsed.

    Addendum

    If you find this package useful or have any other kind of feedback, I’d be happy if you let me know. Otherwise, if you need more functionality for additional message types, please feel free to create an issue or a pull request on GitHub.

    Filed under: R Tagged: Finance, R, RITCH, Visualisation

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    A crazy day in the Bitcoin World

    Thu, 11/30/2017 - 03:11

    (This article was first published on R – insightR, and kindly contributed to R-bloggers)

    By Gabriel Vasconcelos

    Today, November 29, 2017 was a crazy day in the Bitcoin world and the craziness is still going on as I write this post. The price range was of thousands of Dollars in a few hours. Bitcoins were today the main topic in all discussion groups I participate. Some people believe we are in the middle of a giant bubble and are very skeptical about Bitcoins intrinsic value and other people believe cryptocurrencies are the future and are already counting on a price of hundreds of thousands of dollars in a few years. I am no expert and I have no idea which group is right, but I hope it is the second because I really like the Bitcoin idea as the money of the future.

    My objective here is to have a brief look at what happened with Bitcoins prices and transactions today (2017-11-29) compared to two days ago (2017-11-27), which was an ordinary day given the current levels of Bitcoin prices and volume. I will use data available by a Brazilian Bitcoin exchange called “Mercado Bitcoin (MB) (Bitcoin Market)”. You can see more details in their github page here. The prices are in Brazilian Real, which trades today for approximately 31 cents of US Dollar. Since some readers may be only interested in the results, I will put all codes to download the data and generate the plots in the end of the post.

    First I will show a small table that demonstrates how volume and prices changed from one day to the other. The table shows that transactions and the number of Bitcoins traded increased something close 500% and the average amount of Bitcoins in each transaction also increased. The price range was of less than 2000 BRL in November 27th compared to more than 10000 BRL today (November 29th).

    stat Nov.29 Nov.27 Transactions 31838.000 6975.000 Amount 2583.511 446.133 Avg.Amount 0.081 0.064 Avg.Price 40063.902 34274.563 Min.Price 33002.000 33500.000 Max.Price 44980.000 34965.000

    The boxplot helps us to understand how the prices were distributed. The distance between the 25% and the 75% percentiles of today covers much more than the entire range of prices from November 27th. If you are more interested in the dynamics of prices and volume you should look at the figure below the boxplot. It shows how prices and volume behaved across the days (the data is coerced to minutes). Both figures show that November 29th was clearly an unusual day in the Bitcoin world.

    Codes library(jsonlite) library(tidyverse) library(lubridate) library(reshape2) library(grid) library(scales) ## == The data is stored in JSON == ## ## == We can only download 1000 trades each time == ## ## == The while is to download all trades in the day == ## ## == Dates are in UNIX time == ## ## == Data from the last 24h (now it is 23:17 - 2017-11-29) == ## start = 1512004273 - 86400 end = 1512004273 datalist = list() while(start &lt; end){ file = paste("https://www.mercadobitcoin.net/api/BTC/trades/",start,"/",sep="") data &lt;- fromJSON(file, flatten = TRUE) start = max(data$date) + 1 datalist[[length(datalist) + 1]] = data } df_present = Reduce("rbind", datalist) df_present = df_present %&gt;% filter(date&lt;=end) ## == Data from the same time 2 days before == ## start = 1512004273 - 86400 * 2 end = 1512004273 - 86400 * 1 datalist = list() while(start &lt; end){ file = paste("https://www.mercadobitcoin.net/api/BTC/trades/",start,"/",sep="") data &lt;- fromJSON(file, flatten = TRUE) start = max(data$date) + 1 datalist[[length(datalist) + 1]] = data } df_past = Reduce("rbind", datalist) df_past = df_past %&gt;% filter(date &lt;= end) ## = adjust date = ## df_past$date = as.POSIXct(df_past$date, origin = "1970-01-01") df_present$date = as.POSIXct(df_present$date, origin = "1970-01-01") # = statistics = # past = c(nrow(df_past), sum(df_past$amount, na.rm = TRUE), mean(df_past$amount, na.rm = TRUE), mean(df_past$price), range(df_past$price)) present = c(nrow(df_present), sum(df_present$amount, na.rm = TRUE), mean(df_present$amount, na.rm = TRUE), mean(df_present$price), range(df_present$price)) stat = round(data.frame("Nov.29" = present,"Nov.27" = past), 3) stat = data.frame(stat = c("Transactions","Amount","Avg.Amount","Avg.Price","Min.Price","Max.Price"), stat) ## = make data by minute = ## df_present_min = df_present %&gt;% mutate(day = 1 + day(date) - min(day(date)), hour = hour(date), min = minute(date)) %&gt;% mutate(date = make_datetime(day = day, hour = hour,min = min,tz = "BRST")) %&gt;% group_by(date) %&gt;% summarise(price = tail(price, 1) ,vol = sum(amount)) df_past_min=df_past %&gt;% mutate(day = 1 + day(date) - min(day(date)), hour = hour(date) ,min = minute(date)) %&gt;% mutate(date = make_datetime(day = day, hour = hour,min = min, tz="BRST")) %&gt;% group_by(date) %&gt;% summarise(price = tail(price, 1), vol = sum(amount)) df_min = full_join(df_present_min, df_past_min,by=c("date")) %&gt;% arrange(date) df_min$price.x = na.locf(df_min$price.x, na.rm = FALSE) df_min$price.y = na.locf(df_min$price.y, na.rm = FALSE) ## = Plots = ## df1 = melt(df_min[, c(1, 2, 4)], id.vars = "date") df2 = melt(df_min[, c(1, 3, 5)], id.vars = "date") p0 = ggplot() + geom_boxplot(data = df1, aes(variable, value)) + labs(x="Day", y="Price") + scale_x_discrete(labels = c("Nov. 29", "Nov. 27")) p1 = ggplot() + geom_line(data = df1, aes(x = date, y = value, color = factor(variable, labels = c("Nov. 29", "Nov. 27")))) + labs(x = "Time",y = "Price") + guides(color = guide_legend(title = "Day")) + scale_x_datetime(labels = date_format("%H:%M")) p2 = ggplot() + geom_line(data = df2,aes(x = date,y = value,color = factor(variable, labels=c("Nov. 29", "Nov. 27")))) + labs(x = "Time", y = "Volume") + guides(color = guide_legend(title = "Day")) + scale_x_datetime(labels = date_format("%H:%M")) p0 grid.draw(rbind(ggplotGrob(p1), ggplotGrob(p2), size = "first"))

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    Using Heatmap Coloring on a Density Plot Using R to Visualize Distributions

    Thu, 11/30/2017 - 00:40

    (This article was first published on R – Displayr, and kindly contributed to R-bloggers)

    Lots of different visualizations have been proposed for understanding distributions. In this post, I am going show how to create my current favorite, which is a density plot using heatmap shading. I find them particularly useful for showing duration data.

    Example: time to purchase

    The table below shows the number of days it took 200 people to purchase a product, after their first trial of the product. The average time taken is 131 days and the median is 54 days. The rest of the post looks at a number of different ways of visualizing this data, finishing with the heated density plot.

    Density plot

    One of the classic ways of plotting this type of data is as a density plot. The standard R version is shown below. I have set the default from argument to better display this data, as otherwise density plots tend to show negative values even when all the data contains no negative values.

    plot(density(days, from = 0), main = "Density plot", xlab = "Number of days since trial started")

    This plot clearly shows that purchases occur at relatively close to 0 days since the trial started. But, unless you use a ruler, there is no way to work out precisely where the peak occurs. Is it at 20 days or 50 days? The values shown on the y-axis have no obvious meaning (unless you read the technical documentation, and even then they are not numbers that can be explained in a useful way to non-technical people).

    We can make this easier to read by only plotting the data for the first 180 days (to = 180), and changing the bandwidth used in estimating the density (adjust = 0.2) to make the plot less smooth. We can now see that the peak is around 30. While the plot does a good job at describing the shape of the data, it does not allow us to draw conclusions regarding the cumulative proportion of people to purchase by a specific time. For example, what proportion of people buy in the first 100 days? We need a different plot.

    plot(density(days, from = 0, to = 180, adjust = 0.2), main = "Density plot - Up to 180 days (86% of data)", xlab = "Number of days since trial started")

    Survival curve

    Survival curves have been invented for this type of data. An example is shown below. In this case, the survival curve shows the proportion of people who have yet to purchase at each point in time. The relatively sharp drop in the survival function at 30 is the same pattern that was revealed by the peak in the density plot. We can see also see that at about 100 days, around 26% or so of people have “survived” (i.e., not purchased). The plot also reveals that around 14% of customers have not purchased during the time interval shown (as this is where the line crosses the right-side of the plot). To my mind the survival plot is more informative than the density plot. But, it is also harder work. It is hard to see most non-technical audiences finding all the key patterns in this plot without guidance.

    library(survival) surv.days = Surv(days) surv.fit = survfit(surv.days~1) plot(surv.fit, main = "Kaplan-Meier estimate with 95% confidence bounds (86% of data)", xlab = "Days since trial started", xlim = c(0, 180), ylab = "Survival function") grid(20, 10, lwd = 2)

    Heated density plot

    I call the visualization below a heated density plot. No doubt somebody invented this before we did, so please tell me if there is a more appropriate name. It is identical to the density plot from earlier in this post, except that:

    • The heatmap coloring shows the cumulative proportion to purchase, ranging from red (0%), to yellow (50%, the median), to blue (100%). Thus, we can now see that the median is at about 55, which could not be ascertained from the earlier density plots.
    • If you hover your mouse pointer (if you have one) over the plot, it will show you the cumulative percentage of people to purchase. We can readily see that the peak occurs near 30 days. Similarly, we can see that 93% of people purchased within 500 days.
    HeatedDensityPlot(days, from = 0, title = "Heated density plot", x.title = "Number of days since trial started", legend.title = "% of buyers")

    Below I show the plot limited to the first 180 days with the bandwidth adjusted as in the earlier density plot. The use of the legend on the right allows the viewer to readily see that only a little over 85% of people have purchased.

    HeatedDensityPlot(days, from = 0, to = 180, adjust = 0.2, title = "Heated density plot - Up to 180 days (86% of data)", x.title = "Number of days since trial started", legend.title = "% of buyers")

    Bonus: they are awesome for discrete data

    The visualization below shows randomly-generated data where I have generated whole numbers in the range of 0 through 12 (i.e., one 0; six 1s, seven 2s, etc). In all the previous heated density plots the color transition was relatively smooth. In the visualization below, the discrete nature of the data has been emphasized by the clear vertical lines between each color. This is not a feature that can be seen on either a traditional density plot or histogram with small sample sizes.

    set.seed(12230) x = rpois(100, 5) HeatedDensityPlot(x, from = 0, title = "Poisson(5), n = 100", x.title = "x")

    Conclusion

    The heated density plot is, to my mind, a straightforward improvement on traditional density plots. It shows additional information about the cumulative values of the distribution and reveals when the data is discrete. However, the function we have written is still a bit rough around the edges. For example, sometimes a faint line appears at the top of the plot, and a whole host of warnings appear in R when the plot is created. If you can improve on this, please tell us how!

    The code

    The R function used to create the plot is shown below. If you run this code you will get a plot of a normal distribution. If you want to reproduce my exact examples, please click here to sign into Displayr and access the document that contains all my code and charts. This document contains further additional examples of the use of this plot. To see the code, just click on one of the charts in the document, and the code should be shown on the right in Properties > R CODE.

    HeatedDensityPlot <- function(x, title = "", x.title = "x", colors = c('#d7191c','#fdae61','#ffffbf','#abdda4','#2b83ba'), show.legend = TRUE, legend.title = "Cumulative %", n = 512, # Number of unique values of x in the heatmap/density estimation n.breaks = 5, # Number of values of x to show on the x-axis. Final number may differ. ...) { # Checking inputs. n.obs <- length(x) if (any(nas <- is.na(x))) { warning(sum(nas), " observations with missing values have been removed.") x <- x[!nas] n.obs <- length(x) } if (n.obs < 4) stop(n.obs, " is too few observations for a valid density plot. See Silverman (1986) Density Estimation for Statistics and Data Analysis.") # Computing densities dens <- density(x, ...) y.max <- max(dens$y) * 1.1 # To ensure top of plot isn't too close to title x.to.plot.true <- x.to.plot <- dens$x y.seq <- c(0, y.max/2, y.max) y.to.plot <- dens$y range.x = range(x) cum.dens <- ecdf(x)(x.to.plot)# * 100 #Due to plotly blug that misaligns heatmap with ensuing white line, #putting blanks at beginnig of data. n.blanks <- 10 cum.dens <- c(rep(NA, n.blanks), cum.dens) diff <- x.to.plot[1] - x.to.plot[2] blanks <- diff * (n.blanks:1) + x.to.plot[1] x.to.plot <- c(blanks, x.to.plot) y.to.plot <- c(rep(0, n.blanks), y.to.plot) # Creating the matrix of heatmap values cum.perc <- cum.dens * 100 z.mat <- matrix(cum.perc, byrow = TRUE, nrow = 3, ncol = n + n.blanks, dimnames = list(y = y.seq, x = x.to.plot)) # Specifying the colors col.fun <- scales::col_numeric(colors, domain = 0:1, na.color = "white")#range.x) x.as.colors <- col.fun(cum.dens) z.to.plot.scaled <- scales::rescale(cum.perc) color.lookup <- setNames(data.frame(z.to.plot.scaled, x.as.colors), NULL) # Creating the base heatmap. require(plotly) p <- plot_ly(z = z.mat, xsrc = x.to.plot, ysrc = y.seq, type = "heatmap", colorscale = color.lookup, cauto = FALSE, hoverinfo = "none", colorbar = list(title = legend.title), showscale = show.legend) # Placing white on top of the bits of the heatmap to hide p <- add_trace(p, x = c(1:(n + n.blanks), (n + n.blanks):1), y = c(y.to.plot, rep(y.max * 1.10, n + n.blanks)), fill = "tonexty", hoverinfo = "none", showlegend = FALSE, type = "scatter", mode = "line", showscale = FALSE, line = list(color = "white", width = 0), fillcolor = "white") # Adding the tooltips p <- add_trace(p, x = 1:(n + n.blanks), y = y.to.plot, name = "", hoverinfo = "text", text = sprintf(paste0(x.title,": %.0f %% < %.1f"), cum.perc, x.to.plot), type = "scatter", mode = "lines", line = list(color = "white", width = 0), showlegend=FALSE, showscale=FALSE) p <- plotly::config(p, displayModeBar = FALSE) # Formatting the x axis x.text <- pretty(x.to.plot, n = n.breaks) x.tick <- 1 + (x.text - x.to.plot[1]) / (x.to.plot[n + n.blanks] - x.to.plot[1]) * (n + n.blanks - 1) p <- layout(p, title = title, xaxis = list(title = x.title, tickmode = "array", tickvals = x.tick, ticktext = x.text), yaxis = list(title = "", showline = FALSE, ticks = "", showticklabels = FALSE, range= c(0, y.max)), margin = list(t = 30, l = 5, b = 50, r = 5)) p } Acknowledgements

    My colleague Carmen wrote most of the code. It is written using the wonderful plotly package.

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    Sentiment Analysis of “A Christmas Carol”

    Thu, 11/30/2017 - 00:16

    (This article was first published on R – rud.is, and kindly contributed to R-bloggers)

    Our family has been reading, listening to and watching “A Christmas Carol” for just abt 30 years now. I got it into my crazy noggin to perform a sentiment analysis on it the other day and tweeted out the results, but a large chunk of the R community is not on Twitter and it would be good to get a holiday-themed post or two up for the season.

    One reason I embarked on this endeavour is that @juliasilge & @drob made it so gosh darn easy to do so with:

    (btw: That makes an excellent holiday gift for the data scientist[s] in your life.)

    Let us begin!

    STAVE I: hrbrmstr’s Code

    We need the text of this book to work with and thankfully it’s long been in the public domain. As @drob noted, we can use the gutenbergr package to retrieve it. We’ll use an RStudio project structure for this and cache the results locally to avoid burning bandwidth:

    library(rprojroot) library(gutenbergr) library(hrbrthemes) library(stringi) library(tidytext) library(tidyverse) rt <- find_rstudio_root_file() carol_rds <- file.path(rt, "data", "carol.rds") if (!file.exists(carol_rds)) { carol_df <- gutenberg_download("46") write_rds(carol_df, carol_rds) } else { carol_df <- read_rds(carol_rds) }

    How did I know to use 46? We can use gutenberg_works() to get to that info:

    gutenberg_works(author=="Dickens, Charles") ## # A tibble: 74 x 8 ## gutenberg_id title ## ## 1 46 A Christmas Carol in Prose; Being a Ghost Story of Christmas ## 2 98 A Tale of Two Cities ## 3 564 The Mystery of Edwin Drood ## 4 580 The Pickwick Papers ## 5 588 Master Humphrey's Clock ## 6 644 The Haunted Man and the Ghost's Bargain ## 7 650 Pictures from Italy ## 8 653 "The Chimes\r\nA Goblin Story of Some Bells That Rang an Old Year out and a New Year In" ## 9 675 American Notes ## 10 678 The Cricket on the Hearth: A Fairy Tale of Home ## # ... with 64 more rows, and 6 more variables: author , gutenberg_author_id , language , ## # gutenberg_bookshelf , rights , has_text STAVE II: The first of three wrangles

    We’re eventually going to make a ggplot2 faceted chart of the sentiments by paragraphs in each stave (chapter). I wanted nicer titles for the facets so we’ll clean up the stave titles first:

    #' Convenience only carol_txt <- carol_df$text # Just want the chapters (staves) carol_txt <- carol_txt[-(1:(which(grepl("STAVE I:", carol_txt)))-1)] #' We'll need this later to make prettier facet titles data_frame( stave = 1:5, title = sprintf("Stave %s: %s", stave, carol_txt[stri_detect_fixed(carol_txt, "STAVE")] %>% stri_replace_first_regex("STAVE [[:alpha:]]{1,3}: ", "") %>% stri_trans_totitle()) ) -> stave_titles

    stri_trans_totitle() is a super-handy function and all we’re doing here is extracting the stave titles and doing a small transformation. There are scads of ways to do this, so don’t get stuck on this example. Try out other ways of doing this munging.

    You’ll also see that I made sure we started at the first stave break vs include the title bits in the analysis.

    Now, we need to prep the text for text analysis.

    STAVE III: The second of three wrangles

    There are other text mining packages and processes in R. I’m using tidytext because it takes care of so many details for you and does so elegantly. I was also at the rOpenSci Unconf where the idea was spawned & worked on and I’m glad it blossomed into such a great package and a book!

    Since we (I) want to do the analysis by stave & paragraph, let’s break the text into those chunks. Note that I’m doing an extra break by sentence in the event folks out there want to replicate this work but do so on a more granular level.

    #' Break the text up into chapters, paragraphs, sentences, and words, #' preserving the hierarchy so we can use it later. data_frame(txt = carol_txt) %>% unnest_tokens(chapter, txt, token="regex", pattern="STAVE [[:alpha:]]{1,3}: [[:alpha:] [:punct:]]+") %>% mutate(stave = 1:n()) %>% unnest_tokens(paragraph, chapter, token = "paragraphs") %>% group_by(stave) %>% mutate(para = 1:n()) %>% ungroup() %>% unnest_tokens(sentence, paragraph, token="sentences") %>% group_by(stave, para) %>% mutate(sent = 1:n()) %>% ungroup() %>% unnest_tokens(word, sentence) -> carol_tokens carol_tokens ## A tibble: 28,710 x 4 ## stave para sent word ## ## 1 1 1 1 marley ## 2 1 1 1 was ## 3 1 1 1 dead ## 4 1 1 1 to ## 5 1 1 1 begin ## 6 1 1 1 with ## 7 1 1 1 there ## 8 1 1 1 is ## 9 1 1 1 no ## 0 1 1 1 doubt ## ... with 28,700 more rows

    By indexing each hierarchy level, we have the flexibility to do all sorts of structured analyses just by choosing grouping combinations.

    STAVE IV: The third of three wrangles

    Now, we need to layer in some sentiments and do some basic sentiment calculations. Many of these sentiment-al posts (including this one) take a naive approach with basic match and only looking at 1-grams. One reason I didn’t go further was to make the code accessible to new R folk (since I primarily blog for new R folk :-). I’m prepping some 2018 posts with more involved text analysis themes and will likely add some complexity then with other texts.

    #' Retrieve sentiments and compute them. #' #' I left the `index` in vs just use `paragraph` since it'll make this easier to reuse #' this block (which I'm not doing but thought I might). inner_join(carol_tokens, get_sentiments("nrc"), "word") %>% count(stave, index = para, sentiment) %>% spread(sentiment, n, fill = 0) %>% mutate(sentiment = positive - negative) %>% left_join(stave_titles, "stave") -> carol_with_sent STAVE V: The end of it

    Now, we just need to do some really basic ggplot-ing to to get to our desired result:

    ggplot(carol_with_sent) + geom_segment(aes(index, sentiment, xend=index, yend=0, color=title), size=0.33) + scale_x_comma(limits=range(carol_with_sent$index)) + scale_y_comma() + scale_color_ipsum() + facet_wrap(~title, scales="free_x", ncol=5) + labs(x=NULL, y="Sentiment", title="Sentiment Analysis of A Christmas Carol", subtitle="By stave & ¶", caption="Humbug!") + theme_ipsum_rc(grid="Y", axis_text_size = 8, strip_text_face = "italic", strip_text_size = 10.5) + theme(legend.position="none")

    You’ll want to tap/click on that to make it bigger.

    Despite using a naive analysis, I think it tracks pretty well with the flow of the book.

    Stave one is quite bleak. Marley is morose and frightening. There is no joy apart from Fred’s brief appearance.

    The truly terrible (-10 sentiment) paragraph also makes sense:

    Marley’s face. It was not in impenetrable shadow as the other objects in the yard were, but had a dismal light about it, like a bad lobster in a dark cellar. It was not angry or ferocious, but looked at Scrooge as Marley used to look: with ghostly spectacles turned up on its ghostly forehead. The hair was curiously stirred, as if by breath or hot air; and, though the eyes were wide open, they were perfectly motionless. That, and its livid colour, made it horrible; but its horror seemed to be in spite of the face and beyond its control, rather than a part of its own expression.

    (I got to that via this snippet which you can use as a template for finding the other significant sentiment points:)

    filter( carol_tokens, stave == 1, para == filter(carol_with_sent, stave==1) %>% filter(sentiment == min(sentiment)) %>% pull(index) )

    Stave two (Christmas past) is all about Scrooge’s youth and includes details about Fezziwig’s party so the mostly-positive tone also makes sense.

    Stave three (Christmas present) has the highest:

    The Grocers’! oh, the Grocers’! nearly closed, with perhaps two shutters down, or one; but through those gaps such glimpses! It was not alone that the scales descending on the counter made a merry sound, or that the twine and roller parted company so briskly, or that the canisters were rattled up and down like juggling tricks, or even that the blended scents of tea and coffee were so grateful to the nose, or even that the raisins were so plentiful and rare, the almonds so extremely white, the sticks of cinnamon so long and straight, the other spices so delicious, the candied fruits so caked and spotted with molten sugar as to make the coldest lookers-on feel faint and subsequently bilious. Nor was it that the figs were moist and pulpy, or that the French plums blushed in modest tartness from their highly-decorated boxes, or that everything was good to eat and in its Christmas dress; but the customers were all so hurried and so eager in the hopeful promise of the day, that they tumbled up against each other at the door, crashing their wicker baskets wildly, and left their purchases upon the counter, and came running back to fetch them, and committed hundreds of the like mistakes, in the best humour possible; while the Grocer and his people were so frank and fresh that the polished hearts with which they fastened their aprons behind might have been their own, worn outside for general inspection, and for Christmas daws to peck at if they chose.

    and lowest (sentiment) points of the entire book:

    And now, without a word of warning from the Ghost, they stood upon a bleak and desert moor, where monstrous masses of rude stone were cast about, as though it were the burial-place of giants; and water spread itself wheresoever it listed, or would have done so, but for the frost that held it prisoner; and nothing grew but moss and furze, and coarse rank grass. Down in the west the setting sun had left a streak of fiery red, which glared upon the desolation for an instant, like a sullen eye, and frowning lower, lower, lower yet, was lost in the thick gloom of darkest night.

    Stave four (Christmas yet to come) is fairly middling. I had expected to see lower marks here. The standout negative sentiment paragraph (and the one that follows) are pretty dark, though:

    They left the busy scene, and went into an obscure part of the town, where Scrooge had never penetrated before, although he recognised its situation, and its bad repute. The ways were foul and narrow; the shops and houses wretched; the people half-naked, drunken, slipshod, ugly. Alleys and archways, like so many cesspools, disgorged their offences of smell, and dirt, and life, upon the straggling streets; and the whole quarter reeked with crime, with filth, and misery.

    Finally, Stave five is both short and positive (whew!). Which I heartily agree with!

    FIN

    The code is up on GitHub and I hope that it will inspire more folks to experiment with this fun (& useful!) aspect of data science.

    Make sure to send links to anything you create and shoot over PRs for anything you think I did that was awry.

    For those who celebrate Christmas, I hope you keep Christmas as well as or even better than old Scrooge. “May that be truly said of us, and all of us! And so, as Tiny Tim observed, God bless Us, Every One!”

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