8817 lines
1.3 MiB
8817 lines
1.3 MiB
<!DOCTYPE html>
|
||
|
||
<html lang="en">
|
||
<head><meta charset="utf-8"/>
|
||
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
|
||
<title>DiD_analysis</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
|
||
<style type="text/css">
|
||
pre { line-height: 125%; }
|
||
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
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td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
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.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
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.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
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.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
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.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
|
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.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
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||
.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
|
||
.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
|
||
.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
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.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
|
||
.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
|
||
.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
|
||
.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
|
||
.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
|
||
.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
|
||
.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
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.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
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.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
|
||
.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
|
||
.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
|
||
.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
|
||
.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
|
||
.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
|
||
.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
|
||
.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
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||
.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
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||
.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
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||
.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
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||
.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
|
||
.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
|
||
.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
|
||
.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
|
||
.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
|
||
.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
|
||
.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
|
||
.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
|
||
.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
|
||
.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
|
||
.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
|
||
.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
|
||
.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
|
||
.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
|
||
.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
|
||
</style>
|
||
<style type="text/css">
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*
|
||
* Mozilla scrollbar styling
|
||
*/
|
||
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||
/* use standard opaque scrollbars for most nodes */
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[data-jp-theme-scrollbars='true'] {
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scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
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var(--jp-scrollbar-background-color);
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}
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/* for code nodes, use a transparent style of scrollbar. These selectors
|
||
* will match lower in the tree, and so will override the above */
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[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
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[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
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scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
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}
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/* tiny scrollbar */
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.jp-scrollbar-tiny {
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scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
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scrollbar-width: thin;
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}
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/* tiny scrollbar */
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.jp-scrollbar-tiny::-webkit-scrollbar,
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.jp-scrollbar-tiny::-webkit-scrollbar-corner {
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background-color: transparent;
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height: 4px;
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width: 4px;
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}
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.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
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background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
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}
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.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
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border-left: 0 solid transparent;
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border-right: 0 solid transparent;
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}
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.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
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border-top: 0 solid transparent;
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border-bottom: 0 solid transparent;
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}
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/*
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||
* Lumino
|
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*/
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.lm-ScrollBar[data-orientation='horizontal'] {
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min-height: 16px;
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max-height: 16px;
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min-width: 45px;
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border-top: 1px solid #a0a0a0;
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}
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|
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.lm-ScrollBar[data-orientation='vertical'] {
|
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min-width: 16px;
|
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max-width: 16px;
|
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min-height: 45px;
|
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border-left: 1px solid #a0a0a0;
|
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}
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.lm-ScrollBar-button {
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background-color: #f0f0f0;
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background-position: center center;
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min-height: 15px;
|
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max-height: 15px;
|
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min-width: 15px;
|
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max-width: 15px;
|
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}
|
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.lm-ScrollBar-button:hover {
|
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background-color: #dadada;
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}
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.lm-ScrollBar-button.lm-mod-active {
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background-color: #cdcdcd;
|
||
}
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.lm-ScrollBar-track {
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background: #f0f0f0;
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}
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.lm-ScrollBar-thumb {
|
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background: #cdcdcd;
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}
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.lm-ScrollBar-thumb:hover {
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background: #bababa;
|
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}
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.lm-ScrollBar-thumb.lm-mod-active {
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background: #a0a0a0;
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}
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.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
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height: 100%;
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min-width: 15px;
|
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border-left: 1px solid #a0a0a0;
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border-right: 1px solid #a0a0a0;
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}
|
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|
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.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
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width: 100%;
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min-height: 15px;
|
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border-top: 1px solid #a0a0a0;
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border-bottom: 1px solid #a0a0a0;
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}
|
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|
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.lm-ScrollBar[data-orientation='horizontal']
|
||
.lm-ScrollBar-button[data-action='decrement'] {
|
||
background-image: var(--jp-icon-caret-left);
|
||
background-size: 17px;
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}
|
||
|
||
.lm-ScrollBar[data-orientation='horizontal']
|
||
.lm-ScrollBar-button[data-action='increment'] {
|
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background-image: var(--jp-icon-caret-right);
|
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background-size: 17px;
|
||
}
|
||
|
||
.lm-ScrollBar[data-orientation='vertical']
|
||
.lm-ScrollBar-button[data-action='decrement'] {
|
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background-image: var(--jp-icon-caret-up);
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background-size: 17px;
|
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}
|
||
|
||
.lm-ScrollBar[data-orientation='vertical']
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||
.lm-ScrollBar-button[data-action='increment'] {
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background-image: var(--jp-icon-caret-down);
|
||
background-size: 17px;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-Widget {
|
||
box-sizing: border-box;
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position: relative;
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overflow: hidden;
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}
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||
|
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.lm-Widget.lm-mod-hidden {
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display: none !important;
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||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.lm-AccordionPanel[data-orientation='horizontal'] > .lm-AccordionPanel-title {
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/* Title is rotated for horizontal accordion panel using CSS */
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display: block;
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||
transform-origin: top left;
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||
transform: rotate(-90deg) translate(-100%);
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-CommandPalette {
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||
display: flex;
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flex-direction: column;
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-webkit-user-select: none;
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-moz-user-select: none;
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-ms-user-select: none;
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user-select: none;
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}
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.lm-CommandPalette-search {
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flex: 0 0 auto;
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}
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.lm-CommandPalette-content {
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flex: 1 1 auto;
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margin: 0;
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padding: 0;
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min-height: 0;
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overflow: auto;
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list-style-type: none;
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}
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.lm-CommandPalette-header {
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overflow: hidden;
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white-space: nowrap;
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text-overflow: ellipsis;
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}
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|
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.lm-CommandPalette-item {
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display: flex;
|
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flex-direction: row;
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}
|
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|
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.lm-CommandPalette-itemIcon {
|
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flex: 0 0 auto;
|
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}
|
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|
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.lm-CommandPalette-itemContent {
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flex: 1 1 auto;
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overflow: hidden;
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}
|
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|
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.lm-CommandPalette-itemShortcut {
|
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flex: 0 0 auto;
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}
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|
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.lm-CommandPalette-itemLabel {
|
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overflow: hidden;
|
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white-space: nowrap;
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text-overflow: ellipsis;
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}
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|
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.lm-close-icon {
|
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border: 1px solid transparent;
|
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background-color: transparent;
|
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position: absolute;
|
||
z-index: 1;
|
||
right: 3%;
|
||
top: 0;
|
||
bottom: 0;
|
||
margin: auto;
|
||
padding: 7px 0;
|
||
display: none;
|
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vertical-align: middle;
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outline: 0;
|
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cursor: pointer;
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}
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.lm-close-icon:after {
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content: 'X';
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display: block;
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width: 15px;
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height: 15px;
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text-align: center;
|
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color: #000;
|
||
font-weight: normal;
|
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font-size: 12px;
|
||
cursor: pointer;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-DockPanel {
|
||
z-index: 0;
|
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}
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|
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.lm-DockPanel-widget {
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z-index: 0;
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}
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.lm-DockPanel-tabBar {
|
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z-index: 1;
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}
|
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|
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.lm-DockPanel-handle {
|
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z-index: 2;
|
||
}
|
||
|
||
.lm-DockPanel-handle.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-DockPanel-handle:after {
|
||
position: absolute;
|
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top: 0;
|
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left: 0;
|
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width: 100%;
|
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height: 100%;
|
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content: '';
|
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}
|
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|
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.lm-DockPanel-handle[data-orientation='horizontal'] {
|
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cursor: ew-resize;
|
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}
|
||
|
||
.lm-DockPanel-handle[data-orientation='vertical'] {
|
||
cursor: ns-resize;
|
||
}
|
||
|
||
.lm-DockPanel-handle[data-orientation='horizontal']:after {
|
||
left: 50%;
|
||
min-width: 8px;
|
||
transform: translateX(-50%);
|
||
}
|
||
|
||
.lm-DockPanel-handle[data-orientation='vertical']:after {
|
||
top: 50%;
|
||
min-height: 8px;
|
||
transform: translateY(-50%);
|
||
}
|
||
|
||
.lm-DockPanel-overlay {
|
||
z-index: 3;
|
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box-sizing: border-box;
|
||
pointer-events: none;
|
||
}
|
||
|
||
.lm-DockPanel-overlay.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-Menu {
|
||
z-index: 10000;
|
||
position: absolute;
|
||
white-space: nowrap;
|
||
overflow-x: hidden;
|
||
overflow-y: auto;
|
||
outline: none;
|
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-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.lm-Menu-content {
|
||
margin: 0;
|
||
padding: 0;
|
||
display: table;
|
||
list-style-type: none;
|
||
}
|
||
|
||
.lm-Menu-item {
|
||
display: table-row;
|
||
}
|
||
|
||
.lm-Menu-item.lm-mod-hidden,
|
||
.lm-Menu-item.lm-mod-collapsed {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-Menu-itemIcon,
|
||
.lm-Menu-itemSubmenuIcon {
|
||
display: table-cell;
|
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text-align: center;
|
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}
|
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|
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.lm-Menu-itemLabel {
|
||
display: table-cell;
|
||
text-align: left;
|
||
}
|
||
|
||
.lm-Menu-itemShortcut {
|
||
display: table-cell;
|
||
text-align: right;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-MenuBar {
|
||
outline: none;
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.lm-MenuBar-content {
|
||
margin: 0;
|
||
padding: 0;
|
||
display: flex;
|
||
flex-direction: row;
|
||
list-style-type: none;
|
||
}
|
||
|
||
.lm-MenuBar-item {
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.lm-MenuBar-itemIcon,
|
||
.lm-MenuBar-itemLabel {
|
||
display: inline-block;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-ScrollBar {
|
||
display: flex;
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.lm-ScrollBar[data-orientation='horizontal'] {
|
||
flex-direction: row;
|
||
}
|
||
|
||
.lm-ScrollBar[data-orientation='vertical'] {
|
||
flex-direction: column;
|
||
}
|
||
|
||
.lm-ScrollBar-button {
|
||
box-sizing: border-box;
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-ScrollBar-track {
|
||
box-sizing: border-box;
|
||
position: relative;
|
||
overflow: hidden;
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
.lm-ScrollBar-thumb {
|
||
box-sizing: border-box;
|
||
position: absolute;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-SplitPanel-child {
|
||
z-index: 0;
|
||
}
|
||
|
||
.lm-SplitPanel-handle {
|
||
z-index: 1;
|
||
}
|
||
|
||
.lm-SplitPanel-handle.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-SplitPanel-handle:after {
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
width: 100%;
|
||
height: 100%;
|
||
content: '';
|
||
}
|
||
|
||
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
|
||
cursor: ew-resize;
|
||
}
|
||
|
||
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
|
||
cursor: ns-resize;
|
||
}
|
||
|
||
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
|
||
left: 50%;
|
||
min-width: 8px;
|
||
transform: translateX(-50%);
|
||
}
|
||
|
||
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
|
||
top: 50%;
|
||
min-height: 8px;
|
||
transform: translateY(-50%);
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-TabBar {
|
||
display: flex;
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.lm-TabBar[data-orientation='horizontal'] {
|
||
flex-direction: row;
|
||
align-items: flex-end;
|
||
}
|
||
|
||
.lm-TabBar[data-orientation='vertical'] {
|
||
flex-direction: column;
|
||
align-items: flex-end;
|
||
}
|
||
|
||
.lm-TabBar-content {
|
||
margin: 0;
|
||
padding: 0;
|
||
display: flex;
|
||
flex: 1 1 auto;
|
||
list-style-type: none;
|
||
}
|
||
|
||
.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
|
||
flex-direction: row;
|
||
}
|
||
|
||
.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
|
||
flex-direction: column;
|
||
}
|
||
|
||
.lm-TabBar-tab {
|
||
display: flex;
|
||
flex-direction: row;
|
||
box-sizing: border-box;
|
||
overflow: hidden;
|
||
touch-action: none; /* Disable native Drag/Drop */
|
||
}
|
||
|
||
.lm-TabBar-tabIcon,
|
||
.lm-TabBar-tabCloseIcon {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-TabBar-tabLabel {
|
||
flex: 1 1 auto;
|
||
overflow: hidden;
|
||
white-space: nowrap;
|
||
}
|
||
|
||
.lm-TabBar-tabInput {
|
||
user-select: all;
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.lm-TabBar-tab.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-TabBar-addButton.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
|
||
position: relative;
|
||
}
|
||
|
||
.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
|
||
left: 0;
|
||
transition: left 150ms ease;
|
||
}
|
||
|
||
.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
|
||
top: 0;
|
||
transition: top 150ms ease;
|
||
}
|
||
|
||
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
|
||
transition: none;
|
||
}
|
||
|
||
.lm-TabBar-tabLabel .lm-TabBar-tabInput {
|
||
user-select: all;
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
background: inherit;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-TabPanel-tabBar {
|
||
z-index: 1;
|
||
}
|
||
|
||
.lm-TabPanel-stackedPanel {
|
||
z-index: 0;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Collapse {
|
||
display: flex;
|
||
flex-direction: column;
|
||
align-items: stretch;
|
||
}
|
||
|
||
.jp-Collapse-header {
|
||
padding: 1px 12px;
|
||
background-color: var(--jp-layout-color1);
|
||
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
|
||
color: var(--jp-ui-font-color1);
|
||
cursor: pointer;
|
||
display: flex;
|
||
align-items: center;
|
||
font-size: var(--jp-ui-font-size0);
|
||
font-weight: 600;
|
||
text-transform: uppercase;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-Collapser-icon {
|
||
height: 16px;
|
||
}
|
||
|
||
.jp-Collapse-header-collapsed .jp-Collapser-icon {
|
||
transform: rotate(-90deg);
|
||
margin: auto 0;
|
||
}
|
||
|
||
.jp-Collapser-title {
|
||
line-height: 25px;
|
||
}
|
||
|
||
.jp-Collapse-contents {
|
||
padding: 0 12px;
|
||
background-color: var(--jp-layout-color1);
|
||
color: var(--jp-ui-font-color1);
|
||
overflow: auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
|
||
|
||
/**
|
||
* (DEPRECATED) Support for consuming icons as CSS background images
|
||
*/
|
||
|
||
/* Icons urls */
|
||
|
||
:root {
|
||
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-add-below: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5OCkiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik05LjI1IDEwLjA2OTNMNy4zNzUgMTAuMDY5M0w3LjM3NSA4LjE5NDM0QzcuMzc1IDcuOTg4MDkgNy4yMDYyNSA3LjgxOTM0IDcgNy44MTkzNEM2Ljc5Mzc1IDcuODE5MzQgNi42MjUgNy45ODgwOSA2LjYyNSA4LjE5NDM0TDYuNjI1IDEwLjA2OTNMNC43NSAxMC4wNjkzQzQuNTQzNzUgMTAuMDY5MyA0LjM3NSAxMC4yMzgxIDQuMzc1IDEwLjQ0NDNDNC4zNzUgMTAuNjUwNiA0LjU0Mzc1IDEwLjgxOTMgNC43NSAxMC44MTkzTDYuNjI1IDEwLjgxOTNMNi42MjUgMTIuNjk0M0M2LjYyNSAxMi45MDA2IDYuNzkzNzUgMTMuMDY5MyA3IDEzLjA2OTNDNy4yMDYyNSAxMy4wNjkzIDcuMzc1IDEyLjkwMDYgNy4zNzUgMTIuNjk0M0w3LjM3NSAxMC44MTkzTDkuMjUgMTAuODE5M0M5LjQ1NjI1IDEwLjgxOTMgOS42MjUgMTAuNjUwNiA5LjYyNSAxMC40NDQzQzkuNjI1IDEwLjIzODEgOS40NTYyNSAxMC4wNjkzIDkuMjUgMTAuMDY5M1oiIGZpbGw9IiM2MTYxNjEiIHN0cm9rZT0iIzYxNjE2MSIgc3Ryb2tlLXdpZHRoPSIwLjciLz4KPC9nPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJNMi41IDUuNUwyLjUgMy41TDExLjUgMy41TDExLjUgNS41TDIuNSA1LjVaTTIgN0MxLjQ0NzcyIDcgMSA2LjU1MjI4IDEgNkwxIDNDMSAyLjQ0NzcyIDEuNDQ3NzIgMiAyIDJMMTIgMkMxMi41NTIzIDIgMTMgMi40NDc3MiAxMyAzTDEzIDZDMTMgNi41NTIyOSAxMi41NTIzIDcgMTIgN0wyIDdaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5OCI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KDEgMS43NDg0NmUtMDcgMS43NDg0NmUtMDcgLTEgNCAxMy40NDQzKSIvPgo8L2NsaXBQYXRoPgo8L2RlZnM+Cjwvc3ZnPgo=);
|
||
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
|
||
--jp-icon-bell: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
|
||
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|
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--jp-icon-python: url(data:image/svg+xml;base64,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);
|
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--jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-react: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
|
||
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
|
||
--jp-icon-regex: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
|
||
--jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
|
||
--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
|
||
--jp-icon-search: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-settings: url(data:image/svg+xml;base64,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);
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|
||
--jp-icon-users: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
|
||
--jp-icon-word: url(data:image/svg+xml;base64,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);
|
||
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
|
||
}
|
||
|
||
/* Icon CSS class declarations */
|
||
|
||
.jp-AddAboveIcon {
|
||
background-image: var(--jp-icon-add-above);
|
||
}
|
||
|
||
.jp-AddBelowIcon {
|
||
background-image: var(--jp-icon-add-below);
|
||
}
|
||
|
||
.jp-AddIcon {
|
||
background-image: var(--jp-icon-add);
|
||
}
|
||
|
||
.jp-BellIcon {
|
||
background-image: var(--jp-icon-bell);
|
||
}
|
||
|
||
.jp-BugDotIcon {
|
||
background-image: var(--jp-icon-bug-dot);
|
||
}
|
||
|
||
.jp-BugIcon {
|
||
background-image: var(--jp-icon-bug);
|
||
}
|
||
|
||
.jp-BuildIcon {
|
||
background-image: var(--jp-icon-build);
|
||
}
|
||
|
||
.jp-CaretDownEmptyIcon {
|
||
background-image: var(--jp-icon-caret-down-empty);
|
||
}
|
||
|
||
.jp-CaretDownEmptyThinIcon {
|
||
background-image: var(--jp-icon-caret-down-empty-thin);
|
||
}
|
||
|
||
.jp-CaretDownIcon {
|
||
background-image: var(--jp-icon-caret-down);
|
||
}
|
||
|
||
.jp-CaretLeftIcon {
|
||
background-image: var(--jp-icon-caret-left);
|
||
}
|
||
|
||
.jp-CaretRightIcon {
|
||
background-image: var(--jp-icon-caret-right);
|
||
}
|
||
|
||
.jp-CaretUpEmptyThinIcon {
|
||
background-image: var(--jp-icon-caret-up-empty-thin);
|
||
}
|
||
|
||
.jp-CaretUpIcon {
|
||
background-image: var(--jp-icon-caret-up);
|
||
}
|
||
|
||
.jp-CaseSensitiveIcon {
|
||
background-image: var(--jp-icon-case-sensitive);
|
||
}
|
||
|
||
.jp-CheckIcon {
|
||
background-image: var(--jp-icon-check);
|
||
}
|
||
|
||
.jp-CircleEmptyIcon {
|
||
background-image: var(--jp-icon-circle-empty);
|
||
}
|
||
|
||
.jp-CircleIcon {
|
||
background-image: var(--jp-icon-circle);
|
||
}
|
||
|
||
.jp-ClearIcon {
|
||
background-image: var(--jp-icon-clear);
|
||
}
|
||
|
||
.jp-CloseIcon {
|
||
background-image: var(--jp-icon-close);
|
||
}
|
||
|
||
.jp-CodeCheckIcon {
|
||
background-image: var(--jp-icon-code-check);
|
||
}
|
||
|
||
.jp-CodeIcon {
|
||
background-image: var(--jp-icon-code);
|
||
}
|
||
|
||
.jp-CollapseAllIcon {
|
||
background-image: var(--jp-icon-collapse-all);
|
||
}
|
||
|
||
.jp-ConsoleIcon {
|
||
background-image: var(--jp-icon-console);
|
||
}
|
||
|
||
.jp-CopyIcon {
|
||
background-image: var(--jp-icon-copy);
|
||
}
|
||
|
||
.jp-CopyrightIcon {
|
||
background-image: var(--jp-icon-copyright);
|
||
}
|
||
|
||
.jp-CutIcon {
|
||
background-image: var(--jp-icon-cut);
|
||
}
|
||
|
||
.jp-DeleteIcon {
|
||
background-image: var(--jp-icon-delete);
|
||
}
|
||
|
||
.jp-DownloadIcon {
|
||
background-image: var(--jp-icon-download);
|
||
}
|
||
|
||
.jp-DuplicateIcon {
|
||
background-image: var(--jp-icon-duplicate);
|
||
}
|
||
|
||
.jp-EditIcon {
|
||
background-image: var(--jp-icon-edit);
|
||
}
|
||
|
||
.jp-EllipsesIcon {
|
||
background-image: var(--jp-icon-ellipses);
|
||
}
|
||
|
||
.jp-ErrorIcon {
|
||
background-image: var(--jp-icon-error);
|
||
}
|
||
|
||
.jp-ExpandAllIcon {
|
||
background-image: var(--jp-icon-expand-all);
|
||
}
|
||
|
||
.jp-ExtensionIcon {
|
||
background-image: var(--jp-icon-extension);
|
||
}
|
||
|
||
.jp-FastForwardIcon {
|
||
background-image: var(--jp-icon-fast-forward);
|
||
}
|
||
|
||
.jp-FileIcon {
|
||
background-image: var(--jp-icon-file);
|
||
}
|
||
|
||
.jp-FileUploadIcon {
|
||
background-image: var(--jp-icon-file-upload);
|
||
}
|
||
|
||
.jp-FilterDotIcon {
|
||
background-image: var(--jp-icon-filter-dot);
|
||
}
|
||
|
||
.jp-FilterIcon {
|
||
background-image: var(--jp-icon-filter);
|
||
}
|
||
|
||
.jp-FilterListIcon {
|
||
background-image: var(--jp-icon-filter-list);
|
||
}
|
||
|
||
.jp-FolderFavoriteIcon {
|
||
background-image: var(--jp-icon-folder-favorite);
|
||
}
|
||
|
||
.jp-FolderIcon {
|
||
background-image: var(--jp-icon-folder);
|
||
}
|
||
|
||
.jp-HomeIcon {
|
||
background-image: var(--jp-icon-home);
|
||
}
|
||
|
||
.jp-Html5Icon {
|
||
background-image: var(--jp-icon-html5);
|
||
}
|
||
|
||
.jp-ImageIcon {
|
||
background-image: var(--jp-icon-image);
|
||
}
|
||
|
||
.jp-InfoIcon {
|
||
background-image: var(--jp-icon-info);
|
||
}
|
||
|
||
.jp-InspectorIcon {
|
||
background-image: var(--jp-icon-inspector);
|
||
}
|
||
|
||
.jp-JsonIcon {
|
||
background-image: var(--jp-icon-json);
|
||
}
|
||
|
||
.jp-JuliaIcon {
|
||
background-image: var(--jp-icon-julia);
|
||
}
|
||
|
||
.jp-JupyterFaviconIcon {
|
||
background-image: var(--jp-icon-jupyter-favicon);
|
||
}
|
||
|
||
.jp-JupyterIcon {
|
||
background-image: var(--jp-icon-jupyter);
|
||
}
|
||
|
||
.jp-JupyterlabWordmarkIcon {
|
||
background-image: var(--jp-icon-jupyterlab-wordmark);
|
||
}
|
||
|
||
.jp-KernelIcon {
|
||
background-image: var(--jp-icon-kernel);
|
||
}
|
||
|
||
.jp-KeyboardIcon {
|
||
background-image: var(--jp-icon-keyboard);
|
||
}
|
||
|
||
.jp-LaunchIcon {
|
||
background-image: var(--jp-icon-launch);
|
||
}
|
||
|
||
.jp-LauncherIcon {
|
||
background-image: var(--jp-icon-launcher);
|
||
}
|
||
|
||
.jp-LineFormIcon {
|
||
background-image: var(--jp-icon-line-form);
|
||
}
|
||
|
||
.jp-LinkIcon {
|
||
background-image: var(--jp-icon-link);
|
||
}
|
||
|
||
.jp-ListIcon {
|
||
background-image: var(--jp-icon-list);
|
||
}
|
||
|
||
.jp-MarkdownIcon {
|
||
background-image: var(--jp-icon-markdown);
|
||
}
|
||
|
||
.jp-MoveDownIcon {
|
||
background-image: var(--jp-icon-move-down);
|
||
}
|
||
|
||
.jp-MoveUpIcon {
|
||
background-image: var(--jp-icon-move-up);
|
||
}
|
||
|
||
.jp-NewFolderIcon {
|
||
background-image: var(--jp-icon-new-folder);
|
||
}
|
||
|
||
.jp-NotTrustedIcon {
|
||
background-image: var(--jp-icon-not-trusted);
|
||
}
|
||
|
||
.jp-NotebookIcon {
|
||
background-image: var(--jp-icon-notebook);
|
||
}
|
||
|
||
.jp-NumberingIcon {
|
||
background-image: var(--jp-icon-numbering);
|
||
}
|
||
|
||
.jp-OfflineBoltIcon {
|
||
background-image: var(--jp-icon-offline-bolt);
|
||
}
|
||
|
||
.jp-PaletteIcon {
|
||
background-image: var(--jp-icon-palette);
|
||
}
|
||
|
||
.jp-PasteIcon {
|
||
background-image: var(--jp-icon-paste);
|
||
}
|
||
|
||
.jp-PdfIcon {
|
||
background-image: var(--jp-icon-pdf);
|
||
}
|
||
|
||
.jp-PythonIcon {
|
||
background-image: var(--jp-icon-python);
|
||
}
|
||
|
||
.jp-RKernelIcon {
|
||
background-image: var(--jp-icon-r-kernel);
|
||
}
|
||
|
||
.jp-ReactIcon {
|
||
background-image: var(--jp-icon-react);
|
||
}
|
||
|
||
.jp-RedoIcon {
|
||
background-image: var(--jp-icon-redo);
|
||
}
|
||
|
||
.jp-RefreshIcon {
|
||
background-image: var(--jp-icon-refresh);
|
||
}
|
||
|
||
.jp-RegexIcon {
|
||
background-image: var(--jp-icon-regex);
|
||
}
|
||
|
||
.jp-RunIcon {
|
||
background-image: var(--jp-icon-run);
|
||
}
|
||
|
||
.jp-RunningIcon {
|
||
background-image: var(--jp-icon-running);
|
||
}
|
||
|
||
.jp-SaveIcon {
|
||
background-image: var(--jp-icon-save);
|
||
}
|
||
|
||
.jp-SearchIcon {
|
||
background-image: var(--jp-icon-search);
|
||
}
|
||
|
||
.jp-SettingsIcon {
|
||
background-image: var(--jp-icon-settings);
|
||
}
|
||
|
||
.jp-ShareIcon {
|
||
background-image: var(--jp-icon-share);
|
||
}
|
||
|
||
.jp-SpreadsheetIcon {
|
||
background-image: var(--jp-icon-spreadsheet);
|
||
}
|
||
|
||
.jp-StopIcon {
|
||
background-image: var(--jp-icon-stop);
|
||
}
|
||
|
||
.jp-TabIcon {
|
||
background-image: var(--jp-icon-tab);
|
||
}
|
||
|
||
.jp-TableRowsIcon {
|
||
background-image: var(--jp-icon-table-rows);
|
||
}
|
||
|
||
.jp-TagIcon {
|
||
background-image: var(--jp-icon-tag);
|
||
}
|
||
|
||
.jp-TerminalIcon {
|
||
background-image: var(--jp-icon-terminal);
|
||
}
|
||
|
||
.jp-TextEditorIcon {
|
||
background-image: var(--jp-icon-text-editor);
|
||
}
|
||
|
||
.jp-TocIcon {
|
||
background-image: var(--jp-icon-toc);
|
||
}
|
||
|
||
.jp-TreeViewIcon {
|
||
background-image: var(--jp-icon-tree-view);
|
||
}
|
||
|
||
.jp-TrustedIcon {
|
||
background-image: var(--jp-icon-trusted);
|
||
}
|
||
|
||
.jp-UndoIcon {
|
||
background-image: var(--jp-icon-undo);
|
||
}
|
||
|
||
.jp-UserIcon {
|
||
background-image: var(--jp-icon-user);
|
||
}
|
||
|
||
.jp-UsersIcon {
|
||
background-image: var(--jp-icon-users);
|
||
}
|
||
|
||
.jp-VegaIcon {
|
||
background-image: var(--jp-icon-vega);
|
||
}
|
||
|
||
.jp-WordIcon {
|
||
background-image: var(--jp-icon-word);
|
||
}
|
||
|
||
.jp-YamlIcon {
|
||
background-image: var(--jp-icon-yaml);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/**
|
||
* (DEPRECATED) Support for consuming icons as CSS background images
|
||
*/
|
||
|
||
.jp-Icon,
|
||
.jp-MaterialIcon {
|
||
background-position: center;
|
||
background-repeat: no-repeat;
|
||
background-size: 16px;
|
||
min-width: 16px;
|
||
min-height: 16px;
|
||
}
|
||
|
||
.jp-Icon-cover {
|
||
background-position: center;
|
||
background-repeat: no-repeat;
|
||
background-size: cover;
|
||
}
|
||
|
||
/**
|
||
* (DEPRECATED) Support for specific CSS icon sizes
|
||
*/
|
||
|
||
.jp-Icon-16 {
|
||
background-size: 16px;
|
||
min-width: 16px;
|
||
min-height: 16px;
|
||
}
|
||
|
||
.jp-Icon-18 {
|
||
background-size: 18px;
|
||
min-width: 18px;
|
||
min-height: 18px;
|
||
}
|
||
|
||
.jp-Icon-20 {
|
||
background-size: 20px;
|
||
min-width: 20px;
|
||
min-height: 20px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-TabBar .lm-TabBar-addButton {
|
||
align-items: center;
|
||
display: flex;
|
||
padding: 4px;
|
||
padding-bottom: 5px;
|
||
margin-right: 1px;
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.lm-TabBar .lm-TabBar-addButton:hover {
|
||
background-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.lm-DockPanel-tabBar .lm-TabBar-tab {
|
||
width: var(--jp-private-horizontal-tab-width);
|
||
}
|
||
|
||
.lm-DockPanel-tabBar .lm-TabBar-content {
|
||
flex: unset;
|
||
}
|
||
|
||
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/**
|
||
* Support for icons as inline SVG HTMLElements
|
||
*/
|
||
|
||
/* recolor the primary elements of an icon */
|
||
.jp-icon0[fill] {
|
||
fill: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon1[fill] {
|
||
fill: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon2[fill] {
|
||
fill: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon3[fill] {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon4[fill] {
|
||
fill: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
.jp-icon0[stroke] {
|
||
stroke: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon1[stroke] {
|
||
stroke: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon2[stroke] {
|
||
stroke: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon3[stroke] {
|
||
stroke: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon4[stroke] {
|
||
stroke: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
/* recolor the accent elements of an icon */
|
||
.jp-icon-accent0[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-accent1[fill] {
|
||
fill: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-accent2[fill] {
|
||
fill: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-accent3[fill] {
|
||
fill: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-accent4[fill] {
|
||
fill: var(--jp-layout-color4);
|
||
}
|
||
|
||
.jp-icon-accent0[stroke] {
|
||
stroke: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-accent1[stroke] {
|
||
stroke: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-accent2[stroke] {
|
||
stroke: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-accent3[stroke] {
|
||
stroke: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-accent4[stroke] {
|
||
stroke: var(--jp-layout-color4);
|
||
}
|
||
|
||
/* set the color of an icon to transparent */
|
||
.jp-icon-none[fill] {
|
||
fill: none;
|
||
}
|
||
|
||
.jp-icon-none[stroke] {
|
||
stroke: none;
|
||
}
|
||
|
||
/* brand icon colors. Same for light and dark */
|
||
.jp-icon-brand0[fill] {
|
||
fill: var(--jp-brand-color0);
|
||
}
|
||
|
||
.jp-icon-brand1[fill] {
|
||
fill: var(--jp-brand-color1);
|
||
}
|
||
|
||
.jp-icon-brand2[fill] {
|
||
fill: var(--jp-brand-color2);
|
||
}
|
||
|
||
.jp-icon-brand3[fill] {
|
||
fill: var(--jp-brand-color3);
|
||
}
|
||
|
||
.jp-icon-brand4[fill] {
|
||
fill: var(--jp-brand-color4);
|
||
}
|
||
|
||
.jp-icon-brand0[stroke] {
|
||
stroke: var(--jp-brand-color0);
|
||
}
|
||
|
||
.jp-icon-brand1[stroke] {
|
||
stroke: var(--jp-brand-color1);
|
||
}
|
||
|
||
.jp-icon-brand2[stroke] {
|
||
stroke: var(--jp-brand-color2);
|
||
}
|
||
|
||
.jp-icon-brand3[stroke] {
|
||
stroke: var(--jp-brand-color3);
|
||
}
|
||
|
||
.jp-icon-brand4[stroke] {
|
||
stroke: var(--jp-brand-color4);
|
||
}
|
||
|
||
/* warn icon colors. Same for light and dark */
|
||
.jp-icon-warn0[fill] {
|
||
fill: var(--jp-warn-color0);
|
||
}
|
||
|
||
.jp-icon-warn1[fill] {
|
||
fill: var(--jp-warn-color1);
|
||
}
|
||
|
||
.jp-icon-warn2[fill] {
|
||
fill: var(--jp-warn-color2);
|
||
}
|
||
|
||
.jp-icon-warn3[fill] {
|
||
fill: var(--jp-warn-color3);
|
||
}
|
||
|
||
.jp-icon-warn0[stroke] {
|
||
stroke: var(--jp-warn-color0);
|
||
}
|
||
|
||
.jp-icon-warn1[stroke] {
|
||
stroke: var(--jp-warn-color1);
|
||
}
|
||
|
||
.jp-icon-warn2[stroke] {
|
||
stroke: var(--jp-warn-color2);
|
||
}
|
||
|
||
.jp-icon-warn3[stroke] {
|
||
stroke: var(--jp-warn-color3);
|
||
}
|
||
|
||
/* icon colors that contrast well with each other and most backgrounds */
|
||
.jp-icon-contrast0[fill] {
|
||
fill: var(--jp-icon-contrast-color0);
|
||
}
|
||
|
||
.jp-icon-contrast1[fill] {
|
||
fill: var(--jp-icon-contrast-color1);
|
||
}
|
||
|
||
.jp-icon-contrast2[fill] {
|
||
fill: var(--jp-icon-contrast-color2);
|
||
}
|
||
|
||
.jp-icon-contrast3[fill] {
|
||
fill: var(--jp-icon-contrast-color3);
|
||
}
|
||
|
||
.jp-icon-contrast0[stroke] {
|
||
stroke: var(--jp-icon-contrast-color0);
|
||
}
|
||
|
||
.jp-icon-contrast1[stroke] {
|
||
stroke: var(--jp-icon-contrast-color1);
|
||
}
|
||
|
||
.jp-icon-contrast2[stroke] {
|
||
stroke: var(--jp-icon-contrast-color2);
|
||
}
|
||
|
||
.jp-icon-contrast3[stroke] {
|
||
stroke: var(--jp-icon-contrast-color3);
|
||
}
|
||
|
||
.jp-icon-dot[fill] {
|
||
fill: var(--jp-warn-color0);
|
||
}
|
||
|
||
.jp-jupyter-icon-color[fill] {
|
||
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
|
||
}
|
||
|
||
.jp-notebook-icon-color[fill] {
|
||
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
|
||
}
|
||
|
||
.jp-json-icon-color[fill] {
|
||
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
|
||
}
|
||
|
||
.jp-console-icon-color[fill] {
|
||
fill: var(--jp-console-icon-color, white);
|
||
}
|
||
|
||
.jp-console-icon-background-color[fill] {
|
||
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
|
||
}
|
||
|
||
.jp-terminal-icon-color[fill] {
|
||
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
|
||
}
|
||
|
||
.jp-terminal-icon-background-color[fill] {
|
||
fill: var(
|
||
--jp-terminal-icon-background-color,
|
||
var(--jp-inverse-layout-color2)
|
||
);
|
||
}
|
||
|
||
.jp-text-editor-icon-color[fill] {
|
||
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
|
||
}
|
||
|
||
.jp-inspector-icon-color[fill] {
|
||
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
|
||
}
|
||
|
||
/* CSS for icons in selected filebrowser listing items */
|
||
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
|
||
fill: #fff;
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
|
||
fill: var(--jp-brand-color1);
|
||
}
|
||
|
||
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
|
||
|
||
/**
|
||
* TODO: come up with non css-hack solution for showing the busy icon on top
|
||
* of the close icon
|
||
* CSS for complex behavior of close icon of tabs in the main area tabbar
|
||
*/
|
||
.lm-DockPanel-tabBar
|
||
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
|
||
> .lm-TabBar-tabCloseIcon
|
||
> :not(:hover)
|
||
> .jp-icon3[fill] {
|
||
fill: none;
|
||
}
|
||
|
||
.lm-DockPanel-tabBar
|
||
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
|
||
> .lm-TabBar-tabCloseIcon
|
||
> :not(:hover)
|
||
> .jp-icon-busy[fill] {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
|
||
|
||
/* CSS for icons in status bar */
|
||
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
|
||
fill: #fff;
|
||
}
|
||
|
||
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
|
||
fill: var(--jp-brand-color1);
|
||
}
|
||
|
||
/* special handling for splash icon CSS. While the theme CSS reloads during
|
||
splash, the splash icon can loose theming. To prevent that, we set a
|
||
default for its color variable */
|
||
:root {
|
||
--jp-warn-color0: var(--md-orange-700);
|
||
}
|
||
|
||
/* not sure what to do with this one, used in filebrowser listing */
|
||
.jp-DragIcon {
|
||
margin-right: 4px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/**
|
||
* Support for alt colors for icons as inline SVG HTMLElements
|
||
*/
|
||
|
||
/* alt recolor the primary elements of an icon */
|
||
.jp-icon-alt .jp-icon0[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon1[fill] {
|
||
fill: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon2[fill] {
|
||
fill: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon3[fill] {
|
||
fill: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon4[fill] {
|
||
fill: var(--jp-layout-color4);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon0[stroke] {
|
||
stroke: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon1[stroke] {
|
||
stroke: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon2[stroke] {
|
||
stroke: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon3[stroke] {
|
||
stroke: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon4[stroke] {
|
||
stroke: var(--jp-layout-color4);
|
||
}
|
||
|
||
/* alt recolor the accent elements of an icon */
|
||
.jp-icon-alt .jp-icon-accent0[fill] {
|
||
fill: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent1[fill] {
|
||
fill: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent2[fill] {
|
||
fill: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent3[fill] {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent4[fill] {
|
||
fill: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent0[stroke] {
|
||
stroke: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent1[stroke] {
|
||
stroke: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent2[stroke] {
|
||
stroke: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent3[stroke] {
|
||
stroke: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-alt .jp-icon-accent4[stroke] {
|
||
stroke: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
|
||
display: none !important;
|
||
}
|
||
|
||
/**
|
||
* Support for hover colors for icons as inline SVG HTMLElements
|
||
*/
|
||
|
||
/**
|
||
* regular colors
|
||
*/
|
||
|
||
/* recolor the primary elements of an icon */
|
||
.jp-icon-hover :hover .jp-icon0-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon1-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon2-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon3-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon4-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
/* recolor the accent elements of an icon */
|
||
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
|
||
fill: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
|
||
fill: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
|
||
fill: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
|
||
fill: var(--jp-layout-color4);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
|
||
stroke: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
|
||
stroke: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
|
||
stroke: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
|
||
stroke: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
|
||
stroke: var(--jp-layout-color4);
|
||
}
|
||
|
||
/* set the color of an icon to transparent */
|
||
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
|
||
fill: none;
|
||
}
|
||
|
||
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
|
||
stroke: none;
|
||
}
|
||
|
||
/**
|
||
* inverse colors
|
||
*/
|
||
|
||
/* inverse recolor the primary elements of an icon */
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
|
||
fill: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
|
||
fill: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
|
||
fill: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
|
||
fill: var(--jp-layout-color4);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
|
||
stroke: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
|
||
stroke: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
|
||
stroke: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
|
||
stroke: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
|
||
stroke: var(--jp-layout-color4);
|
||
}
|
||
|
||
/* inverse recolor the accent elements of an icon */
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
|
||
fill: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color0);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color2);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
|
||
stroke: var(--jp-inverse-layout-color4);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-IFrame {
|
||
width: 100%;
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-IFrame > iframe {
|
||
border: none;
|
||
}
|
||
|
||
/*
|
||
When drag events occur, `lm-mod-override-cursor` is added to the body.
|
||
Because iframes steal all cursor events, the following two rules are necessary
|
||
to suppress pointer events while resize drags are occurring. There may be a
|
||
better solution to this problem.
|
||
*/
|
||
body.lm-mod-override-cursor .jp-IFrame {
|
||
position: relative;
|
||
}
|
||
|
||
body.lm-mod-override-cursor .jp-IFrame::before {
|
||
content: '';
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
right: 0;
|
||
bottom: 0;
|
||
background: transparent;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2016, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-HoverBox {
|
||
position: fixed;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-FormGroup-content fieldset {
|
||
border: none;
|
||
padding: 0;
|
||
min-width: 0;
|
||
width: 100%;
|
||
}
|
||
|
||
/* stylelint-disable selector-max-type */
|
||
|
||
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
|
||
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
|
||
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
|
||
font-size: var(--jp-content-font-size2);
|
||
border-color: var(--jp-input-border-color);
|
||
border-style: solid;
|
||
border-radius: var(--jp-border-radius);
|
||
border-width: 1px;
|
||
padding: 6px 8px;
|
||
background: none;
|
||
color: var(--jp-ui-font-color0);
|
||
height: inherit;
|
||
}
|
||
|
||
.jp-FormGroup-content fieldset input[type='checkbox'] {
|
||
position: relative;
|
||
top: 2px;
|
||
margin-left: 0;
|
||
}
|
||
|
||
.jp-FormGroup-content button.jp-mod-styled {
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-FormGroup-content .checkbox label {
|
||
cursor: pointer;
|
||
font-size: var(--jp-content-font-size1);
|
||
}
|
||
|
||
.jp-FormGroup-content .jp-root > fieldset > legend {
|
||
display: none;
|
||
}
|
||
|
||
.jp-FormGroup-content .jp-root > fieldset > p {
|
||
display: none;
|
||
}
|
||
|
||
/** copy of `input.jp-mod-styled:focus` style */
|
||
.jp-FormGroup-content fieldset input:focus,
|
||
.jp-FormGroup-content fieldset select:focus {
|
||
-moz-outline-radius: unset;
|
||
outline: var(--jp-border-width) solid var(--md-blue-500);
|
||
outline-offset: -1px;
|
||
box-shadow: inset 0 0 4px var(--md-blue-300);
|
||
}
|
||
|
||
.jp-FormGroup-content fieldset input:hover:not(:focus),
|
||
.jp-FormGroup-content fieldset select:hover:not(:focus) {
|
||
background-color: var(--jp-border-color2);
|
||
}
|
||
|
||
/* stylelint-enable selector-max-type */
|
||
|
||
.jp-FormGroup-content .checkbox .field-description {
|
||
/* Disable default description field for checkbox:
|
||
because other widgets do not have description fields,
|
||
we add descriptions to each widget on the field level.
|
||
*/
|
||
display: none;
|
||
}
|
||
|
||
.jp-FormGroup-content #root__description {
|
||
display: none;
|
||
}
|
||
|
||
.jp-FormGroup-content .jp-modifiedIndicator {
|
||
width: 5px;
|
||
background-color: var(--jp-brand-color2);
|
||
margin-top: 0;
|
||
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
|
||
flex-shrink: 0;
|
||
}
|
||
|
||
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
|
||
background-color: var(--jp-error-color0);
|
||
margin-right: 0.5em;
|
||
}
|
||
|
||
/* RJSF ARRAY style */
|
||
|
||
.jp-arrayFieldWrapper legend {
|
||
font-size: var(--jp-content-font-size2);
|
||
color: var(--jp-ui-font-color0);
|
||
flex-basis: 100%;
|
||
padding: 4px 0;
|
||
font-weight: var(--jp-content-heading-font-weight);
|
||
border-bottom: 1px solid var(--jp-border-color2);
|
||
}
|
||
|
||
.jp-arrayFieldWrapper .field-description {
|
||
padding: 4px 0;
|
||
white-space: pre-wrap;
|
||
}
|
||
|
||
.jp-arrayFieldWrapper .array-item {
|
||
width: 100%;
|
||
border: 1px solid var(--jp-border-color2);
|
||
border-radius: 4px;
|
||
margin: 4px;
|
||
}
|
||
|
||
.jp-ArrayOperations {
|
||
display: flex;
|
||
margin-left: 8px;
|
||
}
|
||
|
||
.jp-ArrayOperationsButton {
|
||
margin: 2px;
|
||
}
|
||
|
||
.jp-ArrayOperationsButton .jp-icon3[fill] {
|
||
fill: var(--jp-ui-font-color0);
|
||
}
|
||
|
||
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
|
||
cursor: not-allowed;
|
||
opacity: 0.5;
|
||
}
|
||
|
||
/* RJSF form validation error */
|
||
|
||
.jp-FormGroup-content .validationErrors {
|
||
color: var(--jp-error-color0);
|
||
}
|
||
|
||
/* Hide panel level error as duplicated the field level error */
|
||
.jp-FormGroup-content .panel.errors {
|
||
display: none;
|
||
}
|
||
|
||
/* RJSF normal content (settings-editor) */
|
||
|
||
.jp-FormGroup-contentNormal {
|
||
display: flex;
|
||
align-items: center;
|
||
flex-wrap: wrap;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
|
||
margin-left: 7px;
|
||
color: var(--jp-ui-font-color0);
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .jp-FormGroup-description {
|
||
flex-basis: 100%;
|
||
padding: 4px 7px;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .jp-FormGroup-default {
|
||
flex-basis: 100%;
|
||
padding: 4px 7px;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
|
||
font-size: var(--jp-content-font-size1);
|
||
font-weight: normal;
|
||
min-width: 120px;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
|
||
margin-left: 7px;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
|
||
/* Display `jp-ArrayOperations` buttons side-by-side with content except
|
||
for small screens where flex-wrap will place them one below the other.
|
||
*/
|
||
display: flex;
|
||
align-items: center;
|
||
flex-wrap: wrap;
|
||
}
|
||
|
||
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
|
||
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
|
||
margin-top: 2px;
|
||
}
|
||
|
||
/* RJSF compact content (metadata-form) */
|
||
|
||
.jp-FormGroup-content.jp-FormGroup-contentCompact {
|
||
width: 100%;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact .form-group {
|
||
display: flex;
|
||
padding: 0.5em 0.2em 0.5em 0;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact
|
||
.jp-FormGroup-compactTitle
|
||
.jp-FormGroup-description {
|
||
font-size: var(--jp-ui-font-size1);
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
|
||
padding-bottom: 0.3em;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
|
||
padding-bottom: 7px;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact
|
||
.jp-objectFieldWrapper
|
||
.jp-objectFieldWrapper
|
||
.form-group {
|
||
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
|
||
margin-top: 2px;
|
||
}
|
||
|
||
.jp-FormGroup-contentCompact ul.error-detail {
|
||
margin-block-start: 0.5em;
|
||
margin-block-end: 0.5em;
|
||
padding-inline-start: 1em;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.jp-SidePanel {
|
||
display: flex;
|
||
flex-direction: column;
|
||
min-width: var(--jp-sidebar-min-width);
|
||
overflow-y: auto;
|
||
color: var(--jp-ui-font-color1);
|
||
background: var(--jp-layout-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
}
|
||
|
||
.jp-SidePanel-header {
|
||
flex: 0 0 auto;
|
||
display: flex;
|
||
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
|
||
font-size: var(--jp-ui-font-size0);
|
||
font-weight: 600;
|
||
letter-spacing: 1px;
|
||
margin: 0;
|
||
padding: 2px;
|
||
text-transform: uppercase;
|
||
}
|
||
|
||
.jp-SidePanel-toolbar {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.jp-SidePanel-content {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
.jp-SidePanel-toolbar,
|
||
.jp-AccordionPanel-toolbar {
|
||
height: var(--jp-private-toolbar-height);
|
||
}
|
||
|
||
.jp-SidePanel-toolbar.jp-Toolbar-micro {
|
||
display: none;
|
||
}
|
||
|
||
.lm-AccordionPanel .jp-AccordionPanel-title {
|
||
box-sizing: border-box;
|
||
line-height: 25px;
|
||
margin: 0;
|
||
display: flex;
|
||
align-items: center;
|
||
background: var(--jp-layout-color1);
|
||
color: var(--jp-ui-font-color1);
|
||
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
|
||
box-shadow: var(--jp-toolbar-box-shadow);
|
||
font-size: var(--jp-ui-font-size0);
|
||
}
|
||
|
||
.jp-AccordionPanel-title {
|
||
cursor: pointer;
|
||
user-select: none;
|
||
-moz-user-select: none;
|
||
-webkit-user-select: none;
|
||
text-transform: uppercase;
|
||
}
|
||
|
||
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
|
||
/* Title is rotated for horizontal accordion panel using CSS */
|
||
display: block;
|
||
transform-origin: top left;
|
||
transform: rotate(-90deg) translate(-100%);
|
||
}
|
||
|
||
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
|
||
user-select: none;
|
||
text-overflow: ellipsis;
|
||
white-space: nowrap;
|
||
overflow: hidden;
|
||
}
|
||
|
||
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
|
||
transform: rotate(-90deg);
|
||
margin: auto 0;
|
||
height: 16px;
|
||
}
|
||
|
||
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
|
||
transform: rotate(0deg);
|
||
}
|
||
|
||
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
|
||
background: none;
|
||
box-shadow: none;
|
||
border: none;
|
||
margin-left: auto;
|
||
}
|
||
|
||
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
|
||
background: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-text-truncated {
|
||
overflow: hidden;
|
||
text-overflow: ellipsis;
|
||
white-space: nowrap;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2017, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Spinner {
|
||
position: absolute;
|
||
display: flex;
|
||
justify-content: center;
|
||
align-items: center;
|
||
z-index: 10;
|
||
left: 0;
|
||
top: 0;
|
||
width: 100%;
|
||
height: 100%;
|
||
background: var(--jp-layout-color0);
|
||
outline: none;
|
||
}
|
||
|
||
.jp-SpinnerContent {
|
||
font-size: 10px;
|
||
margin: 50px auto;
|
||
text-indent: -9999em;
|
||
width: 3em;
|
||
height: 3em;
|
||
border-radius: 50%;
|
||
background: var(--jp-brand-color3);
|
||
background: linear-gradient(
|
||
to right,
|
||
#f37626 10%,
|
||
rgba(255, 255, 255, 0) 42%
|
||
);
|
||
position: relative;
|
||
animation: load3 1s infinite linear, fadeIn 1s;
|
||
}
|
||
|
||
.jp-SpinnerContent::before {
|
||
width: 50%;
|
||
height: 50%;
|
||
background: #f37626;
|
||
border-radius: 100% 0 0;
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
content: '';
|
||
}
|
||
|
||
.jp-SpinnerContent::after {
|
||
background: var(--jp-layout-color0);
|
||
width: 75%;
|
||
height: 75%;
|
||
border-radius: 50%;
|
||
content: '';
|
||
margin: auto;
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
bottom: 0;
|
||
right: 0;
|
||
}
|
||
|
||
@keyframes fadeIn {
|
||
0% {
|
||
opacity: 0;
|
||
}
|
||
|
||
100% {
|
||
opacity: 1;
|
||
}
|
||
}
|
||
|
||
@keyframes load3 {
|
||
0% {
|
||
transform: rotate(0deg);
|
||
}
|
||
|
||
100% {
|
||
transform: rotate(360deg);
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2017, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
button.jp-mod-styled {
|
||
font-size: var(--jp-ui-font-size1);
|
||
color: var(--jp-ui-font-color0);
|
||
border: none;
|
||
box-sizing: border-box;
|
||
text-align: center;
|
||
line-height: 32px;
|
||
height: 32px;
|
||
padding: 0 12px;
|
||
letter-spacing: 0.8px;
|
||
outline: none;
|
||
appearance: none;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
}
|
||
|
||
input.jp-mod-styled {
|
||
background: var(--jp-input-background);
|
||
height: 28px;
|
||
box-sizing: border-box;
|
||
border: var(--jp-border-width) solid var(--jp-border-color1);
|
||
padding-left: 7px;
|
||
padding-right: 7px;
|
||
font-size: var(--jp-ui-font-size2);
|
||
color: var(--jp-ui-font-color0);
|
||
outline: none;
|
||
appearance: none;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
}
|
||
|
||
input[type='checkbox'].jp-mod-styled {
|
||
appearance: checkbox;
|
||
-webkit-appearance: checkbox;
|
||
-moz-appearance: checkbox;
|
||
height: auto;
|
||
}
|
||
|
||
input.jp-mod-styled:focus {
|
||
border: var(--jp-border-width) solid var(--md-blue-500);
|
||
box-shadow: inset 0 0 4px var(--md-blue-300);
|
||
}
|
||
|
||
.jp-select-wrapper {
|
||
display: flex;
|
||
position: relative;
|
||
flex-direction: column;
|
||
padding: 1px;
|
||
background-color: var(--jp-layout-color1);
|
||
box-sizing: border-box;
|
||
margin-bottom: 12px;
|
||
}
|
||
|
||
.jp-select-wrapper:not(.multiple) {
|
||
height: 28px;
|
||
}
|
||
|
||
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
|
||
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
|
||
box-shadow: var(--jp-input-box-shadow);
|
||
background-color: var(--jp-input-active-background);
|
||
}
|
||
|
||
select.jp-mod-styled:hover {
|
||
cursor: pointer;
|
||
color: var(--jp-ui-font-color0);
|
||
background-color: var(--jp-input-hover-background);
|
||
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
|
||
}
|
||
|
||
select.jp-mod-styled {
|
||
flex: 1 1 auto;
|
||
width: 100%;
|
||
font-size: var(--jp-ui-font-size2);
|
||
background: var(--jp-input-background);
|
||
color: var(--jp-ui-font-color0);
|
||
padding: 0 25px 0 8px;
|
||
border: var(--jp-border-width) solid var(--jp-input-border-color);
|
||
border-radius: 0;
|
||
outline: none;
|
||
appearance: none;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
}
|
||
|
||
select.jp-mod-styled:not([multiple]) {
|
||
height: 32px;
|
||
}
|
||
|
||
select.jp-mod-styled[multiple] {
|
||
max-height: 200px;
|
||
overflow-y: auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-switch {
|
||
display: flex;
|
||
align-items: center;
|
||
padding-left: 4px;
|
||
padding-right: 4px;
|
||
font-size: var(--jp-ui-font-size1);
|
||
background-color: transparent;
|
||
color: var(--jp-ui-font-color1);
|
||
border: none;
|
||
height: 20px;
|
||
}
|
||
|
||
.jp-switch:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-switch-label {
|
||
margin-right: 5px;
|
||
font-family: var(--jp-ui-font-family);
|
||
}
|
||
|
||
.jp-switch-track {
|
||
cursor: pointer;
|
||
background-color: var(--jp-switch-color, var(--jp-border-color1));
|
||
-webkit-transition: 0.4s;
|
||
transition: 0.4s;
|
||
border-radius: 34px;
|
||
height: 16px;
|
||
width: 35px;
|
||
position: relative;
|
||
}
|
||
|
||
.jp-switch-track::before {
|
||
content: '';
|
||
position: absolute;
|
||
height: 10px;
|
||
width: 10px;
|
||
margin: 3px;
|
||
left: 0;
|
||
background-color: var(--jp-ui-inverse-font-color1);
|
||
-webkit-transition: 0.4s;
|
||
transition: 0.4s;
|
||
border-radius: 50%;
|
||
}
|
||
|
||
.jp-switch[aria-checked='true'] .jp-switch-track {
|
||
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
|
||
}
|
||
|
||
.jp-switch[aria-checked='true'] .jp-switch-track::before {
|
||
/* track width (35) - margins (3 + 3) - thumb width (10) */
|
||
left: 19px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2016, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-private-toolbar-height: calc(
|
||
28px + var(--jp-border-width)
|
||
); /* leave 28px for content */
|
||
}
|
||
|
||
.jp-Toolbar {
|
||
color: var(--jp-ui-font-color1);
|
||
flex: 0 0 auto;
|
||
display: flex;
|
||
flex-direction: row;
|
||
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
|
||
box-shadow: var(--jp-toolbar-box-shadow);
|
||
background: var(--jp-toolbar-background);
|
||
min-height: var(--jp-toolbar-micro-height);
|
||
padding: 2px;
|
||
z-index: 8;
|
||
overflow-x: hidden;
|
||
}
|
||
|
||
/* Toolbar items */
|
||
|
||
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
|
||
flex-grow: 1;
|
||
flex-shrink: 1;
|
||
}
|
||
|
||
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
|
||
display: inline-block;
|
||
width: 32px;
|
||
background-repeat: no-repeat;
|
||
background-position: center;
|
||
background-size: 16px;
|
||
}
|
||
|
||
.jp-Toolbar > .jp-Toolbar-item {
|
||
flex: 0 0 auto;
|
||
display: flex;
|
||
padding-left: 1px;
|
||
padding-right: 1px;
|
||
font-size: var(--jp-ui-font-size1);
|
||
line-height: var(--jp-private-toolbar-height);
|
||
height: 100%;
|
||
}
|
||
|
||
/* Toolbar buttons */
|
||
|
||
/* This is the div we use to wrap the react component into a Widget */
|
||
div.jp-ToolbarButton {
|
||
color: transparent;
|
||
border: none;
|
||
box-sizing: border-box;
|
||
outline: none;
|
||
appearance: none;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
padding: 0;
|
||
margin: 0;
|
||
}
|
||
|
||
button.jp-ToolbarButtonComponent {
|
||
background: var(--jp-layout-color1);
|
||
border: none;
|
||
box-sizing: border-box;
|
||
outline: none;
|
||
appearance: none;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
padding: 0 6px;
|
||
margin: 0;
|
||
height: 24px;
|
||
border-radius: var(--jp-border-radius);
|
||
display: flex;
|
||
align-items: center;
|
||
text-align: center;
|
||
font-size: 14px;
|
||
min-width: unset;
|
||
min-height: unset;
|
||
}
|
||
|
||
button.jp-ToolbarButtonComponent:disabled {
|
||
opacity: 0.4;
|
||
}
|
||
|
||
button.jp-ToolbarButtonComponent > span {
|
||
padding: 0;
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
|
||
font-size: var(--jp-ui-font-size1);
|
||
line-height: 100%;
|
||
padding-left: 2px;
|
||
color: var(--jp-ui-font-color1);
|
||
font-family: var(--jp-ui-font-family);
|
||
}
|
||
|
||
#jp-main-dock-panel[data-mode='single-document']
|
||
.jp-MainAreaWidget
|
||
> .jp-Toolbar.jp-Toolbar-micro {
|
||
padding: 0;
|
||
min-height: 0;
|
||
}
|
||
|
||
#jp-main-dock-panel[data-mode='single-document']
|
||
.jp-MainAreaWidget
|
||
> .jp-Toolbar {
|
||
border: none;
|
||
box-shadow: none;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.jp-WindowedPanel-outer {
|
||
position: relative;
|
||
overflow-y: auto;
|
||
}
|
||
|
||
.jp-WindowedPanel-inner {
|
||
position: relative;
|
||
}
|
||
|
||
.jp-WindowedPanel-window {
|
||
position: absolute;
|
||
left: 0;
|
||
right: 0;
|
||
overflow: visible;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* Sibling imports */
|
||
|
||
body {
|
||
color: var(--jp-ui-font-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
}
|
||
|
||
/* Disable native link decoration styles everywhere outside of dialog boxes */
|
||
a {
|
||
text-decoration: unset;
|
||
color: unset;
|
||
}
|
||
|
||
a:hover {
|
||
text-decoration: unset;
|
||
color: unset;
|
||
}
|
||
|
||
/* Accessibility for links inside dialog box text */
|
||
.jp-Dialog-content a {
|
||
text-decoration: revert;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
.jp-Dialog-content a:hover {
|
||
text-decoration: revert;
|
||
}
|
||
|
||
/* Styles for ui-components */
|
||
.jp-Button {
|
||
color: var(--jp-ui-font-color2);
|
||
border-radius: var(--jp-border-radius);
|
||
padding: 0 12px;
|
||
font-size: var(--jp-ui-font-size1);
|
||
|
||
/* Copy from blueprint 3 */
|
||
display: inline-flex;
|
||
flex-direction: row;
|
||
border: none;
|
||
cursor: pointer;
|
||
align-items: center;
|
||
justify-content: center;
|
||
text-align: left;
|
||
vertical-align: middle;
|
||
min-height: 30px;
|
||
min-width: 30px;
|
||
}
|
||
|
||
.jp-Button:disabled {
|
||
cursor: not-allowed;
|
||
}
|
||
|
||
.jp-Button:empty {
|
||
padding: 0 !important;
|
||
}
|
||
|
||
.jp-Button.jp-mod-small {
|
||
min-height: 24px;
|
||
min-width: 24px;
|
||
font-size: 12px;
|
||
padding: 0 7px;
|
||
}
|
||
|
||
/* Use our own theme for hover styles */
|
||
.jp-Button.jp-mod-minimal:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-Button.jp-mod-minimal {
|
||
background: none;
|
||
}
|
||
|
||
.jp-InputGroup {
|
||
display: block;
|
||
position: relative;
|
||
}
|
||
|
||
.jp-InputGroup input {
|
||
box-sizing: border-box;
|
||
border: none;
|
||
border-radius: 0;
|
||
background-color: transparent;
|
||
color: var(--jp-ui-font-color0);
|
||
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
|
||
padding-bottom: 0;
|
||
padding-top: 0;
|
||
padding-left: 10px;
|
||
padding-right: 28px;
|
||
position: relative;
|
||
width: 100%;
|
||
-webkit-appearance: none;
|
||
-moz-appearance: none;
|
||
appearance: none;
|
||
font-size: 14px;
|
||
font-weight: 400;
|
||
height: 30px;
|
||
line-height: 30px;
|
||
outline: none;
|
||
vertical-align: middle;
|
||
}
|
||
|
||
.jp-InputGroup input:focus {
|
||
box-shadow: inset 0 0 0 var(--jp-border-width)
|
||
var(--jp-input-active-box-shadow-color),
|
||
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
|
||
}
|
||
|
||
.jp-InputGroup input:disabled {
|
||
cursor: not-allowed;
|
||
resize: block;
|
||
background-color: var(--jp-layout-color2);
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.jp-InputGroup input:disabled ~ span {
|
||
cursor: not-allowed;
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.jp-InputGroup input::placeholder,
|
||
input::placeholder {
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.jp-InputGroupAction {
|
||
position: absolute;
|
||
bottom: 1px;
|
||
right: 0;
|
||
padding: 6px;
|
||
}
|
||
|
||
.jp-HTMLSelect.jp-DefaultStyle select {
|
||
background-color: initial;
|
||
border: none;
|
||
border-radius: 0;
|
||
box-shadow: none;
|
||
color: var(--jp-ui-font-color0);
|
||
display: block;
|
||
font-size: var(--jp-ui-font-size1);
|
||
font-family: var(--jp-ui-font-family);
|
||
height: 24px;
|
||
line-height: 14px;
|
||
padding: 0 25px 0 10px;
|
||
text-align: left;
|
||
-moz-appearance: none;
|
||
-webkit-appearance: none;
|
||
}
|
||
|
||
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
|
||
background-color: var(--jp-layout-color2);
|
||
color: var(--jp-ui-font-color2);
|
||
cursor: not-allowed;
|
||
resize: block;
|
||
}
|
||
|
||
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
|
||
cursor: not-allowed;
|
||
}
|
||
|
||
/* Use our own theme for hover and option styles */
|
||
/* stylelint-disable-next-line selector-max-type */
|
||
.jp-HTMLSelect.jp-DefaultStyle select:hover,
|
||
.jp-HTMLSelect.jp-DefaultStyle select > option {
|
||
background-color: var(--jp-layout-color2);
|
||
color: var(--jp-ui-font-color0);
|
||
}
|
||
|
||
select {
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Styles
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-StatusBar-Widget {
|
||
display: flex;
|
||
align-items: center;
|
||
background: var(--jp-layout-color2);
|
||
min-height: var(--jp-statusbar-height);
|
||
justify-content: space-between;
|
||
padding: 0 10px;
|
||
}
|
||
|
||
.jp-StatusBar-Left {
|
||
display: flex;
|
||
align-items: center;
|
||
flex-direction: row;
|
||
}
|
||
|
||
.jp-StatusBar-Middle {
|
||
display: flex;
|
||
align-items: center;
|
||
}
|
||
|
||
.jp-StatusBar-Right {
|
||
display: flex;
|
||
align-items: center;
|
||
flex-direction: row-reverse;
|
||
}
|
||
|
||
.jp-StatusBar-Item {
|
||
max-height: var(--jp-statusbar-height);
|
||
margin: 0 2px;
|
||
height: var(--jp-statusbar-height);
|
||
white-space: nowrap;
|
||
text-overflow: ellipsis;
|
||
color: var(--jp-ui-font-color1);
|
||
padding: 0 6px;
|
||
}
|
||
|
||
.jp-mod-highlighted:hover {
|
||
background-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-mod-clicked {
|
||
background-color: var(--jp-brand-color1);
|
||
}
|
||
|
||
.jp-mod-clicked:hover {
|
||
background-color: var(--jp-brand-color0);
|
||
}
|
||
|
||
.jp-mod-clicked .jp-StatusBar-TextItem {
|
||
color: var(--jp-ui-inverse-font-color1);
|
||
}
|
||
|
||
.jp-StatusBar-HoverItem {
|
||
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
|
||
}
|
||
|
||
.jp-StatusBar-TextItem {
|
||
font-size: var(--jp-ui-font-size1);
|
||
font-family: var(--jp-ui-font-family);
|
||
line-height: 24px;
|
||
color: var(--jp-ui-font-color1);
|
||
}
|
||
|
||
.jp-StatusBar-GroupItem {
|
||
display: flex;
|
||
align-items: center;
|
||
flex-direction: row;
|
||
}
|
||
|
||
.jp-Statusbar-ProgressCircle svg {
|
||
display: block;
|
||
margin: 0 auto;
|
||
width: 16px;
|
||
height: 24px;
|
||
align-self: normal;
|
||
}
|
||
|
||
.jp-Statusbar-ProgressCircle path {
|
||
fill: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-Statusbar-ProgressBar-progress-bar {
|
||
height: 10px;
|
||
width: 100px;
|
||
border: solid 0.25px var(--jp-brand-color2);
|
||
border-radius: 3px;
|
||
overflow: hidden;
|
||
align-self: center;
|
||
}
|
||
|
||
.jp-Statusbar-ProgressBar-progress-bar > div {
|
||
background-color: var(--jp-brand-color2);
|
||
background-image: linear-gradient(
|
||
-45deg,
|
||
rgba(255, 255, 255, 0.2) 25%,
|
||
transparent 25%,
|
||
transparent 50%,
|
||
rgba(255, 255, 255, 0.2) 50%,
|
||
rgba(255, 255, 255, 0.2) 75%,
|
||
transparent 75%,
|
||
transparent
|
||
);
|
||
background-size: 40px 40px;
|
||
float: left;
|
||
width: 0%;
|
||
height: 100%;
|
||
font-size: 12px;
|
||
line-height: 14px;
|
||
color: #fff;
|
||
text-align: center;
|
||
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
|
||
}
|
||
|
||
.jp-Statusbar-ProgressBar-progress-bar p {
|
||
color: var(--jp-ui-font-color1);
|
||
font-family: var(--jp-ui-font-family);
|
||
font-size: var(--jp-ui-font-size1);
|
||
line-height: 10px;
|
||
width: 100px;
|
||
}
|
||
|
||
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
|
||
0% {
|
||
background-position: 0 0;
|
||
}
|
||
|
||
100% {
|
||
background-position: 40px 40px;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-private-commandpalette-search-height: 28px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Overall styles
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-CommandPalette {
|
||
padding-bottom: 0;
|
||
color: var(--jp-ui-font-color1);
|
||
background: var(--jp-layout-color1);
|
||
|
||
/* This is needed so that all font sizing of children done in ems is
|
||
* relative to this base size */
|
||
font-size: var(--jp-ui-font-size1);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Modal variant
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-ModalCommandPalette {
|
||
position: absolute;
|
||
z-index: 10000;
|
||
top: 38px;
|
||
left: 30%;
|
||
margin: 0;
|
||
padding: 4px;
|
||
width: 40%;
|
||
box-shadow: var(--jp-elevation-z4);
|
||
border-radius: 4px;
|
||
background: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-ModalCommandPalette .lm-CommandPalette {
|
||
max-height: 40vh;
|
||
}
|
||
|
||
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
|
||
display: none;
|
||
}
|
||
|
||
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
|
||
display: none;
|
||
}
|
||
|
||
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
|
||
margin-left: 4px;
|
||
margin-right: 4px;
|
||
}
|
||
|
||
.jp-ModalCommandPalette
|
||
.lm-CommandPalette
|
||
.lm-CommandPalette-item.lm-mod-disabled {
|
||
display: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Search
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-CommandPalette-search {
|
||
padding: 4px;
|
||
background-color: var(--jp-layout-color1);
|
||
z-index: 2;
|
||
}
|
||
|
||
.lm-CommandPalette-wrapper {
|
||
overflow: overlay;
|
||
padding: 0 9px;
|
||
background-color: var(--jp-input-active-background);
|
||
height: 30px;
|
||
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
|
||
}
|
||
|
||
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
|
||
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
|
||
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
|
||
}
|
||
|
||
.jp-SearchIconGroup {
|
||
color: white;
|
||
background-color: var(--jp-brand-color1);
|
||
position: absolute;
|
||
top: 4px;
|
||
right: 4px;
|
||
padding: 5px 5px 1px;
|
||
}
|
||
|
||
.jp-SearchIconGroup svg {
|
||
height: 20px;
|
||
width: 20px;
|
||
}
|
||
|
||
.jp-SearchIconGroup .jp-icon3[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.lm-CommandPalette-input {
|
||
background: transparent;
|
||
width: calc(100% - 18px);
|
||
float: left;
|
||
border: none;
|
||
outline: none;
|
||
font-size: var(--jp-ui-font-size1);
|
||
color: var(--jp-ui-font-color0);
|
||
line-height: var(--jp-private-commandpalette-search-height);
|
||
}
|
||
|
||
.lm-CommandPalette-input::-webkit-input-placeholder,
|
||
.lm-CommandPalette-input::-moz-placeholder,
|
||
.lm-CommandPalette-input:-ms-input-placeholder {
|
||
color: var(--jp-ui-font-color2);
|
||
font-size: var(--jp-ui-font-size1);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Results
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-CommandPalette-header:first-child {
|
||
margin-top: 0;
|
||
}
|
||
|
||
.lm-CommandPalette-header {
|
||
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
|
||
color: var(--jp-ui-font-color1);
|
||
cursor: pointer;
|
||
display: flex;
|
||
font-size: var(--jp-ui-font-size0);
|
||
font-weight: 600;
|
||
letter-spacing: 1px;
|
||
margin-top: 8px;
|
||
padding: 8px 0 8px 12px;
|
||
text-transform: uppercase;
|
||
}
|
||
|
||
.lm-CommandPalette-header.lm-mod-active {
|
||
background: var(--jp-layout-color2);
|
||
}
|
||
|
||
.lm-CommandPalette-header > mark {
|
||
background-color: transparent;
|
||
font-weight: bold;
|
||
color: var(--jp-ui-font-color1);
|
||
}
|
||
|
||
.lm-CommandPalette-item {
|
||
padding: 4px 12px 4px 4px;
|
||
color: var(--jp-ui-font-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
font-weight: 400;
|
||
display: flex;
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-disabled {
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-active {
|
||
color: var(--jp-ui-inverse-font-color1);
|
||
background: var(--jp-brand-color1);
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
|
||
color: var(--jp-ui-inverse-font-color0);
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
|
||
fill: var(--jp-layout-color0);
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
|
||
color: var(--jp-ui-inverse-font-color1);
|
||
background: var(--jp-brand-color1);
|
||
}
|
||
|
||
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
|
||
background: var(--jp-layout-color2);
|
||
}
|
||
|
||
.lm-CommandPalette-itemContent {
|
||
overflow: hidden;
|
||
}
|
||
|
||
.lm-CommandPalette-itemLabel > mark {
|
||
color: var(--jp-ui-font-color0);
|
||
background-color: transparent;
|
||
font-weight: bold;
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-disabled mark {
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
|
||
margin: 0 4px 0 0;
|
||
position: relative;
|
||
width: 16px;
|
||
top: 2px;
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
|
||
opacity: 0.6;
|
||
}
|
||
|
||
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-CommandPalette-itemCaption {
|
||
display: none;
|
||
}
|
||
|
||
.lm-CommandPalette-content {
|
||
background-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.lm-CommandPalette-content:empty::after {
|
||
content: 'No results';
|
||
margin: auto;
|
||
margin-top: 20px;
|
||
width: 100px;
|
||
display: block;
|
||
font-size: var(--jp-ui-font-size2);
|
||
font-family: var(--jp-ui-font-family);
|
||
font-weight: lighter;
|
||
}
|
||
|
||
.lm-CommandPalette-emptyMessage {
|
||
text-align: center;
|
||
margin-top: 24px;
|
||
line-height: 1.32;
|
||
padding: 0 8px;
|
||
color: var(--jp-content-font-color3);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2017, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Dialog {
|
||
position: absolute;
|
||
z-index: 10000;
|
||
display: flex;
|
||
flex-direction: column;
|
||
align-items: center;
|
||
justify-content: center;
|
||
top: 0;
|
||
left: 0;
|
||
margin: 0;
|
||
padding: 0;
|
||
width: 100%;
|
||
height: 100%;
|
||
background: var(--jp-dialog-background);
|
||
}
|
||
|
||
.jp-Dialog-content {
|
||
display: flex;
|
||
flex-direction: column;
|
||
margin-left: auto;
|
||
margin-right: auto;
|
||
background: var(--jp-layout-color1);
|
||
padding: 24px 24px 12px;
|
||
min-width: 300px;
|
||
min-height: 150px;
|
||
max-width: 1000px;
|
||
max-height: 500px;
|
||
box-sizing: border-box;
|
||
box-shadow: var(--jp-elevation-z20);
|
||
word-wrap: break-word;
|
||
border-radius: var(--jp-border-radius);
|
||
|
||
/* This is needed so that all font sizing of children done in ems is
|
||
* relative to this base size */
|
||
font-size: var(--jp-ui-font-size1);
|
||
color: var(--jp-ui-font-color1);
|
||
resize: both;
|
||
}
|
||
|
||
.jp-Dialog-content.jp-Dialog-content-small {
|
||
max-width: 500px;
|
||
}
|
||
|
||
.jp-Dialog-button {
|
||
overflow: visible;
|
||
}
|
||
|
||
button.jp-Dialog-button:focus {
|
||
outline: 1px solid var(--jp-brand-color1);
|
||
outline-offset: 4px;
|
||
-moz-outline-radius: 0;
|
||
}
|
||
|
||
button.jp-Dialog-button:focus::-moz-focus-inner {
|
||
border: 0;
|
||
}
|
||
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
|
||
outline-offset: 4px;
|
||
-moz-outline-radius: 0;
|
||
}
|
||
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
|
||
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
|
||
}
|
||
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
|
||
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
|
||
}
|
||
|
||
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
|
||
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
|
||
}
|
||
|
||
button.jp-Dialog-close-button {
|
||
padding: 0;
|
||
height: 100%;
|
||
min-width: unset;
|
||
min-height: unset;
|
||
}
|
||
|
||
.jp-Dialog-header {
|
||
display: flex;
|
||
justify-content: space-between;
|
||
flex: 0 0 auto;
|
||
padding-bottom: 12px;
|
||
font-size: var(--jp-ui-font-size3);
|
||
font-weight: 400;
|
||
color: var(--jp-ui-font-color1);
|
||
}
|
||
|
||
.jp-Dialog-body {
|
||
display: flex;
|
||
flex-direction: column;
|
||
flex: 1 1 auto;
|
||
font-size: var(--jp-ui-font-size1);
|
||
background: var(--jp-layout-color1);
|
||
color: var(--jp-ui-font-color1);
|
||
overflow: auto;
|
||
}
|
||
|
||
.jp-Dialog-footer {
|
||
display: flex;
|
||
flex-direction: row;
|
||
justify-content: flex-end;
|
||
align-items: center;
|
||
flex: 0 0 auto;
|
||
margin-left: -12px;
|
||
margin-right: -12px;
|
||
padding: 12px;
|
||
}
|
||
|
||
.jp-Dialog-checkbox {
|
||
padding-right: 5px;
|
||
}
|
||
|
||
.jp-Dialog-checkbox > input:focus-visible {
|
||
outline: 1px solid var(--jp-input-active-border-color);
|
||
outline-offset: 1px;
|
||
}
|
||
|
||
.jp-Dialog-spacer {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
.jp-Dialog-title {
|
||
overflow: hidden;
|
||
white-space: nowrap;
|
||
text-overflow: ellipsis;
|
||
}
|
||
|
||
.jp-Dialog-body > .jp-select-wrapper {
|
||
width: 100%;
|
||
}
|
||
|
||
.jp-Dialog-body > button {
|
||
padding: 0 16px;
|
||
}
|
||
|
||
.jp-Dialog-body > label {
|
||
line-height: 1.4;
|
||
color: var(--jp-ui-font-color0);
|
||
}
|
||
|
||
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
|
||
margin-right: 12px;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.jp-Input-Boolean-Dialog {
|
||
flex-direction: row-reverse;
|
||
align-items: end;
|
||
width: 100%;
|
||
}
|
||
|
||
.jp-Input-Boolean-Dialog > label {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2016, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-MainAreaWidget > :focus {
|
||
outline: none;
|
||
}
|
||
|
||
.jp-MainAreaWidget .jp-MainAreaWidget-error {
|
||
padding: 6px;
|
||
}
|
||
|
||
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
|
||
width: auto;
|
||
padding: 10px;
|
||
background: var(--jp-error-color3);
|
||
border: var(--jp-border-width) solid var(--jp-error-color1);
|
||
border-radius: var(--jp-border-radius);
|
||
color: var(--jp-ui-font-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
white-space: pre-wrap;
|
||
word-wrap: break-word;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/**
|
||
* google-material-color v1.2.6
|
||
* https://github.com/danlevan/google-material-color
|
||
*/
|
||
:root {
|
||
--md-red-50: #ffebee;
|
||
--md-red-100: #ffcdd2;
|
||
--md-red-200: #ef9a9a;
|
||
--md-red-300: #e57373;
|
||
--md-red-400: #ef5350;
|
||
--md-red-500: #f44336;
|
||
--md-red-600: #e53935;
|
||
--md-red-700: #d32f2f;
|
||
--md-red-800: #c62828;
|
||
--md-red-900: #b71c1c;
|
||
--md-red-A100: #ff8a80;
|
||
--md-red-A200: #ff5252;
|
||
--md-red-A400: #ff1744;
|
||
--md-red-A700: #d50000;
|
||
--md-pink-50: #fce4ec;
|
||
--md-pink-100: #f8bbd0;
|
||
--md-pink-200: #f48fb1;
|
||
--md-pink-300: #f06292;
|
||
--md-pink-400: #ec407a;
|
||
--md-pink-500: #e91e63;
|
||
--md-pink-600: #d81b60;
|
||
--md-pink-700: #c2185b;
|
||
--md-pink-800: #ad1457;
|
||
--md-pink-900: #880e4f;
|
||
--md-pink-A100: #ff80ab;
|
||
--md-pink-A200: #ff4081;
|
||
--md-pink-A400: #f50057;
|
||
--md-pink-A700: #c51162;
|
||
--md-purple-50: #f3e5f5;
|
||
--md-purple-100: #e1bee7;
|
||
--md-purple-200: #ce93d8;
|
||
--md-purple-300: #ba68c8;
|
||
--md-purple-400: #ab47bc;
|
||
--md-purple-500: #9c27b0;
|
||
--md-purple-600: #8e24aa;
|
||
--md-purple-700: #7b1fa2;
|
||
--md-purple-800: #6a1b9a;
|
||
--md-purple-900: #4a148c;
|
||
--md-purple-A100: #ea80fc;
|
||
--md-purple-A200: #e040fb;
|
||
--md-purple-A400: #d500f9;
|
||
--md-purple-A700: #a0f;
|
||
--md-deep-purple-50: #ede7f6;
|
||
--md-deep-purple-100: #d1c4e9;
|
||
--md-deep-purple-200: #b39ddb;
|
||
--md-deep-purple-300: #9575cd;
|
||
--md-deep-purple-400: #7e57c2;
|
||
--md-deep-purple-500: #673ab7;
|
||
--md-deep-purple-600: #5e35b1;
|
||
--md-deep-purple-700: #512da8;
|
||
--md-deep-purple-800: #4527a0;
|
||
--md-deep-purple-900: #311b92;
|
||
--md-deep-purple-A100: #b388ff;
|
||
--md-deep-purple-A200: #7c4dff;
|
||
--md-deep-purple-A400: #651fff;
|
||
--md-deep-purple-A700: #6200ea;
|
||
--md-indigo-50: #e8eaf6;
|
||
--md-indigo-100: #c5cae9;
|
||
--md-indigo-200: #9fa8da;
|
||
--md-indigo-300: #7986cb;
|
||
--md-indigo-400: #5c6bc0;
|
||
--md-indigo-500: #3f51b5;
|
||
--md-indigo-600: #3949ab;
|
||
--md-indigo-700: #303f9f;
|
||
--md-indigo-800: #283593;
|
||
--md-indigo-900: #1a237e;
|
||
--md-indigo-A100: #8c9eff;
|
||
--md-indigo-A200: #536dfe;
|
||
--md-indigo-A400: #3d5afe;
|
||
--md-indigo-A700: #304ffe;
|
||
--md-blue-50: #e3f2fd;
|
||
--md-blue-100: #bbdefb;
|
||
--md-blue-200: #90caf9;
|
||
--md-blue-300: #64b5f6;
|
||
--md-blue-400: #42a5f5;
|
||
--md-blue-500: #2196f3;
|
||
--md-blue-600: #1e88e5;
|
||
--md-blue-700: #1976d2;
|
||
--md-blue-800: #1565c0;
|
||
--md-blue-900: #0d47a1;
|
||
--md-blue-A100: #82b1ff;
|
||
--md-blue-A200: #448aff;
|
||
--md-blue-A400: #2979ff;
|
||
--md-blue-A700: #2962ff;
|
||
--md-light-blue-50: #e1f5fe;
|
||
--md-light-blue-100: #b3e5fc;
|
||
--md-light-blue-200: #81d4fa;
|
||
--md-light-blue-300: #4fc3f7;
|
||
--md-light-blue-400: #29b6f6;
|
||
--md-light-blue-500: #03a9f4;
|
||
--md-light-blue-600: #039be5;
|
||
--md-light-blue-700: #0288d1;
|
||
--md-light-blue-800: #0277bd;
|
||
--md-light-blue-900: #01579b;
|
||
--md-light-blue-A100: #80d8ff;
|
||
--md-light-blue-A200: #40c4ff;
|
||
--md-light-blue-A400: #00b0ff;
|
||
--md-light-blue-A700: #0091ea;
|
||
--md-cyan-50: #e0f7fa;
|
||
--md-cyan-100: #b2ebf2;
|
||
--md-cyan-200: #80deea;
|
||
--md-cyan-300: #4dd0e1;
|
||
--md-cyan-400: #26c6da;
|
||
--md-cyan-500: #00bcd4;
|
||
--md-cyan-600: #00acc1;
|
||
--md-cyan-700: #0097a7;
|
||
--md-cyan-800: #00838f;
|
||
--md-cyan-900: #006064;
|
||
--md-cyan-A100: #84ffff;
|
||
--md-cyan-A200: #18ffff;
|
||
--md-cyan-A400: #00e5ff;
|
||
--md-cyan-A700: #00b8d4;
|
||
--md-teal-50: #e0f2f1;
|
||
--md-teal-100: #b2dfdb;
|
||
--md-teal-200: #80cbc4;
|
||
--md-teal-300: #4db6ac;
|
||
--md-teal-400: #26a69a;
|
||
--md-teal-500: #009688;
|
||
--md-teal-600: #00897b;
|
||
--md-teal-700: #00796b;
|
||
--md-teal-800: #00695c;
|
||
--md-teal-900: #004d40;
|
||
--md-teal-A100: #a7ffeb;
|
||
--md-teal-A200: #64ffda;
|
||
--md-teal-A400: #1de9b6;
|
||
--md-teal-A700: #00bfa5;
|
||
--md-green-50: #e8f5e9;
|
||
--md-green-100: #c8e6c9;
|
||
--md-green-200: #a5d6a7;
|
||
--md-green-300: #81c784;
|
||
--md-green-400: #66bb6a;
|
||
--md-green-500: #4caf50;
|
||
--md-green-600: #43a047;
|
||
--md-green-700: #388e3c;
|
||
--md-green-800: #2e7d32;
|
||
--md-green-900: #1b5e20;
|
||
--md-green-A100: #b9f6ca;
|
||
--md-green-A200: #69f0ae;
|
||
--md-green-A400: #00e676;
|
||
--md-green-A700: #00c853;
|
||
--md-light-green-50: #f1f8e9;
|
||
--md-light-green-100: #dcedc8;
|
||
--md-light-green-200: #c5e1a5;
|
||
--md-light-green-300: #aed581;
|
||
--md-light-green-400: #9ccc65;
|
||
--md-light-green-500: #8bc34a;
|
||
--md-light-green-600: #7cb342;
|
||
--md-light-green-700: #689f38;
|
||
--md-light-green-800: #558b2f;
|
||
--md-light-green-900: #33691e;
|
||
--md-light-green-A100: #ccff90;
|
||
--md-light-green-A200: #b2ff59;
|
||
--md-light-green-A400: #76ff03;
|
||
--md-light-green-A700: #64dd17;
|
||
--md-lime-50: #f9fbe7;
|
||
--md-lime-100: #f0f4c3;
|
||
--md-lime-200: #e6ee9c;
|
||
--md-lime-300: #dce775;
|
||
--md-lime-400: #d4e157;
|
||
--md-lime-500: #cddc39;
|
||
--md-lime-600: #c0ca33;
|
||
--md-lime-700: #afb42b;
|
||
--md-lime-800: #9e9d24;
|
||
--md-lime-900: #827717;
|
||
--md-lime-A100: #f4ff81;
|
||
--md-lime-A200: #eeff41;
|
||
--md-lime-A400: #c6ff00;
|
||
--md-lime-A700: #aeea00;
|
||
--md-yellow-50: #fffde7;
|
||
--md-yellow-100: #fff9c4;
|
||
--md-yellow-200: #fff59d;
|
||
--md-yellow-300: #fff176;
|
||
--md-yellow-400: #ffee58;
|
||
--md-yellow-500: #ffeb3b;
|
||
--md-yellow-600: #fdd835;
|
||
--md-yellow-700: #fbc02d;
|
||
--md-yellow-800: #f9a825;
|
||
--md-yellow-900: #f57f17;
|
||
--md-yellow-A100: #ffff8d;
|
||
--md-yellow-A200: #ff0;
|
||
--md-yellow-A400: #ffea00;
|
||
--md-yellow-A700: #ffd600;
|
||
--md-amber-50: #fff8e1;
|
||
--md-amber-100: #ffecb3;
|
||
--md-amber-200: #ffe082;
|
||
--md-amber-300: #ffd54f;
|
||
--md-amber-400: #ffca28;
|
||
--md-amber-500: #ffc107;
|
||
--md-amber-600: #ffb300;
|
||
--md-amber-700: #ffa000;
|
||
--md-amber-800: #ff8f00;
|
||
--md-amber-900: #ff6f00;
|
||
--md-amber-A100: #ffe57f;
|
||
--md-amber-A200: #ffd740;
|
||
--md-amber-A400: #ffc400;
|
||
--md-amber-A700: #ffab00;
|
||
--md-orange-50: #fff3e0;
|
||
--md-orange-100: #ffe0b2;
|
||
--md-orange-200: #ffcc80;
|
||
--md-orange-300: #ffb74d;
|
||
--md-orange-400: #ffa726;
|
||
--md-orange-500: #ff9800;
|
||
--md-orange-600: #fb8c00;
|
||
--md-orange-700: #f57c00;
|
||
--md-orange-800: #ef6c00;
|
||
--md-orange-900: #e65100;
|
||
--md-orange-A100: #ffd180;
|
||
--md-orange-A200: #ffab40;
|
||
--md-orange-A400: #ff9100;
|
||
--md-orange-A700: #ff6d00;
|
||
--md-deep-orange-50: #fbe9e7;
|
||
--md-deep-orange-100: #ffccbc;
|
||
--md-deep-orange-200: #ffab91;
|
||
--md-deep-orange-300: #ff8a65;
|
||
--md-deep-orange-400: #ff7043;
|
||
--md-deep-orange-500: #ff5722;
|
||
--md-deep-orange-600: #f4511e;
|
||
--md-deep-orange-700: #e64a19;
|
||
--md-deep-orange-800: #d84315;
|
||
--md-deep-orange-900: #bf360c;
|
||
--md-deep-orange-A100: #ff9e80;
|
||
--md-deep-orange-A200: #ff6e40;
|
||
--md-deep-orange-A400: #ff3d00;
|
||
--md-deep-orange-A700: #dd2c00;
|
||
--md-brown-50: #efebe9;
|
||
--md-brown-100: #d7ccc8;
|
||
--md-brown-200: #bcaaa4;
|
||
--md-brown-300: #a1887f;
|
||
--md-brown-400: #8d6e63;
|
||
--md-brown-500: #795548;
|
||
--md-brown-600: #6d4c41;
|
||
--md-brown-700: #5d4037;
|
||
--md-brown-800: #4e342e;
|
||
--md-brown-900: #3e2723;
|
||
--md-grey-50: #fafafa;
|
||
--md-grey-100: #f5f5f5;
|
||
--md-grey-200: #eee;
|
||
--md-grey-300: #e0e0e0;
|
||
--md-grey-400: #bdbdbd;
|
||
--md-grey-500: #9e9e9e;
|
||
--md-grey-600: #757575;
|
||
--md-grey-700: #616161;
|
||
--md-grey-800: #424242;
|
||
--md-grey-900: #212121;
|
||
--md-blue-grey-50: #eceff1;
|
||
--md-blue-grey-100: #cfd8dc;
|
||
--md-blue-grey-200: #b0bec5;
|
||
--md-blue-grey-300: #90a4ae;
|
||
--md-blue-grey-400: #78909c;
|
||
--md-blue-grey-500: #607d8b;
|
||
--md-blue-grey-600: #546e7a;
|
||
--md-blue-grey-700: #455a64;
|
||
--md-blue-grey-800: #37474f;
|
||
--md-blue-grey-900: #263238;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2017, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| RenderedText
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
/* This is the padding value to fill the gaps between lines containing spans with background color. */
|
||
--jp-private-code-span-padding: calc(
|
||
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
|
||
);
|
||
}
|
||
|
||
.jp-RenderedText {
|
||
text-align: left;
|
||
padding-left: var(--jp-code-padding);
|
||
line-height: var(--jp-code-line-height);
|
||
font-family: var(--jp-code-font-family);
|
||
}
|
||
|
||
.jp-RenderedText pre,
|
||
.jp-RenderedJavaScript pre,
|
||
.jp-RenderedHTMLCommon pre {
|
||
color: var(--jp-content-font-color1);
|
||
font-size: var(--jp-code-font-size);
|
||
border: none;
|
||
margin: 0;
|
||
padding: 0;
|
||
}
|
||
|
||
.jp-RenderedText pre a:link {
|
||
text-decoration: none;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
.jp-RenderedText pre a:hover {
|
||
text-decoration: underline;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
.jp-RenderedText pre a:visited {
|
||
text-decoration: none;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
/* console foregrounds and backgrounds */
|
||
.jp-RenderedText pre .ansi-black-fg {
|
||
color: #3e424d;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-red-fg {
|
||
color: #e75c58;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-green-fg {
|
||
color: #00a250;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-yellow-fg {
|
||
color: #ddb62b;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-blue-fg {
|
||
color: #208ffb;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-magenta-fg {
|
||
color: #d160c4;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-cyan-fg {
|
||
color: #60c6c8;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-white-fg {
|
||
color: #c5c1b4;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-black-bg {
|
||
background-color: #3e424d;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-red-bg {
|
||
background-color: #e75c58;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-green-bg {
|
||
background-color: #00a250;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-yellow-bg {
|
||
background-color: #ddb62b;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-blue-bg {
|
||
background-color: #208ffb;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-magenta-bg {
|
||
background-color: #d160c4;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-cyan-bg {
|
||
background-color: #60c6c8;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-white-bg {
|
||
background-color: #c5c1b4;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-black-intense-fg {
|
||
color: #282c36;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-red-intense-fg {
|
||
color: #b22b31;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-green-intense-fg {
|
||
color: #007427;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-yellow-intense-fg {
|
||
color: #b27d12;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-blue-intense-fg {
|
||
color: #0065ca;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-magenta-intense-fg {
|
||
color: #a03196;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-cyan-intense-fg {
|
||
color: #258f8f;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-white-intense-fg {
|
||
color: #a1a6b2;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-black-intense-bg {
|
||
background-color: #282c36;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-red-intense-bg {
|
||
background-color: #b22b31;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-green-intense-bg {
|
||
background-color: #007427;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-yellow-intense-bg {
|
||
background-color: #b27d12;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-blue-intense-bg {
|
||
background-color: #0065ca;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-magenta-intense-bg {
|
||
background-color: #a03196;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-cyan-intense-bg {
|
||
background-color: #258f8f;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-white-intense-bg {
|
||
background-color: #a1a6b2;
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-default-inverse-fg {
|
||
color: var(--jp-ui-inverse-font-color0);
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-default-inverse-bg {
|
||
background-color: var(--jp-inverse-layout-color0);
|
||
padding: var(--jp-private-code-span-padding) 0;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-bold {
|
||
font-weight: bold;
|
||
}
|
||
|
||
.jp-RenderedText pre .ansi-underline {
|
||
text-decoration: underline;
|
||
}
|
||
|
||
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
|
||
background: var(--jp-rendermime-error-background);
|
||
padding-top: var(--jp-code-padding);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| RenderedLatex
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-RenderedLatex {
|
||
color: var(--jp-content-font-color1);
|
||
font-size: var(--jp-content-font-size1);
|
||
line-height: var(--jp-content-line-height);
|
||
}
|
||
|
||
/* Left-justify outputs.*/
|
||
.jp-OutputArea-output.jp-RenderedLatex {
|
||
padding: var(--jp-code-padding);
|
||
text-align: left;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| RenderedHTML
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-RenderedHTMLCommon {
|
||
color: var(--jp-content-font-color1);
|
||
font-family: var(--jp-content-font-family);
|
||
font-size: var(--jp-content-font-size1);
|
||
line-height: var(--jp-content-line-height);
|
||
|
||
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
|
||
padding-right: 20px;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon em {
|
||
font-style: italic;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon strong {
|
||
font-weight: bold;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon u {
|
||
text-decoration: underline;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon a:link {
|
||
text-decoration: none;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon a:hover {
|
||
text-decoration: underline;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon a:visited {
|
||
text-decoration: none;
|
||
color: var(--jp-content-link-color);
|
||
}
|
||
|
||
/* Headings */
|
||
|
||
.jp-RenderedHTMLCommon h1,
|
||
.jp-RenderedHTMLCommon h2,
|
||
.jp-RenderedHTMLCommon h3,
|
||
.jp-RenderedHTMLCommon h4,
|
||
.jp-RenderedHTMLCommon h5,
|
||
.jp-RenderedHTMLCommon h6 {
|
||
line-height: var(--jp-content-heading-line-height);
|
||
font-weight: var(--jp-content-heading-font-weight);
|
||
font-style: normal;
|
||
margin: var(--jp-content-heading-margin-top) 0
|
||
var(--jp-content-heading-margin-bottom) 0;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h1:first-child,
|
||
.jp-RenderedHTMLCommon h2:first-child,
|
||
.jp-RenderedHTMLCommon h3:first-child,
|
||
.jp-RenderedHTMLCommon h4:first-child,
|
||
.jp-RenderedHTMLCommon h5:first-child,
|
||
.jp-RenderedHTMLCommon h6:first-child {
|
||
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h1:last-child,
|
||
.jp-RenderedHTMLCommon h2:last-child,
|
||
.jp-RenderedHTMLCommon h3:last-child,
|
||
.jp-RenderedHTMLCommon h4:last-child,
|
||
.jp-RenderedHTMLCommon h5:last-child,
|
||
.jp-RenderedHTMLCommon h6:last-child {
|
||
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h1 {
|
||
font-size: var(--jp-content-font-size5);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h2 {
|
||
font-size: var(--jp-content-font-size4);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h3 {
|
||
font-size: var(--jp-content-font-size3);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h4 {
|
||
font-size: var(--jp-content-font-size2);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h5 {
|
||
font-size: var(--jp-content-font-size1);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon h6 {
|
||
font-size: var(--jp-content-font-size0);
|
||
}
|
||
|
||
/* Lists */
|
||
|
||
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
|
||
|
||
.jp-RenderedHTMLCommon ul:not(.list-inline),
|
||
.jp-RenderedHTMLCommon ol:not(.list-inline) {
|
||
padding-left: 2em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ul {
|
||
list-style: disc;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ul ul {
|
||
list-style: square;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ul ul ul {
|
||
list-style: circle;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol {
|
||
list-style: decimal;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol ol {
|
||
list-style: upper-alpha;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol ol ol {
|
||
list-style: lower-alpha;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol ol ol ol {
|
||
list-style: lower-roman;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol ol ol ol ol {
|
||
list-style: decimal;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ol,
|
||
.jp-RenderedHTMLCommon ul {
|
||
margin-bottom: 1em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon ul ul,
|
||
.jp-RenderedHTMLCommon ul ol,
|
||
.jp-RenderedHTMLCommon ol ul,
|
||
.jp-RenderedHTMLCommon ol ol {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
|
||
|
||
.jp-RenderedHTMLCommon hr {
|
||
color: var(--jp-border-color2);
|
||
background-color: var(--jp-border-color1);
|
||
margin-top: 1em;
|
||
margin-bottom: 1em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon > pre {
|
||
margin: 1.5em 2em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon pre,
|
||
.jp-RenderedHTMLCommon code {
|
||
border: 0;
|
||
background-color: var(--jp-layout-color0);
|
||
color: var(--jp-content-font-color1);
|
||
font-family: var(--jp-code-font-family);
|
||
font-size: inherit;
|
||
line-height: var(--jp-code-line-height);
|
||
padding: 0;
|
||
white-space: pre-wrap;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon :not(pre) > code {
|
||
background-color: var(--jp-layout-color2);
|
||
padding: 1px 5px;
|
||
}
|
||
|
||
/* Tables */
|
||
|
||
.jp-RenderedHTMLCommon table {
|
||
border-collapse: collapse;
|
||
border-spacing: 0;
|
||
border: none;
|
||
color: var(--jp-ui-font-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
table-layout: fixed;
|
||
margin-left: auto;
|
||
margin-bottom: 1em;
|
||
margin-right: auto;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon thead {
|
||
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
|
||
vertical-align: bottom;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon td,
|
||
.jp-RenderedHTMLCommon th,
|
||
.jp-RenderedHTMLCommon tr {
|
||
vertical-align: middle;
|
||
padding: 0.5em;
|
||
line-height: normal;
|
||
white-space: normal;
|
||
max-width: none;
|
||
border: none;
|
||
}
|
||
|
||
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
|
||
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
|
||
max-width: none;
|
||
}
|
||
|
||
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
|
||
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
|
||
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
|
||
text-align: right;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon th {
|
||
font-weight: bold;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
|
||
background: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
|
||
background: var(--jp-rendermime-table-row-background);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon tbody tr:hover {
|
||
background: var(--jp-rendermime-table-row-hover-background);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon p {
|
||
text-align: left;
|
||
margin: 0;
|
||
margin-bottom: 1em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon img {
|
||
-moz-force-broken-image-icon: 1;
|
||
}
|
||
|
||
/* Restrict to direct children as other images could be nested in other content. */
|
||
.jp-RenderedHTMLCommon > img {
|
||
display: block;
|
||
margin-left: 0;
|
||
margin-right: 0;
|
||
margin-bottom: 1em;
|
||
}
|
||
|
||
/* Change color behind transparent images if they need it... */
|
||
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
|
||
background-color: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
|
||
background-color: var(--jp-inverse-layout-color1);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon img,
|
||
.jp-RenderedImage img,
|
||
.jp-RenderedHTMLCommon svg,
|
||
.jp-RenderedSVG svg {
|
||
max-width: 100%;
|
||
height: auto;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
|
||
.jp-RenderedImage img.jp-mod-unconfined,
|
||
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
|
||
.jp-RenderedSVG svg.jp-mod-unconfined {
|
||
max-width: none;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert {
|
||
padding: var(--jp-notebook-padding);
|
||
border: var(--jp-border-width) solid transparent;
|
||
border-radius: var(--jp-border-radius);
|
||
margin-bottom: 1em;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-info {
|
||
color: var(--jp-info-color0);
|
||
background-color: var(--jp-info-color3);
|
||
border-color: var(--jp-info-color2);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-info hr {
|
||
border-color: var(--jp-info-color3);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-info > p:last-child,
|
||
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-warning {
|
||
color: var(--jp-warn-color0);
|
||
background-color: var(--jp-warn-color3);
|
||
border-color: var(--jp-warn-color2);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-warning hr {
|
||
border-color: var(--jp-warn-color3);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
|
||
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-success {
|
||
color: var(--jp-success-color0);
|
||
background-color: var(--jp-success-color3);
|
||
border-color: var(--jp-success-color2);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-success hr {
|
||
border-color: var(--jp-success-color3);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-success > p:last-child,
|
||
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-danger {
|
||
color: var(--jp-error-color0);
|
||
background-color: var(--jp-error-color3);
|
||
border-color: var(--jp-error-color2);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-danger hr {
|
||
border-color: var(--jp-error-color3);
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
|
||
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon blockquote {
|
||
margin: 1em 2em;
|
||
padding: 0 1em;
|
||
border-left: 5px solid var(--jp-border-color2);
|
||
}
|
||
|
||
a.jp-InternalAnchorLink {
|
||
visibility: hidden;
|
||
margin-left: 8px;
|
||
color: var(--md-blue-800);
|
||
}
|
||
|
||
h1:hover .jp-InternalAnchorLink,
|
||
h2:hover .jp-InternalAnchorLink,
|
||
h3:hover .jp-InternalAnchorLink,
|
||
h4:hover .jp-InternalAnchorLink,
|
||
h5:hover .jp-InternalAnchorLink,
|
||
h6:hover .jp-InternalAnchorLink {
|
||
visibility: visible;
|
||
}
|
||
|
||
.jp-RenderedHTMLCommon kbd {
|
||
background-color: var(--jp-rendermime-table-row-background);
|
||
border: 1px solid var(--jp-border-color0);
|
||
border-bottom-color: var(--jp-border-color2);
|
||
border-radius: 3px;
|
||
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
|
||
display: inline-block;
|
||
font-size: var(--jp-ui-font-size0);
|
||
line-height: 1em;
|
||
padding: 0.2em 0.5em;
|
||
}
|
||
|
||
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
|
||
* At the bottom of cells this is a bit too much as there is also spacing
|
||
* between cells. Going all the way to 0 gets too tight between markdown and
|
||
* code cells.
|
||
*/
|
||
.jp-RenderedHTMLCommon > *:last-child {
|
||
margin-bottom: 0.5em;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
||
|
|
||
| Distributed under the terms of the BSD 3-Clause License.
|
||
|
|
||
| The full license is in the file LICENSE, distributed with this software.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.lm-cursor-backdrop {
|
||
position: fixed;
|
||
width: 200px;
|
||
height: 200px;
|
||
margin-top: -100px;
|
||
margin-left: -100px;
|
||
will-change: transform;
|
||
z-index: 100;
|
||
}
|
||
|
||
.lm-mod-drag-image {
|
||
will-change: transform;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.jp-lineFormSearch {
|
||
padding: 4px 12px;
|
||
background-color: var(--jp-layout-color2);
|
||
box-shadow: var(--jp-toolbar-box-shadow);
|
||
z-index: 2;
|
||
font-size: var(--jp-ui-font-size1);
|
||
}
|
||
|
||
.jp-lineFormCaption {
|
||
font-size: var(--jp-ui-font-size0);
|
||
line-height: var(--jp-ui-font-size1);
|
||
margin-top: 4px;
|
||
color: var(--jp-ui-font-color0);
|
||
}
|
||
|
||
.jp-baseLineForm {
|
||
border: none;
|
||
border-radius: 0;
|
||
position: absolute;
|
||
background-size: 16px;
|
||
background-repeat: no-repeat;
|
||
background-position: center;
|
||
outline: none;
|
||
}
|
||
|
||
.jp-lineFormButtonContainer {
|
||
top: 4px;
|
||
right: 8px;
|
||
height: 24px;
|
||
padding: 0 12px;
|
||
width: 12px;
|
||
}
|
||
|
||
.jp-lineFormButtonIcon {
|
||
top: 0;
|
||
right: 0;
|
||
background-color: var(--jp-brand-color1);
|
||
height: 100%;
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
padding: 4px 6px;
|
||
}
|
||
|
||
.jp-lineFormButton {
|
||
top: 0;
|
||
right: 0;
|
||
background-color: transparent;
|
||
height: 100%;
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.jp-lineFormWrapper {
|
||
overflow: hidden;
|
||
padding: 0 8px;
|
||
border: 1px solid var(--jp-border-color0);
|
||
background-color: var(--jp-input-active-background);
|
||
height: 22px;
|
||
}
|
||
|
||
.jp-lineFormWrapperFocusWithin {
|
||
border: var(--jp-border-width) solid var(--md-blue-500);
|
||
box-shadow: inset 0 0 4px var(--md-blue-300);
|
||
}
|
||
|
||
.jp-lineFormInput {
|
||
background: transparent;
|
||
width: 200px;
|
||
height: 100%;
|
||
border: none;
|
||
outline: none;
|
||
color: var(--jp-ui-font-color0);
|
||
line-height: 28px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) 2014-2016, Jupyter Development Team.
|
||
|
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-JSONEditor {
|
||
display: flex;
|
||
flex-direction: column;
|
||
width: 100%;
|
||
}
|
||
|
||
.jp-JSONEditor-host {
|
||
flex: 1 1 auto;
|
||
border: var(--jp-border-width) solid var(--jp-input-border-color);
|
||
border-radius: 0;
|
||
background: var(--jp-layout-color0);
|
||
min-height: 50px;
|
||
padding: 1px;
|
||
}
|
||
|
||
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
|
||
border-color: red;
|
||
outline-color: red;
|
||
}
|
||
|
||
.jp-JSONEditor-header {
|
||
display: flex;
|
||
flex: 1 0 auto;
|
||
padding: 0 0 0 12px;
|
||
}
|
||
|
||
.jp-JSONEditor-header label {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.jp-JSONEditor-commitButton {
|
||
height: 16px;
|
||
width: 16px;
|
||
background-size: 18px;
|
||
background-repeat: no-repeat;
|
||
background-position: center;
|
||
}
|
||
|
||
.jp-JSONEditor-host.jp-mod-focused {
|
||
background-color: var(--jp-input-active-background);
|
||
border: 1px solid var(--jp-input-active-border-color);
|
||
box-shadow: var(--jp-input-box-shadow);
|
||
}
|
||
|
||
.jp-Editor.jp-mod-dropTarget {
|
||
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
|
||
box-shadow: var(--jp-input-box-shadow);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
.jp-DocumentSearch-input {
|
||
border: none;
|
||
outline: none;
|
||
color: var(--jp-ui-font-color0);
|
||
font-size: var(--jp-ui-font-size1);
|
||
background-color: var(--jp-layout-color0);
|
||
font-family: var(--jp-ui-font-family);
|
||
padding: 2px 1px;
|
||
resize: none;
|
||
}
|
||
|
||
.jp-DocumentSearch-overlay {
|
||
position: absolute;
|
||
background-color: var(--jp-toolbar-background);
|
||
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
|
||
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
|
||
top: 0;
|
||
right: 0;
|
||
z-index: 7;
|
||
min-width: 405px;
|
||
padding: 2px;
|
||
font-size: var(--jp-ui-font-size1);
|
||
|
||
--jp-private-document-search-button-height: 20px;
|
||
}
|
||
|
||
.jp-DocumentSearch-overlay button {
|
||
background-color: var(--jp-toolbar-background);
|
||
outline: 0;
|
||
}
|
||
|
||
.jp-DocumentSearch-overlay button:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DocumentSearch-overlay button:active {
|
||
background-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-DocumentSearch-overlay-row {
|
||
display: flex;
|
||
align-items: center;
|
||
margin-bottom: 2px;
|
||
}
|
||
|
||
.jp-DocumentSearch-button-content {
|
||
display: inline-block;
|
||
cursor: pointer;
|
||
box-sizing: border-box;
|
||
width: 100%;
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-DocumentSearch-button-content svg {
|
||
width: 100%;
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-DocumentSearch-input-wrapper {
|
||
border: var(--jp-border-width) solid var(--jp-border-color0);
|
||
display: flex;
|
||
background-color: var(--jp-layout-color0);
|
||
margin: 2px;
|
||
}
|
||
|
||
.jp-DocumentSearch-input-wrapper:focus-within {
|
||
border-color: var(--jp-cell-editor-active-border-color);
|
||
}
|
||
|
||
.jp-DocumentSearch-toggle-wrapper,
|
||
.jp-DocumentSearch-button-wrapper {
|
||
all: initial;
|
||
overflow: hidden;
|
||
display: inline-block;
|
||
border: none;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.jp-DocumentSearch-toggle-wrapper {
|
||
width: 14px;
|
||
height: 14px;
|
||
}
|
||
|
||
.jp-DocumentSearch-button-wrapper {
|
||
width: var(--jp-private-document-search-button-height);
|
||
height: var(--jp-private-document-search-button-height);
|
||
}
|
||
|
||
.jp-DocumentSearch-toggle-wrapper:focus,
|
||
.jp-DocumentSearch-button-wrapper:focus {
|
||
outline: var(--jp-border-width) solid
|
||
var(--jp-cell-editor-active-border-color);
|
||
outline-offset: -1px;
|
||
}
|
||
|
||
.jp-DocumentSearch-toggle-wrapper,
|
||
.jp-DocumentSearch-button-wrapper,
|
||
.jp-DocumentSearch-button-content:focus {
|
||
outline: none;
|
||
}
|
||
|
||
.jp-DocumentSearch-toggle-placeholder {
|
||
width: 5px;
|
||
}
|
||
|
||
.jp-DocumentSearch-input-button::before {
|
||
display: block;
|
||
padding-top: 100%;
|
||
}
|
||
|
||
.jp-DocumentSearch-input-button-off {
|
||
opacity: var(--jp-search-toggle-off-opacity);
|
||
}
|
||
|
||
.jp-DocumentSearch-input-button-off:hover {
|
||
opacity: var(--jp-search-toggle-hover-opacity);
|
||
}
|
||
|
||
.jp-DocumentSearch-input-button-on {
|
||
opacity: var(--jp-search-toggle-on-opacity);
|
||
}
|
||
|
||
.jp-DocumentSearch-index-counter {
|
||
padding-left: 10px;
|
||
padding-right: 10px;
|
||
user-select: none;
|
||
min-width: 35px;
|
||
display: inline-block;
|
||
}
|
||
|
||
.jp-DocumentSearch-up-down-wrapper {
|
||
display: inline-block;
|
||
padding-right: 2px;
|
||
margin-left: auto;
|
||
white-space: nowrap;
|
||
}
|
||
|
||
.jp-DocumentSearch-spacer {
|
||
margin-left: auto;
|
||
}
|
||
|
||
.jp-DocumentSearch-up-down-wrapper button {
|
||
outline: 0;
|
||
border: none;
|
||
width: var(--jp-private-document-search-button-height);
|
||
height: var(--jp-private-document-search-button-height);
|
||
vertical-align: middle;
|
||
margin: 1px 5px 2px;
|
||
}
|
||
|
||
.jp-DocumentSearch-up-down-button:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DocumentSearch-up-down-button:active {
|
||
background-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-DocumentSearch-filter-button {
|
||
border-radius: var(--jp-border-radius);
|
||
}
|
||
|
||
.jp-DocumentSearch-filter-button:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DocumentSearch-filter-button-enabled {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DocumentSearch-filter-button-enabled:hover {
|
||
background-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-DocumentSearch-search-options {
|
||
padding: 0 8px;
|
||
margin-left: 3px;
|
||
width: 100%;
|
||
display: grid;
|
||
justify-content: start;
|
||
grid-template-columns: 1fr 1fr;
|
||
align-items: center;
|
||
justify-items: stretch;
|
||
}
|
||
|
||
.jp-DocumentSearch-search-filter-disabled {
|
||
color: var(--jp-ui-font-color2);
|
||
}
|
||
|
||
.jp-DocumentSearch-search-filter {
|
||
display: flex;
|
||
align-items: center;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-DocumentSearch-regex-error {
|
||
color: var(--jp-error-color0);
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-button-wrapper {
|
||
overflow: hidden;
|
||
display: inline-block;
|
||
box-sizing: border-box;
|
||
border: var(--jp-border-width) solid var(--jp-border-color0);
|
||
margin: auto 2px;
|
||
padding: 1px 4px;
|
||
height: calc(var(--jp-private-document-search-button-height) + 2px);
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-button-wrapper:focus {
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-button {
|
||
display: inline-block;
|
||
text-align: center;
|
||
cursor: pointer;
|
||
box-sizing: border-box;
|
||
color: var(--jp-ui-font-color1);
|
||
|
||
/* height - 2 * (padding of wrapper) */
|
||
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
|
||
width: 100%;
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-button:focus {
|
||
outline: none;
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-wrapper-class {
|
||
margin-left: 14px;
|
||
display: flex;
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-toggle {
|
||
border: none;
|
||
background-color: var(--jp-toolbar-background);
|
||
border-radius: var(--jp-border-radius);
|
||
}
|
||
|
||
.jp-DocumentSearch-replace-toggle:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.cm-editor {
|
||
line-height: var(--jp-code-line-height);
|
||
font-size: var(--jp-code-font-size);
|
||
font-family: var(--jp-code-font-family);
|
||
border: 0;
|
||
border-radius: 0;
|
||
height: auto;
|
||
|
||
/* Changed to auto to autogrow */
|
||
}
|
||
|
||
.cm-editor pre {
|
||
padding: 0 var(--jp-code-padding);
|
||
}
|
||
|
||
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
|
||
background-color: var(--jp-layout-color0);
|
||
color: var(--jp-content-font-color1);
|
||
}
|
||
|
||
.jp-CodeMirrorEditor {
|
||
cursor: text;
|
||
}
|
||
|
||
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
|
||
@media screen and (min-width: 2138px) and (max-width: 4319px) {
|
||
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
|
||
border-left: var(--jp-code-cursor-width1) solid
|
||
var(--jp-editor-cursor-color);
|
||
}
|
||
}
|
||
|
||
/* When zoomed out less than 33% */
|
||
@media screen and (min-width: 4320px) {
|
||
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
|
||
border-left: var(--jp-code-cursor-width2) solid
|
||
var(--jp-editor-cursor-color);
|
||
}
|
||
}
|
||
|
||
.cm-editor.jp-mod-readOnly .cm-cursor {
|
||
display: none;
|
||
}
|
||
|
||
.jp-CollaboratorCursor {
|
||
border-left: 5px solid transparent;
|
||
border-right: 5px solid transparent;
|
||
border-top: none;
|
||
border-bottom: 3px solid;
|
||
background-clip: content-box;
|
||
margin-left: -5px;
|
||
margin-right: -5px;
|
||
}
|
||
|
||
.cm-searching,
|
||
.cm-searching span {
|
||
/* `.cm-searching span`: we need to override syntax highlighting */
|
||
background-color: var(--jp-search-unselected-match-background-color);
|
||
color: var(--jp-search-unselected-match-color);
|
||
}
|
||
|
||
.cm-searching::selection,
|
||
.cm-searching span::selection {
|
||
background-color: var(--jp-search-unselected-match-background-color);
|
||
color: var(--jp-search-unselected-match-color);
|
||
}
|
||
|
||
.jp-current-match > .cm-searching,
|
||
.jp-current-match > .cm-searching span,
|
||
.cm-searching > .jp-current-match,
|
||
.cm-searching > .jp-current-match span {
|
||
background-color: var(--jp-search-selected-match-background-color);
|
||
color: var(--jp-search-selected-match-color);
|
||
}
|
||
|
||
.jp-current-match > .cm-searching::selection,
|
||
.cm-searching > .jp-current-match::selection,
|
||
.jp-current-match > .cm-searching span::selection {
|
||
background-color: var(--jp-search-selected-match-background-color);
|
||
color: var(--jp-search-selected-match-color);
|
||
}
|
||
|
||
.cm-trailingspace {
|
||
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
|
||
background-position: center left;
|
||
background-repeat: repeat-x;
|
||
}
|
||
|
||
.jp-CollaboratorCursor-hover {
|
||
position: absolute;
|
||
z-index: 1;
|
||
transform: translateX(-50%);
|
||
color: white;
|
||
border-radius: 3px;
|
||
padding-left: 4px;
|
||
padding-right: 4px;
|
||
padding-top: 1px;
|
||
padding-bottom: 1px;
|
||
text-align: center;
|
||
font-size: var(--jp-ui-font-size1);
|
||
white-space: nowrap;
|
||
}
|
||
|
||
.jp-CodeMirror-ruler {
|
||
border-left: 1px dashed var(--jp-border-color2);
|
||
}
|
||
|
||
/* Styles for shared cursors (remote cursor locations and selected ranges) */
|
||
.jp-CodeMirrorEditor .cm-ySelectionCaret {
|
||
position: relative;
|
||
border-left: 1px solid black;
|
||
margin-left: -1px;
|
||
margin-right: -1px;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
|
||
white-space: nowrap;
|
||
position: absolute;
|
||
top: -1.15em;
|
||
padding-bottom: 0.05em;
|
||
left: -1px;
|
||
font-size: 0.95em;
|
||
font-family: var(--jp-ui-font-family);
|
||
font-weight: bold;
|
||
line-height: normal;
|
||
user-select: none;
|
||
color: white;
|
||
padding-left: 2px;
|
||
padding-right: 2px;
|
||
z-index: 101;
|
||
transition: opacity 0.3s ease-in-out;
|
||
}
|
||
|
||
.jp-CodeMirrorEditor .cm-ySelectionInfo {
|
||
transition-delay: 0.7s;
|
||
opacity: 0;
|
||
}
|
||
|
||
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
|
||
opacity: 1;
|
||
transition-delay: 0s;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-MimeDocument {
|
||
outline: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-private-filebrowser-button-height: 28px;
|
||
--jp-private-filebrowser-button-width: 48px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-FileBrowser .jp-SidePanel-content {
|
||
display: flex;
|
||
flex-direction: column;
|
||
}
|
||
|
||
.jp-FileBrowser-toolbar.jp-Toolbar {
|
||
flex-wrap: wrap;
|
||
row-gap: 12px;
|
||
border-bottom: none;
|
||
height: auto;
|
||
margin: 8px 12px 0;
|
||
box-shadow: none;
|
||
padding: 0;
|
||
justify-content: flex-start;
|
||
}
|
||
|
||
.jp-FileBrowser-Panel {
|
||
flex: 1 1 auto;
|
||
display: flex;
|
||
flex-direction: column;
|
||
}
|
||
|
||
.jp-BreadCrumbs {
|
||
flex: 0 0 auto;
|
||
margin: 8px 12px;
|
||
}
|
||
|
||
.jp-BreadCrumbs-item {
|
||
margin: 0 2px;
|
||
padding: 0 2px;
|
||
border-radius: var(--jp-border-radius);
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-BreadCrumbs-item:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-BreadCrumbs-item:first-child {
|
||
margin-left: 0;
|
||
}
|
||
|
||
.jp-BreadCrumbs-item.jp-mod-dropTarget {
|
||
background-color: var(--jp-brand-color2);
|
||
opacity: 0.7;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Buttons
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
|
||
flex: 0 0 auto;
|
||
padding-left: 0;
|
||
padding-right: 2px;
|
||
align-items: center;
|
||
height: unset;
|
||
}
|
||
|
||
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
|
||
width: 40px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Other styles
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-FileDialog.jp-mod-conflict input {
|
||
color: var(--jp-error-color1);
|
||
}
|
||
|
||
.jp-FileDialog .jp-new-name-title {
|
||
margin-top: 12px;
|
||
}
|
||
|
||
.jp-LastModified-hidden {
|
||
display: none;
|
||
}
|
||
|
||
.jp-FileSize-hidden {
|
||
display: none;
|
||
}
|
||
|
||
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
|
||
display: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| DirListing
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-DirListing {
|
||
flex: 1 1 auto;
|
||
display: flex;
|
||
flex-direction: column;
|
||
outline: 0;
|
||
}
|
||
|
||
.jp-DirListing-header {
|
||
flex: 0 0 auto;
|
||
display: flex;
|
||
flex-direction: row;
|
||
align-items: center;
|
||
overflow: hidden;
|
||
border-top: var(--jp-border-width) solid var(--jp-border-color2);
|
||
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
|
||
box-shadow: var(--jp-toolbar-box-shadow);
|
||
z-index: 2;
|
||
}
|
||
|
||
.jp-DirListing-headerItem {
|
||
padding: 4px 12px 2px;
|
||
font-weight: 500;
|
||
}
|
||
|
||
.jp-DirListing-headerItem:hover {
|
||
background: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DirListing-headerItem.jp-id-name {
|
||
flex: 1 0 84px;
|
||
}
|
||
|
||
.jp-DirListing-headerItem.jp-id-modified {
|
||
flex: 0 0 112px;
|
||
border-left: var(--jp-border-width) solid var(--jp-border-color2);
|
||
text-align: right;
|
||
}
|
||
|
||
.jp-DirListing-headerItem.jp-id-filesize {
|
||
flex: 0 0 75px;
|
||
border-left: var(--jp-border-width) solid var(--jp-border-color2);
|
||
text-align: right;
|
||
}
|
||
|
||
.jp-id-narrow {
|
||
display: none;
|
||
flex: 0 0 5px;
|
||
padding: 4px;
|
||
border-left: var(--jp-border-width) solid var(--jp-border-color2);
|
||
text-align: right;
|
||
color: var(--jp-border-color2);
|
||
}
|
||
|
||
.jp-DirListing-narrow .jp-id-narrow {
|
||
display: block;
|
||
}
|
||
|
||
.jp-DirListing-narrow .jp-id-modified,
|
||
.jp-DirListing-narrow .jp-DirListing-itemModified {
|
||
display: none;
|
||
}
|
||
|
||
.jp-DirListing-headerItem.jp-mod-selected {
|
||
font-weight: 600;
|
||
}
|
||
|
||
/* increase specificity to override bundled default */
|
||
.jp-DirListing-content {
|
||
flex: 1 1 auto;
|
||
margin: 0;
|
||
padding: 0;
|
||
list-style-type: none;
|
||
overflow: auto;
|
||
background-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-DirListing-content mark {
|
||
color: var(--jp-ui-font-color0);
|
||
background-color: transparent;
|
||
font-weight: bold;
|
||
}
|
||
|
||
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
|
||
color: var(--jp-ui-inverse-font-color0);
|
||
}
|
||
|
||
/* Style the directory listing content when a user drops a file to upload */
|
||
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
|
||
outline: 5px dashed rgba(128, 128, 128, 0.5);
|
||
outline-offset: -10px;
|
||
cursor: copy;
|
||
}
|
||
|
||
.jp-DirListing-item {
|
||
display: flex;
|
||
flex-direction: row;
|
||
align-items: center;
|
||
padding: 4px 12px;
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-DirListing-checkboxWrapper {
|
||
/* Increases hit area of checkbox. */
|
||
padding: 4px;
|
||
}
|
||
|
||
.jp-DirListing-header
|
||
.jp-DirListing-checkboxWrapper
|
||
+ .jp-DirListing-headerItem {
|
||
padding-left: 4px;
|
||
}
|
||
|
||
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
|
||
position: relative;
|
||
left: -4px;
|
||
margin: -4px 0 -4px -8px;
|
||
}
|
||
|
||
.jp-DirListing-checkboxWrapper.jp-mod-visible {
|
||
visibility: visible;
|
||
}
|
||
|
||
/* For devices that support hovering, hide checkboxes until hovered, selected...
|
||
*/
|
||
@media (hover: hover) {
|
||
.jp-DirListing-checkboxWrapper {
|
||
visibility: hidden;
|
||
}
|
||
|
||
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
|
||
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
|
||
visibility: visible;
|
||
}
|
||
}
|
||
|
||
.jp-DirListing-item[data-is-dot] {
|
||
opacity: 75%;
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-selected {
|
||
color: var(--jp-ui-inverse-font-color1);
|
||
background: var(--jp-brand-color1);
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-dropTarget {
|
||
background: var(--jp-brand-color3);
|
||
}
|
||
|
||
.jp-DirListing-item:hover:not(.jp-mod-selected) {
|
||
background: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-DirListing-itemIcon {
|
||
flex: 0 0 20px;
|
||
margin-right: 4px;
|
||
}
|
||
|
||
.jp-DirListing-itemText {
|
||
flex: 1 0 64px;
|
||
white-space: nowrap;
|
||
overflow: hidden;
|
||
text-overflow: ellipsis;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-DirListing-itemText:focus {
|
||
outline-width: 2px;
|
||
outline-color: var(--jp-inverse-layout-color1);
|
||
outline-style: solid;
|
||
outline-offset: 1px;
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
|
||
outline-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-DirListing-itemModified {
|
||
flex: 0 0 125px;
|
||
text-align: right;
|
||
}
|
||
|
||
.jp-DirListing-itemFileSize {
|
||
flex: 0 0 90px;
|
||
text-align: right;
|
||
}
|
||
|
||
.jp-DirListing-editor {
|
||
flex: 1 0 64px;
|
||
outline: none;
|
||
border: none;
|
||
color: var(--jp-ui-font-color1);
|
||
background-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
|
||
color: var(--jp-success-color1);
|
||
content: '\25CF';
|
||
font-size: 8px;
|
||
position: absolute;
|
||
left: -8px;
|
||
}
|
||
|
||
.jp-DirListing-item.jp-mod-running.jp-mod-selected
|
||
.jp-DirListing-itemIcon::before {
|
||
color: var(--jp-ui-inverse-font-color1);
|
||
}
|
||
|
||
.jp-DirListing-item.lm-mod-drag-image,
|
||
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
|
||
font-size: var(--jp-ui-font-size1);
|
||
padding-left: 4px;
|
||
margin-left: 4px;
|
||
width: 160px;
|
||
background-color: var(--jp-ui-inverse-font-color2);
|
||
box-shadow: var(--jp-elevation-z2);
|
||
border-radius: 0;
|
||
color: var(--jp-ui-font-color1);
|
||
transform: translateX(-40%) translateY(-58%);
|
||
}
|
||
|
||
.jp-Document {
|
||
min-width: 120px;
|
||
min-height: 120px;
|
||
outline: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Main OutputArea
|
||
| OutputArea has a list of Outputs
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-OutputArea {
|
||
overflow-y: auto;
|
||
}
|
||
|
||
.jp-OutputArea-child {
|
||
display: table;
|
||
table-layout: fixed;
|
||
width: 100%;
|
||
overflow: hidden;
|
||
}
|
||
|
||
.jp-OutputPrompt {
|
||
width: var(--jp-cell-prompt-width);
|
||
color: var(--jp-cell-outprompt-font-color);
|
||
font-family: var(--jp-cell-prompt-font-family);
|
||
padding: var(--jp-code-padding);
|
||
letter-spacing: var(--jp-cell-prompt-letter-spacing);
|
||
line-height: var(--jp-code-line-height);
|
||
font-size: var(--jp-code-font-size);
|
||
border: var(--jp-border-width) solid transparent;
|
||
opacity: var(--jp-cell-prompt-opacity);
|
||
|
||
/* Right align prompt text, don't wrap to handle large prompt numbers */
|
||
text-align: right;
|
||
white-space: nowrap;
|
||
overflow: hidden;
|
||
text-overflow: ellipsis;
|
||
|
||
/* Disable text selection */
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-OutputArea-prompt {
|
||
display: table-cell;
|
||
vertical-align: top;
|
||
}
|
||
|
||
.jp-OutputArea-output {
|
||
display: table-cell;
|
||
width: 100%;
|
||
height: auto;
|
||
overflow: auto;
|
||
user-select: text;
|
||
-moz-user-select: text;
|
||
-webkit-user-select: text;
|
||
-ms-user-select: text;
|
||
}
|
||
|
||
.jp-OutputArea .jp-RenderedText {
|
||
padding-left: 1ch;
|
||
}
|
||
|
||
/**
|
||
* Prompt overlay.
|
||
*/
|
||
|
||
.jp-OutputArea-promptOverlay {
|
||
position: absolute;
|
||
top: 0;
|
||
width: var(--jp-cell-prompt-width);
|
||
height: 100%;
|
||
opacity: 0.5;
|
||
}
|
||
|
||
.jp-OutputArea-promptOverlay:hover {
|
||
background: var(--jp-layout-color2);
|
||
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
|
||
cursor: zoom-out;
|
||
}
|
||
|
||
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
|
||
cursor: zoom-in;
|
||
}
|
||
|
||
/**
|
||
* Isolated output.
|
||
*/
|
||
.jp-OutputArea-output.jp-mod-isolated {
|
||
width: 100%;
|
||
display: block;
|
||
}
|
||
|
||
/*
|
||
When drag events occur, `lm-mod-override-cursor` is added to the body.
|
||
Because iframes steal all cursor events, the following two rules are necessary
|
||
to suppress pointer events while resize drags are occurring. There may be a
|
||
better solution to this problem.
|
||
*/
|
||
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
|
||
position: relative;
|
||
}
|
||
|
||
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
|
||
content: '';
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
right: 0;
|
||
bottom: 0;
|
||
background: transparent;
|
||
}
|
||
|
||
/* pre */
|
||
|
||
.jp-OutputArea-output pre {
|
||
border: none;
|
||
margin: 0;
|
||
padding: 0;
|
||
overflow-x: auto;
|
||
overflow-y: auto;
|
||
word-break: break-all;
|
||
word-wrap: break-word;
|
||
white-space: pre-wrap;
|
||
}
|
||
|
||
/* tables */
|
||
|
||
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
|
||
margin-left: 0;
|
||
margin-right: 0;
|
||
}
|
||
|
||
/* description lists */
|
||
|
||
.jp-OutputArea-output dl,
|
||
.jp-OutputArea-output dt,
|
||
.jp-OutputArea-output dd {
|
||
display: block;
|
||
}
|
||
|
||
.jp-OutputArea-output dl {
|
||
width: 100%;
|
||
overflow: hidden;
|
||
padding: 0;
|
||
margin: 0;
|
||
}
|
||
|
||
.jp-OutputArea-output dt {
|
||
font-weight: bold;
|
||
float: left;
|
||
width: 20%;
|
||
padding: 0;
|
||
margin: 0;
|
||
}
|
||
|
||
.jp-OutputArea-output dd {
|
||
float: left;
|
||
width: 80%;
|
||
padding: 0;
|
||
margin: 0;
|
||
}
|
||
|
||
.jp-TrimmedOutputs pre {
|
||
background: var(--jp-layout-color3);
|
||
font-size: calc(var(--jp-code-font-size) * 1.4);
|
||
text-align: center;
|
||
text-transform: uppercase;
|
||
}
|
||
|
||
/* Hide the gutter in case of
|
||
* - nested output areas (e.g. in the case of output widgets)
|
||
* - mirrored output areas
|
||
*/
|
||
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
|
||
display: none;
|
||
}
|
||
|
||
/* Hide empty lines in the output area, for instance due to cleared widgets */
|
||
.jp-OutputArea-prompt:empty {
|
||
padding: 0;
|
||
border: 0;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| executeResult is added to any Output-result for the display of the object
|
||
| returned by a cell
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-OutputArea-output.jp-OutputArea-executeResult {
|
||
margin-left: 0;
|
||
width: 100%;
|
||
}
|
||
|
||
/* Text output with the Out[] prompt needs a top padding to match the
|
||
* alignment of the Out[] prompt itself.
|
||
*/
|
||
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
|
||
padding-top: var(--jp-code-padding);
|
||
border-top: var(--jp-border-width) solid transparent;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| The Stdin output
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Stdin-prompt {
|
||
color: var(--jp-content-font-color0);
|
||
padding-right: var(--jp-code-padding);
|
||
vertical-align: baseline;
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.jp-Stdin-input {
|
||
font-family: var(--jp-code-font-family);
|
||
font-size: inherit;
|
||
color: inherit;
|
||
background-color: inherit;
|
||
width: 42%;
|
||
min-width: 200px;
|
||
|
||
/* make sure input baseline aligns with prompt */
|
||
vertical-align: baseline;
|
||
|
||
/* padding + margin = 0.5em between prompt and cursor */
|
||
padding: 0 0.25em;
|
||
margin: 0 0.25em;
|
||
flex: 0 0 70%;
|
||
}
|
||
|
||
.jp-Stdin-input::placeholder {
|
||
opacity: 0;
|
||
}
|
||
|
||
.jp-Stdin-input:focus {
|
||
box-shadow: none;
|
||
}
|
||
|
||
.jp-Stdin-input:focus::placeholder {
|
||
opacity: 1;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Output Area View
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-LinkedOutputView .jp-OutputArea {
|
||
height: 100%;
|
||
display: block;
|
||
}
|
||
|
||
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
|
||
height: 100%;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Printing
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
@media print {
|
||
.jp-OutputArea-child {
|
||
break-inside: avoid-page;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Mobile
|
||
|----------------------------------------------------------------------------*/
|
||
@media only screen and (max-width: 760px) {
|
||
.jp-OutputPrompt {
|
||
display: table-row;
|
||
text-align: left;
|
||
}
|
||
|
||
.jp-OutputArea-child .jp-OutputArea-output {
|
||
display: table-row;
|
||
margin-left: var(--jp-notebook-padding);
|
||
}
|
||
}
|
||
|
||
/* Trimmed outputs warning */
|
||
.jp-TrimmedOutputs > a {
|
||
margin: 10px;
|
||
text-decoration: none;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-TrimmedOutputs > a:hover {
|
||
text-decoration: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Table of Contents
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-private-toc-active-width: 4px;
|
||
}
|
||
|
||
.jp-TableOfContents {
|
||
display: flex;
|
||
flex-direction: column;
|
||
background: var(--jp-layout-color1);
|
||
color: var(--jp-ui-font-color1);
|
||
font-size: var(--jp-ui-font-size1);
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-TableOfContents-placeholder {
|
||
text-align: center;
|
||
}
|
||
|
||
.jp-TableOfContents-placeholderContent {
|
||
color: var(--jp-content-font-color2);
|
||
padding: 8px;
|
||
}
|
||
|
||
.jp-TableOfContents-placeholderContent > h3 {
|
||
margin-bottom: var(--jp-content-heading-margin-bottom);
|
||
}
|
||
|
||
.jp-TableOfContents .jp-SidePanel-content {
|
||
overflow-y: auto;
|
||
}
|
||
|
||
.jp-TableOfContents-tree {
|
||
margin: 4px;
|
||
}
|
||
|
||
.jp-TableOfContents ol {
|
||
list-style-type: none;
|
||
}
|
||
|
||
/* stylelint-disable-next-line selector-max-type */
|
||
.jp-TableOfContents li > ol {
|
||
/* Align left border with triangle icon center */
|
||
padding-left: 11px;
|
||
}
|
||
|
||
.jp-TableOfContents-content {
|
||
/* left margin for the active heading indicator */
|
||
margin: 0 0 0 var(--jp-private-toc-active-width);
|
||
padding: 0;
|
||
background-color: var(--jp-layout-color1);
|
||
}
|
||
|
||
.jp-tocItem {
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.jp-tocItem-heading {
|
||
display: flex;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-tocItem-heading:hover {
|
||
background-color: var(--jp-layout-color2);
|
||
}
|
||
|
||
.jp-tocItem-content {
|
||
display: block;
|
||
padding: 4px 0;
|
||
white-space: nowrap;
|
||
text-overflow: ellipsis;
|
||
overflow-x: hidden;
|
||
}
|
||
|
||
.jp-tocItem-collapser {
|
||
height: 20px;
|
||
margin: 2px 2px 0;
|
||
padding: 0;
|
||
background: none;
|
||
border: none;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-tocItem-collapser:hover {
|
||
background-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
/* Active heading indicator */
|
||
|
||
.jp-tocItem-heading::before {
|
||
content: ' ';
|
||
background: transparent;
|
||
width: var(--jp-private-toc-active-width);
|
||
height: 24px;
|
||
position: absolute;
|
||
left: 0;
|
||
border-radius: var(--jp-border-radius);
|
||
}
|
||
|
||
.jp-tocItem-heading.jp-tocItem-active::before {
|
||
background-color: var(--jp-brand-color1);
|
||
}
|
||
|
||
.jp-tocItem-heading:hover.jp-tocItem-active::before {
|
||
background: var(--jp-brand-color0);
|
||
opacity: 1;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Collapser {
|
||
flex: 0 0 var(--jp-cell-collapser-width);
|
||
padding: 0;
|
||
margin: 0;
|
||
border: none;
|
||
outline: none;
|
||
background: transparent;
|
||
border-radius: var(--jp-border-radius);
|
||
opacity: 1;
|
||
}
|
||
|
||
.jp-Collapser-child {
|
||
display: block;
|
||
width: 100%;
|
||
box-sizing: border-box;
|
||
|
||
/* height: 100% doesn't work because the height of its parent is computed from content */
|
||
position: absolute;
|
||
top: 0;
|
||
bottom: 0;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Printing
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*
|
||
Hiding collapsers in print mode.
|
||
|
||
Note: input and output wrappers have "display: block" propery in print mode.
|
||
*/
|
||
|
||
@media print {
|
||
.jp-Collapser {
|
||
display: none;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Header/Footer
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* Hidden by zero height by default */
|
||
.jp-CellHeader,
|
||
.jp-CellFooter {
|
||
height: 0;
|
||
width: 100%;
|
||
padding: 0;
|
||
margin: 0;
|
||
border: none;
|
||
outline: none;
|
||
background: transparent;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Input
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* All input areas */
|
||
.jp-InputArea {
|
||
display: table;
|
||
table-layout: fixed;
|
||
width: 100%;
|
||
overflow: hidden;
|
||
}
|
||
|
||
.jp-InputArea-editor {
|
||
display: table-cell;
|
||
overflow: hidden;
|
||
vertical-align: top;
|
||
|
||
/* This is the non-active, default styling */
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
|
||
border-radius: 0;
|
||
background: var(--jp-cell-editor-background);
|
||
}
|
||
|
||
.jp-InputPrompt {
|
||
display: table-cell;
|
||
vertical-align: top;
|
||
width: var(--jp-cell-prompt-width);
|
||
color: var(--jp-cell-inprompt-font-color);
|
||
font-family: var(--jp-cell-prompt-font-family);
|
||
padding: var(--jp-code-padding);
|
||
letter-spacing: var(--jp-cell-prompt-letter-spacing);
|
||
opacity: var(--jp-cell-prompt-opacity);
|
||
line-height: var(--jp-code-line-height);
|
||
font-size: var(--jp-code-font-size);
|
||
border: var(--jp-border-width) solid transparent;
|
||
|
||
/* Right align prompt text, don't wrap to handle large prompt numbers */
|
||
text-align: right;
|
||
white-space: nowrap;
|
||
overflow: hidden;
|
||
text-overflow: ellipsis;
|
||
|
||
/* Disable text selection */
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Mobile
|
||
|----------------------------------------------------------------------------*/
|
||
@media only screen and (max-width: 760px) {
|
||
.jp-InputArea-editor {
|
||
display: table-row;
|
||
margin-left: var(--jp-notebook-padding);
|
||
}
|
||
|
||
.jp-InputPrompt {
|
||
display: table-row;
|
||
text-align: left;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Placeholder
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Placeholder {
|
||
display: table;
|
||
table-layout: fixed;
|
||
width: 100%;
|
||
}
|
||
|
||
.jp-Placeholder-prompt {
|
||
display: table-cell;
|
||
box-sizing: border-box;
|
||
}
|
||
|
||
.jp-Placeholder-content {
|
||
display: table-cell;
|
||
padding: 4px 6px;
|
||
border: 1px solid transparent;
|
||
border-radius: 0;
|
||
background: none;
|
||
box-sizing: border-box;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-Placeholder-contentContainer {
|
||
display: flex;
|
||
}
|
||
|
||
.jp-Placeholder-content:hover,
|
||
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
|
||
border-color: var(--jp-layout-color3);
|
||
}
|
||
|
||
.jp-Placeholder-content .jp-MoreHorizIcon {
|
||
width: 32px;
|
||
height: 16px;
|
||
border: 1px solid transparent;
|
||
border-radius: var(--jp-border-radius);
|
||
}
|
||
|
||
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
|
||
border: 1px solid var(--jp-border-color1);
|
||
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
|
||
background-color: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-PlaceholderText {
|
||
white-space: nowrap;
|
||
overflow-x: hidden;
|
||
color: var(--jp-inverse-layout-color3);
|
||
font-family: var(--jp-code-font-family);
|
||
}
|
||
|
||
.jp-InputPlaceholder > .jp-Placeholder-content {
|
||
border-color: var(--jp-cell-editor-border-color);
|
||
background: var(--jp-cell-editor-background);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Private CSS variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-private-cell-scrolling-output-offset: 5px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Cell
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Cell {
|
||
padding: var(--jp-cell-padding);
|
||
margin: 0;
|
||
border: none;
|
||
outline: none;
|
||
background: transparent;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Common input/output
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Cell-inputWrapper,
|
||
.jp-Cell-outputWrapper {
|
||
display: flex;
|
||
flex-direction: row;
|
||
padding: 0;
|
||
margin: 0;
|
||
|
||
/* Added to reveal the box-shadow on the input and output collapsers. */
|
||
overflow: visible;
|
||
}
|
||
|
||
/* Only input/output areas inside cells */
|
||
.jp-Cell-inputArea,
|
||
.jp-Cell-outputArea {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Collapser
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* Make the output collapser disappear when there is not output, but do so
|
||
* in a manner that leaves it in the layout and preserves its width.
|
||
*/
|
||
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
|
||
border: none !important;
|
||
background: transparent !important;
|
||
}
|
||
|
||
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
|
||
min-height: var(--jp-cell-collapser-min-height);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Output
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* Put a space between input and output when there IS output */
|
||
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
|
||
margin-top: 5px;
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
|
||
overflow-y: auto;
|
||
max-height: 24em;
|
||
margin-left: var(--jp-private-cell-scrolling-output-offset);
|
||
resize: vertical;
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
|
||
max-height: unset;
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
|
||
content: ' ';
|
||
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
|
||
width: 100%;
|
||
height: 100%;
|
||
position: sticky;
|
||
bottom: 0;
|
||
top: 0;
|
||
margin-top: -50%;
|
||
float: left;
|
||
display: block;
|
||
pointer-events: none;
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
|
||
padding-top: 6px;
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
|
||
width: calc(
|
||
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
|
||
);
|
||
}
|
||
|
||
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
|
||
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| CodeCell
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| MarkdownCell
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-MarkdownOutput {
|
||
display: table-cell;
|
||
width: 100%;
|
||
margin-top: 0;
|
||
margin-bottom: 0;
|
||
padding-left: var(--jp-code-padding);
|
||
}
|
||
|
||
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
|
||
overflow: auto;
|
||
}
|
||
|
||
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
|
||
.jp-collapseHeadingButton {
|
||
display: flex;
|
||
min-height: var(--jp-cell-collapser-min-height);
|
||
font-size: var(--jp-code-font-size);
|
||
position: absolute;
|
||
background-color: transparent;
|
||
background-size: 25px;
|
||
background-repeat: no-repeat;
|
||
background-position-x: center;
|
||
background-position-y: top;
|
||
background-image: var(--jp-icon-caret-down);
|
||
right: 0;
|
||
top: 0;
|
||
bottom: 0;
|
||
}
|
||
|
||
.jp-collapseHeadingButton.jp-mod-collapsed {
|
||
background-image: var(--jp-icon-caret-right);
|
||
}
|
||
|
||
/*
|
||
set the container font size to match that of content
|
||
so that the nested collapse buttons have the right size
|
||
*/
|
||
.jp-MarkdownCell .jp-InputPrompt {
|
||
font-size: var(--jp-content-font-size1);
|
||
}
|
||
|
||
/*
|
||
Align collapseHeadingButton with cell top header
|
||
The font sizes are identical to the ones in packages/rendermime/style/base.css
|
||
*/
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
|
||
font-size: var(--jp-content-font-size5);
|
||
background-position-y: calc(0.3 * var(--jp-content-font-size5));
|
||
}
|
||
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
|
||
font-size: var(--jp-content-font-size4);
|
||
background-position-y: calc(0.3 * var(--jp-content-font-size4));
|
||
}
|
||
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
|
||
font-size: var(--jp-content-font-size3);
|
||
background-position-y: calc(0.3 * var(--jp-content-font-size3));
|
||
}
|
||
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
|
||
font-size: var(--jp-content-font-size2);
|
||
background-position-y: calc(0.3 * var(--jp-content-font-size2));
|
||
}
|
||
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
|
||
font-size: var(--jp-content-font-size1);
|
||
background-position-y: top;
|
||
}
|
||
|
||
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
|
||
font-size: var(--jp-content-font-size0);
|
||
background-position-y: top;
|
||
}
|
||
|
||
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
|
||
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
|
||
display: none;
|
||
}
|
||
|
||
.jp-Notebook.jp-mod-showHiddenCellsButton
|
||
:is(.jp-MarkdownCell:hover, .jp-mod-active)
|
||
.jp-collapseHeadingButton {
|
||
display: flex;
|
||
}
|
||
|
||
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
|
||
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
|
||
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
|
||
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
|
||
margin-top: var(--jp-code-padding);
|
||
border: 1px solid var(--jp-border-color2);
|
||
background-color: var(--jp-border-color3) !important;
|
||
color: var(--jp-content-font-color0) !important;
|
||
display: flex;
|
||
}
|
||
|
||
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
|
||
background-color: var(--jp-border-color2) !important;
|
||
}
|
||
|
||
.jp-showHiddenCellsButton {
|
||
display: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Printing
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*
|
||
Using block instead of flex to allow the use of the break-inside CSS property for
|
||
cell outputs.
|
||
*/
|
||
|
||
@media print {
|
||
.jp-Cell-inputWrapper,
|
||
.jp-Cell-outputWrapper {
|
||
display: block;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Styles
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-NotebookPanel-toolbar {
|
||
padding: var(--jp-notebook-toolbar-padding);
|
||
|
||
/* disable paint containment from lumino 2.0 default strict CSS containment */
|
||
contain: style size !important;
|
||
}
|
||
|
||
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
|
||
border: none;
|
||
box-shadow: none;
|
||
}
|
||
|
||
.jp-Notebook-toolbarCellTypeDropdown select {
|
||
height: 24px;
|
||
font-size: var(--jp-ui-font-size1);
|
||
line-height: 14px;
|
||
border-radius: 0;
|
||
display: block;
|
||
}
|
||
|
||
.jp-Notebook-toolbarCellTypeDropdown span {
|
||
top: 5px !important;
|
||
}
|
||
|
||
.jp-Toolbar-responsive-popup {
|
||
position: absolute;
|
||
height: fit-content;
|
||
display: flex;
|
||
flex-direction: row;
|
||
flex-wrap: wrap;
|
||
justify-content: flex-end;
|
||
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
|
||
box-shadow: var(--jp-toolbar-box-shadow);
|
||
background: var(--jp-toolbar-background);
|
||
min-height: var(--jp-toolbar-micro-height);
|
||
padding: var(--jp-notebook-toolbar-padding);
|
||
z-index: 1;
|
||
right: 0;
|
||
top: 0;
|
||
}
|
||
|
||
.jp-Toolbar > .jp-Toolbar-responsive-opener {
|
||
margin-left: auto;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Styles
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Notebook-ExecutionIndicator {
|
||
position: relative;
|
||
display: inline-block;
|
||
height: 100%;
|
||
z-index: 9997;
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator-tooltip {
|
||
visibility: hidden;
|
||
height: auto;
|
||
width: max-content;
|
||
width: -moz-max-content;
|
||
background-color: var(--jp-layout-color2);
|
||
color: var(--jp-ui-font-color1);
|
||
text-align: justify;
|
||
border-radius: 6px;
|
||
padding: 0 5px;
|
||
position: fixed;
|
||
display: table;
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator-tooltip.up {
|
||
transform: translateX(-50%) translateY(-100%) translateY(-32px);
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator-tooltip.down {
|
||
transform: translateX(calc(-100% + 16px)) translateY(5px);
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
|
||
display: none;
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
|
||
visibility: visible;
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator span {
|
||
font-size: var(--jp-ui-font-size1);
|
||
font-family: var(--jp-ui-font-family);
|
||
color: var(--jp-ui-font-color1);
|
||
line-height: 24px;
|
||
display: block;
|
||
}
|
||
|
||
.jp-Notebook-ExecutionIndicator-progress-bar {
|
||
display: flex;
|
||
justify-content: center;
|
||
height: 100%;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
/*
|
||
* Execution indicator
|
||
*/
|
||
.jp-tocItem-content::after {
|
||
content: '';
|
||
|
||
/* Must be identical to form a circle */
|
||
width: 12px;
|
||
height: 12px;
|
||
background: none;
|
||
border: none;
|
||
position: absolute;
|
||
right: 0;
|
||
}
|
||
|
||
.jp-tocItem-content[data-running='0']::after {
|
||
border-radius: 50%;
|
||
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
|
||
background: none;
|
||
}
|
||
|
||
.jp-tocItem-content[data-running='1']::after {
|
||
border-radius: 50%;
|
||
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
|
||
background-color: var(--jp-inverse-layout-color3);
|
||
}
|
||
|
||
.jp-tocItem-content[data-running='0'],
|
||
.jp-tocItem-content[data-running='1'] {
|
||
margin-right: 12px;
|
||
}
|
||
|
||
/*
|
||
* Copyright (c) Jupyter Development Team.
|
||
* Distributed under the terms of the Modified BSD License.
|
||
*/
|
||
|
||
.jp-Notebook-footer {
|
||
height: 27px;
|
||
margin-left: calc(
|
||
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
|
||
var(--jp-cell-padding)
|
||
);
|
||
width: calc(
|
||
100% -
|
||
(
|
||
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
|
||
var(--jp-cell-padding) + var(--jp-cell-padding)
|
||
)
|
||
);
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
|
||
color: var(--jp-ui-font-color3);
|
||
margin-top: 6px;
|
||
background: none;
|
||
cursor: pointer;
|
||
}
|
||
|
||
.jp-Notebook-footer:focus {
|
||
border-color: var(--jp-cell-editor-active-border-color);
|
||
}
|
||
|
||
/* For devices that support hovering, hide footer until hover */
|
||
@media (hover: hover) {
|
||
.jp-Notebook-footer {
|
||
opacity: 0;
|
||
}
|
||
|
||
.jp-Notebook-footer:focus,
|
||
.jp-Notebook-footer:hover {
|
||
opacity: 1;
|
||
}
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Copyright (c) Jupyter Development Team.
|
||
| Distributed under the terms of the Modified BSD License.
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Imports
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| CSS variables
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
:root {
|
||
--jp-side-by-side-output-size: 1fr;
|
||
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
|
||
--jp-private-notebook-dragImage-width: 304px;
|
||
--jp-private-notebook-dragImage-height: 36px;
|
||
--jp-private-notebook-selected-color: var(--md-blue-400);
|
||
--jp-private-notebook-active-color: var(--md-green-400);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Notebook
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* stylelint-disable selector-max-class */
|
||
|
||
.jp-NotebookPanel {
|
||
display: block;
|
||
height: 100%;
|
||
}
|
||
|
||
.jp-NotebookPanel.jp-Document {
|
||
min-width: 240px;
|
||
min-height: 120px;
|
||
}
|
||
|
||
.jp-Notebook {
|
||
padding: var(--jp-notebook-padding);
|
||
outline: none;
|
||
overflow: auto;
|
||
background: var(--jp-layout-color0);
|
||
}
|
||
|
||
.jp-Notebook.jp-mod-scrollPastEnd::after {
|
||
display: block;
|
||
content: '';
|
||
min-height: var(--jp-notebook-scroll-padding);
|
||
}
|
||
|
||
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
|
||
contain: strict;
|
||
}
|
||
|
||
.jp-Notebook .jp-Cell {
|
||
overflow: visible;
|
||
}
|
||
|
||
.jp-Notebook .jp-Cell .jp-InputPrompt {
|
||
cursor: move;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Notebook state related styling
|
||
|
|
||
| The notebook and cells each have states, here are the possibilities:
|
||
|
|
||
| - Notebook
|
||
| - Command
|
||
| - Edit
|
||
| - Cell
|
||
| - None
|
||
| - Active (only one can be active)
|
||
| - Selected (the cells actions are applied to)
|
||
| - Multiselected (when multiple selected, the cursor)
|
||
| - No outputs
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
/* Command or edit modes */
|
||
|
||
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
|
||
opacity: var(--jp-cell-prompt-not-active-opacity);
|
||
color: var(--jp-cell-prompt-not-active-font-color);
|
||
}
|
||
|
||
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
|
||
opacity: var(--jp-cell-prompt-not-active-opacity);
|
||
color: var(--jp-cell-prompt-not-active-font-color);
|
||
}
|
||
|
||
/* cell is active */
|
||
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
|
||
background: var(--jp-brand-color1);
|
||
}
|
||
|
||
/* cell is dirty */
|
||
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
|
||
color: var(--jp-warn-color1);
|
||
}
|
||
|
||
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
|
||
color: var(--jp-warn-color1);
|
||
content: '•';
|
||
}
|
||
|
||
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
|
||
background: var(--jp-warn-color1);
|
||
}
|
||
|
||
/* collapser is hovered */
|
||
.jp-Notebook .jp-Cell .jp-Collapser:hover {
|
||
box-shadow: var(--jp-elevation-z2);
|
||
background: var(--jp-brand-color1);
|
||
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
|
||
}
|
||
|
||
/* cell is active and collapser is hovered */
|
||
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
|
||
background: var(--jp-brand-color0);
|
||
opacity: 1;
|
||
}
|
||
|
||
/* Command mode */
|
||
|
||
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
|
||
background: var(--jp-notebook-multiselected-color);
|
||
}
|
||
|
||
.jp-Notebook.jp-mod-commandMode
|
||
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
|
||
background: transparent;
|
||
}
|
||
|
||
/* Edit mode */
|
||
|
||
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
|
||
box-shadow: var(--jp-input-box-shadow);
|
||
background-color: var(--jp-cell-editor-active-background);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Notebook drag and drop
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Notebook-cell.jp-mod-dropSource {
|
||
opacity: 0.5;
|
||
}
|
||
|
||
.jp-Notebook-cell.jp-mod-dropTarget,
|
||
.jp-Notebook.jp-mod-commandMode
|
||
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
|
||
border-top-color: var(--jp-private-notebook-selected-color);
|
||
border-top-style: solid;
|
||
border-top-width: 2px;
|
||
}
|
||
|
||
.jp-dragImage {
|
||
display: block;
|
||
flex-direction: row;
|
||
width: var(--jp-private-notebook-dragImage-width);
|
||
height: var(--jp-private-notebook-dragImage-height);
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
|
||
background: var(--jp-cell-editor-background);
|
||
overflow: visible;
|
||
}
|
||
|
||
.jp-dragImage-singlePrompt {
|
||
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
|
||
}
|
||
|
||
.jp-dragImage .jp-dragImage-content {
|
||
flex: 1 1 auto;
|
||
z-index: 2;
|
||
font-size: var(--jp-code-font-size);
|
||
font-family: var(--jp-code-font-family);
|
||
line-height: var(--jp-code-line-height);
|
||
padding: var(--jp-code-padding);
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
|
||
background: var(--jp-cell-editor-background-color);
|
||
color: var(--jp-content-font-color3);
|
||
text-align: left;
|
||
margin: 4px 4px 4px 0;
|
||
}
|
||
|
||
.jp-dragImage .jp-dragImage-prompt {
|
||
flex: 0 0 auto;
|
||
min-width: 36px;
|
||
color: var(--jp-cell-inprompt-font-color);
|
||
padding: var(--jp-code-padding);
|
||
padding-left: 12px;
|
||
font-family: var(--jp-cell-prompt-font-family);
|
||
letter-spacing: var(--jp-cell-prompt-letter-spacing);
|
||
line-height: 1.9;
|
||
font-size: var(--jp-code-font-size);
|
||
border: var(--jp-border-width) solid transparent;
|
||
}
|
||
|
||
.jp-dragImage-multipleBack {
|
||
z-index: -1;
|
||
position: absolute;
|
||
height: 32px;
|
||
width: 300px;
|
||
top: 8px;
|
||
left: 8px;
|
||
background: var(--jp-layout-color2);
|
||
border: var(--jp-border-width) solid var(--jp-input-border-color);
|
||
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Cell toolbar
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-NotebookTools {
|
||
display: block;
|
||
min-width: var(--jp-sidebar-min-width);
|
||
color: var(--jp-ui-font-color1);
|
||
background: var(--jp-layout-color1);
|
||
|
||
/* This is needed so that all font sizing of children done in ems is
|
||
* relative to this base size */
|
||
font-size: var(--jp-ui-font-size1);
|
||
overflow: auto;
|
||
}
|
||
|
||
.jp-ActiveCellTool {
|
||
padding: 12px 0;
|
||
display: flex;
|
||
}
|
||
|
||
.jp-ActiveCellTool-Content {
|
||
flex: 1 1 auto;
|
||
}
|
||
|
||
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
|
||
background: var(--jp-cell-editor-background);
|
||
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
|
||
border-radius: 0;
|
||
min-height: 29px;
|
||
}
|
||
|
||
.jp-ActiveCellTool .jp-InputPrompt {
|
||
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
|
||
}
|
||
|
||
.jp-ActiveCellTool-CellContent > pre {
|
||
padding: 5px 4px;
|
||
margin: 0;
|
||
white-space: normal;
|
||
}
|
||
|
||
.jp-MetadataEditorTool {
|
||
flex-direction: column;
|
||
padding: 12px 0;
|
||
}
|
||
|
||
.jp-RankedPanel > :not(:first-child) {
|
||
margin-top: 12px;
|
||
}
|
||
|
||
.jp-KeySelector select.jp-mod-styled {
|
||
font-size: var(--jp-ui-font-size1);
|
||
color: var(--jp-ui-font-color0);
|
||
border: var(--jp-border-width) solid var(--jp-border-color1);
|
||
}
|
||
|
||
.jp-KeySelector label,
|
||
.jp-MetadataEditorTool label,
|
||
.jp-NumberSetter label {
|
||
line-height: 1.4;
|
||
}
|
||
|
||
.jp-NotebookTools .jp-select-wrapper {
|
||
margin-top: 4px;
|
||
margin-bottom: 0;
|
||
}
|
||
|
||
.jp-NumberSetter input {
|
||
width: 100%;
|
||
margin-top: 4px;
|
||
}
|
||
|
||
.jp-NotebookTools .jp-Collapse {
|
||
margin-top: 16px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Presentation Mode (.jp-mod-presentationMode)
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-mod-presentationMode .jp-Notebook {
|
||
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
|
||
--jp-code-font-size: var(--jp-code-presentation-font-size);
|
||
}
|
||
|
||
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
|
||
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
|
||
flex: 0 0 110px;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Side-by-side Mode (.jp-mod-sideBySide)
|
||
|----------------------------------------------------------------------------*/
|
||
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
|
||
margin-top: 3em;
|
||
margin-bottom: 3em;
|
||
margin-left: 5%;
|
||
margin-right: 5%;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
|
||
display: grid;
|
||
grid-template-columns: minmax(0, 1fr) min-content minmax(
|
||
0,
|
||
var(--jp-side-by-side-output-size)
|
||
);
|
||
grid-template-rows: auto minmax(0, 1fr) auto;
|
||
grid-template-areas:
|
||
'header header header'
|
||
'input handle output'
|
||
'footer footer footer';
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
|
||
grid-template-columns: minmax(0, 1fr) min-content minmax(
|
||
0,
|
||
var(--jp-side-by-side-resized-cell)
|
||
);
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
|
||
grid-area: header;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
|
||
grid-area: input;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
|
||
/* overwrite the default margin (no vertical separation needed in side by side move */
|
||
margin-top: 0;
|
||
grid-area: output;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
|
||
grid-area: footer;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
|
||
grid-area: handle;
|
||
user-select: none;
|
||
display: block;
|
||
height: 100%;
|
||
cursor: ew-resize;
|
||
padding: 0 var(--jp-cell-padding);
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
|
||
content: '';
|
||
display: block;
|
||
background: var(--jp-border-color2);
|
||
height: 100%;
|
||
width: 5px;
|
||
}
|
||
|
||
.jp-mod-sideBySide.jp-Notebook
|
||
.jp-CodeCell.jp-mod-resizedCell
|
||
.jp-CellResizeHandle::after {
|
||
background: var(--jp-border-color0);
|
||
}
|
||
|
||
.jp-CellResizeHandle {
|
||
display: none;
|
||
}
|
||
|
||
/*-----------------------------------------------------------------------------
|
||
| Placeholder
|
||
|----------------------------------------------------------------------------*/
|
||
|
||
.jp-Cell-Placeholder {
|
||
padding-left: 55px;
|
||
}
|
||
|
||
.jp-Cell-Placeholder-wrapper {
|
||
background: #fff;
|
||
border: 1px solid;
|
||
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
|
||
border-radius: 4px;
|
||
-webkit-border-radius: 4px;
|
||
margin: 10px 15px;
|
||
}
|
||
|
||
.jp-Cell-Placeholder-wrapper-inner {
|
||
padding: 15px;
|
||
position: relative;
|
||
}
|
||
|
||
.jp-Cell-Placeholder-wrapper-body {
|
||
background-repeat: repeat;
|
||
background-size: 50% auto;
|
||
}
|
||
|
||
.jp-Cell-Placeholder-wrapper-body div {
|
||
background: #f6f7f8;
|
||
background-image: -webkit-linear-gradient(
|
||
left,
|
||
#f6f7f8 0%,
|
||
#edeef1 20%,
|
||
#f6f7f8 40%,
|
||
#f6f7f8 100%
|
||
);
|
||
background-repeat: no-repeat;
|
||
background-size: 800px 104px;
|
||
height: 104px;
|
||
position: absolute;
|
||
right: 15px;
|
||
left: 15px;
|
||
top: 15px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-h1 {
|
||
top: 20px;
|
||
height: 20px;
|
||
left: 15px;
|
||
width: 150px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-h2 {
|
||
left: 15px;
|
||
top: 50px;
|
||
height: 10px;
|
||
width: 100px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-content-1,
|
||
div.jp-Cell-Placeholder-content-2,
|
||
div.jp-Cell-Placeholder-content-3 {
|
||
left: 15px;
|
||
right: 15px;
|
||
height: 10px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-content-1 {
|
||
top: 100px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-content-2 {
|
||
top: 120px;
|
||
}
|
||
|
||
div.jp-Cell-Placeholder-content-3 {
|
||
top: 140px;
|
||
}
|
||
|
||
</style>
|
||
<style type="text/css">
|
||
/*-----------------------------------------------------------------------------
|
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| Copyright (c) Jupyter Development Team.
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| Distributed under the terms of the Modified BSD License.
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|----------------------------------------------------------------------------*/
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/*
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The following CSS variables define the main, public API for styling JupyterLab.
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These variables should be used by all plugins wherever possible. In other
|
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words, plugins should not define custom colors, sizes, etc unless absolutely
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necessary. This enables users to change the visual theme of JupyterLab
|
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by changing these variables.
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Many variables appear in an ordered sequence (0,1,2,3). These sequences
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are designed to work well together, so for example, `--jp-border-color1` should
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be used with `--jp-layout-color1`. The numbers have the following meanings:
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* 0: super-primary, reserved for special emphasis
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* 1: primary, most important under normal situations
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* 2: secondary, next most important under normal situations
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* 3: tertiary, next most important under normal situations
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Throughout JupyterLab, we are mostly following principles from Google's
|
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Material Design when selecting colors. We are not, however, following
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all of MD as it is not optimized for dense, information rich UIs.
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*/
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:root {
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||
/* Elevation
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*
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* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
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*
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* https://github.com/material-components/material-components-web
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* https://material-components-web.appspot.com/elevation.html
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*/
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--jp-shadow-base-lightness: 0;
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--jp-shadow-umbra-color: rgba(
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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0.2
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);
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--jp-shadow-penumbra-color: rgba(
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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0.14
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);
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--jp-shadow-ambient-color: rgba(
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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var(--jp-shadow-base-lightness),
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0.12
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);
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||
--jp-elevation-z0: none;
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--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
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0 1px 1px 0 var(--jp-shadow-penumbra-color),
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0 1px 3px 0 var(--jp-shadow-ambient-color);
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--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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0 2px 2px 0 var(--jp-shadow-penumbra-color),
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0 1px 5px 0 var(--jp-shadow-ambient-color);
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--jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
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0 4px 5px 0 var(--jp-shadow-penumbra-color),
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0 1px 10px 0 var(--jp-shadow-ambient-color);
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--jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
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0 6px 10px 0 var(--jp-shadow-penumbra-color),
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0 1px 18px 0 var(--jp-shadow-ambient-color);
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--jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
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0 8px 10px 1px var(--jp-shadow-penumbra-color),
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0 3px 14px 2px var(--jp-shadow-ambient-color);
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--jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
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0 12px 17px 2px var(--jp-shadow-penumbra-color),
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0 5px 22px 4px var(--jp-shadow-ambient-color);
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--jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
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0 16px 24px 2px var(--jp-shadow-penumbra-color),
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0 6px 30px 5px var(--jp-shadow-ambient-color);
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--jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
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0 20px 31px 3px var(--jp-shadow-penumbra-color),
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0 8px 38px 7px var(--jp-shadow-ambient-color);
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--jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
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0 24px 38px 3px var(--jp-shadow-penumbra-color),
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0 9px 46px 8px var(--jp-shadow-ambient-color);
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|
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/* Borders
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||
*
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* The following variables, specify the visual styling of borders in JupyterLab.
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||
*/
|
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--jp-border-width: 1px;
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--jp-border-color0: var(--md-grey-400);
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--jp-border-color1: var(--md-grey-400);
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--jp-border-color2: var(--md-grey-300);
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--jp-border-color3: var(--md-grey-200);
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--jp-inverse-border-color: var(--md-grey-600);
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--jp-border-radius: 2px;
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|
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/* UI Fonts
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||
*
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* The UI font CSS variables are used for the typography all of the JupyterLab
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* user interface elements that are not directly user generated content.
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||
*
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||
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
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||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
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||
*/
|
||
|
||
--jp-ui-font-scale-factor: 1.2;
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||
--jp-ui-font-size0: 0.83333em;
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||
--jp-ui-font-size1: 13px; /* Base font size */
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||
--jp-ui-font-size2: 1.2em;
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||
--jp-ui-font-size3: 1.44em;
|
||
--jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
|
||
helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
|
||
'Segoe UI Symbol';
|
||
|
||
/*
|
||
* Use these font colors against the corresponding main layout colors.
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||
* In a light theme, these go from dark to light.
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||
*/
|
||
|
||
/* Defaults use Material Design specification */
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||
--jp-ui-font-color0: rgba(0, 0, 0, 1);
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||
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
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||
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
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||
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
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/*
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||
* Use these against the brand/accent/warn/error colors.
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||
* These will typically go from light to darker, in both a dark and light theme.
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||
*/
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--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
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--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
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--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
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||
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
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||
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/* Content Fonts
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||
*
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||
* Content font variables are used for typography of user generated content.
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||
*
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||
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
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||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
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||
*/
|
||
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||
--jp-content-line-height: 1.6;
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||
--jp-content-font-scale-factor: 1.2;
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||
--jp-content-font-size0: 0.83333em;
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||
--jp-content-font-size1: 14px; /* Base font size */
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||
--jp-content-font-size2: 1.2em;
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||
--jp-content-font-size3: 1.44em;
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||
--jp-content-font-size4: 1.728em;
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||
--jp-content-font-size5: 2.0736em;
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||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-content-presentation-font-size1: 17px;
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||
--jp-content-heading-line-height: 1;
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||
--jp-content-heading-margin-top: 1.2em;
|
||
--jp-content-heading-margin-bottom: 0.8em;
|
||
--jp-content-heading-font-weight: 500;
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-content-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
|
||
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
|
||
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
|
||
--jp-content-link-color: var(--md-blue-900);
|
||
--jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
|
||
'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
|
||
'Segoe UI Emoji', 'Segoe UI Symbol';
|
||
|
||
/*
|
||
* Code Fonts
|
||
*
|
||
* Code font variables are used for typography of code and other monospaces content.
|
||
*/
|
||
|
||
--jp-code-font-size: 13px;
|
||
--jp-code-line-height: 1.3077; /* 17px for 13px base */
|
||
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
|
||
--jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
|
||
--jp-code-font-family: var(--jp-code-font-family-default);
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-code-presentation-font-size: 16px;
|
||
|
||
/* may need to tweak cursor width if you change font size */
|
||
--jp-code-cursor-width0: 1.4px;
|
||
--jp-code-cursor-width1: 2px;
|
||
--jp-code-cursor-width2: 4px;
|
||
|
||
/* Layout
|
||
*
|
||
* The following are the main layout colors use in JupyterLab. In a light
|
||
* theme these would go from light to dark.
|
||
*/
|
||
|
||
--jp-layout-color0: white;
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||
--jp-layout-color1: white;
|
||
--jp-layout-color2: var(--md-grey-200);
|
||
--jp-layout-color3: var(--md-grey-400);
|
||
--jp-layout-color4: var(--md-grey-600);
|
||
|
||
/* Inverse Layout
|
||
*
|
||
* The following are the inverse layout colors use in JupyterLab. In a light
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||
* theme these would go from dark to light.
|
||
*/
|
||
|
||
--jp-inverse-layout-color0: #111;
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||
--jp-inverse-layout-color1: var(--md-grey-900);
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||
--jp-inverse-layout-color2: var(--md-grey-800);
|
||
--jp-inverse-layout-color3: var(--md-grey-700);
|
||
--jp-inverse-layout-color4: var(--md-grey-600);
|
||
|
||
/* Brand/accent */
|
||
|
||
--jp-brand-color0: var(--md-blue-900);
|
||
--jp-brand-color1: var(--md-blue-700);
|
||
--jp-brand-color2: var(--md-blue-300);
|
||
--jp-brand-color3: var(--md-blue-100);
|
||
--jp-brand-color4: var(--md-blue-50);
|
||
--jp-accent-color0: var(--md-green-900);
|
||
--jp-accent-color1: var(--md-green-700);
|
||
--jp-accent-color2: var(--md-green-300);
|
||
--jp-accent-color3: var(--md-green-100);
|
||
|
||
/* State colors (warn, error, success, info) */
|
||
|
||
--jp-warn-color0: var(--md-orange-900);
|
||
--jp-warn-color1: var(--md-orange-700);
|
||
--jp-warn-color2: var(--md-orange-300);
|
||
--jp-warn-color3: var(--md-orange-100);
|
||
--jp-error-color0: var(--md-red-900);
|
||
--jp-error-color1: var(--md-red-700);
|
||
--jp-error-color2: var(--md-red-300);
|
||
--jp-error-color3: var(--md-red-100);
|
||
--jp-success-color0: var(--md-green-900);
|
||
--jp-success-color1: var(--md-green-700);
|
||
--jp-success-color2: var(--md-green-300);
|
||
--jp-success-color3: var(--md-green-100);
|
||
--jp-info-color0: var(--md-cyan-900);
|
||
--jp-info-color1: var(--md-cyan-700);
|
||
--jp-info-color2: var(--md-cyan-300);
|
||
--jp-info-color3: var(--md-cyan-100);
|
||
|
||
/* Cell specific styles */
|
||
|
||
--jp-cell-padding: 5px;
|
||
--jp-cell-collapser-width: 8px;
|
||
--jp-cell-collapser-min-height: 20px;
|
||
--jp-cell-collapser-not-active-hover-opacity: 0.6;
|
||
--jp-cell-editor-background: var(--md-grey-100);
|
||
--jp-cell-editor-border-color: var(--md-grey-300);
|
||
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-cell-editor-active-background: var(--jp-layout-color0);
|
||
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
|
||
--jp-cell-prompt-width: 64px;
|
||
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
|
||
--jp-cell-prompt-letter-spacing: 0;
|
||
--jp-cell-prompt-opacity: 1;
|
||
--jp-cell-prompt-not-active-opacity: 0.5;
|
||
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
|
||
|
||
/* A custom blend of MD grey and blue 600
|
||
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
|
||
--jp-cell-inprompt-font-color: #307fc1;
|
||
|
||
/* A custom blend of MD grey and orange 600
|
||
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
|
||
--jp-cell-outprompt-font-color: #bf5b3d;
|
||
|
||
/* Notebook specific styles */
|
||
|
||
--jp-notebook-padding: 10px;
|
||
--jp-notebook-select-background: var(--jp-layout-color1);
|
||
--jp-notebook-multiselected-color: var(--md-blue-50);
|
||
|
||
/* The scroll padding is calculated to fill enough space at the bottom of the
|
||
notebook to show one single-line cell (with appropriate padding) at the top
|
||
when the notebook is scrolled all the way to the bottom. We also subtract one
|
||
pixel so that no scrollbar appears if we have just one single-line cell in the
|
||
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
|
||
*/
|
||
--jp-notebook-scroll-padding: calc(
|
||
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
|
||
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
|
||
);
|
||
|
||
/* Rendermime styles */
|
||
|
||
--jp-rendermime-error-background: #fdd;
|
||
--jp-rendermime-table-row-background: var(--md-grey-100);
|
||
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
|
||
|
||
/* Dialog specific styles */
|
||
|
||
--jp-dialog-background: rgba(0, 0, 0, 0.25);
|
||
|
||
/* Console specific styles */
|
||
|
||
--jp-console-padding: 10px;
|
||
|
||
/* Toolbar specific styles */
|
||
|
||
--jp-toolbar-border-color: var(--jp-border-color1);
|
||
--jp-toolbar-micro-height: 8px;
|
||
--jp-toolbar-background: var(--jp-layout-color1);
|
||
--jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
|
||
--jp-toolbar-header-margin: 4px 4px 0 4px;
|
||
--jp-toolbar-active-background: var(--md-grey-300);
|
||
|
||
/* Statusbar specific styles */
|
||
|
||
--jp-statusbar-height: 24px;
|
||
|
||
/* Input field styles */
|
||
|
||
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-input-active-background: var(--jp-layout-color1);
|
||
--jp-input-hover-background: var(--jp-layout-color1);
|
||
--jp-input-background: var(--md-grey-100);
|
||
--jp-input-border-color: var(--jp-inverse-border-color);
|
||
--jp-input-active-border-color: var(--jp-brand-color1);
|
||
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
|
||
|
||
/* General editor styles */
|
||
|
||
--jp-editor-selected-background: #d9d9d9;
|
||
--jp-editor-selected-focused-background: #d7d4f0;
|
||
--jp-editor-cursor-color: var(--jp-ui-font-color0);
|
||
|
||
/* Code mirror specific styles */
|
||
|
||
--jp-mirror-editor-keyword-color: #008000;
|
||
--jp-mirror-editor-atom-color: #88f;
|
||
--jp-mirror-editor-number-color: #080;
|
||
--jp-mirror-editor-def-color: #00f;
|
||
--jp-mirror-editor-variable-color: var(--md-grey-900);
|
||
--jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
|
||
--jp-mirror-editor-variable-3-color: #085;
|
||
--jp-mirror-editor-punctuation-color: #05a;
|
||
--jp-mirror-editor-property-color: #05a;
|
||
--jp-mirror-editor-operator-color: #a2f;
|
||
--jp-mirror-editor-comment-color: #408080;
|
||
--jp-mirror-editor-string-color: #ba2121;
|
||
--jp-mirror-editor-string-2-color: #708;
|
||
--jp-mirror-editor-meta-color: #a2f;
|
||
--jp-mirror-editor-qualifier-color: #555;
|
||
--jp-mirror-editor-builtin-color: #008000;
|
||
--jp-mirror-editor-bracket-color: #997;
|
||
--jp-mirror-editor-tag-color: #170;
|
||
--jp-mirror-editor-attribute-color: #00c;
|
||
--jp-mirror-editor-header-color: blue;
|
||
--jp-mirror-editor-quote-color: #090;
|
||
--jp-mirror-editor-link-color: #00c;
|
||
--jp-mirror-editor-error-color: #f00;
|
||
--jp-mirror-editor-hr-color: #999;
|
||
|
||
/*
|
||
RTC user specific colors.
|
||
These colors are used for the cursor, username in the editor,
|
||
and the icon of the user.
|
||
*/
|
||
|
||
--jp-collaborator-color1: #ffad8e;
|
||
--jp-collaborator-color2: #dac83d;
|
||
--jp-collaborator-color3: #72dd76;
|
||
--jp-collaborator-color4: #00e4d0;
|
||
--jp-collaborator-color5: #45d4ff;
|
||
--jp-collaborator-color6: #e2b1ff;
|
||
--jp-collaborator-color7: #ff9de6;
|
||
|
||
/* Vega extension styles */
|
||
|
||
--jp-vega-background: white;
|
||
|
||
/* Sidebar-related styles */
|
||
|
||
--jp-sidebar-min-width: 250px;
|
||
|
||
/* Search-related styles */
|
||
|
||
--jp-search-toggle-off-opacity: 0.5;
|
||
--jp-search-toggle-hover-opacity: 0.8;
|
||
--jp-search-toggle-on-opacity: 1;
|
||
--jp-search-selected-match-background-color: rgb(245, 200, 0);
|
||
--jp-search-selected-match-color: black;
|
||
--jp-search-unselected-match-background-color: var(
|
||
--jp-inverse-layout-color0
|
||
);
|
||
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
|
||
|
||
/* Icon colors that work well with light or dark backgrounds */
|
||
--jp-icon-contrast-color0: var(--md-purple-600);
|
||
--jp-icon-contrast-color1: var(--md-green-600);
|
||
--jp-icon-contrast-color2: var(--md-pink-600);
|
||
--jp-icon-contrast-color3: var(--md-blue-600);
|
||
|
||
/* Button colors */
|
||
--jp-accept-color-normal: var(--md-blue-700);
|
||
--jp-accept-color-hover: var(--md-blue-800);
|
||
--jp-accept-color-active: var(--md-blue-900);
|
||
--jp-warn-color-normal: var(--md-red-700);
|
||
--jp-warn-color-hover: var(--md-red-800);
|
||
--jp-warn-color-active: var(--md-red-900);
|
||
--jp-reject-color-normal: var(--md-grey-600);
|
||
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* a slash.
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<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
|
||
<main><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
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<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||
<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">create_engine</span>
|
||
<span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
|
||
<span class="kn">from</span> <span class="nn">dotenv</span> <span class="kn">import</span> <span class="n">load_dotenv</span>
|
||
<span class="n">load_dotenv</span><span class="p">()</span>
|
||
|
||
<span class="kn">import</span> <span class="nn">os</span>
|
||
|
||
<span class="c1"># Database connection details from zshrc environment variables</span>
|
||
<span class="c1"># use .env file for local development</span>
|
||
|
||
|
||
<span class="n">db_name</span> <span class="o">=</span> <span class="s1">'colorado_spills'</span>
|
||
<span class="n">user</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'DB_USER'</span><span class="p">)</span>
|
||
<span class="n">password</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'DB_PASSWORD'</span><span class="p">)</span>
|
||
<span class="n">host</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'DB_HOST'</span><span class="p">)</span>
|
||
<span class="n">port</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'DB_PORT'</span><span class="p">)</span>
|
||
|
||
|
||
<span class="c1"># Create an engine to connect to the PostgreSQL database</span>
|
||
<span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span><span class="sa">f</span><span class="s1">'postgresql+psycopg2://</span><span class="si">{</span><span class="n">user</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s1">@</span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">db_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Read in the spills_with_demographics_geog as spills</span>
|
||
<span class="n">spills</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql_table</span><span class="p">(</span><span class="s1">'spills_with_demographics_geog'</span><span class="p">,</span> <span class="n">engine</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/pandas/io/sql.py:1725: SAWarning: Did not recognize type 'geometry' of column 'geometry'
|
||
self.meta.reflect(bind=self.con, only=[table_name], views=True)
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># use longitude and latitude to create a GeoDataFrame from spills data</span>
|
||
<span class="n">spills</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">points_from_xy</span><span class="p">(</span><span class="n">spills</span><span class="p">[</span><span class="s1">'Longitude'</span><span class="p">],</span> <span class="n">spills</span><span class="p">[</span><span class="s1">'Latitude'</span><span class="p">])</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">spills</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="s1">'EPSG:4326'</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Ensure date columns are in datetime format</span>
|
||
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">])</span>
|
||
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">])</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># drop 2024 data for date of discovery and initial report date</span>
|
||
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span> <span class="o"><</span> <span class="mi">2024</span><span class="p">]</span>
|
||
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span> <span class="o"><</span> <span class="mi">2024</span><span class="p">]</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Separate Historical vs. Recent Spills</span>
|
||
<span class="n">historical_spills</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Spill Type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Historical'</span><span class="p">]</span>
|
||
<span class="n">recent_spills</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Spill Type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Recent'</span><span class="p">]</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Compare Reporting Delay</span>
|
||
<span class="n">report_delay_summary</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Spill Type'</span><span class="p">)[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">report_delay_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Ensure the output directory exists</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Save the report delay summary to a markdown file</span>
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'report_delay_summary.md'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"# Report Delay Summary</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">report_delay_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre> count mean std min 25% 50% 75% max
|
||
Spill Type
|
||
Historical 5312.0 21.490023 205.615913 0.0 0.0 1.0 2.0 9261.0
|
||
Recent 10678.0 3.964506 46.997749 0.0 0.0 1.0 2.0 2232.0
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="c1"># Compare Spill Trends Over Time</span>
|
||
<span class="n">spills_by_year</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Report Year'</span><span class="p">,</span> <span class="s1">'Spill Type'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">unstack</span><span class="p">()</span>
|
||
<span class="n">ax</span> <span class="o">=</span> <span class="n">spills_by_year</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">),</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Spills by Year and Type'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Year'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of Spills'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">'Spill Type'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Ensure the output directory exists</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Save the plot</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">output_dir</span><span class="si">}</span><span class="s1">/spills_by_year_and_type.png'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Display the plot</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
<img alt="No description has been provided for this image" class="" src="data:image/png;base64,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"/>
|
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</div>
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</div>
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="cm-editor cm-s-jupyter">
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
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<span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
|
||
|
||
<span class="c1"># Ensure spills_gdf is already a GeoDataFrame with the necessary data</span>
|
||
<span class="c1"># spills_gdf = gpd.GeoDataFrame(...)</span>
|
||
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||
<span class="c1"># Create a plot for the spatial distribution of spills</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
|
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<span class="c1"># Plot the GeoDataFrame with optimizations</span>
|
||
<span class="n">spills_gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
|
||
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
|
||
<span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span>
|
||
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="c1"># Adjust transparency for better visibility</span>
|
||
<span class="n">column</span><span class="o">=</span><span class="s1">'Spill Type'</span><span class="p">,</span>
|
||
<span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
<span class="n">legend_kwds</span><span class="o">=</span><span class="p">{</span><span class="s1">'title'</span><span class="p">:</span> <span class="s2">"Spill Type"</span><span class="p">},</span> <span class="c1"># Correctly specify the legend title</span>
|
||
<span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span> <span class="c1"># Use a colormap for better visual distinction</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="c1"># Set plot title and labels</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Spatial Distribution of Historical vs. Recent Spills'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Longitude'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Latitude'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Improve layout</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
|
||
<span class="c1"># Display the plot</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
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</div>
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<img alt="No description has been provided for this image" class="" 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"/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
<span class="kn">import</span> <span class="nn">contextily</span> <span class="k">as</span> <span class="nn">ctx</span>
|
||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||
|
||
<span class="c1"># Use your ORIGINAL plot code exactly as it is, just adding the basemap at the end</span>
|
||
<span class="c1"># This ensures all your data points, colors and categories remain intact</span>
|
||
|
||
<span class="c1"># Starting with your original code:</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
|
||
<span class="c1"># Plot the GeoDataFrame with optimizations - YOUR ORIGINAL PLOTTING CODE</span>
|
||
<span class="n">spills_gdf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
|
||
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
|
||
<span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span>
|
||
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="c1"># Adjust transparency for better visibility</span>
|
||
<span class="n">column</span><span class="o">=</span><span class="s1">'Spill Type'</span><span class="p">,</span>
|
||
<span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||
<span class="n">legend_kwds</span><span class="o">=</span><span class="p">{</span><span class="s1">'title'</span><span class="p">:</span> <span class="s2">"Spill Type"</span><span class="p">},</span> <span class="c1"># Correctly specify the legend title</span>
|
||
<span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span> <span class="c1"># Use a colormap for better visual distinction</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="c1"># Set plot title and labels</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Spatial Distribution of Historical vs. Recent Spills in Colorado'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Longitude'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Latitude'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Store the current axis limits to restore them after adding basemap</span>
|
||
<span class="n">xlim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()</span>
|
||
<span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
|
||
|
||
<span class="c1"># Before adding the basemap, convert the axes coordinates to Web Mercator</span>
|
||
<span class="c1"># This is a crucial step that lets us keep your original points exactly as they are</span>
|
||
<span class="kn">from</span> <span class="nn">pyproj</span> <span class="kn">import</span> <span class="n">Transformer</span>
|
||
|
||
<span class="c1"># Create transformer from lat/lon to Web Mercator</span>
|
||
<span class="n">transformer</span> <span class="o">=</span> <span class="n">Transformer</span><span class="o">.</span><span class="n">from_crs</span><span class="p">(</span><span class="s2">"EPSG:4326"</span><span class="p">,</span> <span class="s2">"EPSG:3857"</span><span class="p">,</span> <span class="n">always_xy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Transform the limits</span>
|
||
<span class="n">west</span><span class="p">,</span> <span class="n">south</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||
<span class="n">east</span><span class="p">,</span> <span class="n">north</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||
|
||
<span class="c1"># Add the basemap - this will be UNDER your existing points</span>
|
||
<span class="n">ctx</span><span class="o">.</span><span class="n">add_basemap</span><span class="p">(</span>
|
||
<span class="n">ax</span><span class="p">,</span>
|
||
<span class="n">crs</span><span class="o">=</span><span class="n">spills_gdf</span><span class="o">.</span><span class="n">crs</span><span class="p">,</span>
|
||
<span class="n">source</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">providers</span><span class="o">.</span><span class="n">OpenStreetMap</span><span class="o">.</span><span class="n">Mapnik</span><span class="p">,</span>
|
||
<span class="n">zoom</span><span class="o">=</span><span class="s2">"auto"</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="c1"># Restore original axis limits</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xlim</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Improve layout</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
|
||
<span class="c1"># Save the plot to the analysis/output directory</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">output_dir</span><span class="si">}</span><span class="s1">/spatial_distribution_spills.png'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Show the plot</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
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"/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Add a column to indicate whether the spill occurred before or after 2020</span>
|
||
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Report Year'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'Before 2020'</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">2020</span> <span class="k">else</span> <span class="s1">'2020 and After'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Group by the new period column and calculate the mean response time</span>
|
||
<span class="n">response_time_summary</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Period'</span><span class="p">)[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">response_time_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Ensure the output directory exists</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Save the response time summary to a markdown file</span>
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'response_time_summary.md'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"# Response Time Summary</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">response_time_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre> count mean std min 25% 50% 75% max
|
||
Period
|
||
2020 and After 7177.0 6.924342 124.965839 0.0 0.0 1.0 1.0 9261.0
|
||
Before 2020 8813.0 12.117554 124.705876 0.0 0.0 1.0 2.0 5681.0
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># t-test for the difference in response time before and after 2020</span>
|
||
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind</span>
|
||
|
||
<span class="n">before_2020</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Before 2020'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
<span class="n">after_2020</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'2020 and After'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
|
||
<span class="n">t_stat</span><span class="p">,</span> <span class="n">p_value</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">before_2020</span><span class="p">,</span> <span class="n">after_2020</span><span class="p">,</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'T-Statistic: </span><span class="si">{</span><span class="n">t_stat</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'P-Value: </span><span class="si">{</span><span class="n">p_value</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Check if the difference is statistically significant</span>
|
||
<span class="k">if</span> <span class="n">p_value</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time is statistically significant.'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time is not statistically significant.'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Create a plot for the response time distribution before and after 2020</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
|
||
<span class="c1"># Plot the response time distribution for each period</span>
|
||
<span class="n">spills_gdf</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>T-Statistic: 2.6161
|
||
P-Value: 0.0089
|
||
The difference in response time is statistically significant.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child jp-OutputArea-executeResult">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
|
||
<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
|
||
<pre><Axes: title={'center': 'Report Delay (Days)'}, xlabel='Period'></pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
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"/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">pip</span> install tabulate
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>Requirement already satisfied: tabulate in /home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages (0.9.0)
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>Note: you may need to restart the kernel to use updated packages.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tabulate</span>
|
||
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind</span>
|
||
|
||
<span class="c1"># Ensure the output directory exists</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Add a column to indicate whether the spill occurred before or after 2020 for historical and recent spills</span>
|
||
<span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Report Year'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'Before 2020'</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">2020</span> <span class="k">else</span> <span class="s1">'2020 and After'</span><span class="p">)</span>
|
||
<span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Report Year'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'Before 2020'</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">2020</span> <span class="k">else</span> <span class="s1">'2020 and After'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Historical Spills Analysis</span>
|
||
<span class="n">historical_report_delay_summary</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Historical Spills Report Delay Summary:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">historical_report_delay_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Recent Spills Analysis</span>
|
||
<span class="n">recent_report_delay_summary</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Recent Spills Report Delay Summary:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">recent_report_delay_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Historical Spills Response Time Summary</span>
|
||
<span class="n">historical_response_time_summary</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Period'</span><span class="p">)[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Historical Spills Response Time Summary:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">historical_response_time_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Recent Spills Response Time Summary</span>
|
||
<span class="n">recent_response_time_summary</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Period'</span><span class="p">)[</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Recent Spills Response Time Summary:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">recent_response_time_summary</span><span class="p">)</span>
|
||
|
||
<span class="c1"># t-test for the difference in response time before and after 2020 for historical spills</span>
|
||
<span class="n">historical_before_2020</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Before 2020'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
<span class="n">historical_after_2020</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'2020 and After'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
|
||
<span class="n">t_stat_historical</span><span class="p">,</span> <span class="n">p_value_historical</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">historical_before_2020</span><span class="p">,</span> <span class="n">historical_after_2020</span><span class="p">,</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Historical Spills T-Statistic: </span><span class="si">{</span><span class="n">t_stat_historical</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Historical Spills P-Value: </span><span class="si">{</span><span class="n">p_value_historical</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Check if the difference is statistically significant for historical spills</span>
|
||
<span class="k">if</span> <span class="n">p_value_historical</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is statistically significant.'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is not statistically significant.'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># t-test for the difference in response time before and after 2020 for recent spills</span>
|
||
<span class="n">recent_before_2020</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Before 2020'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
<span class="n">recent_after_2020</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'2020 and After'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
|
||
<span class="n">t_stat_recent</span><span class="p">,</span> <span class="n">p_value_recent</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">recent_before_2020</span><span class="p">,</span> <span class="n">recent_after_2020</span><span class="p">,</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Recent Spills T-Statistic: </span><span class="si">{</span><span class="n">t_stat_recent</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Recent Spills P-Value: </span><span class="si">{</span><span class="n">p_value_recent</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Check if the difference is statistically significant for recent spills</span>
|
||
<span class="k">if</span> <span class="n">p_value_recent</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is statistically significant.'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is not statistically significant.'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Save the summaries and t-test results to a markdown file</span>
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'analysis_results.md'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"# Analysis Results</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"## Historical Spills Report Delay Summary:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">historical_report_delay_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"## Recent Spills Report Delay Summary:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">recent_report_delay_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"## Historical Spills Response Time Summary:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">historical_response_time_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"## Recent Spills Response Time Summary:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">recent_response_time_summary</span><span class="o">.</span><span class="n">to_markdown</span><span class="p">()</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"## T-Test Results for Historical Spills</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"T-Statistic: </span><span class="si">{</span><span class="n">t_stat_historical</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"P-Value: </span><span class="si">{</span><span class="n">p_value_historical</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">p_value_historical</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is not statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">## T-Test Results for Recent Spills</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"T-Statistic: </span><span class="si">{</span><span class="n">t_stat_recent</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"P-Value: </span><span class="si">{</span><span class="n">p_value_recent</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">p_value_recent</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is not statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>Historical Spills Report Delay Summary:
|
||
count 5312.000000
|
||
mean 21.490023
|
||
std 205.615913
|
||
min 0.000000
|
||
25% 0.000000
|
||
50% 1.000000
|
||
75% 2.000000
|
||
max 9261.000000
|
||
Name: Report Delay (Days), dtype: float64
|
||
|
||
Recent Spills Report Delay Summary:
|
||
count 10678.000000
|
||
mean 3.964506
|
||
std 46.997749
|
||
min 0.000000
|
||
25% 0.000000
|
||
50% 1.000000
|
||
75% 2.000000
|
||
max 2232.000000
|
||
Name: Report Delay (Days), dtype: float64
|
||
|
||
Historical Spills Response Time Summary:
|
||
count mean std min 25% 50% 75% max
|
||
Period
|
||
2020 and After 2705.0 13.533087 200.352339 0.0 0.0 0.0 1.0 9261.0
|
||
Before 2020 2607.0 29.746068 210.659491 0.0 0.0 1.0 2.0 5681.0
|
||
|
||
Recent Spills Response Time Summary:
|
||
count mean std min 25% 50% 75% max
|
||
Period
|
||
2020 and After 4472.0 2.926878 27.302062 0.0 0.0 1.0 1.0 1329.0
|
||
Before 2020 6206.0 4.712214 57.116100 0.0 1.0 1.0 2.0 2232.0
|
||
|
||
Historical Spills T-Statistic: 2.8723
|
||
Historical Spills P-Value: 0.0041
|
||
The difference in response time for historical spills is statistically significant.
|
||
|
||
Recent Spills T-Statistic: 2.1457
|
||
Recent Spills P-Value: 0.0319
|
||
The difference in response time for recent spills is statistically significant.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
|
||
<pre>/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning:
|
||
A value is trying to be set on a copy of a slice from a DataFrame.
|
||
Try using .loc[row_indexer,col_indexer] = value instead
|
||
|
||
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
||
super().__setitem__(key, value)
|
||
/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/geopandas/geodataframe.py:1819: SettingWithCopyWarning:
|
||
A value is trying to be set on a copy of a slice from a DataFrame.
|
||
Try using .loc[row_indexer,col_indexer] = value instead
|
||
|
||
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
||
super().__setitem__(key, value)
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
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|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
|
||
|
||
<span class="c1"># Ensure the output directory exists</span>
|
||
<span class="n">output_dir</span> <span class="o">=</span> <span class="s1">'analysis/output'</span>
|
||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Save the t-test results to a markdown file</span>
|
||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'t_test_results.md'</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"## T-Test Results for Historical Spills</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"T-Statistic: </span><span class="si">{</span><span class="n">t_stat_historical</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"P-Value: </span><span class="si">{</span><span class="n">p_value_historical</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">p_value_historical</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is not statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">## T-Test Results for Recent Spills</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"T-Statistic: </span><span class="si">{</span><span class="n">t_stat_recent</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"P-Value: </span><span class="si">{</span><span class="n">p_value_recent</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="k">if</span> <span class="n">p_value_recent</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is not statistically significant.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Create and save the plots</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
<span class="n">historical_spills</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Historical Spills Response Time Distribution Before and After 2020'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'historical_spills_response_time_distribution.png'</span><span class="p">))</span>
|
||
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
<span class="n">recent_spills</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Recent Spills Response Time Distribution Before and After 2020'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s1">'recent_spills_response_time_distribution.png'</span><span class="p">))</span>
|
||
|
||
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># t-test for the difference in response time before and after 2020 for historical spills</span>
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<span class="n">historical_before_2020</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Before 2020'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
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<span class="n">historical_after_2020</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'2020 and After'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
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<span class="n">t_stat</span><span class="p">,</span> <span class="n">p_value</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">historical_before_2020</span><span class="p">,</span> <span class="n">historical_after_2020</span><span class="p">,</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Historical Spills T-Statistic: </span><span class="si">{</span><span class="n">t_stat</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Historical Spills P-Value: </span><span class="si">{</span><span class="n">p_value</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Check if the difference is statistically significant for historical spills</span>
|
||
<span class="k">if</span> <span class="n">p_value</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is statistically significant.'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for historical spills is not statistically significant.'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># t-test for the difference in response time before and after 2020 for recent spills</span>
|
||
<span class="n">recent_before_2020</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Before 2020'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
<span class="n">recent_after_2020</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'2020 and After'</span><span class="p">][</span><span class="s1">'Report Delay (Days)'</span><span class="p">]</span>
|
||
|
||
<span class="n">t_stat</span><span class="p">,</span> <span class="n">p_value</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">recent_before_2020</span><span class="p">,</span> <span class="n">recent_after_2020</span><span class="p">,</span> <span class="n">equal_var</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Recent Spills T-Statistic: </span><span class="si">{</span><span class="n">t_stat</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Recent Spills P-Value: </span><span class="si">{</span><span class="n">p_value</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Check if the difference is statistically significant for recent spills</span>
|
||
<span class="k">if</span> <span class="n">p_value</span> <span class="o"><</span> <span class="mf">0.05</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is statistically significant.'</span><span class="p">)</span>
|
||
<span class="k">else</span><span class="p">:</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s1">'The difference in response time for recent spills is not statistically significant.'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Create a plot for the response time distribution before and after 2020 for historical and recent spills</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
|
||
<span class="c1"># Plot the response time distribution for historical spills</span>
|
||
<span class="n">historical_spills</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">positions</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
|
||
|
||
<span class="c1"># Plot the response time distribution for recent spills</span>
|
||
<span class="n">recent_spills</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s1">'Report Delay (Days)'</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">positions</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
|
||
|
||
<span class="c1"># Save the plot to the /output directory</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'analysis/output/response_time_distribution.png'</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>
|
||
Historical Spills T-Statistic: 2.8723
|
||
Historical Spills P-Value: 0.0041
|
||
The difference in response time for historical spills is statistically significant.
|
||
|
||
Recent Spills T-Statistic: 2.1457
|
||
Recent Spills P-Value: 0.0319
|
||
The difference in response time for recent spills is statistically significant.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
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"/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Historical only</span>
|
||
<span class="n">historical_spills</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||
<span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Post2020'</span><span class="p">]</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="s1">'2020 and After'</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||
<span class="n">model_hist</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s1">'Q("Report Delay (Days)") ~ Post2020'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">historical_spills</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Historical Spills:</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">model_hist</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
|
||
|
||
<span class="c1"># Recent only</span>
|
||
<span class="n">recent_spills</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||
<span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Post2020'</span><span class="p">]</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="s1">'2020 and After'</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||
<span class="n">model_recent</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s1">'Q("Report Delay (Days)") ~ Post2020'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">recent_spills</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Recent Spills:</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">model_recent</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre>Historical Spills:
|
||
OLS Regression Results
|
||
====================================================================================
|
||
Dep. Variable: Q("Report Delay (Days)") R-squared: 0.002
|
||
Model: OLS Adj. R-squared: 0.001
|
||
Method: Least Squares F-statistic: 8.265
|
||
Date: Mon, 31 Mar 2025 Prob (F-statistic): 0.00406
|
||
Time: 13:58:43 Log-Likelihood: -35825.
|
||
No. Observations: 5312 AIC: 7.165e+04
|
||
Df Residuals: 5310 BIC: 7.167e+04
|
||
Df Model: 1
|
||
Covariance Type: nonrobust
|
||
==============================================================================
|
||
coef std err t P>|t| [0.025 0.975]
|
||
------------------------------------------------------------------------------
|
||
Intercept 29.7461 4.024 7.392 0.000 21.857 37.635
|
||
Post2020 -16.2130 5.639 -2.875 0.004 -27.269 -5.157
|
||
==============================================================================
|
||
Omnibus: 12943.048 Durbin-Watson: 1.944
|
||
Prob(Omnibus): 0.000 Jarque-Bera (JB): 189644913.057
|
||
Skew: 25.310 Prob(JB): 0.00
|
||
Kurtosis: 927.266 Cond. No. 2.64
|
||
==============================================================================
|
||
|
||
Notes:
|
||
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
|
||
|
||
Recent Spills:
|
||
OLS Regression Results
|
||
====================================================================================
|
||
Dep. Variable: Q("Report Delay (Days)") R-squared: 0.000
|
||
Model: OLS Adj. R-squared: 0.000
|
||
Method: Least Squares F-statistic: 3.752
|
||
Date: Mon, 31 Mar 2025 Prob (F-statistic): 0.0528
|
||
Time: 13:58:43 Log-Likelihood: -56260.
|
||
No. Observations: 10678 AIC: 1.125e+05
|
||
Df Residuals: 10676 BIC: 1.125e+05
|
||
Df Model: 1
|
||
Covariance Type: nonrobust
|
||
==============================================================================
|
||
coef std err t P>|t| [0.025 0.975]
|
||
------------------------------------------------------------------------------
|
||
Intercept 4.7122 0.597 7.900 0.000 3.543 5.881
|
||
Post2020 -1.7853 0.922 -1.937 0.053 -3.592 0.021
|
||
==============================================================================
|
||
Omnibus: 28541.449 Durbin-Watson: 1.643
|
||
Prob(Omnibus): 0.000 Jarque-Bera (JB): 676851501.928
|
||
Skew: 32.390 Prob(JB): 0.00
|
||
Kurtosis: 1234.707 Cond. No. 2.47
|
||
==============================================================================
|
||
|
||
Notes:
|
||
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Label both groups, combine</span>
|
||
<span class="n">historical_spills</span><span class="p">[</span><span class="s1">'Group'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'Historical'</span>
|
||
<span class="n">recent_spills</span><span class="p">[</span><span class="s1">'Group'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'Recent'</span>
|
||
<span class="n">combined</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">historical_spills</span><span class="p">,</span> <span class="n">recent_spills</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="n">combined</span><span class="p">[</span><span class="s1">'Post2020'</span><span class="p">]</span> <span class="o">=</span> <span class="n">combined</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="s1">'2020 and After'</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Run regression with interactions by group</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s1">'Q("Report Delay (Days)") ~ Post2020 * Group'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">combined</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre> OLS Regression Results
|
||
====================================================================================
|
||
Dep. Variable: Q("Report Delay (Days)") R-squared: 0.006
|
||
Model: OLS Adj. R-squared: 0.006
|
||
Method: Least Squares F-statistic: 31.12
|
||
Date: Mon, 31 Mar 2025 Prob (F-statistic): 4.78e-20
|
||
Time: 13:53:41 Log-Likelihood: -99827.
|
||
No. Observations: 15990 AIC: 1.997e+05
|
||
Df Residuals: 15986 BIC: 1.997e+05
|
||
Df Model: 3
|
||
Covariance Type: nonrobust
|
||
============================================================================================
|
||
coef std err t P>|t| [0.025 0.975]
|
||
--------------------------------------------------------------------------------------------
|
||
Intercept 29.7461 2.438 12.200 0.000 24.967 34.525
|
||
Group[T.Recent] -25.0339 2.906 -8.616 0.000 -30.729 -19.339
|
||
Post2020 -16.2130 3.417 -4.745 0.000 -22.910 -9.516
|
||
Post2020:Group[T.Recent] 14.4276 4.200 3.435 0.001 6.196 22.660
|
||
==============================================================================
|
||
Omnibus: 45995.174 Durbin-Watson: 1.915
|
||
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3525625525.882
|
||
Skew: 38.959 Prob(JB): 0.00
|
||
Kurtosis: 2302.059 Cond. No. 8.21
|
||
==============================================================================
|
||
|
||
Notes:
|
||
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="c1"># Data</span>
|
||
<span class="n">pre_means</span> <span class="o">=</span> <span class="p">[</span><span class="mf">29.75</span><span class="p">,</span> <span class="mf">4.71</span><span class="p">]</span>
|
||
<span class="n">post_means</span> <span class="o">=</span> <span class="p">[</span><span class="mf">29.75</span> <span class="o">-</span> <span class="mf">16.21</span><span class="p">,</span> <span class="mf">4.71</span> <span class="o">-</span> <span class="mf">1.79</span><span class="p">]</span>
|
||
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Historical Spills'</span><span class="p">,</span> <span class="s1">'Recent Spills'</span><span class="p">]</span>
|
||
|
||
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">))</span>
|
||
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>
|
||
|
||
<span class="c1"># Plot</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="n">i</span> <span class="o">-</span> <span class="n">width</span> <span class="o">/</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">],</span> <span class="n">pre_means</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Pre-2020'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'skyblue'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="n">i</span> <span class="o">+</span> <span class="n">width</span> <span class="o">/</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">],</span> <span class="n">post_means</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Post-2020'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'salmon'</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Labels and aesthetics</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Mean Report Delay (Days)'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mean Report Delay Before and After 2020 by Spill Type'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
|
||
|
||
<span class="c1"># Save and show</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'report_delay_barplot.png'</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
<img alt="No description has been provided for this image" class="" 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"/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>Group</th>
|
||
<th>Pre-2020 Mean</th>
|
||
<th>Post-2020 Change (coef)</th>
|
||
<th>p-value</th>
|
||
<th>Significant (p < 0.05)</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Historical Spills</td>
|
||
<td>29.75</td>
|
||
<td>-16.21</td>
|
||
<td>0.004</td>
|
||
<td>Yes</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Recent Spills</td>
|
||
<td>4.71</td>
|
||
<td>-1.79</td>
|
||
<td>0.053</td>
|
||
<td>No</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h3 id="Analysis-of-Reporting-Delays-for-Historical-and-Recent-Spills">Analysis of Reporting Delays for Historical and Recent Spills<a class="anchor-link" href="#Analysis-of-Reporting-Delays-for-Historical-and-Recent-Spills">¶</a></h3><p>We analyzed reporting delays for historical and recent spills before and after 2020 using simple OLS regressions. For historical spills, the average delay decreased significantly by approximately 16.2 days after 2020 (p = 0.004), dropping from a pre-2020 mean of 29.75 days. In contrast, the reduction for recent spills was smaller—about 1.8 days—and only marginally significant (p = 0.053), starting from a much lower pre-2020 average of 4.71 days. These results suggest that the most substantial improvements in timeliness occurred within the historical spill reporting process.</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h2 id="Differences-in-Reporting-Delays-by-Spill-Type">Differences in Reporting Delays by Spill Type<a class="anchor-link" href="#Differences-in-Reporting-Delays-by-Spill-Type">¶</a></h2>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||
<span class="kn">import</span> <span class="nn">statsmodels.formula.api</span> <span class="k">as</span> <span class="nn">smf</span>
|
||
|
||
<span class="c1"># Combine the datasets and label the groups</span>
|
||
<span class="n">historical_spills</span><span class="p">[</span><span class="s1">'SpillType'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># historical = 0</span>
|
||
<span class="n">recent_spills</span><span class="p">[</span><span class="s1">'SpillType'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># recent = 1</span>
|
||
|
||
<span class="n">combined</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">historical_spills</span><span class="p">,</span> <span class="n">recent_spills</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Convert 'Period' to binary: 0 = Before 2020, 1 = 2020 and After</span>
|
||
<span class="n">combined</span><span class="p">[</span><span class="s1">'Post2020'</span><span class="p">]</span> <span class="o">=</span> <span class="n">combined</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="s1">'2020 and After'</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Interaction term is created implicitly in the regression</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s1">'Q("Report Delay (Days)") ~ Post2020 + SpillType + Post2020:SpillType'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">combined</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
|
||
<pre> OLS Regression Results
|
||
====================================================================================
|
||
Dep. Variable: Q("Report Delay (Days)") R-squared: 0.006
|
||
Model: OLS Adj. R-squared: 0.006
|
||
Method: Least Squares F-statistic: 31.12
|
||
Date: Mon, 31 Mar 2025 Prob (F-statistic): 4.78e-20
|
||
Time: 14:02:43 Log-Likelihood: -99827.
|
||
No. Observations: 15990 AIC: 1.997e+05
|
||
Df Residuals: 15986 BIC: 1.997e+05
|
||
Df Model: 3
|
||
Covariance Type: nonrobust
|
||
======================================================================================
|
||
coef std err t P>|t| [0.025 0.975]
|
||
--------------------------------------------------------------------------------------
|
||
Intercept 29.7461 2.438 12.200 0.000 24.967 34.525
|
||
Post2020 -16.2130 3.417 -4.745 0.000 -22.910 -9.516
|
||
SpillType -25.0339 2.906 -8.616 0.000 -30.729 -19.339
|
||
Post2020:SpillType 14.4276 4.200 3.435 0.001 6.196 22.660
|
||
==============================================================================
|
||
Omnibus: 45995.174 Durbin-Watson: 1.915
|
||
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3525625525.882
|
||
Skew: 38.959 Prob(JB): 0.00
|
||
Kurtosis: 2302.059 Cond. No. 8.21
|
||
==============================================================================
|
||
|
||
Notes:
|
||
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>Term</th>
|
||
<th>Coefficient</th>
|
||
<th>Std. Error</th>
|
||
<th>p-value</th>
|
||
<th>Interpretation</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Intercept</td>
|
||
<td>29.75</td>
|
||
<td>2.44</td>
|
||
<td><0.001</td>
|
||
<td>Historical spills, pre-2020 average report delay</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Post2020</td>
|
||
<td>-16.21</td>
|
||
<td>3.42</td>
|
||
<td><0.001</td>
|
||
<td>Historical spills saw a 16.2-day reduction in delay after 2020</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Group: Recent Spills</td>
|
||
<td>-25.03</td>
|
||
<td>2.91</td>
|
||
<td><0.001</td>
|
||
<td>Recent spills had a 25-day shorter delay than historical spills pre-2020</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Post2020 × Recent Spills</td>
|
||
<td>+14.43</td>
|
||
<td>4.20</td>
|
||
<td>0.001</td>
|
||
<td>Recent spills improved <strong>14.4 days less</strong> than historical spills post-2020</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
|
||
<div class="jp-Cell-inputWrapper" tabindex="0">
|
||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||
</div>
|
||
<div class="jp-InputArea jp-Cell-inputArea">
|
||
<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div>
|
||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||
|
||
<span class="c1"># X-axis positions: 0 = Before 2020, 1 = 2020 and After</span>
|
||
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
|
||
|
||
<span class="c1"># Y-values for each group</span>
|
||
<span class="n">historical</span> <span class="o">=</span> <span class="p">[</span><span class="mf">29.75</span><span class="p">,</span> <span class="mf">29.75</span> <span class="o">-</span> <span class="mf">16.21</span><span class="p">]</span> <span class="c1"># Before and after for historical</span>
|
||
<span class="n">recent</span> <span class="o">=</span> <span class="p">[</span><span class="mf">4.71</span><span class="p">,</span> <span class="mf">4.71</span> <span class="o">-</span> <span class="mf">1.79</span><span class="p">]</span> <span class="c1"># Before and after for recent</span>
|
||
|
||
<span class="c1"># Plot</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">historical</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Historical Spills'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">recent</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Recent Spills'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||
|
||
<span class="c1"># Aesthetics</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s1">'Before 2020'</span><span class="p">,</span> <span class="s1">'2020 and After'</span><span class="p">])</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Mean Report Delay (Days)'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Difference-in-Differences: Report Delay by Spill Type'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
<img alt="No description has been provided for this image" class="" 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"/>
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</div>
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</div>
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</div>
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</div>
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</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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<div class="jp-Cell-inputWrapper" tabindex="0">
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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</div>
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<div class="jp-InputArea jp-Cell-inputArea">
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<div class="jp-InputPrompt jp-InputArea-prompt">In [25]:</div>
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<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="cm-editor cm-s-jupyter">
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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||
<span class="n">improvements</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">16.21</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.79</span><span class="p">]</span>
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||
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Historical Spills'</span><span class="p">,</span> <span class="s1">'Recent Spills'</span><span class="p">]</span>
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<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">improvements</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'steelblue'</span><span class="p">,</span> <span class="s1">'orange'</span><span class="p">])</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Change in Report Delay (Days)'</span><span class="p">)</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Change in Report Delay After 2020'</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="jp-Cell-outputWrapper">
|
||
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
|
||
</div>
|
||
<div class="jp-OutputArea jp-Cell-outputArea">
|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
|
||
<img alt="No description has been provided for this image" class="" 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<p><strong>Interpretation of Difference-in-Differences Model</strong></p>
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<p>This Difference-in-Differences model estimates how report delays changed after 2020 for two groups—historical and recent spills—and compares the <em>magnitude of change</em> between them. The pre-2020 average delay for historical spills was approximately 29.75 days. After 2020, historical spills saw a statistically significant reduction of about 16.2 days (p < 0.001).</p>
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<p>Recent spills, by contrast, already had much shorter average delays (about 25 days less than historical spills before 2020). The DiD interaction term (+14.4 days, p = 0.001) shows that the improvement in timeliness for recent spills was significantly smaller than that for historical spills. In other words, while both groups saw reductions in report delays after 2020, the gains were <strong>substantially greater</strong> for historical spills.</p>
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<p>This suggests that any policy or process changes implemented around 2020 had a <strong>disproportionately stronger effect</strong> on addressing delays in the historical spill category.</p>
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