8213 lines
855 KiB
HTML
8213 lines
855 KiB
HTML
<!DOCTYPE html>
|
||
|
||
<html lang="en">
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||
<head><meta charset="utf-8"/>
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<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
|
||
<style type="text/css">
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||
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; }
|
||
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) }
|
||
.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 */
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||
.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
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||
.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 */
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||
.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
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||
.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
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||
.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
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||
.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
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||
.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
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||
.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 */
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||
.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
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||
.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
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||
.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
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||
.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
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||
.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
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||
.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
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.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 */
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||
.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
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||
.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
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||
.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 */
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||
.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 */
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||
.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
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||
</style>
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||
<style type="text/css">
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/*-----------------------------------------------------------------------------
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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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||
* Mozilla scrollbar styling
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*/
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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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||
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||
/* for code nodes, use a transparent style of scrollbar. These selectors
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* 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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||
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||
/* tiny scrollbar */
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||
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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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||
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/* tiny scrollbar */
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||
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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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||
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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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||
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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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||
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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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||
/*
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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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||
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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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||
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||
.lm-ScrollBar-button:hover {
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||
background-color: #dadada;
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||
}
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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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||
}
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||
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||
.lm-ScrollBar-track {
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||
background: #f0f0f0;
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||
}
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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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||
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||
.lm-ScrollBar-thumb:hover {
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||
background: #bababa;
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||
}
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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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||
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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']
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||
.lm-ScrollBar-button[data-action='decrement'] {
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||
background-image: var(--jp-icon-caret-left);
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||
background-size: 17px;
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||
}
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||
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||
.lm-ScrollBar[data-orientation='horizontal']
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||
.lm-ScrollBar-button[data-action='increment'] {
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||
background-image: var(--jp-icon-caret-right);
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||
background-size: 17px;
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||
}
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||
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||
.lm-ScrollBar[data-orientation='vertical']
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||
.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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||
}
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||
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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;
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||
}
|
||
|
||
/*
|
||
* 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.
|
||
|----------------------------------------------------------------------------*/
|
||
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||
.lm-Widget {
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||
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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||
}
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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%);
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||
}
|
||
|
||
/*
|
||
* 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 {
|
||
display: flex;
|
||
flex-direction: column;
|
||
-webkit-user-select: none;
|
||
-moz-user-select: none;
|
||
-ms-user-select: none;
|
||
user-select: none;
|
||
}
|
||
|
||
.lm-CommandPalette-search {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-CommandPalette-content {
|
||
flex: 1 1 auto;
|
||
margin: 0;
|
||
padding: 0;
|
||
min-height: 0;
|
||
overflow: auto;
|
||
list-style-type: none;
|
||
}
|
||
|
||
.lm-CommandPalette-header {
|
||
overflow: hidden;
|
||
white-space: nowrap;
|
||
text-overflow: ellipsis;
|
||
}
|
||
|
||
.lm-CommandPalette-item {
|
||
display: flex;
|
||
flex-direction: row;
|
||
}
|
||
|
||
.lm-CommandPalette-itemIcon {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-CommandPalette-itemContent {
|
||
flex: 1 1 auto;
|
||
overflow: hidden;
|
||
}
|
||
|
||
.lm-CommandPalette-itemShortcut {
|
||
flex: 0 0 auto;
|
||
}
|
||
|
||
.lm-CommandPalette-itemLabel {
|
||
overflow: hidden;
|
||
white-space: nowrap;
|
||
text-overflow: ellipsis;
|
||
}
|
||
|
||
.lm-close-icon {
|
||
border: 1px solid transparent;
|
||
background-color: transparent;
|
||
position: absolute;
|
||
z-index: 1;
|
||
right: 3%;
|
||
top: 0;
|
||
bottom: 0;
|
||
margin: auto;
|
||
padding: 7px 0;
|
||
display: none;
|
||
vertical-align: middle;
|
||
outline: 0;
|
||
cursor: pointer;
|
||
}
|
||
.lm-close-icon:after {
|
||
content: 'X';
|
||
display: block;
|
||
width: 15px;
|
||
height: 15px;
|
||
text-align: center;
|
||
color: #000;
|
||
font-weight: normal;
|
||
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;
|
||
}
|
||
|
||
.lm-DockPanel-widget {
|
||
z-index: 0;
|
||
}
|
||
|
||
.lm-DockPanel-tabBar {
|
||
z-index: 1;
|
||
}
|
||
|
||
.lm-DockPanel-handle {
|
||
z-index: 2;
|
||
}
|
||
|
||
.lm-DockPanel-handle.lm-mod-hidden {
|
||
display: none !important;
|
||
}
|
||
|
||
.lm-DockPanel-handle:after {
|
||
position: absolute;
|
||
top: 0;
|
||
left: 0;
|
||
width: 100%;
|
||
height: 100%;
|
||
content: '';
|
||
}
|
||
|
||
.lm-DockPanel-handle[data-orientation='horizontal'] {
|
||
cursor: ew-resize;
|
||
}
|
||
|
||
.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;
|
||
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;
|
||
-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;
|
||
text-align: center;
|
||
}
|
||
|
||
.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,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);
|
||
--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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMTUwIDE1MCA1NDEuOSAyOTUuMyI+CiAgPGcgY2xhc3M9ImpwLWljb24tYnJhbmQyIGpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iIzYxREFGQiI+CiAgICA8cGF0aCBkPSJNNjY2LjMgMjk2LjVjMC0zMi41LTQwLjctNjMuMy0xMDMuMS04Mi40IDE0LjQtNjMuNiA4LTExNC4yLTIwLjItMTMwLjQtNi41LTMuOC0xNC4xLTUuNi0yMi40LTUuNnYyMi4zYzQuNiAwIDguMy45IDExLjQgMi42IDEzLjYgNy44IDE5LjUgMzcuNSAxNC45IDc1LjctMS4xIDkuNC0yLjkgMTkuMy01LjEgMjkuNC0xOS42LTQuOC00MS04LjUtNjMuNS0xMC45LTEzLjUtMTguNS0yNy41LTM1LjMtNDEuNi01MCAzMi42LTMwLjMgNjMuMi00Ni45IDg0LTQ2LjlWNzhjLTI3LjUgMC02My41IDE5LjYtOTkuOSA1My42LTM2LjQtMzMuOC03Mi40LTUzLjItOTkuOS01My4ydjIyLjNjMjAuNyAwIDUxLjQgMTYuNSA4NCA0Ni42LTE0IDE0LjctMjggMzEuNC00MS4zIDQ5LjktMjIuNiAyLjQtNDQgNi4xLTYzLjYgMTEtMi4zLTEwLTQtMTkuNy01LjItMjktNC43LTM4LjIgMS4xLTY3LjkgMTQuNi03NS44IDMtMS44IDYuOS0yLjYgMTEuNS0yLjZWNzguNWMtOC40IDAtMTYgMS44LTIyLjYgNS42LTI4LjEgMTYuMi0zNC40IDY2LjctMTkuOSAxMzAuMS02Mi4yIDE5LjItMTAyLjcgNDkuOS0xMDIuNyA4Mi4zIDAgMzIuNSA0MC43IDYzLjMgMTAzLjEgODIuNC0xNC40IDYzLjYtOCAxMTQuMiAyMC4yIDEzMC40IDYuNSAzLjggMTQuMSA1LjYgMjIuNSA1LjYgMjcuNSAwIDYzLjUtMTkuNiA5OS45LTUzLjYgMzYuNCAzMy44IDcyLjQgNTMuMiA5OS45IDUzLjIgOC40IDAgMTYtMS44IDIyLjYtNS42IDI4LjEtMTYuMiAzNC40LTY2LjcgMTkuOS0xMzAuMSA2Mi0xOS4xIDEwMi41LTQ5LjkgMTAyLjUtODIuM3ptLTEzMC4yLTY2LjdjLTMuNyAxMi45LTguMyAyNi4yLTEzLjUgMzkuNS00LjEtOC04LjQtMTYtMTMuMS0yNC00LjYtOC05LjUtMTUuOC0xNC40LTIzLjQgMTQuMiAyLjEgMjcuOSA0LjcgNDEgNy45em0tNDUuOCAxMDYuNWMtNy44IDEzLjUtMTUuOCAyNi4zLTI0LjEgMzguMi0xNC45IDEuMy0zMCAyLTQ1LjIgMi0xNS4xIDAtMzAuMi0uNy00NS0xLjktOC4zLTExLjktMTYuNC0yNC42LTI0LjItMzgtNy42LTEzLjEtMTQuNS0yNi40LTIwLjgtMzkuOCA2LjItMTMuNCAxMy4yLTI2LjggMjAuNy0zOS45IDcuOC0xMy41IDE1LjgtMjYuMyAyNC4xLTM4LjIgMTQuOS0xLjMgMzAtMiA0NS4yLTIgMTUuMSAwIDMwLjIuNyA0NSAxLjkgOC4zIDExLjkgMTYuNCAyNC42IDI0LjIgMzggNy42IDEzLjEgMTQuNSAyNi40IDIwLjggMzkuOC02LjMgMTMuNC0xMy4yIDI2LjgtMjAuNyAzOS45em0zMi4zLTEzYzUuNCAxMy40IDEwIDI2LjggMTMuOCAzOS44LTEzLjEgMy4yLTI2LjkgNS45LTQxLjIgOCA0LjktNy43IDkuOC0xNS42IDE0LjQtMjMuNyA0LjYtOCA4LjktMTYuMSAxMy0yNC4xek00MjEuMiA0MzBjLTkuMy05LjYtMTguNi0yMC4zLTI3LjgtMzIgOSAuNCAxOC4yLjcgMjcuNS43IDkuNCAwIDE4LjctLjIgMjcuOC0uNy05IDExLjctMTguMyAyMi40LTI3LjUgMzJ6bS03NC40LTU4LjljLTE0LjItMi4xLTI3LjktNC43LTQxLTcuOSAzLjctMTIuOSA4LjMtMjYuMiAxMy41LTM5LjUgNC4xIDggOC40IDE2IDEzLjEgMjQgNC43IDggOS41IDE1LjggMTQuNCAyMy40ek00MjAuNyAxNjNjOS4zIDkuNiAxOC42IDIwLjMgMjcuOCAzMi05LS40LTE4LjItLjctMjcuNS0uNy05LjQgMC0xOC43LjItMjcuOC43IDktMTEuNyAxOC4zLTIyLjQgMjcuNS0zMnptLTc0IDU4LjljLTQuOSA3LjctOS44IDE1LjYtMTQuNCAyMy43LTQuNiA4LTguOSAxNi0xMyAyNC01LjQtMTMuNC0xMC0yNi44LTEzLjgtMzkuOCAxMy4xLTMuMSAyNi45LTUuOCA0MS4yLTcuOXptLTkwLjUgMTI1LjJjLTM1LjQtMTUuMS01OC4zLTM0LjktNTguMy01MC42IDAtMTUuNyAyMi45LTM1LjYgNTguMy01MC42IDguNi0zLjcgMTgtNyAyNy43LTEwLjEgNS43IDE5LjYgMTMuMiA0MCAyMi41IDYwLjktOS4yIDIwLjgtMTYuNiA0MS4xLTIyLjIgNjAuNi05LjktMy4xLTE5LjMtNi41LTI4LTEwLjJ6TTMxMCA0OTBjLTEzLjYtNy44LTE5LjUtMzcuNS0xNC45LTc1LjcgMS4xLTkuNCAyLjktMTkuMyA1LjEtMjkuNCAxOS42IDQuOCA0MSA4LjUgNjMuNSAxMC45IDEzLjUgMTguNSAyNy41IDM1LjMgNDEuNiA1MC0zMi42IDMwLjMtNjMuMiA0Ni45LTg0IDQ2LjktNC41LS4xLTguMy0xLTExLjMtMi43em0yMzcuMi03Ni4yYzQuNyAzOC4yLTEuMSA2Ny45LTE0LjYgNzUuOC0zIDEuOC02LjkgMi42LTExLjUgMi42LTIwLjcgMC01MS40LTE2LjUtODQtNDYuNiAxNC0xNC43IDI4LTMxLjQgNDEuMy00OS45IDIyLjYtMi40IDQ0LTYuMSA2My42LTExIDIuMyAxMC4xIDQuMSAxOS44IDUuMiAyOS4xem0zOC41LTY2LjdjLTguNiAzLjctMTggNy0yNy43IDEwLjEtNS43LTE5LjYtMTMuMi00MC0yMi41LTYwLjkgOS4yLTIwLjggMTYuNi00MS4xIDIyLjItNjAuNiA5LjkgMy4xIDE5LjMgNi41IDI4LjEgMTAuMiAzNS40IDE1LjEgNTguMyAzNC45IDU4LjMgNTAuNi0uMSAxNS43LTIzIDM1LjYtNTguNCA1MC42ek0zMjAuOCA3OC40eiIvPgogICAgPGNpcmNsZSBjeD0iNDIwLjkiIGN5PSIyOTYuNSIgcj0iNDUuNyIvPgogIDwvZz4KPC9zdmc+Cg==);
|
||
--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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<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
|
||
<main>
|
||
<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>
|
||
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|
||
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
|
||
<h1 id="Tutorial:-Predicting-Lid-driven-cavity-problem-parameters-with-POD-RBF">Tutorial: Predicting Lid-driven cavity problem parameters with POD-RBF<a class="anchor-link" href="#Tutorial:-Predicting-Lid-driven-cavity-problem-parameters-with-POD-RBF">¶</a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial14/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></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">
|
||
<p>In this tutorial we will show how to use the <strong>PINA</strong> library to predict the distributions of velocity and pressure the Lid-driven Cavity problem, a benchmark in Computational Fluid Dynamics. The problem consists of a square cavity with a lid on top moving with tangential velocity (by convention to the right), with the addition of no-slip conditions on the walls of the cavity and null static pressure on the lower left angle.</p>
|
||
<p>Our goal is to predict the distributions of velocity and pressure of the fluid inside the cavity as the Reynolds number of the inlet fluid varies. To do so we're using a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). The parametric solution manifold is approximated here with Radial Basis Function (RBF) Interpolation, a common mesh-free interpolation method that doesn't require trainers or solvers as the found radial basis functions are used to interpolate new points.</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">
|
||
<p>Let's start with the necessary imports. We're particularly interested in the <code>PODBlock</code> and <code>RBFBlock</code> classes which will allow us to define the POD-RBF model.</p>
|
||
</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 [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="c1">## routine needed to run the notebook on Google Colab</span>
|
||
<span class="k">try</span><span class="p">:</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">google.colab</span>
|
||
|
||
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">True</span>
|
||
<span class="k">except</span><span class="p">:</span>
|
||
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">False</span>
|
||
<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
|
||
<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"pina-mathlab"</span><span class="w"> </span>
|
||
|
||
<span class="o">%</span><span class="k">matplotlib</span> inline
|
||
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">pina</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
|
||
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model.block</span><span class="w"> </span><span class="kn">import</span> <span class="n">PODBlock</span><span class="p">,</span> <span class="n">RBFBlock</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">pina</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelTensor</span>
|
||
|
||
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
|
||
</pre></div>
|
||
</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">
|
||
<p>In this tutorial we're gonna use the <code>LidCavity</code> class from the <a href="https://github.com/mathLab/Smithers">Smithers</a> library, which contains a set of parametric solutions of the Lid-driven cavity problem in a square domain. The dataset consists of 300 snapshots of the parameter fields, which in this case are the magnitude of velocity and the pressure, and the corresponding parameter values $u$ and $p$. Each snapshot corresponds to a different value of the tangential velocity $\mu$ of the lid, which has been sampled uniformly between 0.01 m/s and 1 m/s.</p>
|
||
<p>Let's start by importing the dataset:</p>
|
||
</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="kn">import</span><span class="w"> </span><span class="nn">smithers</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">smithers.dataset</span><span class="w"> </span><span class="kn">import</span> <span class="n">LidCavity</span>
|
||
|
||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">LidCavity</span><span class="p">()</span>
|
||
</pre></div>
|
||
</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">
|
||
<p>Let's plot two the data points and the corresponding solution for both parameters at different snapshots, in order to better visualise the data we're using:</p>
|
||
</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 [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">fig</span><span class="p">,</span> <span class="n">axs</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"mag(v)"</span><span class="p">][:</span><span class="mi">3</span><span class="p">]):</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</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="sa">f</span><span class="s2">"$u$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][:</span><span class="mi">3</span><span class="p">]):</span>
|
||
<span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</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="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
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|
||
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"/>
|
||
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||
<p>To train the model we only need the snapshots for the two parameters. In order to be able to work with the snapshots in <strong>PINA</strong> we first need to assure they're in a compatible format, hence why we start by casting them into <code>LabelTensor</code> objects:</p>
|
||
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|
||
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||
<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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||
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||
<div class="cm-editor cm-s-jupyter">
|
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""velocity magnitude data, 5041 for each snapshot"""</span>
|
||
|
||
<span class="n">u</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"mag(v)"</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
|
||
<span class="n">u</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
|
||
<span class="sd">"""pressure data, 5041 for each snapshot"""</span>
|
||
<span class="n">p</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
|
||
<span class="n">p</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
|
||
<span class="sd">"""mu corresponding to each snapshot"""</span>
|
||
<span class="n">mu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
|
||
<span class="n">mu</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">])</span>
|
||
</pre></div>
|
||
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|
||
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|
||
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|
||
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||
<p>The goal of our training is to be able to predict the solution for new test parameters. The first thing we need to do is validate the accuracy of the model, and in order to do so we split the 300 snapshots in training and testing dataset. In the example we set the training <code>ratio</code> to 0.9, which means that 90% of the total snapshots is used for training and the remaining 10% for testing.</p>
|
||
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|
||
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|
||
<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="sd">"""number of snapshots"""</span>
|
||
|
||
<span class="n">n</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||
<span class="sd">"""training over total snapshots ratio and number of training snapshots"""</span>
|
||
<span class="n">ratio</span> <span class="o">=</span> <span class="mf">0.9</span>
|
||
<span class="n">n_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span>
|
||
<span class="sd">"""split u and p data"""</span>
|
||
<span class="n">u_train</span><span class="p">,</span> <span class="n">u_test</span> <span class="o">=</span> <span class="n">u</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span> <span class="c1"># for mag(v)</span>
|
||
<span class="n">p_train</span><span class="p">,</span> <span class="n">p_test</span> <span class="o">=</span> <span class="n">p</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span> <span class="c1"># for p</span>
|
||
<span class="sd">"""split snapshots"""</span>
|
||
<span class="n">mu_train</span><span class="p">,</span> <span class="n">mu_test</span> <span class="o">=</span> <span class="n">mu</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">mu</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span>
|
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<p>We now proceed by defining the model we intend to use. We inherit from the <code>torch.nn.Module</code> class, but in addition we require a <code>pod_rank</code> for the POD part and a function <code>rbf_kernel</code> in order to perform the RBF part:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">PODRBF</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
||
<span class="sd"> Proper orthogonal decomposition with Radial Basis Function interpolation model.</span>
|
||
<span class="sd"> """</span>
|
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|
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<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pod_rank</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="p">):</span>
|
||
|
||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span> <span class="o">=</span> <span class="n">PODBlock</span><span class="p">(</span><span class="n">pod_rank</span><span class="p">)</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span> <span class="o">=</span> <span class="n">RBFBlock</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">rbf_kernel</span><span class="p">)</span>
|
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</pre></div>
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<p>We complete our model by adding two crucial methods. The first is <code>forward</code>, and it expands the input POD coefficients. After being expanded the POD layer needs to be fit, hence why we add a <code>fit</code> method that gives us the POD basis (current <strong>PINA</strong> default is by performing truncated Singular Value Decomposition). The same method then uses the basis to fit the RBF interpolation. Overall, the completed class looks like this:</p>
|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">PODRBF</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
||
<span class="w"> </span><span class="sd">"""</span>
|
||
<span class="sd"> Proper orthogonal decomposition with Radial Basis Function interpolation model.</span>
|
||
<span class="sd"> """</span>
|
||
|
||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pod_rank</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="p">):</span>
|
||
|
||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span> <span class="o">=</span> <span class="n">PODBlock</span><span class="p">(</span><span class="n">pod_rank</span><span class="p">)</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span> <span class="o">=</span> <span class="n">RBFBlock</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">rbf_kernel</span><span class="p">)</span>
|
||
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||
<span class="w"> </span><span class="sd">"""</span>
|
||
<span class="sd"> Defines the computation performed at every call.</span>
|
||
<span class="sd"> :param x: The tensor to apply the forward pass.</span>
|
||
<span class="sd"> :type x: torch.Tensor</span>
|
||
<span class="sd"> :return: the output computed by the model.</span>
|
||
<span class="sd"> :rtype: torch.Tensor</span>
|
||
<span class="sd"> """</span>
|
||
<span class="n">coefficients</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rbf</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)</span>
|
||
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||
<span class="w"> </span><span class="sd">"""</span>
|
||
<span class="sd"> Call the :meth:`pina.model.layers.PODBlock.fit` method of the</span>
|
||
<span class="sd"> :attr:`pina.model.layers.PODBlock` attribute to perform the POD,</span>
|
||
<span class="sd"> and the :meth:`pina.model.layers.RBFBlock.fit` method of the</span>
|
||
<span class="sd"> :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation.</span>
|
||
<span class="sd"> """</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
|
||
</pre></div>
|
||
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|
||
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|
||
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|
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|
||
<p>Now that we've built our class, we can fit the model and ask it to predict the parameters for the remaining snapshots. We remember that we don't need to train the model, as it doesn't involve any learnable parameter. The only things we have to set are the rank of the decomposition and the radial basis function (here we use thin plate). Here we focus on predicting the magnitude of velocity:</p>
|
||
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|
||
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|
||
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
|
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|
||
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|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""create the model"""</span>
|
||
|
||
<span class="n">pod_rbfu</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
|
||
|
||
<span class="sd">"""fit the model to velocity training data"""</span>
|
||
<span class="n">pod_rbfu</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mu_train</span><span class="p">,</span> <span class="n">u_train</span><span class="p">)</span>
|
||
|
||
<span class="sd">"""predict the parameter using the fitted model"""</span>
|
||
<span class="n">u_train_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_train</span><span class="p">)</span>
|
||
<span class="n">u_test_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_test</span><span class="p">)</span>
|
||
</pre></div>
|
||
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|
||
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|
||
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|
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|
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|
||
<p>Finally we can calculate the relative error for our model:</p>
|
||
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|
||
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|
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|
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
|
||
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|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="n">relative_u_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_train_rbf</span> <span class="o">-</span> <span class="n">u_train</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_train</span><span class="p">)</span>
|
||
<span class="n">relative_u_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_test_rbf</span> <span class="o">-</span> <span class="n">u_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_test</span><span class="p">)</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">relative_u_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">relative_u_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
</pre></div>
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
<pre>Error summary for POD-RBF model:
|
||
Train: 8.186969e-03
|
||
Test: 5.062145e-02
|
||
</pre>
|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
||
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|
||
<p>The results are promising! Now let's visualise them, comparing four random predicted snapshots to the true ones:</p>
|
||
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
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|
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
|
||
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|
||
<div class="cm-editor cm-s-jupyter">
|
||
<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib</span>
|
||
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
|
||
|
||
<span class="n">idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">u_test</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,))</span>
|
||
<span class="n">u_idx_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
|
||
|
||
<span class="n">relative_u_error_rbf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">-</span> <span class="n">u_idx_rbf</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
|
||
<span class="n">relative_u_error_rbf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
|
||
<span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o"><</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">,</span> <span class="n">relative_u_error_rbf</span> <span class="o">/</span> <span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
|
||
<span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">idx_</span><span class="p">,</span> <span class="n">rbf_</span><span class="p">,</span> <span class="n">rbf_err_</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
|
||
<span class="nb">zip</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">u_idx_rbf</span><span class="p">,</span> <span class="n">relative_u_error_rbf</span><span class="p">)</span>
|
||
<span class="p">):</span>
|
||
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Prediction for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span>
|
||
<span class="s2">"True snapshot for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span>
|
||
<span class="p">)</span>
|
||
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Error for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
|
||
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span>
|
||
<span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">rbf_</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
|
||
<span class="p">)</span> <span class="c1"># POD-RBF prediction</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
|
||
|
||
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="c1"># Truth</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
|
||
|
||
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tripcolor</span><span class="p">(</span>
|
||
<span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">rbf_err_</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">colors</span><span class="o">.</span><span class="n">LogNorm</span><span class="p">()</span>
|
||
<span class="p">)</span> <span class="c1"># Error for POD-RBF</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</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">
|
||
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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">
|
||
<p>Overall we have reached a good level of approximation while avoiding time-consuming training procedures. Let's try doing the same to predict the pressure snapshots:</p>
|
||
</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="sd">"""create the model"""</span>
|
||
|
||
<span class="n">pod_rbfp</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
|
||
|
||
<span class="sd">"""fit the model to pressure training data"""</span>
|
||
<span class="n">pod_rbfp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mu_train</span><span class="p">,</span> <span class="n">p_train</span><span class="p">)</span>
|
||
|
||
<span class="sd">"""predict the parameter using the fitted model"""</span>
|
||
<span class="n">p_train_rbf</span> <span class="o">=</span> <span class="n">pod_rbfp</span><span class="p">(</span><span class="n">mu_train</span><span class="p">)</span>
|
||
<span class="n">p_test_rbf</span> <span class="o">=</span> <span class="n">pod_rbfp</span><span class="p">(</span><span class="n">mu_test</span><span class="p">)</span>
|
||
|
||
<span class="n">relative_p_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_train_rbf</span> <span class="o">-</span> <span class="n">p_train</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_train</span><span class="p">)</span>
|
||
<span class="n">relative_p_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_test_rbf</span> <span class="o">-</span> <span class="n">p_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_test</span><span class="p">)</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">relative_p_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">relative_p_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</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>Error summary for POD-RBF model:
|
||
Train: 4.524322e-02
|
||
Test: 4.513036e+06
|
||
</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">
|
||
<p>Unfortunately here we obtain a very high relative test error, although this is likely due to the nature of the available data. Looking at the plots we can see that the pressure field is subject to high variations between subsequent snapshots, especially here:</p>
|
||
</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="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
|
||
<span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">66</span><span class="p">:</span><span class="mi">72</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="mi">66</span><span class="p">:</span><span class="mi">72</span><span class="p">]</span>
|
||
<span class="p">):</span>
|
||
<span class="n">cm</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</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="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</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">
|
||
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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">
|
||
<p>Or here:</p>
|
||
</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="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
|
||
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
|
||
<span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">98</span><span class="p">:</span><span class="mi">104</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="mi">98</span><span class="p">:</span><span class="mi">104</span><span class="p">]</span>
|
||
<span class="p">):</span>
|
||
<span class="n">cm</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</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="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</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">
|
||
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|
||
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|
||
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|
||
<div class="jp-OutputArea-child">
|
||
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
|
||
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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 class="jp-Cell-inputWrapper" tabindex="0">
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<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">
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</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
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||
<p>Scrolling through the velocity snapshots we can observe a more regular behaviour, with no such variations in subsequent snapshots. Moreover, if we decide not to consider the abovementioned "problematic" snapshots, we can already observe a huge improvement:</p>
|
||
</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">
|
||
<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">
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""excluding problematic snapshots"""</span>
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|
||
<span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">))</span>
|
||
<span class="n">data_to_consider</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="mi">67</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="mi">71</span><span class="p">:</span><span class="mi">100</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="mi">102</span><span class="p">:]</span>
|
||
<span class="sd">"""proceed as before"""</span>
|
||
<span class="n">newp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="n">data_to_consider</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
|
||
<span class="n">newp</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">newp</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">newp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
|
||
|
||
<span class="n">newmu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">data_to_consider</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
|
||
<span class="n">newmu</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">newmu</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">])</span>
|
||
|
||
<span class="n">newn</span> <span class="o">=</span> <span class="n">newp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||
<span class="n">ratio</span> <span class="o">=</span> <span class="mf">0.9</span>
|
||
<span class="n">new_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">newn</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span>
|
||
|
||
<span class="n">new_p_train</span><span class="p">,</span> <span class="n">new_p_test</span> <span class="o">=</span> <span class="n">newp</span><span class="p">[:</span><span class="n">new_train</span><span class="p">],</span> <span class="n">newp</span><span class="p">[</span><span class="n">new_train</span><span class="p">:]</span>
|
||
|
||
<span class="n">new_mu_train</span><span class="p">,</span> <span class="n">new_mu_test</span> <span class="o">=</span> <span class="n">newmu</span><span class="p">[:</span><span class="n">new_train</span><span class="p">],</span> <span class="n">newmu</span><span class="p">[</span><span class="n">new_train</span><span class="p">:]</span>
|
||
|
||
<span class="n">new_pod_rbfp</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
|
||
|
||
<span class="n">new_pod_rbfp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">new_mu_train</span><span class="p">,</span> <span class="n">new_p_train</span><span class="p">)</span>
|
||
|
||
<span class="n">new_p_train_rbf</span> <span class="o">=</span> <span class="n">new_pod_rbfp</span><span class="p">(</span><span class="n">new_mu_train</span><span class="p">)</span>
|
||
<span class="n">new_p_test_rbf</span> <span class="o">=</span> <span class="n">new_pod_rbfp</span><span class="p">(</span><span class="n">new_mu_test</span><span class="p">)</span>
|
||
|
||
<span class="n">new_relative_p_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
|
||
<span class="n">new_p_train_rbf</span> <span class="o">-</span> <span class="n">new_p_train</span>
|
||
<span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">new_p_train</span><span class="p">)</span>
|
||
<span class="n">new_relative_p_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
|
||
<span class="n">new_p_test_rbf</span> <span class="o">-</span> <span class="n">new_p_test</span>
|
||
<span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">new_p_test</span><span class="p">)</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">new_relative_p_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">new_relative_p_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</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>Error summary for POD-RBF model:
|
||
Train: 4.368320e-02
|
||
Test: 2.537675e-01
|
||
</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">
|
||
<h2 id="What's-next?">What's next?<a class="anchor-link" href="#What's-next?">¶</a></h2><p>Congratulations on completing the <strong>PINA</strong> tutorial on building and using a custom POD class! Now you can try:</p>
|
||
<ol>
|
||
<li><p>Varying the inputs of the model (for a list of the supported RB functions look at the <code>rbf_layer.py</code> file in <code>pina.layers</code>)</p>
|
||
</li>
|
||
<li><p>Changing the POD model, for example using Artificial Neural Networks. For a more in depth overview of POD-NN and a comparison with the POD-RBF model already shown, look at <a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb">Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems</a></p>
|
||
</li>
|
||
<li><p>Building your own classes or adapt the one shown to other datasets/problems</p>
|
||
</li>
|
||
</ol>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</main>
|
||
</body>
|
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</html>
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