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PINA/docs/source/tutorials/tutorial8/tutorial.html
github-actions[bot] ae1fd2680f export tutorials changed in c161138 (#498) 
Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>
2025-04-17 10:48:31 +02:00

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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
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--jp-icon-add-above: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5MikiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik00Ljc1IDQuOTMwNjZINi42MjVWNi44MDU2NkM2LjYyNSA3LjAxMTkxIDYuNzkzNzUgNy4xODA2NiA3IDcuMTgwNjZDNy4yMDYyNSA3LjE4MDY2IDcuMzc1IDcuMDExOTEgNy4zNzUgNi44MDU2NlY0LjkzMDY2SDkuMjVDOS40NTYyNSA0LjkzMDY2IDkuNjI1IDQuNzYxOTEgOS42MjUgNC41NTU2NkM5LjYyNSA0LjM0OTQxIDkuNDU2MjUgNC4xODA2NiA5LjI1IDQuMTgwNjZINy4zNzVWMi4zMDU2NkM3LjM3NSAyLjA5OTQxIDcuMjA2MjUgMS45MzA2NiA3IDEuOTMwNjZDNi43OTM3NSAxLjkzMDY2IDYuNjI1IDIuMDk5NDEgNi42MjUgMi4zMDU2NlY0LjE4MDY2SDQuNzVDNC41NDM3NSA0LjE4MDY2IDQuMzc1IDQuMzQ5NDEgNC4zNzUgNC41NTU2NkM0LjM3NSA0Ljc2MTkxIDQuNTQzNzUgNC45MzA2NiA0Ljc1IDQuOTMwNjZaIiBmaWxsPSIjNjE2MTYxIiBzdHJva2U9IiM2MTYxNjEiIHN0cm9rZS13aWR0aD0iMC43Ii8+CjwvZz4KPHBhdGggY2xhc3M9ImpwLWljb24zIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiIGNsaXAtcnVsZT0iZXZlbm9kZCIgZD0iTTExLjUgOS41VjExLjVMMi41IDExLjVWOS41TDExLjUgOS41Wk0xMiA4QzEyLjU1MjMgOCAxMyA4LjQ0NzcyIDEzIDlWMTJDMTMgMTIuNTUyMyAxMi41NTIzIDEzIDEyIDEzTDIgMTNDMS40NDc3MiAxMyAxIDEyLjU1MjMgMSAxMlY5QzEgOC40NDc3MiAxLjQ0NzcxIDggMiA4TDEyIDhaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5MiI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KC0xIDAgMCAxIDEwIDEuNTU1NjYpIi8+CjwvY2xpcFBhdGg+CjwvZGVmcz4KPC9zdmc+Cg==);
--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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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KICA8ZyBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiM0MTQxNDEiPgogICAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiAgPC9nPgoKICA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiBmaWxsPSIjRkZGIj4KICAgIDxjaXJjbGUgY2xhc3M9InN0MiIgY3g9IjUuNSIgY3k9IjE0LjUiIHI9IjEuNSIvPgogICAgPHJlY3QgeD0iMTIiIHk9IjQiIGNsYXNzPSJzdDIiIHdpZHRoPSIxIiBoZWlnaHQ9IjgiLz4KICAgIDxyZWN0IHg9IjguNSIgeT0iNy41IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjg2NiAtMC41IDAuNSAwLjg2NiAtMi4zMjU1IDcuMzIxOSkiIGNsYXNzPSJzdDIiIHdpZHRoPSI4IiBoZWlnaHQ9IjEiLz4KICAgIDxyZWN0IHg9IjEyIiB5PSI0IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjUgLTAuODY2IDAuODY2IDAuNSAtMC42Nzc5IDE0LjgyNTIpIiBjbGFzcz0ic3QyIiB3aWR0aD0iMSIgaGVpZ2h0PSI4Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--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">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<h1 id="Tutorial:-Reduced-order-models-(POD-NN-and-POD-RBF)-for-parametric-problems">Tutorial: Reduced order models (POD-NN and POD-RBF) for parametric problems<a class="anchor-link" href="#Tutorial:-Reduced-order-models-(POD-NN-and-POD-RBF)-for-parametric-problems"></a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
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</div>
</div>
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<p>The tutorial aims to show how to employ the <strong>PINA</strong> library in order to apply a reduced order modeling technique [1]. Such methodologies have several similarities with machine learning approaches, since the main goal consists in predicting the solution of differential equations (typically parametric PDEs) in a real-time fashion.</p>
<p>In particular we are going to use the Proper Orthogonal Decomposition with either Radial Basis Function Interpolation (POD-RBF) or Neural Network (POD-NN) [2]. Here we basically perform a dimensional reduction using the POD approach, approximating the parametric solution manifold (at the reduced space) using a regression technique (NN) and comparing it to an RBF interpolation. In this example, we use a simple multilayer perceptron, but the plenty of different architectures can be plugged as well.</p>
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<p>Let's start with the necessary imports.
It's important to note the minimum PINA version to run this tutorial is the <code>0.1</code>.</p>
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<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="o">%</span><span class="k">matplotlib</span> inline
<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="kn">import</span><span class="w"> </span><span class="nn">torch</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">warnings</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">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model</span><span class="w"> </span><span class="kn">import</span> <span class="n">FeedForward</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.solver</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedSolver</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.optim</span><span class="w"> </span><span class="kn">import</span> <span class="n">TorchOptimizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.problem.zoo</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedProblem</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="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>
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<p>We exploit the <a href="https://github.com/mathLab/Smithers">Smithers</a> library to collect the parametric snapshots. In particular, we use the <code>NavierStokesDataset</code> class that contains a set of parametric solutions of the Navier-Stokes equations in a 2D L-shape domain. The parameter is the inflow velocity.
The dataset is composed by 500 snapshots of the velocity (along $x$, $y$, and the magnitude) and pressure fields, and the corresponding parameter values.</p>
<p>To visually check the snapshots, let's plot also the data points and the reference solution: this is the expected output of our model.</p>
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<div class="highlight hl-ipython3"><pre><span></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">NavierStokesDataset</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">NavierStokesDataset</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">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">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">p</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">4</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">4</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">"$\mu$ = </span><span class="si">{</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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"/>
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<p>The <em>snapshots</em> - aka the numerical solutions computed for several parameters - and the corresponding parameters are the only data we need to train the model, in order to predict the solution for any new test parameter. To properly validate the accuracy, we will split the 500 snapshots into the training dataset (90% of the original data) and the testing one (the reamining 10%) inside the <code>Trainer</code>.</p>
<p>It is now time to define the problem!</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class="highlight hl-ipython3"><pre><span></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">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">params</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">SupervisedProblem</span><span class="p">(</span><span class="n">input_</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">output_</span><span class="o">=</span><span class="n">u</span><span class="p">)</span>
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<p>We can then build a <code>POD-NN</code> model (using an MLP architecture as approximation) and compare it with a <code>POD-RBF</code> model (using a Radial Basis Function interpolation as approximation).</p>
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<h2 id="POD-NN-reduced-order-model">POD-NN reduced order model<a class="anchor-link" href="#POD-NN-reduced-order-model"></a></h2>
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<p>Let's build the <code>PODNN</code> class</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">PODNN</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 neural network 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">layers</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
<span class="w"> </span><span class="sd">""" """</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">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="n">pod_rank</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="n">layers</span><span class="p">,</span>
<span class="n">func</span><span class="o">=</span><span class="n">func</span><span class="p">,</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">coefficents</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</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">coefficents</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit_pod</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"> Just call the :meth:`pina.model.layers.PODBlock.fit` method of the</span>
<span class="sd"> :attr:`pina.model.layers.PODBlock` attribute.</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>
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<p>We highlight that the POD modes are directly computed by means of the singular value decomposition (computed over the input data), and not trained using the backpropagation approach. Only the weights of the MLP are actually trained during the optimization loop.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">pod_nn</span> <span class="o">=</span> <span class="n">PODNN</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">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">)</span>
<span class="n">pod_nn_stokes</span> <span class="o">=</span> <span class="n">SupervisedSolver</span><span class="p">(</span>
<span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">pod_nn</span><span class="p">,</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">TorchOptimizer</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">),</span>
<span class="n">use_lt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
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<p>Before starting we need to fit the POD basis on the training dataset, this can be easily done in PINA as well:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">solver</span><span class="o">=</span><span class="n">pod_nn_stokes</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># fit the pod basis</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">setup</span><span class="p">(</span><span class="s2">"fit"</span><span class="p">)</span> <span class="c1"># set up the dataset</span>
<span class="n">x_train</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">conditions_dict</span><span class="p">[</span><span class="s2">"data"</span><span class="p">][</span>
<span class="s2">"target"</span>
<span class="p">]</span> <span class="c1"># extract data for training</span>
<span class="n">pod_nn</span><span class="o">.</span><span class="n">fit_pod</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x_train</span><span class="p">)</span>
<span class="c1"># now train</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<pre>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>Missing logger folder: /home/runner/work/PINA/PINA/tutorials/tutorial8/lightning_logs
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<pre>
| Name | Type | Params | Mode
----------------------------------------------------
0 | _pina_models | ModuleList | 460 | train
1 | _loss | MSELoss | 0 | train
----------------------------------------------------
460 Trainable params
0 Non-trainable params
460 Total params
0.002 Total estimated model params size (MB)
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<pre>`Trainer.fit` stopped: `max_epochs=1000` reached.
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<p>Done! Now that the computational expensive part is over, we can load in future the model to infer new parameters (simply loading the checkpoint file automatically created by <code>Lightning</code>) or test its performances. We measure the relative error for the training and test datasets, printing the mean one.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># extract train and test data</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">setup</span><span class="p">(</span><span class="s2">"test"</span><span class="p">)</span> <span class="c1"># set up the dataset</span>
<span class="n">p_train</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">conditions_dict</span><span class="p">[</span><span class="s2">"data"</span><span class="p">][</span><span class="s2">"input"</span><span class="p">]</span>
<span class="n">u_train</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">conditions_dict</span><span class="p">[</span><span class="s2">"data"</span><span class="p">][</span><span class="s2">"target"</span><span class="p">]</span>
<span class="n">p_test</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">conditions_dict</span><span class="p">[</span><span class="s2">"data"</span><span class="p">][</span><span class="s2">"input"</span><span class="p">]</span>
<span class="n">u_test</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">conditions_dict</span><span class="p">[</span><span class="s2">"data"</span><span class="p">][</span><span class="s2">"target"</span><span class="p">]</span>
<span class="c1"># compute statistics</span>
<span class="n">u_test_nn</span> <span class="o">=</span> <span class="n">pod_nn_stokes</span><span class="p">(</span><span class="n">p_test</span><span class="p">)</span>
<span class="n">u_train_nn</span> <span class="o">=</span> <span class="n">pod_nn_stokes</span><span class="p">(</span><span class="n">p_train</span><span class="p">)</span>
<span class="n">relative_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_nn</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_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_nn</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-NN 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_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_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>
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<pre>Error summary for POD-NN model:
Train: 2.402595e-01
Test: 2.638434e-01
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<h2 id="POD-RBF-reduced-order-model">POD-RBF reduced order model<a class="anchor-link" href="#POD-RBF-reduced-order-model"></a></h2>
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<p>Then, we define the model we want to use, with the POD (<code>PODBlock</code>) and the RBF (<code>RBFBlock</code>) objects.</p>
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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="w"> </span><span class="sd">""" """</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">coefficents</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">coefficents</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>
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<p>We can then fit the model and ask it to predict the required field for unseen values of the parameters. Note that this model does not need a <code>Trainer</code> since it does not include any neural network or learnable parameters.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">pod_rbf</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">pod_rbf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">p_train</span><span class="p">,</span> <span class="n">u_train</span><span class="p">)</span>
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<p>Compute errors</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">u_test_rbf</span> <span class="o">=</span> <span class="n">pod_rbf</span><span class="p">(</span><span class="n">p_test</span><span class="p">)</span>
<span class="n">u_train_rbf</span> <span class="o">=</span> <span class="n">pod_rbf</span><span class="p">(</span><span class="n">p_train</span><span class="p">)</span>
<span class="n">relative_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_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_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_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>
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<pre>Error summary for POD-RBF model:
Train: 8.315196e-05
Test: 8.554822e-05
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<h2 id="POD-RBF-vs-POD-NN">POD-RBF vs POD-NN<a class="anchor-link" href="#POD-RBF-vs-POD-NN"></a></h2>
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<p>We can of course also plot the solutions predicted by the <code>PODRBF</code> and by the <code>PODNN</code> model, comparing them to the original ones. We can note here, in the <code>PODNN</code> model and for low velocities, some differences, but improvements can be accomplished thanks to longer training.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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<div class="highlight hl-ipython3"><pre><span></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_rbf</span><span class="p">(</span><span class="n">p_test</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
<span class="n">u_idx_nn</span> <span class="o">=</span> <span class="n">pod_nn_stokes</span><span class="p">(</span><span class="n">p_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">4</span><span class="p">,</span> <span class="mi">5</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">9</span><span class="p">))</span>
<span class="n">relative_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_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">&lt;</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_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="n">relative_error_nn</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_nn</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
<span class="n">relative_error_nn</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">&lt;</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_error_nn</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">nn_</span><span class="p">,</span> <span class="n">rbf_err_</span><span class="p">,</span> <span class="n">nn_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">u_idx_nn</span><span class="p">,</span> <span class="n">relative_error_rbf</span><span class="p">,</span> <span class="n">relative_error_nn</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Real Snapshots"</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"POD-RBF"</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="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"POD-NN"</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="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Error POD-RBF"</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="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Error POD-NN"</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="n">i</span><span class="p">,</span> <span class="mi">0</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="n">i</span><span class="p">,</span> <span class="mi">0</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="n">i</span><span class="p">,</span> <span class="mi">1</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">nn_</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="p">)</span> <span class="c1"># POD-NN 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="n">i</span><span class="p">,</span> <span class="mi">1</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="n">i</span><span class="p">,</span> <span class="mi">2</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="n">i</span><span class="p">,</span> <span class="mi">2</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="n">i</span><span class="p">,</span> <span class="mi">3</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="n">i</span><span class="p">,</span> <span class="mi">3</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="n">i</span><span class="p">,</span> <span class="mi">4</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">nn_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-NN</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="n">i</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h4 id="References">References<a class="anchor-link" href="#References"></a></h4><ol>
<li>Rozza G., Stabile G., Ballarin F. (2022). Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, Society for Industrial and Applied Mathematics.</li>
<li>Hesthaven, J. S., &amp; Ubbiali, S. (2018). Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363, 55-78.</li>
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