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PINA/docs/source/tutorials/tutorial8/tutorial.html
github-actions[bot] 578c5bc2f4 export tutorials changed in 5aec5f5 (#540)
2025-04-17 10:48:32 +02:00

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This file contains invisible Unicode characters
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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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/* Icons urls */
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yMCA4aC0yLjgxYy0uNDUtLjc4LTEuMDctMS40NS0xLjgyLTEuOTZMMTcgNC40MSAxNS41OSAzbC0yLjE3IDIuMTdDMTIuOTYgNS4wNiAxMi40OSA1IDEyIDVjLS40OSAwLS45Ni4wNi0xLjQxLjE3TDguNDEgMyA3IDQuNDFsMS42MiAxLjYzQzcuODggNi41NSA3LjI2IDcuMjIgNi44MSA4SDR2MmgyLjA5Yy0uMDUuMzMtLjA5LjY2LS4wOSAxdjFINHYyaDJ2MWMwIC4zNC4wNC42Ny4wOSAxSDR2MmgyLjgxYzEuMDQgMS43OSAyLjk3IDMgNS4xOSAzczQuMTUtMS4yMSA1LjE5LTNIMjB2LTJoLTIuMDljLjA1LS4zMy4wOS0uNjYuMDktMXYtMWgydi0yaC0ydi0xYzAtLjM0LS4wNC0uNjctLjA5LTFIMjBWOHptLTYgOGgtNHYtMmg0djJ6bTAtNGgtNHYtMmg0djJ6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--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,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);
--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,PHN2ZyBmaWxsPSJub25lIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI1Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgc3Ryb2tlPSIjMzMzMzMzIiBzdHJva2Utd2lkdGg9IjIiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDIgMykiIGQ9Ik0xLjg2MDk0IDExLjQ0MDlDMC44MjY0NDggOC43NzAyNyAwLjg2Mzc3OSA2LjA1NzY0IDEuMjQ5MDcgNC4xOTkzMkMyLjQ4MjA2IDMuOTMzNDcgNC4wODA2OCAzLjQwMzQ3IDUuNjAxMDIgMi44NDQ5QzcuMjM1NDkgMi4yNDQ0IDguODU2NjYgMS41ODE1IDkuOTg3NiAxLjA5NTM5QzExLjA1OTcgMS41ODM0MSAxMi42MDk0IDIuMjQ0NCAxNC4yMTggMi44NDMzOUMxNS43NTAzIDMuNDEzOTQgMTcuMzk5NSAzLjk1MjU4IDE4Ljc1MzkgNC4yMTM4NUMxOS4xMzY0IDYuMDcxNzcgMTkuMTcwOSA4Ljc3NzIyIDE4LjEzOSAxMS40NDA5QzE3LjAzMDMgMTQuMzAzMiAxNC42NjY4IDE3LjE4NDQgOS45OTk5OSAxOC45MzU0QzUuMzMzMiAxNy4xODQ0IDIuOTY5NjggMTQuMzAzMiAxLjg2MDk0IDExLjQ0MDlaIi8+CiAgICA8cGF0aCBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiMzMzMzMzMiIHN0cm9rZT0iIzMzMzMzMyIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoOCA5Ljg2NzE5KSIgZD0iTTIuODYwMTUgNC44NjUzNUwwLjcyNjU0OSAyLjk5OTU5TDAgMy42MzA0NUwyLjg2MDE1IDYuMTMxNTdMOCAwLjYzMDg3Mkw3LjI3ODU3IDBMMi44NjAxNSA0Ljg2NTM1WiIvPgo8L3N2Zz4K);
--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: 3.088735e-01
Test: 2.973566e-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="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="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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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: 6.569416e-05
Test: 6.045619e-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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