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PINA/docs/source/tutorials/tutorial14/tutorial.html
github-actions[bot] 578c5bc2f4 export tutorials changed in 5aec5f5 (#540)
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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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/* Icons urls */
:root {
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KICA8ZyBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiM0MTQxNDEiPgogICAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiAgPC9nPgoKICA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiBmaWxsPSIjRkZGIj4KICAgIDxjaXJjbGUgY2xhc3M9InN0MiIgY3g9IjUuNSIgY3k9IjE0LjUiIHI9IjEuNSIvPgogICAgPHJlY3QgeD0iMTIiIHk9IjQiIGNsYXNzPSJzdDIiIHdpZHRoPSIxIiBoZWlnaHQ9IjgiLz4KICAgIDxyZWN0IHg9IjguNSIgeT0iNy41IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjg2NiAtMC41IDAuNSAwLjg2NiAtMi4zMjU1IDcuMzIxOSkiIGNsYXNzPSJzdDIiIHdpZHRoPSI4IiBoZWlnaHQ9IjEiLz4KICAgIDxyZWN0IHg9IjEyIiB5PSI0IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjUgLTAuODY2IDAuODY2IDAuNSAtMC42Nzc5IDE0LjgyNTIpIiBjbGFzcz0ic3QyIiB3aWR0aD0iMSIgaGVpZ2h0PSI4Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<h1 id="Tutorial:-Predicting-Lid-driven-cavity-problem-parameters-with-POD-RBF">Tutorial: Predicting Lid-driven cavity problem parameters with POD-RBF<a class="anchor-link" href="#Tutorial:-Predicting-Lid-driven-cavity-problem-parameters-with-POD-RBF"></a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial14/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
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<p>In this tutorial we will show how to use the <strong>PINA</strong> library to predict the distributions of velocity and pressure the Lid-driven Cavity problem, a benchmark in Computational Fluid Dynamics. The problem consists of a square cavity with a lid on top moving with tangential velocity (by convention to the right), with the addition of no-slip conditions on the walls of the cavity and null static pressure on the lower left angle.</p>
<p>Our goal is to predict the distributions of velocity and pressure of the fluid inside the cavity as the Reynolds number of the inlet fluid varies. To do so we're using a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD). The parametric solution manifold is approximated here with Radial Basis Function (RBF) Interpolation, a common mesh-free interpolation method that doesn't require trainers or solvers as the found radial basis functions are used to interpolate new points.</p>
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<p>Let's start with the necessary imports. We're particularly interested in the <code>PODBlock</code> and <code>RBFBlock</code> classes which will allow us to define the POD-RBF model.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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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.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pina</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model.block</span><span class="w"> </span><span class="kn">import</span> <span class="n">PODBlock</span><span class="p">,</span> <span class="n">RBFBlock</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelTensor</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
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<p>In this tutorial we're gonna use the <code>LidCavity</code> class from the <a href="https://github.com/mathLab/Smithers">Smithers</a> library, which contains a set of parametric solutions of the Lid-driven cavity problem in a square domain. The dataset consists of 300 snapshots of the parameter fields, which in this case are the magnitude of velocity and the pressure, and the corresponding parameter values $u$ and $p$. Each snapshot corresponds to a different value of the tangential velocity $\mu$ of the lid, which has been sampled uniformly between 0.01 m/s and 1 m/s.</p>
<p>Let's start by importing the dataset:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">smithers</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">smithers.dataset</span><span class="w"> </span><span class="kn">import</span> <span class="n">LidCavity</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">LidCavity</span><span class="p">()</span>
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<p>Let's plot two the data points and the corresponding solution for both parameters at different snapshots, in order to better visualise the data we're using:</p>
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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">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"mag(v)"</span><span class="p">][:</span><span class="mi">3</span><span class="p">]):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"$u$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][:</span><span class="mi">3</span><span class="p">]):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<p>To train the model we only need the snapshots for the two parameters. In order to be able to work with the snapshots in <strong>PINA</strong> we first need to assure they're in a compatible format, hence why we start by casting them into <code>LabelTensor</code> objects:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""velocity magnitude data, 5041 for each snapshot"""</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"mag(v)"</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">u</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
<span class="sd">"""pressure data, 5041 for each snapshot"""</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
<span class="sd">"""mu corresponding to each snapshot"""</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">])</span>
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<p>The goal of our training is to be able to predict the solution for new test parameters. The first thing we need to do is validate the accuracy of the model, and in order to do so we split the 300 snapshots in training and testing dataset. In the example we set the training <code>ratio</code> to 0.9, which means that 90% of the total snapshots is used for training and the remaining 10% for testing.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""number of snapshots"""</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="sd">"""training over total snapshots ratio and number of training snapshots"""</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">n_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span>
<span class="sd">"""split u and p data"""</span>
<span class="n">u_train</span><span class="p">,</span> <span class="n">u_test</span> <span class="o">=</span> <span class="n">u</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span> <span class="c1"># for mag(v)</span>
<span class="n">p_train</span><span class="p">,</span> <span class="n">p_test</span> <span class="o">=</span> <span class="n">p</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span> <span class="c1"># for p</span>
<span class="sd">"""split snapshots"""</span>
<span class="n">mu_train</span><span class="p">,</span> <span class="n">mu_test</span> <span class="o">=</span> <span class="n">mu</span><span class="p">[:</span><span class="n">n_train</span><span class="p">],</span> <span class="n">mu</span><span class="p">[</span><span class="n">n_train</span><span class="p">:]</span>
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<p>We now proceed by defining the model we intend to use. We inherit from the <code>torch.nn.Module</code> class, but in addition we require a <code>pod_rank</code> for the POD part and a function <code>rbf_kernel</code> in order to perform the RBF part:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">PODRBF</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Proper orthogonal decomposition with Radial Basis Function interpolation model.</span>
<span class="sd"> """</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pod_rank</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span> <span class="o">=</span> <span class="n">PODBlock</span><span class="p">(</span><span class="n">pod_rank</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span> <span class="o">=</span> <span class="n">RBFBlock</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">rbf_kernel</span><span class="p">)</span>
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<p>We complete our model by adding two crucial methods. The first is <code>forward</code>, and it expands the input POD coefficients. After being expanded the POD layer needs to be fit, hence why we add a <code>fit</code> method that gives us the POD basis (current <strong>PINA</strong> default is by performing truncated Singular Value Decomposition). The same method then uses the basis to fit the RBF interpolation. Overall, the completed class looks like this:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">PODRBF</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Proper orthogonal decomposition with Radial Basis Function interpolation model.</span>
<span class="sd"> """</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pod_rank</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span> <span class="o">=</span> <span class="n">PODBlock</span><span class="p">(</span><span class="n">pod_rank</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span> <span class="o">=</span> <span class="n">RBFBlock</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">rbf_kernel</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Defines the computation performed at every call.</span>
<span class="sd"> :param x: The tensor to apply the forward pass.</span>
<span class="sd"> :type x: torch.Tensor</span>
<span class="sd"> :return: the output computed by the model.</span>
<span class="sd"> :rtype: torch.Tensor</span>
<span class="sd"> """</span>
<span class="n">coefficients</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rbf</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">coefficients</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Call the :meth:`pina.model.layers.PODBlock.fit` method of the</span>
<span class="sd"> :attr:`pina.model.layers.PODBlock` attribute to perform the POD,</span>
<span class="sd"> and the :meth:`pina.model.layers.RBFBlock.fit` method of the</span>
<span class="sd"> :attr:`pina.model.layers.RBFBlock` attribute to fit the interpolation.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rbf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pod</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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<p>Now that we've built our class, we can fit the model and ask it to predict the parameters for the remaining snapshots. We remember that we don't need to train the model, as it doesn't involve any learnable parameter. The only things we have to set are the rank of the decomposition and the radial basis function (here we use thin plate). Here we focus on predicting the magnitude of velocity:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""create the model"""</span>
<span class="n">pod_rbfu</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
<span class="sd">"""fit the model to velocity training data"""</span>
<span class="n">pod_rbfu</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mu_train</span><span class="p">,</span> <span class="n">u_train</span><span class="p">)</span>
<span class="sd">"""predict the parameter using the fitted model"""</span>
<span class="n">u_train_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_train</span><span class="p">)</span>
<span class="n">u_test_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_test</span><span class="p">)</span>
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<p>Finally we can calculate the relative error for our model:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">relative_u_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_train_rbf</span> <span class="o">-</span> <span class="n">u_train</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_train</span><span class="p">)</span>
<span class="n">relative_u_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_test_rbf</span> <span class="o">-</span> <span class="n">u_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">u_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">relative_u_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">relative_u_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Error summary for POD-RBF model:
Train: 8.187051e-03
Test: 5.066957e-02
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<p>The results are promising! Now let's visualise them, comparing four random predicted snapshots to the true ones:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">u_test</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,))</span>
<span class="n">u_idx_rbf</span> <span class="o">=</span> <span class="n">pod_rbfu</span><span class="p">(</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">relative_u_error_rbf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">-</span> <span class="n">u_idx_rbf</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
<span class="n">relative_u_error_rbf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
<span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">&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_u_error_rbf</span> <span class="o">/</span> <span class="n">u_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">idx_</span><span class="p">,</span> <span class="n">rbf_</span><span class="p">,</span> <span class="n">rbf_err_</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
<span class="nb">zip</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">u_idx_rbf</span><span class="p">,</span> <span class="n">relative_u_error_rbf</span><span class="p">)</span>
<span class="p">):</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Prediction for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span>
<span class="s2">"True snapshot for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Error for "</span> <span class="sa">f</span><span class="s2">"$\mu$ = </span><span class="si">{</span><span class="n">mu_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">rbf_</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="p">)</span> <span class="c1"># POD-RBF prediction</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u_test</span><span class="p">[</span><span class="n">idx_</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="c1"># Truth</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tripcolor</span><span class="p">(</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">rbf_err_</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">colors</span><span class="o">.</span><span class="n">LogNorm</span><span class="p">()</span>
<span class="p">)</span> <span class="c1"># Error for POD-RBF</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>Overall we have reached a good level of approximation while avoiding time-consuming training procedures. Let's try doing the same to predict the pressure snapshots:</p>
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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="sd">"""create the model"""</span>
<span class="n">pod_rbfp</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
<span class="sd">"""fit the model to pressure training data"""</span>
<span class="n">pod_rbfp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mu_train</span><span class="p">,</span> <span class="n">p_train</span><span class="p">)</span>
<span class="sd">"""predict the parameter using the fitted model"""</span>
<span class="n">p_train_rbf</span> <span class="o">=</span> <span class="n">pod_rbfp</span><span class="p">(</span><span class="n">mu_train</span><span class="p">)</span>
<span class="n">p_test_rbf</span> <span class="o">=</span> <span class="n">pod_rbfp</span><span class="p">(</span><span class="n">mu_test</span><span class="p">)</span>
<span class="n">relative_p_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_train_rbf</span> <span class="o">-</span> <span class="n">p_train</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_train</span><span class="p">)</span>
<span class="n">relative_p_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_test_rbf</span> <span class="o">-</span> <span class="n">p_test</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">p_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">relative_p_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">relative_p_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Error summary for POD-RBF model:
Train: 4.455698e-02
Test: 4.281982e+06
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<p>Unfortunately here we obtain a very high relative test error, although this is likely due to the nature of the available data. Looking at the plots we can see that the pressure field is subject to high variations between subsequent snapshots, especially here:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
<span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">66</span><span class="p">:</span><span class="mi">72</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="mi">66</span><span class="p">:</span><span class="mi">72</span><span class="p">]</span>
<span class="p">):</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>Or here:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">par</span><span class="p">,</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
<span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">98</span><span class="p">:</span><span class="mi">104</span><span class="p">],</span> <span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="mi">98</span><span class="p">:</span><span class="mi">104</span><span class="p">]</span>
<span class="p">):</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">triang</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"$p$ field for $\mu$ = </span><span class="si">{</span><span class="n">par</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<p>Scrolling through the velocity snapshots we can observe a more regular behaviour, with no such variations in subsequent snapshots. Moreover, if we decide not to consider the abovementioned "problematic" snapshots, we can already observe a huge improvement:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""excluding problematic snapshots"""</span>
<span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">))</span>
<span class="n">data_to_consider</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="mi">67</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="mi">71</span><span class="p">:</span><span class="mi">100</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="mi">102</span><span class="p">:]</span>
<span class="sd">"""proceed as before"""</span>
<span class="n">newp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">snapshots</span><span class="p">[</span><span class="s2">"p"</span><span class="p">][</span><span class="n">data_to_consider</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">newp</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">newp</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="sa">f</span><span class="s2">"s</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">newp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])])</span>
<span class="n">newmu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">data_to_consider</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">newmu</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">newmu</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">])</span>
<span class="n">newn</span> <span class="o">=</span> <span class="n">newp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">new_train</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">newn</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span>
<span class="n">new_p_train</span><span class="p">,</span> <span class="n">new_p_test</span> <span class="o">=</span> <span class="n">newp</span><span class="p">[:</span><span class="n">new_train</span><span class="p">],</span> <span class="n">newp</span><span class="p">[</span><span class="n">new_train</span><span class="p">:]</span>
<span class="n">new_mu_train</span><span class="p">,</span> <span class="n">new_mu_test</span> <span class="o">=</span> <span class="n">newmu</span><span class="p">[:</span><span class="n">new_train</span><span class="p">],</span> <span class="n">newmu</span><span class="p">[</span><span class="n">new_train</span><span class="p">:]</span>
<span class="n">new_pod_rbfp</span> <span class="o">=</span> <span class="n">PODRBF</span><span class="p">(</span><span class="n">pod_rank</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">rbf_kernel</span><span class="o">=</span><span class="s2">"thin_plate_spline"</span><span class="p">)</span>
<span class="n">new_pod_rbfp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">new_mu_train</span><span class="p">,</span> <span class="n">new_p_train</span><span class="p">)</span>
<span class="n">new_p_train_rbf</span> <span class="o">=</span> <span class="n">new_pod_rbfp</span><span class="p">(</span><span class="n">new_mu_train</span><span class="p">)</span>
<span class="n">new_p_test_rbf</span> <span class="o">=</span> <span class="n">new_pod_rbfp</span><span class="p">(</span><span class="n">new_mu_test</span><span class="p">)</span>
<span class="n">new_relative_p_error_train</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
<span class="n">new_p_train_rbf</span> <span class="o">-</span> <span class="n">new_p_train</span>
<span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">new_p_train</span><span class="p">)</span>
<span class="n">new_relative_p_error_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
<span class="n">new_p_test_rbf</span> <span class="o">-</span> <span class="n">new_p_test</span>
<span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">new_p_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Error summary for POD-RBF model:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Train: </span><span class="si">{</span><span class="n">new_relative_p_error_train</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Test: </span><span class="si">{</span><span class="n">new_relative_p_error_test</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Error summary for POD-RBF model:
Train: 2.012740e-01
Test: 3.015688e-01
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<h2 id="What's-next?">What's next?<a class="anchor-link" href="#What's-next?"></a></h2><p>Congratulations on completing the <strong>PINA</strong> tutorial on building and using a custom POD class! Now you can try:</p>
<ol>
<li><p>Varying the inputs of the model (for a list of the supported RB functions look at the <code>rbf_layer.py</code> file in <code>pina.layers</code>)</p>
</li>
<li><p>Changing the POD model, for example using Artificial Neural Networks. For a more in depth overview of POD-NN and a comparison with the POD-RBF model already shown, look at <a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial9/tutorial.ipynb">Tutorial: Reduced order model (POD-RBF or POD-NN) for parametric problems</a></p>
</li>
<li><p>Building your own classes or adapt the one shown to other datasets/problems</p>
</li>
</ol>
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