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PINA/docs/source/tutorials/tutorial3/tutorial.html
github-actions[bot] d10c525e74 export tutorials changed in dd88513 (#559) 
Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>
2025-04-23 18:53:30 +02:00

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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
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--jp-icon-add-above: url(data:image/svg+xml;base64,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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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE2IDE2IiB2ZXJzaW9uPSIxLjEiPgogICA8cGF0aCBjbGFzcz0ianAtaWNvbjIganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMzMzMzMzIgogICAgICBkPSJtOCAwLjI5Yy0xLjQgMC0yLjcgMC43My0zLjYgMS44LTEuMiAxLjUtMS40IDMuNC0xLjUgNS4yLTAuMTggMi4yLTAuNDQgNC0yLjMgNS4zbDAuMjggMS4zaDVjMC4wMjYgMC42NiAwLjMyIDEuMSAwLjcxIDEuNSAwLjg0IDAuNjEgMiAwLjYxIDIuOCAwIDAuNTItMC40IDAuNi0xIDAuNzEtMS41aDVsMC4yOC0xLjNjLTEuOS0wLjk3LTIuMi0zLjMtMi4zLTUuMy0wLjEzLTEuOC0wLjI2LTMuNy0xLjUtNS4yLTAuODUtMS0yLjItMS44LTMuNi0xLjh6bTAgMS40YzAuODggMCAxLjkgMC41NSAyLjUgMS4zIDAuODggMS4xIDEuMSAyLjcgMS4yIDQuNCAwLjEzIDEuNyAwLjIzIDMuNiAxLjMgNS4yaC0xMGMxLjEtMS42IDEuMi0zLjQgMS4zLTUuMiAwLjEzLTEuNyAwLjMtMy4zIDEuMi00LjQgMC41OS0wLjcyIDEuNi0xLjMgMi41LTEuM3ptLTAuNzQgMTJoMS41Yy0wLjAwMTUgMC4yOCAwLjAxNSAwLjc5LTAuNzQgMC43OS0wLjczIDAuMDAxNi0wLjcyLTAuNTMtMC43NC0wLjc5eiIgLz4KPC9zdmc+Cg==);
--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,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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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:-Applying-Hard-Constraints-in-PINNs-to-solve-the-Wave-Problem">Tutorial: Applying Hard Constraints in PINNs to solve the Wave Problem<a class="anchor-link" href="#Tutorial:-Applying-Hard-Constraints-in-PINNs-to-solve-the-Wave-Problem"></a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
<p>In this tutorial, we will present how to solve the wave equation using <strong>hard constraint Physics-Informed Neural Networks (PINNs)</strong>. To achieve this, we will build a custom <code>torch</code> model and pass it to the <strong>PINN solver</strong>.</p>
<p>First of all, some useful imports.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">## routine needed to run the notebook on Google Colab</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">google.colab</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"pina-mathlab[tutorial]"</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">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">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina</span><span class="w"> </span><span class="kn">import</span> <span class="n">Condition</span><span class="p">,</span> <span class="n">LabelTensor</span><span class="p">,</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.problem</span><span class="w"> </span><span class="kn">import</span> <span class="n">SpatialProblem</span><span class="p">,</span> <span class="n">TimeDependentProblem</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.operator</span><span class="w"> </span><span class="kn">import</span> <span class="n">laplacian</span><span class="p">,</span> <span class="n">grad</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.domain</span><span class="w"> </span><span class="kn">import</span> <span class="n">CartesianDomain</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.solver</span><span class="w"> </span><span class="kn">import</span> <span class="n">PINN</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.equation</span><span class="w"> </span><span class="kn">import</span> <span class="n">Equation</span><span class="p">,</span> <span class="n">FixedValue</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.callback</span><span class="w"> </span><span class="kn">import</span> <span class="n">MetricTracker</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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<h2 id="The-problem-definition">The problem definition<a class="anchor-link" href="#The-problem-definition"></a></h2><p>The problem is described by the following system of partial differential equations (PDEs):</p>
<p>\begin{equation}
\begin{cases}
\Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\
u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\
u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4,
\end{cases}
\end{equation}</p>
<p>Where:</p>
<ul>
<li>$D$ is a square domain $[0, 1]^2$.</li>
<li>$\Gamma_i$, where $i = 1, \dots, 4$, are the boundaries of the square where Dirichlet conditions are applied.</li>
<li>The velocity in the standard wave equation is fixed to $1$.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">wave_equation</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">output_</span><span class="p">):</span>
<span class="n">u_t</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">output_</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"u"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"t"</span><span class="p">])</span>
<span class="n">u_tt</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">u_t</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"dudt"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"t"</span><span class="p">])</span>
<span class="n">nabla_u</span> <span class="o">=</span> <span class="n">laplacian</span><span class="p">(</span><span class="n">output_</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"u"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">])</span>
<span class="k">return</span> <span class="n">nabla_u</span> <span class="o">-</span> <span class="n">u_tt</span>
<span class="k">def</span><span class="w"> </span><span class="nf">initial_condition</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">output_</span><span class="p">):</span>
<span class="n">u_expected</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">input_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">input_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">output_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"u"</span><span class="p">])</span> <span class="o">-</span> <span class="n">u_expected</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Wave</span><span class="p">(</span><span class="n">TimeDependentProblem</span><span class="p">,</span> <span class="n">SpatialProblem</span><span class="p">):</span>
<span class="n">output_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"u"</span><span class="p">]</span>
<span class="n">spatial_domain</span> <span class="o">=</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
<span class="n">temporal_domain</span> <span class="o">=</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
<span class="n">domains</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"g1"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g2"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g3"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g4"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"initial"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="mi">0</span><span class="p">}),</span>
<span class="s2">"D"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="p">}</span>
<span class="n">conditions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"g1"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g1"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g2"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g2"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g3"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g3"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g4"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g4"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"initial"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span>
<span class="n">domain</span><span class="o">=</span><span class="s2">"initial"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">Equation</span><span class="p">(</span><span class="n">initial_condition</span><span class="p">)</span>
<span class="p">),</span>
<span class="s2">"D"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"D"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">Equation</span><span class="p">(</span><span class="n">wave_equation</span><span class="p">)),</span>
<span class="p">}</span>
<span class="k">def</span><span class="w"> </span><span class="nf">solution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pts</span><span class="p">):</span>
<span class="n">f</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_variables</span><span class="p">)</span>
<span class="c1"># define problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">Wave</span><span class="p">()</span>
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<h2 id="Hard-Constraint-Model">Hard Constraint Model<a class="anchor-link" href="#Hard-Constraint-Model"></a></h2><p>Once the problem is defined, a <strong>torch</strong> model is needed to solve the PINN. While <strong>PINA</strong> provides several pre-implemented models, users have the option to build their own custom model using <strong>torch</strong>. The hard constraint we impose is on the boundary of the spatial domain. Specifically, the solution is written as:</p>
<p>$$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), $$</p>
<p>where $NN$ represents the neural network output. This neural network takes the spatial coordinates $x$, $y$, and time $t$ as input and provides the unknown field $u$. By construction, the solution is zero at the boundaries.</p>
<p>The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the <code>discretise_domain</code> method). The loss function minimized by the neural network is the sum of the residuals.</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">HardMLP</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="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">input_dim</span><span class="p">,</span> <span class="n">output_dim</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">layers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</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">Linear</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="mi">40</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">ReLU</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">Linear</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">40</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">ReLU</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">Linear</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># here in the foward we implement the hard constraints</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="n">hard</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">hard</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<h2 id="Train-and-Inference">Train and Inference<a class="anchor-link" href="#Train-and-Inference"></a></h2><p>In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in <code>PINN</code>).</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># generate the data</span>
<span class="n">problem</span><span class="o">.</span><span class="n">discretise_domain</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">"random"</span><span class="p">,</span> <span class="n">domains</span><span class="o">=</span><span class="s2">"all"</span><span class="p">)</span>
<span class="c1"># define model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">HardMLP</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">input_variables</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">output_variables</span><span class="p">))</span>
<span class="c1"># crete the solver</span>
<span class="n">pinn</span> <span class="o">=</span> <span class="n">PINN</span><span class="p">(</span><span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
<span class="c1"># create trainer and train</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span>
<span class="n">enable_model_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">MetricTracker</span><span class="p">([</span><span class="s2">"train_loss"</span><span class="p">,</span> <span class="s2">"initial_loss"</span><span class="p">,</span> <span class="s2">"D_loss"</span><span class="p">])],</span>
<span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<pre>You are using the plain ModelCheckpoint callback. Consider using LitModelCheckpoint which with seamless uploading to Model registry.
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<pre>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>`Trainer.fit` stopped: `max_epochs=1000` reached.
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<p>Let's now plot the losses inside <code>MetricTracker</code> to see how they vary during training.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">trainer_metrics</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">callbacks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">metrics</span>
<span class="k">for</span> <span class="n">metric</span><span class="p">,</span> <span class="n">loss</span> <span class="ow">in</span> <span class="n">trainer_metrics</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">loss</span><span class="p">)),</span> <span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">metric</span><span class="p">)</span>
<span class="c1"># plotting</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"epoch"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"loss"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
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<pre>&lt;matplotlib.legend.Legend at 0x7fb0b8588e20&gt;</pre>
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"/>
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<p>Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! Once the training is completed, we can plot the results using <code>matplotlib</code>. We will display the predicted output on the left side, the true solution in the center, and the difference between them on the right side using the <code>plot_solution</code> function.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
<span class="c1"># get the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">problem</span>
<span class="c1"># get spatial points</span>
<span class="n">spatial_samples</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">spatial_domain</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">)</span>
<span class="c1"># get temporal value</span>
<span class="n">time</span> <span class="o">=</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="n">time</span><span class="p">]]),</span> <span class="s2">"t"</span><span class="p">)</span>
<span class="c1"># cross data</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">spatial_samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"cross"</span><span class="p">)</span>
<span class="c1"># compute pinn solution, true solution and absolute difference</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"PINN solution"</span><span class="p">:</span> <span class="n">solver</span><span class="p">(</span><span class="n">points</span><span class="p">),</span>
<span class="s2">"True solution"</span><span class="p">:</span> <span class="n">problem</span><span class="o">.</span><span class="n">solution</span><span class="p">(</span><span class="n">points</span><span class="p">),</span>
<span class="s2">"Absolute Difference"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
<span class="n">solver</span><span class="p">(</span><span class="n">points</span><span class="p">)</span> <span class="o">-</span> <span class="n">problem</span><span class="o">.</span><span class="n">solution</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="c1"># plot the solution</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Solution for time </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">field</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</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">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span> <span class="c1"># convert to torch tensor + flatten</span>
<span class="n">points</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="s2">"x"</span><span class="p">)</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
<span class="n">points</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="s2">"y"</span><span class="p">)</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
<span class="n">field</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</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">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<p>Let's take a look at the results at different times, for example <code>0.0</code>, <code>0.5</code> and <code>1.0</code>:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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"/>
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<p>The results are not ideal, and we can clearly see that as time progresses, the solution deteriorates. Can we do better?</p>
<p>One valid approach is to impose the initial condition as a hard constraint as well. Specifically, we modify the solution to:</p>
<p>$$
u_{\rm{pinn}} = xy(1-x)(1-y) \cdot NN(x, y, t) \cdot t + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y),
$$</p>
<p>Now, let us start by building the neural network.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">HardMLPtime</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="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">input_dim</span><span class="p">,</span> <span class="n">output_dim</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">layers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</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">Linear</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="mi">40</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">ReLU</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">Linear</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">40</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">ReLU</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">Linear</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># here in the foward we implement the hard constraints</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="n">hard_space</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="p">)</span>
<span class="n">hard_t</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">hard_space</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span> <span class="o">+</span> <span class="n">hard_t</span>
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<p>Now let's train with the same configuration as the previous test</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># define model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">HardMLPtime</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">input_variables</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">output_variables</span><span class="p">))</span>
<span class="c1"># crete the solver</span>
<span class="n">pinn</span> <span class="o">=</span> <span class="n">PINN</span><span class="p">(</span><span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
<span class="c1"># create trainer and train</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span>
<span class="n">enable_model_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">MetricTracker</span><span class="p">([</span><span class="s2">"train_loss"</span><span class="p">,</span> <span class="s2">"initial_loss"</span><span class="p">,</span> <span class="s2">"D_loss"</span><span class="p">])],</span>
<span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<pre>You are using the plain ModelCheckpoint callback. Consider using LitModelCheckpoint which with seamless uploading to Model registry.
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<pre>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>`Trainer.fit` stopped: `max_epochs=1000` reached.
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<p>We can clearly see that the loss is way lower now. Let's plot the results</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<p>We can now see that the results are much better! This improvement is due to the fact that, previously, the network was not correctly learning the initial condition, which led to a poor solution as time evolved. By imposing the initial condition as a hard constraint, the network is now able to correctly solve the problem.</p>
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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 two-dimensional Wave tutorial of <strong>PINA</strong>! Now that you’ve got the basics down, there are several directions you can explore:</p>
<ol>
<li><p><strong>Train the Network for Longer</strong>: Train the network for a longer duration or experiment with different layer sizes to assess the final accuracy.</p>
</li>
<li><p><strong>Propose New Types of Hard Constraints in Time</strong>: Experiment with new time-dependent hard constraints, for example:</p>
<p>$$
u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y)
$$</p>
</li>
<li><p><strong>Exploit Extrafeature Training</strong>: Apply extrafeature training techniques to improve models from 1 and 2.</p>
</li>
<li><p><strong>...and many more!</strong>: The possibilities are endless! Keep experimenting and pushing the boundaries.</p>
</li>
</ol>
<p>For more resources and tutorials, check out the <a href="https://mathlab.github.io/PINA/">PINA Documentation</a>.</p>
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