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PINA/docs/source/tutorials/tutorial4/tutorial.html
github-actions[bot] ae1fd2680f export tutorials changed in c161138 (#498) 
Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>
2025-04-17 10:48:31 +02:00

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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
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--jp-icon-add-above: url(data:image/svg+xml;base64,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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:-Unstructured-convolutional-autoencoder-via-continuous-convolution">Tutorial: Unstructured convolutional autoencoder via continuous convolution<a class="anchor-link" href="#Tutorial:-Unstructured-convolutional-autoencoder-via-continuous-convolution">¶</a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
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<p>In this tutorial, we will show how to use the Continuous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work <a href="https://arxiv.org/abs/2210.13416"><em>A Continuous Convolutional Trainable Filter for Modelling Unstructured Data</em></a>.</p>
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<p>First of all we import the modules needed for the tutorial:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">## routine needed to run the notebook on Google Colab</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">google.colab</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"pina-mathlab"</span>
<span class="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">torchvision</span> <span class="c1"># for MNIST dataset</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina</span><span class="w"> </span><span class="kn">import</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.problem.zoo</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedProblem</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.solver</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedSolver</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.trainer</span><span class="w"> </span><span class="kn">import</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model.block</span><span class="w"> </span><span class="kn">import</span> <span class="n">ContinuousConvBlock</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model</span><span class="w"> </span><span class="kn">import</span> <span class="n">FeedForward</span> <span class="c1"># for building AE and MNIST classification</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
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<p>The tutorial is structured as follow:</p>
<ul>
<li><a href="#continuous-filter-background">Continuous filter background</a>: understand how the convolutional filter works and how to use it.</li>
<li><a href="#building-a-mnist-classifier">Building a MNIST Classifier</a>: show how to build a simple classifier using the MNIST dataset and how to combine a continuous convolutional layer with a feedforward neural network.</li>
<li><a href="#building-a-continuous-convolutional-autoencoder">Building a Continuous Convolutional Autoencoder</a>: show how to use the continuous filter to work with unstructured data for autoencoding and up-sampling.</li>
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<h2 id="Continuous-filter-background">Continuous filter background<a class="anchor-link" href="#Continuous-filter-background">¶</a></h2>
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<p>As reported by the authors in the original paper: in contrast to discrete convolution, continuous convolution is mathematically defined as:</p>
<p>$$
\mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}} \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau},
$$
where $\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}$ is the <em>continuous filter</em> function, and $\mathcal{I} : \Omega \subset \mathbb{R}^N \rightarrow \mathbb{R}$ is the input function. The continuous filter function is approximated using a FeedForward Neural Network, thus trainable during the training phase. The way in which the integral is approximated can be different, currently on <strong>PINA</strong> we approximate it using a simple sum, as suggested by the authors. Thus, given $\{\mathbf{x}_i\}_{i=1}^{n}$ points in $\mathbb{R}^N$ of the input function mapped on the $\mathcal{X}$ filter domain, we approximate the above equation as:
$$
\mathcal{I}_{\rm{out}}(\mathbf{\tilde{x}}_i) = \sum_{{\mathbf{x}_i}\in\mathcal{X}} \mathcal{I}(\mathbf{x}_i + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{x}_i),
$$
where $\mathbf{\tau} \in \mathcal{S}$, with $\mathcal{S}$ the set of available strides, corresponds to the current stride position of the filter, and $\mathbf{\tilde{x}}_i$ points are obtained by taking the centroid of the filter position mapped on the $\Omega$ domain.</p>
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<p>We will now try to pratically see how to work with the filter. From the above definition we see that what is needed is:</p>
<ol>
<li>A domain and a function defined on that domain (the input)</li>
<li>A stride, corresponding to the positions where the filter needs to be $\rightarrow$ <code>stride</code> variable in <code>ContinuousConv</code></li>
<li>The filter rectangular domain $\rightarrow$ <code>filter_dim</code> variable in <code>ContinuousConv</code></li>
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<h3 id="Input-function">Input function<a class="anchor-link" href="#Input-function">¶</a></h3><p>The input function for the continuous filter is defined as a tensor of shape: $$[B \times N_{in} \times N \times D]$$ where $B$ is the batch_size, $N_{in}$ is the number of input fields, $N$ the number of points in the mesh, $D$ the dimension of the problem. In particular:</p>
<ul>
<li>$D$ is the number of spatial variables + 1. The last column must contain the field value. For example for 2D problems $D=3$ and the tensor will be something like <code>[first coordinate, second coordinate, field value]</code></li>
<li>$N_{in}$ represents the number of vectorial function presented. For example a vectorial function $f = [f_1, f_2]$ will have $N_{in}=2$</li>
</ul>
<p>Let's see an example to clear the ideas. We will be verbose to explain in details the input form. We wish to create the function:
$$
f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1]
$$</p>
<p>using a batch size equal to 1.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># batch size fixed to 1</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># points in the mesh fixed to 200</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">200</span>
<span class="c1"># vectorial 2 dimensional function, number_input_fields=2</span>
<span class="n">number_input_fields</span> <span class="o">=</span> <span class="mi">2</span>
<span class="c1"># 2 dimensional spatial variables, D = 2 + 1 = 3</span>
<span class="n">D</span> <span class="o">=</span> <span class="mi">3</span>
<span class="c1"># create the function f domain as random 2d points in [0, 1]</span>
<span class="n">domain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">number_input_fields</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">D</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Domain has shape: </span><span class="si">{</span><span class="n">domain</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># create the functions</span>
<span class="n">pi</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</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="o">-</span><span class="mf">1.0</span><span class="p">]))</span> <span class="c1"># pi value</span>
<span class="n">f1</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">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</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">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">f2</span> <span class="o">=</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">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</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">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># stacking the input domain and field values</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">number_input_fields</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">D</span><span class="p">))</span>
<span class="n">data</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">domain</span> <span class="c1"># copy the domain</span>
<span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1</span> <span class="c1"># copy first field value</span>
<span class="n">data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1</span> <span class="c1"># copy second field value</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter input data has shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Domain has shape: torch.Size([1, 2, 200, 2])
Filter input data has shape: torch.Size([1, 2, 200, 3])
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<h3 id="Stride">Stride<a class="anchor-link" href="#Stride">¶</a></h3><p>The stride is passed as a dictionary <code>stride</code> which tells the filter where to go. Here is an example for the $[0,1]\times[0,5]$ domain:</p>
<div class="highlight"><pre><span></span><span class="c1"># stride definition</span>
<span class="n">stride</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jump"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">}</span>
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<p>This tells the filter:</p>
<ol>
<li><code>domain</code>: square domain (the only implemented) $[0,1]\times[0,5]$. The minimum value is always zero, while the maximum is specified by the user</li>
<li><code>start</code>: start position of the filter, coordinate $(0, 0)$</li>
<li><code>jump</code>: the jumps of the centroid of the filter to the next position $(0.1, 0.3)$</li>
<li><code>direction</code>: the directions of the jump, with <code>1 = right</code>, <code>0 = no jump</code>, <code>-1 = left</code> with respect to the current position</li>
</ol>
<p><strong>Note</strong></p>
<p>We are planning to release the possibility to directly pass a list of possible strides!</p>
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<h3 id="Filter-definition">Filter definition<a class="anchor-link" href="#Filter-definition">¶</a></h3><p>Having defined all the previous blocks, we are now able to construct the continuous filter.</p>
<p>Suppose we would like to get an output with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># filter dim</span>
<span class="n">filter_dim</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span>
<span class="c1"># stride</span>
<span class="n">stride</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jump"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.08</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">}</span>
<span class="c1"># creating the filter</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="p">)</span>
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<p>That's it! In just one line of code we have created the continuous convolutional filter. By default the <code>pina.model.FeedForward</code> neural network is intitialised, more on the <a href="https://mathlab.github.io/PINA/_rst/fnn.html">documentation</a>. In case the mesh doesn't change during training we can set the <code>optimize</code> flag equals to <code>True</code>, to exploit optimizations for finding the points to convolve.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># creating the filter + optimization</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
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<p>Let's try to do a forward pass:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter input data has shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># input to the filter</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">cConv</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter output data has shape: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Filter input data has shape: torch.Size([1, 2, 200, 3])
Filter output data has shape: torch.Size([1, 1, 169, 3])
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<p>If we don't want to use the default <code>FeedForward</code> neural network, we can pass a specified torch model in the <code>model</code> keyword as follow:</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">SimpleKernel</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="o">-&gt;</span> <span class="kc">None</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">model</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="mi">2</span><span class="p">,</span> <span class="mi">20</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">20</span><span class="p">,</span> <span class="mi">20</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">20</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">SimpleKernel</span><span class="p">,</span>
<span class="p">)</span>
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<p>Notice that we pass the class and not an already built object!</p>
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<h2 id="Building-a-MNIST-Classifier">Building a MNIST Classifier<a class="anchor-link" href="#Building-a-MNIST-Classifier">¶</a></h2><p>Let's see how we can build a MNIST classifier using a continuous convolutional filter. We will use the MNIST dataset from PyTorch. In order to keep small training times we use only 6000 samples for training and 1000 samples for testing.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">torch.utils.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">SubsetRandomSampler</span>
<span class="n">numb_training</span> <span class="o">=</span> <span class="mi">6000</span> <span class="c1"># get just 6000 images for training</span>
<span class="n">numb_testing</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># get just 1000 images for training</span>
<span class="n">seed</span> <span class="o">=</span> <span class="mi">111</span> <span class="c1"># for reproducibility</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span> <span class="c1"># setting batch size</span>
<span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="c1"># downloading the dataset</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
<span class="s2">"./data/"</span><span class="p">,</span>
<span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,)),</span>
<span class="p">]</span>
<span class="p">),</span>
<span class="p">)</span>
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<pre>Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz
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<pre>Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw
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Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz
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<pre>Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
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Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
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<p>Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape <code>[batch, 1, 28, 28]</code>, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels values are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">transform_input</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">dim_grid</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># creating the n dimensional mesh grid for a single channel image</span>
<span class="n">values_mesh</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dim_grid</span><span class="p">]</span>
<span class="n">mesh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">values_mesh</span><span class="p">)</span>
<span class="n">coordinates_mesh</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mesh</span><span class="p">]</span>
<span class="n">coordinates</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">coordinates_mesh</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="o">.</span><span class="n">repeat</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">coordinates</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
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<p>We can now build a simple classifier! We will use just one convolutional filter followed by a feedforward neural network</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ContinuousClassifier</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="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># number of classes for classification</span>
<span class="n">numb_class</span> <span class="o">=</span> <span class="mi">10</span>
<span class="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">27</span><span class="p">,</span> <span class="mi">27</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="mi">196</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="n">numb_class</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">120</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
<span class="n">func</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># transform input + convolution</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">transform_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># feed forward classification</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
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<p>We now aim to solve the classification problem. For this we will use the <code>SupervisedSolver</code> and the <code>SupervisedProblem</code>. The input of the supervised problems are the images, while the output the corresponding class.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">SupervisedProblem</span><span class="p">(</span>
<span class="n">input_</span><span class="o">=</span><span class="n">train_data</span><span class="o">.</span><span class="n">train_data</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="c1"># adding channel dimension</span>
<span class="n">output_</span><span class="o">=</span><span class="n">train_data</span><span class="o">.</span><span class="n">train_labels</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># setting the solver</span>
<span class="n">solver</span> <span class="o">=</span> <span class="n">SupervisedSolver</span><span class="p">(</span>
<span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">ContinuousClassifier</span><span class="p">(),</span>
<span class="n">loss</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">CrossEntropyLoss</span><span class="p">(),</span>
<span class="n">use_lt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># setting the trainer</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">solver</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1</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">0.7</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">64</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>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>Missing logger folder: /home/runner/work/PINA/PINA/tutorials/tutorial4/lightning_logs
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<pre>`Trainer.fit` stopped: `max_epochs=1` reached.
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<p>Let's see the performance on the test set!</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">setup</span><span class="p">(</span><span class="s2">"test"</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">test_dataloader</span><span class="p">():</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"data"</span><span class="p">]</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s2">"input"</span><span class="p">],</span> <span class="n">test_data</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="c1"># calculate outputs by running images through the network</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">solver</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="c1"># the class with the highest energy is what we choose as prediction</span>
<span class="n">_</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">total</span> <span class="o">+=</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy of the network on the test images: </span><span class="si">{</span><span class="p">(</span><span class="n">correct</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">total</span><span class="p">)</span><span class="si">:</span><span class="s2">.3%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy of the network on the test images: 82.600%
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<p>As we can see we have very good performance for having trained only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now.</p>
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<h2 id="Building-a-Continuous-Convolutional-Autoencoder">Building a Continuous Convolutional Autoencoder<a class="anchor-link" href="#Building-a-Continuous-Convolutional-Autoencoder">¶</a></h2><p>Just as toy problem, we will now build an autoencoder for the following function $f(x,y)=\sin(\pi x)\sin(\pi y)$ on the unit circle domain centered in $(0.5, 0.5)$. We will also see the ability to up-sample (once trained) the results without retraining. Let's first create the input and visualize it, we will use firstly a mesh of $100$ points.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># create inputs</span>
<span class="k">def</span><span class="w"> </span><span class="nf">circle_grid</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Generate points withing a unit 2D circle centered in (0.5, 0.5)</span>
<span class="sd"> :param N: number of points</span>
<span class="sd"> :type N: float</span>
<span class="sd"> :return: [x, y] array of points</span>
<span class="sd"> :rtype: torch.tensor</span>
<span class="sd"> """</span>
<span class="n">PI</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">R</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">centerX</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">centerY</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">R</span> <span class="o">*</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">rand</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
<span class="n">theta</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PI</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">centerX</span> <span class="o">+</span> <span class="n">r</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">theta</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">centerY</span> <span class="o">+</span> <span class="n">r</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">theta</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
<span class="c1"># create the grid</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">500</span><span class="p">)</span>
<span class="c1"># create input</span>
<span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid</span>
<span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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">pi</span> <span class="o">*</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</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">pi</span> <span class="o">*</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># visualize data</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Training sample with 500 points"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</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">colorbar</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>Let's now build a simple autoencoder using the continuous convolutional filter. The data is clearly unstructured and a simple convolutional filter might not work without projecting or interpolating first. Let's first build and <code>Encoder</code> and <code>Decoder</code> class, and then a <code>Autoencoder</code> class that contains both.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">Encoder</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">hidden_dimension</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="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="n">hidden_dimension</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">240</span><span class="p">,</span> <span class="mi">120</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># convolution</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># feed forward pass</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Decoder</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">hidden_dimension</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="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="n">hidden_dimension</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">120</span><span class="p">,</span> <span class="mi">240</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grid</span><span class="p">):</span>
<span class="c1"># feed forward pass</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="c1"># transpose convolution</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">grid</span><span class="p">))</span>
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<p>Very good! Notice that in the <code>Decoder</code> class in the <code>forward</code> pass we have used the <code>.transpose()</code> method of the <code>ContinuousConvolution</code> class. This method accepts the <code>weights</code> for upsampling and the <code>grid</code> on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the <code>hidden_dimension</code> variable. We apply the sigmoid on the output since the field value is between $[0, 1]$.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">Autoencoder</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">hidden_dimension</span><span class="o">=</span><span class="mi">10</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">encoder</span> <span class="o">=</span> <span class="n">Encoder</span><span class="p">(</span><span class="n">hidden_dimension</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">Decoder</span><span class="p">(</span><span class="n">hidden_dimension</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># saving grid for later upsampling</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># encoder</span>
<span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># decoder</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grid</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span>
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<p>Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. We use the <code>SupervisedSolver</code> as solver, and the problem is a simple problem created by inheriting from <code>AbstractProblem</code>. It takes approximately two minutes to train on CPU.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># define the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">SupervisedProblem</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
<span class="c1"># define the solver</span>
<span class="n">solver</span> <span class="o">=</span> <span class="n">SupervisedSolver</span><span class="p">(</span>
<span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">Autoencoder</span><span class="p">(),</span>
<span class="n">loss</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">MSELoss</span><span class="p">(),</span>
<span class="n">use_lt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># 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="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">100</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="c1"># we train on CPU and avoid model summary at beginning of training (optional)</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="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>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=100` reached.
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<p>Let's visualize the two solutions side by side!</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">solver</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># get output and detach from computational graph for plotting</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># visualize data</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Autoencoder"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">l2_error</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">input_</span> <span class="o">-</span> <span class="n">target</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
<span class="n">input_</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>l2 error: 4.73%
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<p>More or less $4\%$ in $l_2$ error, which is really low considering the fact that we use just <strong>one</strong> convolutional layer and a simple feedforward to decrease the dimension. Let's see now some peculiarity of the filter.</p>
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<h3 id="Filter-for-upsampling">Filter for upsampling<a class="anchor-link" href="#Filter-for-upsampling">¶</a></h3><p>Suppose we have already the hidden representation and we want to upsample on a differen grid with more points. Let's see how to do it:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">grid2</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">1500</span><span class="p">)</span> <span class="c1"># triple number of points</span>
<span class="n">input_data2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid2</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</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">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># get the hidden representation from original input</span>
<span class="n">latent</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
<span class="c1"># upsample on the second input_data2</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">latent</span><span class="p">,</span> <span class="n">input_data2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># show the picture</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Up-sampling"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
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<pre>l2 error: 9.68%
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<h3 id="Autoencoding-at-different-resolutions">Autoencoding at different resolutions<a class="anchor-link" href="#Autoencoding-at-different-resolutions">¶</a></h3><p>In the previous example we already had the hidden representation (of the original input) and we used it to upsample. Sometimes however we could have a finer mesh solution and we would simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">grid2</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">3500</span><span class="p">)</span> <span class="c1"># very fine mesh</span>
<span class="n">input_data2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid2</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</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">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># get the hidden representation from finer mesh input</span>
<span class="n">latent</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">input_data2</span><span class="p">)</span>
<span class="c1"># upsample on the second input_data2</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">latent</span><span class="p">,</span> <span class="n">input_data2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># show the picture</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Autoencoder not re-trained"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># calculate l2 error</span>
<span class="nb">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
</pre></div>
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<pre>l2 error: 9.53%
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<h2 id="What's-next?">What's next?<a class="anchor-link" href="#What's-next?">¶</a></h2><p>We have shown the basic usage of a convolutional filter. There are additional extensions possible:</p>
<ol>
<li><p>Train using Physics Informed strategies</p>
</li>
<li><p>Use the filter to build an unstructured convolutional autoencoder for reduced order modelling</p>
</li>
<li><p>Many more...</p>
</li>
</ol>
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