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FYS-STK4155/doc/LectureNotes/exercisesweek42.html
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lars f66400f741 Excercises for week 42
2025-10-15 10:59:41 +02:00

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/* Icons urls */
:root {
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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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),
0 2px 2px 0 var(--jp-shadow-penumbra-color),
0 1px 5px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
0 4px 5px 0 var(--jp-shadow-penumbra-color),
0 1px 10px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
0 6px 10px 0 var(--jp-shadow-penumbra-color),
0 1px 18px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
0 8px 10px 1px var(--jp-shadow-penumbra-color),
0 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
0 12px 17px 2px var(--jp-shadow-penumbra-color),
0 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
0 16px 24px 2px var(--jp-shadow-penumbra-color),
0 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
0 20px 31px 3px var(--jp-shadow-penumbra-color),
0 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
0 24px 38px 3px var(--jp-shadow-penumbra-color),
0 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-inverse-border-color: var(--md-grey-600);
--jp-border-radius: 2px;
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* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
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--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
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/*
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* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
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--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
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* Content font variables are used for typography of user generated content.
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* The font sizing here is done assuming that the body font size of --jp-content-font-size1
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--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
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--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
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--jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
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--jp-code-cursor-width1: 2px;
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/* Layout
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* The following are the inverse layout colors use in JupyterLab. In a light
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--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
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--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
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--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
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--jp-cell-collapser-width: 8px;
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--jp-notebook-multiselected-color: var(--md-blue-50);
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/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
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--jp-toolbar-background: var(--jp-layout-color1);
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--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
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--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-inverse-border-color);
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/* General editor styles */
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--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #a2f;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #a2f;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
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RTC user specific colors.
These colors are used for the cursor, username in the editor,
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--jp-collaborator-color2: #dac83d;
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--jp-collaborator-color4: #00e4d0;
--jp-collaborator-color5: #45d4ff;
--jp-collaborator-color6: #e2b1ff;
--jp-collaborator-color7: #ff9de6;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
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--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
/* Button colors */
--jp-accept-color-normal: var(--md-blue-700);
--jp-accept-color-hover: var(--md-blue-800);
--jp-accept-color-active: var(--md-blue-900);
--jp-warn-color-normal: var(--md-red-700);
--jp-warn-color-hover: var(--md-red-800);
--jp-warn-color-active: var(--md-red-900);
--jp-reject-color-normal: var(--md-grey-600);
--jp-reject-color-hover: var(--md-grey-700);
--jp-reject-color-active: var(--md-grey-800);
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--jp-notebook-icon-color: #f37626;
--jp-json-icon-color: var(--md-orange-700);
--jp-console-icon-background-color: var(--md-blue-700);
--jp-console-icon-color: white;
--jp-terminal-icon-background-color: var(--md-grey-800);
--jp-terminal-icon-color: var(--md-grey-200);
--jp-text-editor-icon-color: var(--md-grey-700);
--jp-inspector-icon-color: var(--md-grey-700);
--jp-switch-color: var(--md-grey-400);
--jp-switch-true-position-color: var(--md-orange-900);
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<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
<main>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h1 id="Exercises-week-42">Exercises week 42<a class="anchor-link" href="#Exercises-week-42"></a></h1><p><strong>Python Code can be found at <a href="https://github.uio.no/larsbog/FYS-STK4155">https://github.uio.no/larsbog/FYS-STK4155</a></strong></p>
<p><strong>October 13-17, 2025</strong></p>
<p>Date: <strong>Deadline is Friday October 17 at midnight</strong></p>
</div>
</div>
</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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<h1 id="Overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week<a class="anchor-link" href="#Overarching-aims-of-the-exercises-this-week"></a></h1><p>The aim of the exercises this week is to train the neural network you implemented last week.</p>
<p>To train neural networks, we use gradient descent, since there is no analytical expression for the optimal parameters. This means you will need to compute the gradient of the cost function wrt. the network parameters. And then you will need to implement some gradient method.</p>
<p>You will begin by computing gradients for a network with one layer, then two layers, then any number of layers. Keeping track of the shapes and doing things step by step will be very important this week.</p>
<p>We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the neural network correctly, and running small parts of the code at a time will be important for understanding the methods. If you have trouble running a notebook, you can run this notebook in google colab instead(<a href="https://colab.research.google.com/drive/1FfvbN0XlhV-lATRPyGRTtTBnJr3zNuHL#offline=true&amp;sandboxMode=true">https://colab.research.google.com/drive/1FfvbN0XlhV-lATRPyGRTtTBnJr3zNuHL#offline=true&amp;sandboxMode=true</a>), though we recommend that you set up VSCode and your python environment to run code like this locally.</p>
<p>First, some setup code that you will need.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">autograd.numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span> <span class="c1"># We need to use this numpy wrapper to make automatic differentiation work later</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">autograd</span><span class="w"> </span><span class="kn">import</span> <span class="n">grad</span><span class="p">,</span> <span class="n">elementwise_grad</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</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">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="c1"># Defining some activation functions</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ReLU</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># Derivative of the ReLU function</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ReLU_der</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">z</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">mse</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">predict</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
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<h1 id="Exercise-1---Understand-the-feed-forward-pass">Exercise 1 - Understand the feed forward pass<a class="anchor-link" href="#Exercise-1---Understand-the-feed-forward-pass"></a></h1><p><strong>a)</strong> Complete last weeks' exercises if you haven't already (recommended).</p>
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<h1 id="Exercise-2---Gradient-with-one-layer-using-autograd">Exercise 2 - Gradient with one layer using autograd<a class="anchor-link" href="#Exercise-2---Gradient-with-one-layer-using-autograd"></a></h1><p>For the first few exercises, we will not use batched inputs. Only a single input vector is passed through the layer at a time.</p>
<p>In this exercise you will compute the gradient of a single layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though!</p>
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<p><strong>a)</strong> If the weights and bias of a layer has shapes (10, 4) and (10), what will the shapes of the gradients of the cost function wrt. these weights and this bias be?</p>
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The gradients need to have the same shape as the weights and biases, as we update them as new = old - lr * gradient
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<p><strong>b)</strong> Complete the feed_forward_one_layer function. It should use the sigmoid activation function. Also define the weigth and bias with the correct shapes.</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">feed_forward_one_layer</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost_one_layer</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_one_layer</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
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<p><strong>c)</strong> Compute the gradient of the cost function wrt. the weigth and bias by running the cell below. You will not need to change anything, just make sure it runs by defining things correctly in the cell above. This code uses the autograd package which uses backprogagation to compute the gradient!</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">autograd_one_layer</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">cost_one_layer</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">W_g</span><span class="p">,</span> <span class="n">b_g</span> <span class="o">=</span> <span class="n">autograd_one_layer</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">W_g</span><span class="p">,</span> <span class="n">b_g</span><span class="p">)</span>
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<pre>[[-0.0012661 -0.00171951]
[-0.00421828 -0.00572891]
[ 0.00930977 0.01264372]] [-0.01193713 -0.03977107 0.08777494]
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<h1 id="Exercise-3---Gradient-with-one-layer-writing-backpropagation-by-hand">Exercise 3 - Gradient with one layer writing backpropagation by hand<a class="anchor-link" href="#Exercise-3---Gradient-with-one-layer-writing-backpropagation-by-hand"></a></h1><p>Before you use the gradient you found using autograd, you will have to find the gradient "manually", to better understand how the backpropagation computation works. To do backpropagation "manually", you will need to write out expressions for many derivatives along the computation.</p>
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<p>We want to find the gradient of the cost function wrt. the weight and bias. This is quite hard to do directly, so we instead use the chain rule to combine multiple derivatives which are easier to compute.</p>
<p>$$
\frac{dC}{dW} = \frac{dC}{da}\frac{da}{dz}\frac{dz}{dW}
$$</p>
<p>$$
\frac{dC}{db} = \frac{dC}{da}\frac{da}{dz}\frac{dz}{db}
$$</p>
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<p><strong>a)</strong> Which intermediary results can be reused between the two expressions?</p>
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<p>The derivatives $\frac{\mathrm d C}{\mathrm d a} \frac{\mathrm d a}{\mathrm d z}$ are the same in both expressions and can be reused.</p>
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<p><strong>b)</strong> What is the derivative of the cost wrt. the final activation? You can use the autograd calculation to make sure you get the correct result. Remember that we compute the mean in mse.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">a</span>
<span class="k">def</span><span class="w"> </span><span class="nf">mse_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">predict</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span> <span class="o">/</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mse_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">))</span>
<span class="n">cost_autograd</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">mse</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">cost_autograd</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">))</span>
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<pre>[-0.06423077 -0.18044784 0.43834371]
[-0.06423077 -0.18044784 0.43834371]
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<p><strong>c)</strong> What is the expression for the derivative of the sigmoid activation function? You can use the autograd calculation to make sure you get the correct result.</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">sigmoid_der</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sigmoid_der</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
<span class="n">sigmoid_autograd</span> <span class="o">=</span> <span class="n">elementwise_grad</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sigmoid_autograd</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
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<pre>[0.18584758 0.22040205 0.20024227]
[0.18584758 0.22040205 0.20024227]
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<p><strong>d)</strong> Using the two derivatives you just computed, compute this intermetidary gradient you will use later:</p>
<p>$$
\frac{dC}{dz} = \frac{dC}{da}\frac{da}{dz}
$$</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dC_da</span> <span class="o">=</span> <span class="n">mse_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">dC_dz</span> <span class="o">=</span> <span class="n">dC_da</span> <span class="o">*</span> <span class="n">sigmoid_der</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
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<p><strong>e)</strong> What is the derivative of the intermediary z wrt. the weight and bias? What should the shapes be? The one for the weights is a little tricky, it can be easier to play around in the next exercise first. You can also try computing it with autograd to get a hint.</p>
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The derivative of z wrt to the bias is 1, since z = Wx + b. The derivative of z wrt the weights is x, since z_i = sum_j W_ij * x_j + b_i
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<p><strong>f)</strong> Now combine the expressions you have worked with so far to compute the gradients! Note that you always need to do a feed forward pass while saving the zs and as before you do backpropagation, as they are used in the derivative expressions</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">dC_dz</span>
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<pre>(array([0.10606414, 0.14404709]),
array([-0.01193713, -0.03977107, 0.08777494]))</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dC_da</span> <span class="o">=</span> <span class="n">mse_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">dC_dz</span> <span class="o">=</span> <span class="n">dC_da</span> <span class="o">*</span> <span class="n">sigmoid_der</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">dC_dW</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">dC_dz</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">dC_db</span> <span class="o">=</span> <span class="n">dC_dz</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dC_dW</span><span class="p">,</span> <span class="n">dC_db</span><span class="p">)</span>
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<pre>[[-0.0012661 -0.00171951]
[-0.00421828 -0.00572891]
[ 0.00930977 0.01264372]] [-0.01193713 -0.03977107 0.08777494]
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<p>You should get the same results as with autograd.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">W_g</span><span class="p">,</span> <span class="n">b_g</span> <span class="o">=</span> <span class="n">autograd_one_layer</span><span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">W_g</span><span class="p">,</span> <span class="n">b_g</span><span class="p">)</span>
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<pre>[[-0.0012661 -0.00171951]
[-0.00421828 -0.00572891]
[ 0.00930977 0.01264372]] [-0.01193713 -0.03977107 0.08777494]
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<h1 id="Exercise-4---Gradient-with-two-layers-writing-backpropagation-by-hand">Exercise 4 - Gradient with two layers writing backpropagation by hand<a class="anchor-link" href="#Exercise-4---Gradient-with-two-layers-writing-backpropagation-by-hand"></a></h1>
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<p>Now that you have implemented backpropagation for one layer, you have found most of the expressions you will need for more layers. Let's move up to two layers.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="n">W1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">b1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">W2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">b2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[(</span><span class="n">W1</span><span class="p">,</span> <span class="n">b1</span><span class="p">),</span> <span class="p">(</span><span class="n">W2</span><span class="p">,</span> <span class="n">b2</span><span class="p">)]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">z1</span> <span class="o">=</span> <span class="n">W1</span> <span class="o">@</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b1</span>
<span class="n">a1</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z1</span><span class="p">)</span>
<span class="n">z2</span> <span class="o">=</span> <span class="n">W2</span> <span class="o">@</span> <span class="n">a1</span> <span class="o">+</span> <span class="n">b2</span>
<span class="n">a2</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z2</span><span class="p">)</span>
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<p>We begin by computing the gradients of the last layer, as the gradients must be propagated backwards from the end.</p>
<p><strong>a)</strong> Compute the gradients of the last layer, just like you did the single layer in the previous exercise.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dC_da2</span> <span class="o">=</span> <span class="n">mse_der</span><span class="p">(</span><span class="n">a2</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">dC_dz2</span> <span class="o">=</span> <span class="n">dC_da2</span> <span class="o">*</span> <span class="n">sigmoid_der</span><span class="p">(</span><span class="n">z2</span><span class="p">)</span>
<span class="n">dC_dW2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">dC_dz2</span><span class="p">,</span> <span class="n">a1</span><span class="p">)</span>
<span class="n">dC_db2</span> <span class="o">=</span> <span class="n">dC_dz2</span>
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<p>To find the derivative of the cost wrt. the activation of the first layer, we need a new expression, the one furthest to the right in the following.</p>
<p>$$
\frac{dC}{da_1} = \frac{dC}{dz_2}\frac{dz_2}{da_1}
$$</p>
<p><strong>b)</strong> What is the derivative of the second layer intermetiate wrt. the first layer activation? (First recall how you compute $z_2$)</p>
<p>$$
\frac{dz_2}{da_1}
$$</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dz2_da1</span> <span class="o">=</span> <span class="n">W2</span><span class="o">.</span><span class="n">T</span>
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<p><strong>c)</strong> Use this expression, together with expressions which are equivelent to ones for the last layer to compute all the derivatives of the first layer.</p>
<p>$$
\frac{dC}{dW_1} = \frac{dC}{da_1}\frac{da_1}{dz_1}\frac{dz_1}{dW_1}
$$</p>
<p>$$
\frac{dC}{db_1} = \frac{dC}{da_1}\frac{da_1}{dz_1}\frac{dz_1}{db_1}
$$</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dC_da1</span> <span class="o">=</span> <span class="n">dz2_da1</span> <span class="o">@</span> <span class="n">dC_dz2</span>
<span class="n">dC_dz1</span> <span class="o">=</span> <span class="n">dC_da1</span> <span class="o">*</span> <span class="n">sigmoid_der</span><span class="p">(</span><span class="n">z1</span><span class="p">)</span>
<span class="n">dC_dW1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">dC_dz1</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">dC_db1</span> <span class="o">=</span> <span class="n">dC_dz1</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">dC_dW1</span><span class="p">,</span> <span class="n">dC_db1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dC_dW2</span><span class="p">,</span> <span class="n">dC_db2</span><span class="p">)</span>
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<pre>[[0.00446139 0.00071062]
[0.00983449 0.00156645]
[0.0024775 0.00039462]] [0.00524627 0.01156463 0.00291336]
[[ 0.03644459 0.02409418 0.03224462]
[ 0.05648327 0.03734211 0.04997399]
[-0.00813619 -0.00537898 -0.00719855]
[ 0.0034844 0.0023036 0.00308285]] [ 0.04508103 0.06986838 -0.01006426 0.00431011]
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<p><strong>d)</strong> Make sure you got the same gradient as the following code which uses autograd to do backpropagation.</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">feed_forward_two_layers</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">W1</span><span class="p">,</span> <span class="n">b1</span> <span class="o">=</span> <span class="n">layers</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">z1</span> <span class="o">=</span> <span class="n">W1</span> <span class="o">@</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b1</span>
<span class="n">a1</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z1</span><span class="p">)</span>
<span class="n">W2</span><span class="p">,</span> <span class="n">b2</span> <span class="o">=</span> <span class="n">layers</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">z2</span> <span class="o">=</span> <span class="n">W2</span> <span class="o">@</span> <span class="n">a1</span> <span class="o">+</span> <span class="n">b2</span>
<span class="n">a2</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a2</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">cost_two_layers</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_two_layers</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">grad_two_layers</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">cost_two_layers</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">grad_two_layers</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
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<pre>[(array([[0.00446139, 0.00071062],
[0.00983449, 0.00156645],
[0.0024775 , 0.00039462]]),
array([0.00524627, 0.01156463, 0.00291336])),
(array([[ 0.03644459, 0.02409418, 0.03224462],
[ 0.05648327, 0.03734211, 0.04997399],
[-0.00813619, -0.00537898, -0.00719855],
[ 0.0034844 , 0.0023036 , 0.00308285]]),
array([ 0.04508103, 0.06986838, -0.01006426, 0.00431011]))]</pre>
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<p><strong>e)</strong> How would you use the gradient from this layer to compute the gradient of an even earlier layer? Would the expressions be any different?</p>
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To compute the gradient of an earlier layer we can continue this scheme of using the chain rule, just like we did for the second layer. The expressions would not be different, just the indices would change.
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<h1 id="Exercise-5---Gradient-with-any-number-of-layers-writing-backpropagation-by-hand">Exercise 5 - Gradient with any number of layers writing backpropagation by hand<a class="anchor-link" href="#Exercise-5---Gradient-with-any-number-of-layers-writing-backpropagation-by-hand"></a></h1>
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<p>Well done on getting this far! Now it's time to compute the gradient with any number of layers.</p>
<p>First, some code from the general neural network code from last week. Note that we are still sending in one input vector at a time. We will change it to use batched inputs later.</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">create_layers</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">):</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">network_input_size</span>
<span class="k">for</span> <span class="n">layer_output_size</span> <span class="ow">in</span> <span class="n">layer_output_sizes</span><span class="p">:</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">layer_output_size</span><span class="p">,</span> <span class="n">i_size</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">layer_output_size</span><span class="p">)</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">layer_output_size</span>
<span class="k">return</span> <span class="n">layers</span>
<span class="k">def</span><span class="w"> </span><span class="nf">feed_forward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="nb">input</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">activation_func</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">activation_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
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<p>You might have already have noticed a very important detail in backpropagation: You need the values from the forward pass to compute all the gradients! The feed forward method above is great for efficiency and for using autograd, as it only cares about computing the final output, but now we need to also save the results along the way.</p>
<p>Here is a function which does that for you.</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">feed_forward_saver</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">layer_inputs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">zs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">a</span> <span class="o">=</span> <span class="nb">input</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">activation_func</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">layer_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">activation_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">zs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">layer_inputs</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="n">a</span>
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<p><strong>a)</strong> Now, complete the backpropagation function so that it returns the gradient of the cost function wrt. all the weigths and biases. Use the autograd calculation below to make sure you get the correct answer.</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">backpropagation</span><span class="p">(</span>
<span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">activation_ders</span><span class="p">,</span> <span class="n">cost_der</span><span class="o">=</span><span class="n">mse_der</span>
<span class="p">):</span>
<span class="n">layer_inputs</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_saver</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
<span class="n">layer_grads</span> <span class="o">=</span> <span class="p">[()</span> <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">]</span>
<span class="c1"># We loop over the layers, from the last to the first</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">))):</span>
<span class="n">layer_input</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">activation_der</span> <span class="o">=</span> <span class="n">layer_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">zs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation_ders</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># For last layer we use cost derivative as dC_da(L) can be computed directly</span>
<span class="n">dC_da</span> <span class="o">=</span> <span class="n">cost_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># For other layers we build on previous z derivative, as dC_da(i) = dC_dz(i+1) * dz(i+1)_da(i)</span>
<span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="o">=</span> <span class="n">layers</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">dC_da</span> <span class="o">=</span> <span class="n">W</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">dC_dz</span>
<span class="n">dC_dz</span> <span class="o">=</span> <span class="n">dC_da</span> <span class="o">*</span> <span class="n">activation_der</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">dC_dW</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">dC_dz</span><span class="p">,</span> <span class="n">layer_input</span><span class="p">)</span>
<span class="n">dC_db</span> <span class="o">=</span> <span class="n">dC_dz</span>
<span class="n">layer_grads</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dC_dW</span><span class="p">,</span> <span class="n">dC_db</span><span class="p">)</span>
<span class="k">return</span> <span class="n">layer_grads</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">network_input_size</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">layer_output_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="n">activation_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">ReLU</span><span class="p">]</span>
<span class="n">activation_ders</span> <span class="o">=</span> <span class="p">[</span><span class="n">sigmoid_der</span><span class="p">,</span> <span class="n">ReLU_der</span><span class="p">]</span>
<span class="n">layers</span> <span class="o">=</span> <span class="n">create_layers</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">layer_grads</span> <span class="o">=</span> <span class="n">backpropagation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">activation_ders</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">layer_grads</span><span class="p">)</span>
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<pre>[(array([[0.02299964, 0.09565439],
[0.01274242, 0.05299513],
[0.01038055, 0.04317222]]), array([0.09661863, 0.05352934, 0.04360741])), (array([[-0. , -0. , -0. ],
[ 0.23150223, 0.16003629, 0.36267149],
[-0. , -0. , -0. ],
[-0.20226612, -0.13982552, -0.31687019]]), array([-0. , 0.41494126, -0. , -0.36253888]))]
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">cost_grad</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">cost_grad</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">ReLU</span><span class="p">],</span> <span class="n">target</span><span class="p">)</span>
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<pre>[(array([[0.02299964, 0.09565439],
[0.01274242, 0.05299513],
[0.01038055, 0.04317222]]),
array([0.09661863, 0.05352934, 0.04360741])),
(array([[ 0. , 0. , 0. ],
[ 0.23150223, 0.16003629, 0.36267149],
[ 0. , 0. , 0. ],
[-0.20226612, -0.13982552, -0.31687019]]),
array([ 0. , 0.41494126, 0. , -0.36253888]))]</pre>
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<h1 id="Exercise-6---Batched-inputs">Exercise 6 - Batched inputs<a class="anchor-link" href="#Exercise-6---Batched-inputs"></a></h1><p>Make new versions of all the functions in exercise 5 which now take batched inputs instead. See last weeks exercise 5 for details on how to batch inputs to neural networks. You will also need to update the backpropogation function.</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">feed_forward_saver_batch</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">layer_inputs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">zs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">a</span> <span class="o">=</span> <span class="nb">input</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">activation_func</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">layer_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">W</span><span class="o">.</span><span class="n">T</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">activation_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">zs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">layer_inputs</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="n">a</span>
<span class="k">def</span><span class="w"> </span><span class="nf">backpropagation_batch</span><span class="p">(</span>
<span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">activation_ders</span><span class="p">,</span> <span class="n">cost_der</span><span class="o">=</span><span class="n">mse_der</span>
<span class="p">):</span>
<span class="n">layer_inputs</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_saver_batch</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
<span class="n">layer_grads</span> <span class="o">=</span> <span class="p">[()</span> <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">]</span>
<span class="c1"># We loop over the layers, from the last to the first</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">))):</span>
<span class="n">layer_input</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">activation_der</span> <span class="o">=</span> <span class="n">layer_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">zs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation_ders</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># For last layer we use cost derivative as dC_da(L) can be computed directly</span>
<span class="n">dC_da</span> <span class="o">=</span> <span class="n">cost_der</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># For other layers we build on previous z derivative, as dC_da(i) = dC_dz(i+1) * dz(i+1)_da(i)</span>
<span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="o">=</span> <span class="n">layers</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">dC_da</span> <span class="o">=</span> <span class="p">(</span><span class="n">W</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">dC_dz</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
<span class="c1"># print(f"Layer {i}: dC_da shape: {dC_da.shape}, dC_dz shape: {dC_dz.shape if 'dC_dz' in locals() else 'N/A'}, layer_input shape: {layer_input.shape}")</span>
<span class="n">dC_dz</span> <span class="o">=</span> <span class="n">dC_da</span> <span class="o">*</span> <span class="n">activation_der</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="c1"># print(dC_dz.shape)</span>
<span class="n">dC_dW</span> <span class="o">=</span> <span class="n">dC_dz</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">layer_input</span>
<span class="n">dC_db</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dC_dz</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">layer_grads</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dC_dW</span><span class="p">,</span> <span class="n">dC_db</span><span class="p">)</span>
<span class="k">return</span> <span class="n">layer_grads</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="n">network_input_size</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">layer_grads</span> <span class="o">=</span> <span class="n">backpropagation_batch</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">activation_ders</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">layer_grads</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">layer_grads</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
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<pre>((3, 2), (3,))</pre>
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<h1 id="Exercise-7---Training">Exercise 7 - Training<a class="anchor-link" href="#Exercise-7---Training"></a></h1>
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<p><strong>a)</strong> Complete exercise 6 and 7 from last week, but use your own backpropogation implementation to compute the gradient.</p>
<ul>
<li>IMPORTANT: Do not implement the derivative terms for softmax and cross-entropy separately, it will be very hard!</li>
<li>Instead, use the fact that the derivatives multiplied together simplify to <strong>prediction - target</strong> (see <a href="https://medium.com/data-science/derivative-of-the-softmax-function-and-the-categorical-cross-entropy-loss-ffceefc081d1">source1</a>, <a href="https://shivammehta25.github.io/posts/deriving-categorical-cross-entropy-and-softmax/">source2</a>)</li>
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<p><strong>b)</strong> Use stochastic gradient descent with momentum when you train your network.</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">GradientDescent</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">*</span><span class="n">args</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">num_iterations</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">"""Gradient Descent Class</span>
<span class="sd"> Args:</span>
<span class="sd"> learning_rate (float, optional): Learning rate used for step updates. Defaults to 0.1.</span>
<span class="sd"> num_iterations (int, optional): Number of iterations for gradient descent. Defaults to 1000.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_cost_history</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_iterations</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">learning_rate</span>
<span class="bp">self</span><span class="o">.</span><span class="n">num_iterations</span> <span class="o">=</span> <span class="n">num_iterations</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost_history</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Returns the cost history of the optimization process.</span>
<span class="sd"> Returns:</span>
<span class="sd"> np.ndarray: Array of cost values for each iteration.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cost_history</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_epochs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Returns an array of epoch numbers from 0 to num_iterations - 1.</span>
<span class="sd"> Returns:</span>
<span class="sd"> np.ndarray: Array of epoch numbers.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_iterations</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Returns the optimal solution for an optimization problem using the gradient descent</span>
<span class="sd"> Args:</span>
<span class="sd"> X (np.ndarray): X values</span>
<span class="sd"> y (np.ndarray): y values</span>
<span class="sd"> Returns:</span>
<span class="sd"> np.ndarray: Optimal parameters</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="n">X</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">y</span>
<span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</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="mi">1</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_precomp</span><span class="p">()</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_iterations</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_comp_step</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_cost_history</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_cost</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_theta</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_precomp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_comp_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_cost</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_update_theta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_batch</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">NNGradientDescent</span><span class="p">(</span><span class="n">GradientDescent</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">*</span><span class="n">args</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">activation_ders</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">layers</span>
<span class="bp">self</span><span class="o">.</span><span class="n">activation_funcs</span> <span class="o">=</span> <span class="n">activation_funcs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">activation_ders</span> <span class="o">=</span> <span class="n">activation_ders</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_precomp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_comp_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layer_inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_saver_batch</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_funcs</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_cost</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]:</span>
<span class="n">layer_grads</span> <span class="o">=</span> <span class="n">backpropagation_batch</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_funcs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_ders</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">layer_grads</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_update_theta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">dC_dW</span><span class="p">,</span> <span class="n">dC_db</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_compute_grad</span><span class="p">()):</span>
<span class="n">W</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">W</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">dC_dW</span>
<span class="n">b</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">dC_db</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">NNStochasticGradientDescent</span><span class="p">(</span><span class="n">NNGradientDescent</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">*</span><span class="n">args</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">batches_per_epoch</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">):</span>
<span class="c1"># print(self.__class__.__name__) # If you see this: debugging yaaaay, the programmer that wrote this line is stupid...</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="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">batches_per_epoch</span> <span class="o">=</span> <span class="n">batches_per_epoch</span>
<span class="bp">self</span><span class="o">.</span><span class="n">RNG</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_precomp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">RNG</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_comp_step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">RNG</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
<span class="n">batch_indices</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)</span>
<span class="k">if</span> <span class="n">index</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">:</span>
<span class="n">batch_indices</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">N</span><span class="p">)</span>
<span class="n">X_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X</span><span class="p">[</span><span class="n">batch_indices</span><span class="p">]</span>
<span class="n">y_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">batch_indices</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layer_inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">zs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_saver_batch</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_funcs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">X_batch</span> <span class="o">=</span> <span class="n">X_batch</span>
<span class="bp">self</span><span class="o">.</span><span class="n">y_batch</span> <span class="o">=</span> <span class="n">y_batch</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_cost</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="n">mse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_batch</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]:</span>
<span class="n">layer_grads</span> <span class="o">=</span> <span class="n">backpropagation_batch</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">X_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_funcs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_batch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation_ders</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">layer_grads</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_epochs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_iterations</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">batches_per_epoch</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost_history</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Returns the cost history of the optimization process.</span>
<span class="sd"> Returns:</span>
<span class="sd"> np.ndarray: Array of cost values for each epoch.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cost_history</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="bp">self</span><span class="o">.</span><span class="n">batches_per_epoch</span><span class="p">),</span> <span class="n">axis</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">NNMomentum</span><span class="p">(</span><span class="n">NNGradientDescent</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">*</span><span class="n">args</span><span class="p">,</span> <span class="n">delta</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">=</span> <span class="n">delta</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_precomp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_layers</span> <span class="o">=</span> <span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">W</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> <span class="k">for</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">]</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_precomp</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_update_theta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_layers</span> <span class="o">=</span> <span class="p">[(</span><span class="n">W</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">b</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span> <span class="k">for</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">((</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="p">(</span><span class="n">W_prev</span><span class="p">,</span> <span class="n">b_prev</span><span class="p">),</span> <span class="p">(</span><span class="n">dW</span><span class="p">,</span> <span class="n">db</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_layers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_grad</span><span class="p">())):</span>
<span class="n">W</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">*</span> <span class="p">(</span><span class="n">W</span> <span class="o">-</span> <span class="n">W_prev</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">dW</span>
<span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span> <span class="o">*</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">b_prev</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">db</span>
<span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">NNMomentumSGD</span><span class="p">(</span><span class="n">NNMomentum</span><span class="p">,</span> <span class="n">NNStochasticGradientDescent</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">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">NNMomentumSGD</span><span class="p">(</span><span class="n">layers</span><span class="o">=</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="o">=</span><span class="n">activation_funcs</span><span class="p">,</span> <span class="n">activation_ders</span><span class="o">=</span><span class="n">activation_ders</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">num_iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">batches_per_epoch</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">get_epochs</span><span class="p">(),</span> <span class="n">opt</span><span class="o">.</span><span class="n">cost_history</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[27]:</div>
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<pre>[&lt;matplotlib.lines.Line2D at 0x7f2db9fc87d0&gt;]</pre>
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<h1 id="Exercise-8-(Optional)---Object-orientation">Exercise 8 (Optional) - Object orientation<a class="anchor-link" href="#Exercise-8-(Optional)---Object-orientation"></a></h1><p>Passing in the layers, activations functions, activation derivatives and cost derivatives into the functions each time leads to code which is easy to understand in isoloation, but messier when used in a larger context with data splitting, data scaling, gradient methods and so forth. Creating an object which stores these values can lead to code which is much easier to use.</p>
<p><strong>a)</strong> Write a neural network class. You are free to implement it how you see fit, though we strongly recommend to not save any input or output values as class attributes, nor let the neural network class handle gradient methods internally. Gradient methods should be handled outside, by performing general operations on the layer_grads list using functions or classes separate to the neural network.</p>
<p>We provide here a skeleton structure which should get you started.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</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">NeuralNetwork</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">network_input_size</span><span class="p">,</span>
<span class="n">layer_output_sizes</span><span class="p">,</span>
<span class="n">activation_funcs</span><span class="p">,</span>
<span class="n">activation_ders</span><span class="p">,</span>
<span class="n">cost_fun</span><span class="p">,</span>
<span class="n">cost_der</span><span class="p">,</span>
<span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
<span class="c1"># Simple feed forward pass</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_feed_forward_saver</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">compute_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">update_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layer_grads</span><span class="p">):</span>
<span class="k">pass</span>
<span class="c1"># These last two methods are not needed in the project, but they can be nice to have! The first one has a layers parameter so that you can use autograd on it</span>
<span class="k">def</span><span class="w"> </span><span class="nf">autograd_compliant_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">autograd_gradient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="k">pass</span>
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