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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
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<h1><center>Maschinelles Lernen für Wirtschaft und Finanzen<br/>Universität Hamburg</center></h1>
<h2><center><b>Aufgabenserie 1</b></center></h2>
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<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
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<h2 id="Inhaltsverzeichnis:">Inhaltsverzeichnis:<a class="anchor-link" href="#Inhaltsverzeichnis:"></a></h2><ul>
<li><a href="#Vorab">Vorab</a></li>
<li><a href="#Aufbau">Aufbau</a></li>
<li><a href="#Aufgabe-1">Aufgabe 1</a></li>
<li><a href="#Aufgabe-2">Aufgabe 2</a></li>
<li><a href="#Aufgabe-3">Aufgabe 3</a></li>
<li><a href="#Anhang">Anhang</a></li>
<li><a href="#Literatur">Literatur</a></li>
</ul>
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<h3 id="Vorab--">Vorab <a class="anchor" id="Vorab"></a><a class="anchor-link" href="#Vorab--"></a></h3><ul>
<li>Ziel dieses Tutorials ist es, einige der wichtigsten Konzepte zu üben, die in den ersten Wochen des ML Kurses behandelt werden.</li>
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<h3 id="Aufbau-">Aufbau <a class="anchor" id="Aufbau"></a><a class="anchor-link" href="#Aufbau-"></a></h3><p><strong>Die Hauptaufgabe dieses Problemsets besteht darin, die Rendite des US-Aktienmarktes vorherzusagen.</strong> Dazu verwenden wir den Datensatz <code>stockmarketdata.RDS</code> von Welch und Goyal (2007), der auf <em>OpenOlat</em> verfügbar ist. Der Datensatz enthält vierteljährliche Renditen des US-Aktienmarktes ($ret$) sowie mehrere andere Variablen, die von Finanzforschern vorgeschlagen wurden, um Aktienrenditen vorherzusagen. Eine Liste aller Variablen zusammen mit einer Beschreibung finden Sie im Anhang. Für das erste Quartal 1999 (<em>date = 19991</em>) enthält er beispielsweise Variablen wie die Rendite des Aktienmarktes ($ret_{t}$), das Dividenden-zu-Preis-Verhältnis ($DP_{t}$), den Kreditspread ($CS_{t}$) und so weiter. Da das Ziel darin besteht, Renditen im folgenden Quartal vorherzusagen, interessieren uns Modelle der Form</p>
<p>$ret_{t+1} = f (DP_{t}, CS_{t}, ...) + ϵ_{t+1}$</p>
<p>Angenommen, Sie sind ein Vermögensverwalter und es ist Ende 1994, dh Sie haben alle Daten vor 1995 zur Verfügung, um Ihr Modell zu trainieren und zu validieren. Ihr Ziel ist es, ein Modell zu entwickeln, das nicht nur in der Stichprobe funktioniert, sondern auch zukünftige Renditen vorhersagen kann (nach 1995).</p>
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<h2 id="Aufgabe-1:-Vorbereitung-und-Analyse-der-Daten-">Aufgabe 1: Vorbereitung und Analyse der Daten <a class="anchor" id="Aufgabe-1"></a><a class="anchor-link" href="#Aufgabe-1:-Vorbereitung-und-Analyse-der-Daten-"></a></h2><ol>
<li><p>Zuerst müssen wir die Daten so ausrichten, dass eine Zeile, die die Merkmale für das Datum t enthält, die Rendite für das Datum $t + 1$ enthält (anstelle der Rendite für das Datum t, wie es derzeit der Fall ist). Dadurch wird sichergestellt, dass wir tatsächlich die Renditen für das nächste Quartal vorhersagen. Hierfür müssen wir die Zeitreihe der Rendite um einen Zeitraum nach vorne verschieben. (Hinweis: Verwenden Sie die Funktion <code>shift()</code>, um eine neue Variable zum Dataframe hinzuzufügen, die die Rendite um einen Zeitraum verschiebt. Entfernen Sie anschließend die alte Zeitreihe der Rendite aus dem Dataframe.)</p>
</li>
<li><p>Entfernen Sie alle Zeilen, die fehlende Werte im Datensatz enthalten. Google bietet viele verschiedene Möglichkeiten, wie Sie dies tun können. Wenn Sie Schwierigkeiten mit dieser Übung haben, tun Sie Folgendes: Verwenden Sie die kombination der Funktionen <code>.isna().sum()</code> auf das DataFrame, um die Summe aller Zeilen zu bestimmen, die fehlende Werte enthalten. Finden Sie die fehlenden Werte für diese Variablen durch Augeninspektion. Beginnen und beenden Sie die Stichprobe so, dass diese Zeilen mit fehlenden Werten nicht enthalten sind. Verwenden Sie anschließend erneut die Funktion <code>.isna().sum()</code>, um sicherzustellen, dass Sie alle fehlenden Werte entfernt haben.</p>
</li>
<li><p>Teilen Sie die Stichprobe in zwei Teile auf. Daten vor 1995 für das Training und die Validierung und Daten nach und einschließlich 1995 für das Out-of-Sample-Testen.</p>
</li>
<li><p>Berechnen Sie die durchschnittliche vierteljährliche Rendite und ihre Standardabweichung in den Trainings- und Testdaten. Gibt es etwas, das erwähnenswert ist?</p>
</li>
<li><p>Berechnen Sie die Korrelationsmatrix für die Trainingsdaten (einschließlich sowohl der Ergebnisse als auch der Merkmale). Gibt es etwas, das erwähnenswert ist?</p>
</li>
</ol>
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<h3 id="Vorbereitung:">Vorbereitung:<a class="anchor-link" href="#Vorbereitung:"></a></h3><ul>
<li>Einlesen und Grundanpassungen der <code>stockmarketdata.rds</code> Daten</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span> <span class="c1"># Paket zum Unterdrücken von "warnings"</span>
<span class="kn">import</span> <span class="nn">pyreadr</span> <span class="c1"># Paket zum einlesen von RDS Datein - https://github.com/ofajardo/pyreadr</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pyreadr</span><span class="o">.</span><span class="n">read_r</span><span class="p">(</span><span class="s1">'stockmarketdata.rds'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="c1"># Extrahieren des verfügbaren Pandas-DataFrame Objekts.</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="c1"># Zeige die ersten 5 Zeilen des DataFrames an.</span>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>date</th>
<th>ret</th>
<th>DP</th>
<th>CS</th>
<th>ntis</th>
<th>cay</th>
<th>TS</th>
<th>svar</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>19291.0</td>
<td>0.050490</td>
<td>-3.367688</td>
<td>0.010357</td>
<td>0.079805</td>
<td>NaN</td>
<td>-0.0083</td>
<td>0.007982</td>
</tr>
<tr>
<th>1</th>
<td>19292.0</td>
<td>0.087235</td>
<td>-3.412851</td>
<td>0.011105</td>
<td>0.116197</td>
<td>NaN</td>
<td>-0.0113</td>
<td>0.008405</td>
</tr>
<tr>
<th>2</th>
<td>19293.0</td>
<td>0.091067</td>
<td>-3.468392</td>
<td>0.012517</td>
<td>0.121390</td>
<td>NaN</td>
<td>-0.0083</td>
<td>0.008056</td>
</tr>
<tr>
<th>3</th>
<td>19294.0</td>
<td>-0.268418</td>
<td>-3.096184</td>
<td>0.012155</td>
<td>0.163522</td>
<td>NaN</td>
<td>0.0037</td>
<td>0.100171</td>
</tr>
<tr>
<th>4</th>
<td>19301.0</td>
<td>0.165884</td>
<td>-3.252345</td>
<td>0.010554</td>
<td>0.145496</td>
<td>NaN</td>
<td>0.0040</td>
<td>0.004662</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="c1"># Definierung einer Funktion, um die numerische Darstellung in das Format 'JJJJ-QX' zu erreichen.</span>
<span class="k">def</span> <span class="nf">convert_to_quarterly_date</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">):</span>
<span class="n">year</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">)</span> <span class="o">//</span> <span class="mi">10</span> <span class="c1"># Ziehen der Information des Jahres</span>
<span class="n">quarter</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numeric_date</span><span class="p">)</span> <span class="o">%</span> <span class="mi">10</span> <span class="c1"># Ziehen der Information des Quartals</span>
<span class="n">quarter_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'Q</span><span class="si">{</span><span class="n">quarter</span><span class="si">}</span><span class="s1">'</span> <span class="c1"># Umwandling der "float" Quartal Daten zu "string" Format</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">year</span><span class="si">}</span><span class="s1">-Q</span><span class="si">{</span><span class="n">quarter</span><span class="si">}</span><span class="s1">'</span> <span class="c1"># Rückgabe der überschriebenen Schreibweise</span>
<span class="c1"># Anwendung der Fuktion auf die Variable "date".</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">convert_to_quarterly_date</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<table border="1" class="dataframe">
<thead>
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<th></th>
<th>date</th>
<th>ret</th>
<th>DP</th>
<th>CS</th>
<th>ntis</th>
<th>cay</th>
<th>TS</th>
<th>svar</th>
</tr>
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<tbody>
<tr>
<th>360</th>
<td>2019-Q1</td>
<td>0.137489</td>
<td>-3.943400</td>
<td>0.010258</td>
<td>-0.023230</td>
<td>-0.039336</td>
<td>0.0017</td>
<td>0.004651</td>
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<th>361</th>
<td>2019-Q2</td>
<td>0.042688</td>
<td>-3.960033</td>
<td>0.010006</td>
<td>-0.012562</td>
<td>-0.033844</td>
<td>-0.0010</td>
<td>0.003271</td>
</tr>
<tr>
<th>362</th>
<td>2019-Q3</td>
<td>0.017042</td>
<td>-3.951689</td>
<td>0.008505</td>
<td>-0.010862</td>
<td>-0.029529</td>
<td>-0.0019</td>
<td>0.005517</td>
</tr>
<tr>
<th>363</th>
<td>2019-Q4</td>
<td>0.090143</td>
<td>-4.015896</td>
<td>0.008410</td>
<td>-0.007222</td>
<td>-0.033609</td>
<td>0.0032</td>
<td>0.002319</td>
</tr>
<tr>
<th>364</th>
<td>2020-Q1</td>
<td>-0.193794</td>
<td>-3.769992</td>
<td>0.012252</td>
<td>-0.007731</td>
<td>-0.050141</td>
<td>0.0058</td>
<td>0.079049</td>
</tr>
</tbody>
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<h3 id="Aufgabe-1.1:">Aufgabe 1.1:<a class="anchor-link" href="#Aufgabe-1.1:"></a></h3><ul>
<li>Anpassung der Rendite Zeitdaten</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Verschiebung der Spalte Rendite um einen Zeitraum t+1.</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'ret'</span><span class="p">]</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Entfernen der Spalte "ret" aus dem DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'ret'</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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<th></th>
<th>date</th>
<th>DP</th>
<th>CS</th>
<th>ntis</th>
<th>cay</th>
<th>TS</th>
<th>svar</th>
<th>ret_next</th>
</tr>
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<th>360</th>
<td>2019-Q1</td>
<td>-3.943400</td>
<td>0.010258</td>
<td>-0.023230</td>
<td>-0.039336</td>
<td>0.0017</td>
<td>0.004651</td>
<td>0.042688</td>
</tr>
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<th>361</th>
<td>2019-Q2</td>
<td>-3.960033</td>
<td>0.010006</td>
<td>-0.012562</td>
<td>-0.033844</td>
<td>-0.0010</td>
<td>0.003271</td>
<td>0.017042</td>
</tr>
<tr>
<th>362</th>
<td>2019-Q3</td>
<td>-3.951689</td>
<td>0.008505</td>
<td>-0.010862</td>
<td>-0.029529</td>
<td>-0.0019</td>
<td>0.005517</td>
<td>0.090143</td>
</tr>
<tr>
<th>363</th>
<td>2019-Q4</td>
<td>-4.015896</td>
<td>0.008410</td>
<td>-0.007222</td>
<td>-0.033609</td>
<td>0.0032</td>
<td>0.002319</td>
<td>-0.193794</td>
</tr>
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<th>364</th>
<td>2020-Q1</td>
<td>-3.769992</td>
<td>0.012252</td>
<td>-0.007731</td>
<td>-0.050141</td>
<td>0.0058</td>
<td>0.079049</td>
<td>NaN</td>
</tr>
</tbody>
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<h3 id="Aufgabe-1.2:">Aufgabe 1.2:<a class="anchor-link" href="#Aufgabe-1.2:"></a></h3><ul>
<li>Anpassung fehlender Datenwerte im DataFrame</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Aufzählung aller NaN's (je Variable) des DataFrame.</span>
<span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>date 0
DP 0
CS 0
ntis 0
cay 92
TS 0
svar 0
ret_next 1
dtype: int64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># Verwerfen aller Zeilen, bei denen Variablen eine NaN Zelle besitzen.</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
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<pre>date 0
DP 0
CS 0
ntis 0
cay 0
TS 0
svar 0
ret_next 0
dtype: int64</pre>
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<h3 id="Aufgabe-1.3:">Aufgabe 1.3:<a class="anchor-link" href="#Aufgabe-1.3:"></a></h3><ul>
<li>Aufteilung des Datensatzes in zwei Teile. Training Datensatz mit Daten vor 1995 und Validierung Datensatz mit den Daten nach 1995 als Out-of-Sample Datensatz.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Erstellen von Variablen mit der Information "1994-Q4" und der Position, welche als Schnittpunkt dienen.</span>
<span class="n">split_date</span> <span class="o">=</span> <span class="s1">'1994-Q4'</span>
<span class="n">split_ind</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">==</span> <span class="n">split_date</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Aufteilung der Daten zu "train_data" und "test_data".</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="n">split_ind</span><span class="p">]</span> <span class="c1"># für in-sample tests</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">split_ind</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]</span> <span class="c1"># für out-of-sample tests</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"train_data besitzt </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span><span class="si">}</span><span class="s2"> Beobachtung."</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"test_data besitzt </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span><span class="si">}</span><span class="s2"> Beobachtung."</span><span class="p">)</span>
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<pre>train_data besitzt 172 Beobachtung.
test_data besitzt 100 Beobachtung.
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<h3 id="Aufgabe-1.4:">Aufgabe 1.4:<a class="anchor-link" href="#Aufgabe-1.4:"></a></h3><ul>
<li>Berechnung der durchschnittlichen vierteljährlichen Rendite und ihre Standardabweichung in den Trainings- und Testdaten.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">train_mean_ret</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># durchschnittliche vierteljährlichen Rendite (train_data)</span>
<span class="n">train_std_ret</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span> <span class="c1"># Standardabweichung der vierteljährlichen Rendite (train_data)</span>
<span class="n">test_mean_ret</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># durchschnittliche vierteljährlichen Rendite (test_data)</span>
<span class="n">test_std_ret</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span> <span class="c1"># Standardabweichung der vierteljährlichen Rendite (test_data)</span>
<span class="c1"># Ausgabe der Ergebnisse</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Trainingsdaten:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Durchschnittliche Rendite: </span><span class="si">{</span><span class="n">train_mean_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Standardabweichung der Rendite: </span><span class="si">{</span><span class="n">train_std_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Testdaten:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Durchschnittliche Rendite: </span><span class="si">{</span><span class="n">test_mean_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Standardabweichung der Rendite: </span><span class="si">{</span><span class="n">test_std_ret</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Trainingsdaten:
Durchschnittliche Rendite: 0.0306
Standardabweichung der Rendite: 0.0763
Testdaten:
Durchschnittliche Rendite: 0.0252
Standardabweichung der Rendite: 0.0823
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<h3 id="Aufgabe-1.5:">Aufgabe 1.5:<a class="anchor-link" href="#Aufgabe-1.5:"></a></h3><ul>
<li>Berechnung der Korrelationsmatrix für die Trainingsdaten (einschließlich sowohl der Ergebnisse als auch der Merkmale).</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Berechnung der Korrelationsmatrix für die Trainingsdaten</span>
<span class="n">train_cor_matrix</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">train_data</span><span class="o">.</span><span class="n">columns</span> <span class="o">!=</span> <span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">'pearson'</span><span class="p">)</span> <span class="c1"># "date" Spalte ausgelassen (Nur numerische Spalten)</span>
<span class="c1"># Ausgabe der Korrelationsmatrix. Werte auf zwei Nachkommastellen aufgerundet.</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Korrelationsmatrix für Trainingsdaten:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">train_cor_matrix</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
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<pre>Korrelationsmatrix für Trainingsdaten:
DP CS ntis cay TS svar ret_next
DP 1.00 0.38 -0.12 -0.21 -0.14 0.11 0.23
CS 0.38 1.00 -0.31 -0.02 0.21 0.22 0.18
ntis -0.12 -0.31 1.00 -0.40 -0.07 -0.12 -0.19
cay -0.21 -0.02 -0.40 1.00 0.46 0.04 0.17
TS -0.14 0.21 -0.07 0.46 1.00 0.08 0.16
svar 0.11 0.22 -0.12 0.04 0.08 1.00 0.13
ret_next 0.23 0.18 -0.19 0.17 0.16 0.13 1.00
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<li><strong>Zusatz</strong>: Grafische Abbildung der Korrelationsmatrix</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">train_data</span><span class="o">.</span><span class="n">columns</span> <span class="o">!=</span> <span class="s1">'date'</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(),</span>
<span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">linewidths</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span>
<span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
<span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">xticklabels</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span>
<span class="n">yticklabels</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">,</span>
<span class="n">fmt</span><span class="o">=</span><span class="s1">'0.2f'</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="s2">"coolwarm"</span><span class="p">)</span>
<span class="n">s</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Korrelationsmatrix der trainingsdaten Variablen'</span><span class="p">)</span>
<span class="n">s</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">'Variablen'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">'Variablen'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<h2 id="Aufgabe-2:-Vorhersage-von-Renditen-">Aufgabe 2: Vorhersage von Renditen <a class="anchor" id="Aufgabe-2"></a><a class="anchor-link" href="#Aufgabe-2:-Vorhersage-von-Renditen-"></a></h2><p>Nachdem die Daten bereinigt wurden, sind Sie bereit, das erste Modell zur Vorhersage von Renditen zu erstellen.</p>
<ol>
<li><p>Verwenden Sie die Trainingsdaten, um ein lineares Modell unter Verwendung aller Variablen (stellen Sie sicher, dass Sie die Datumsvariable ausschließen) anzupassen. Welche Merkmale sind nützlich für die Vorhersage von Renditen?</p>
</li>
<li><p>Berechnen Sie das R² in der Stichprobe sowie den mittleren quadratischen Fehler. Glauben Sie, dass vierteljährliche Renditen leicht vorhergesagt werden können?</p>
</li>
<li><p>Verwenden Sie eine 5-fache Kreuzvalidierung, um eine Schätzung für den mittleren quadratischen Fehler außerhalb der Stichprobe zu erhalten. Vergleichen Sie diese Schätzung mit dem mittleren quadratischen Fehler in der Stichprobe aus "Aufgabe 2.2".</p>
</li>
<li><p>Basierend auf Ihren Ergebnissen aus "Aufgabe 2.1" wählen Sie nur eine Teilmenge der Merkmale aus, um Ihr Modell zu verbessern. Welche Merkmale wählen Sie aus und warum? Berechnen Sie das R² in der Stichprobe sowie den mittleren quadratischen Fehler für dieses Modell und verwenden Sie eine 5-fache Kreuzvalidierung, um eine Schätzung für den mittleren quadratischen Fehler außerhalb der Stichprobe zu erhalten. Vergleichen Sie Ihre Ergebnisse mit dem Modell unter Verwendung aller Merkmale.</p>
</li>
<li><p>Angenommen, Sie verwenden die beiden Modelle, die Sie erstellt haben, um vierteljährliche Renditen in den nächsten 25 Jahren vorherzusagen. Berechnen Sie die mittleren quadratischen Fehler außerhalb der Stichprobe für die Testdaten. Vergleichen Sie diese Fehler mit den Schätzungen für die außerhalb der Stichprobe erhaltenen Fehler aus der k-fachen Kreuzvalidierung. Interpretieren Sie.</p>
</li>
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<h3 id="Aufgabe-2.1:">Aufgabe 2.1:<a class="anchor-link" href="#Aufgabe-2.1:"></a></h3><ul>
<li>MLR unter Verwendung aller Variablen des Trainingdatensatzes</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
<span class="c1"># Alle nutzen außer - entfernen der "Unabhängige Variablen" und der "qualitativen variablen /bzw. level Variablen" - hier aber die "date" Variable!</span>
<span class="c1"># Möglichkeit 1 - doch ohne weitere Anpassung problematisch:</span>
<span class="c1">#X = train_data.drop(columns=['date','ret_next'])</span>
<span class="c1"># DOCH dann fehlt aber der Intercept in der summary !!</span>
<span class="c1"># Möglichkeit 2:</span>
<span class="c1">#X = pd.DataFrame({'intercept': np.ones(train_data.shape[0]), </span>
<span class="c1"># 'DP': train_data['DP'],</span>
<span class="c1"># 'CS': train_data['CS'],</span>
<span class="c1"># 'ntis': train_data['ntis'],</span>
<span class="c1"># 'cay': train_data['cay'],</span>
<span class="c1"># 'TS': train_data['TS'],</span>
<span class="c1"># 'svar': train_data['svar']</span>
<span class="c1"># })</span>
<span class="c1"># Möglichkeit 3: finale Lösung - Füllung der Exogenen Variable</span>
<span class="c1"># Erstellen der Modell-Matrix:</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'date'</span><span class="p">,</span><span class="s1">'ret_next'</span><span class="p">])</span> <span class="c1"># Entfernen der endogenen und qualitativen Variablen</span>
<span class="n">X</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'intercept'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span> <span class="c1"># Setzen der Endogene Variable</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="c1"># OLS Modell füllen.</span>
<span class="n">fit_lm</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span> <span class="c1"># Fit des univariaten linearen Regressionsmodells.</span>
<span class="n">fit_lm</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> <span class="c1"># Ausgabe der Modellstatistik</span>
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<table class="simpletable">
<caption>OLS Regression Results</caption>
<tr>
<th>Dep. Variable:</th> <td>ret_next</td> <th> R-squared: </th> <td> 0.129</td>
</tr>
<tr>
<th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.097</td>
</tr>
<tr>
<th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 4.063</td>
</tr>
<tr>
<th>Date:</th> <td>Sun, 14 Apr 2024</td> <th> Prob (F-statistic):</th> <td>0.000793</td>
</tr>
<tr>
<th>Time:</th> <td>20:47:06</td> <th> Log-Likelihood: </th> <td> 210.80</td>
</tr>
<tr>
<th>No. Observations:</th> <td> 172</td> <th> AIC: </th> <td> -407.6</td>
</tr>
<tr>
<th>Df Residuals:</th> <td> 165</td> <th> BIC: </th> <td> -385.6</td>
</tr>
<tr>
<th>Df Model:</th> <td> 6</td> <th> </th> <td> </td>
</tr>
<tr>
<th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td>
</tr>
</table>
<table class="simpletable">
<tr>
<td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P&gt;|t|</th> <th>[0.025</th> <th>0.975]</th>
</tr>
<tr>
<th>intercept</th> <td> 0.2968</td> <td> 0.097</td> <td> 3.063</td> <td> 0.003</td> <td> 0.106</td> <td> 0.488</td>
</tr>
<tr>
<th>DP</th> <td> 0.0839</td> <td> 0.028</td> <td> 3.031</td> <td> 0.003</td> <td> 0.029</td> <td> 0.139</td>
</tr>
<tr>
<th>CS</th> <td> 0.4750</td> <td> 1.739</td> <td> 0.273</td> <td> 0.785</td> <td> -2.958</td> <td> 3.908</td>
</tr>
<tr>
<th>ntis</th> <td> -0.3945</td> <td> 0.410</td> <td> -0.961</td> <td> 0.338</td> <td> -1.205</td> <td> 0.416</td>
</tr>
<tr>
<th>cay</th> <td> 0.4215</td> <td> 0.306</td> <td> 1.379</td> <td> 0.170</td> <td> -0.182</td> <td> 1.025</td>
</tr>
<tr>
<th>TS</th> <td> 0.6310</td> <td> 0.482</td> <td> 1.309</td> <td> 0.192</td> <td> -0.320</td> <td> 1.583</td>
</tr>
<tr>
<th>svar</th> <td> 0.8027</td> <td> 0.828</td> <td> 0.969</td> <td> 0.334</td> <td> -0.832</td> <td> 2.438</td>
</tr>
</table>
<table class="simpletable">
<tr>
<th>Omnibus:</th> <td>26.211</td> <th> Durbin-Watson: </th> <td> 1.810</td>
</tr>
<tr>
<th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 40.178</td>
</tr>
<tr>
<th>Skew:</th> <td>-0.823</td> <th> Prob(JB): </th> <td>1.89e-09</td>
</tr>
<tr>
<th>Kurtosis:</th> <td> 4.701</td> <th> Cond. No. </th> <td>1.10e+03</td>
</tr>
</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.1e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems.
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<h3 id="Aufgabe-2.2:">Aufgabe 2.2:<a class="anchor-link" href="#Aufgabe-2.2:"></a></h3><ul>
<li>$R^{2}$-Wert des Trainingdatatensatzes.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Das train_data R^2 beträgt: </span><span class="si">{</span><span class="n">fit_lm</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Das train_data R^2 beträgt: 0.1287
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<ul>
<li>Bestimmung des in-sample MSE Wertes.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">#from sklearn.metrics import mean_squared_error # kann benutzt werden</span>
<span class="c1">#Oder wie hier, selbst definiert werden:</span>
<span class="c1">##### Computing the MSE for the Auto Data: #####</span>
<span class="c1"># Compute predicted values y_head for training data</span>
<span class="n">y_head</span> <span class="o">=</span> <span class="n">fit_lm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># Function to compute the mean squared error (MSE)</span>
<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_head</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">y</span> <span class="o">-</span> <span class="n">y_head</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Compute the mean squared error in the training data</span>
<span class="n">MSE_train_data</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">],</span> <span class="n">y_head</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"in-sample (MSE): </span><span class="si">{</span><span class="n">MSE_train_data</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>in-sample (MSE): 0.0050
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<h3 id="Aufgabe-2.3:">Aufgabe 2.3:<a class="anchor-link" href="#Aufgabe-2.3:"></a></h3><ul>
<li>Vergleich des out-of-sample MSE's mit dem 5-fach Kreuzvalidierung MSE's:</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># Separate features and target variables</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">,</span><span class="s1">'date'</span><span class="p">])</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">,</span><span class="s1">'date'</span><span class="p">])</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="c1"># Train the model on the training data</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Predict on the test data</span>
<span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Calculate the mean squared error (MSE) on the test data</span>
<span class="n">mse_test</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE: </span><span class="si">{</span><span class="n">mse_test</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Perform 5-fold cross-validation and calculate the mean of MSEs</span>
<span class="n">mse_cv</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Cross-validated MSE: </span><span class="si">{</span><span class="n">mse_cv</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Out-of-sample MSE: 0.0086
Cross-validated MSE: 0.0081
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<h3 id="Aufgabe-2.4:">Aufgabe 2.4:<a class="anchor-link" href="#Aufgabe-2.4:"></a></h3><ul>
<li>Bestimmung des out-of-sample MSE's und mittel 5-fache Kreuzvalidierung den Cross-validation MSE<ul>
<li>Auswertung der Modellgüte mittels verschiedener variablen Kombinationen.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
<span class="c1"># Select subset of features</span>
<span class="n">selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'DP'</span><span class="p">]</span>
<span class="c1"># Separate features and target variables for selected features</span>
<span class="n">X_train_selected</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
<span class="n">X_test_selected</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="c1"># Train the model on the training data using selected features</span>
<span class="n">model_selected</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">model_selected</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Predict on the training data</span>
<span class="n">y_pred_train_selected</span> <span class="o">=</span> <span class="n">model_selected</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">)</span>
<span class="c1"># Compute in-sample R^2</span>
<span class="n">r2_in_sample_selected</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample R^2 mit der Variable (DP): </span><span class="si">{</span><span class="n">r2_in_sample_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compute mean squared error (MSE) for the training data</span>
<span class="n">mse_train_selected</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample MSE mit der Variable (DP): </span><span class="si">{</span><span class="n">mse_train_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Perform 5-fold cross-validation and compute out-of-sample MSE for the selected features</span>
<span class="n">mse_cv_selected</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model_selected</span><span class="p">,</span> <span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE bei 5-fold cross-validation mit der Variable (DP): </span><span class="si">{</span><span class="n">mse_cv_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>In-sample R^2 mit der Variable (DP): 0.0541
In-sample MSE mit der Variable (DP): 0.0055
Out-of-sample MSE bei 5-fold cross-validation mit der Variable (DP): 0.0056
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<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
<span class="c1"># Select subset of features</span>
<span class="n">selected_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'DP'</span><span class="p">,</span> <span class="s1">'cay'</span><span class="p">]</span>
<span class="c1"># Separate features and target variables for selected features</span>
<span class="n">X_train_selected</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
<span class="n">X_test_selected</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="n">selected_features</span><span class="p">]</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span>
<span class="c1"># Train the model on the training data using selected features</span>
<span class="n">model_selected</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">model_selected</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Predict on the training data</span>
<span class="n">y_pred_train_selected</span> <span class="o">=</span> <span class="n">model_selected</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_selected</span><span class="p">)</span>
<span class="c1"># Compute in-sample R^2</span>
<span class="n">r2_in_sample_selected</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample R^2 mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">r2_in_sample_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compute mean squared error (MSE) for the training data</span>
<span class="n">mse_train_selected</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train_selected</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample MSE mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">mse_train_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Perform 5-fold cross-validation and compute out-of-sample MSE for the selected features</span>
<span class="n">mse_cv_selected</span> <span class="o">=</span> <span class="o">-</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">model_selected</span><span class="p">,</span> <span class="n">X_train_selected</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample MSE bei 5-fold cross-validation mit den Variablen (DP+cay): </span><span class="si">{</span><span class="n">mse_cv_selected</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>In-sample R^2 mit den Variablen (DP+cay): 0.1040
In-sample MSE mit den Variablen (DP+cay): 0.0052
Out-of-sample MSE bei 5-fold cross-validation mit den Variablen (DP+cay): 0.0053
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<h2 id="Aufgabe-3:-Vorhersage-der-Richtung-des-Aktienmarktes-">Aufgabe 3: Vorhersage der Richtung des Aktienmarktes <a class="anchor" id="Aufgabe-3"></a><a class="anchor-link" href="#Aufgabe-3:-Vorhersage-der-Richtung-des-Aktienmarktes-"></a></h2><p>Statt Renditen quantitativ vorherzusagen, nehmen Sie nun an, dass Sie die Richtung des Aktienmarktes vorhersagen möchten, d. h. ob die Aktien steigen oder fallen. Basierend auf diesen Vorhersagen möchten Sie entweder in Aktien investieren oder nicht.</p>
<ol>
<li><p>Erstellen Sie eine neue Variable sowohl in den Trainings- als auch in den Testdaten, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst.</p>
</li>
<li><p>Berechnen Sie den Anteil positiver Aktienrenditen sowohl in den Trainings- als auch in den Testdaten.</p>
</li>
<li><p>Passen Sie eine logistische Regression an die Trainingsdaten an, um die Richtung des Aktienmarktes vorherzusagen (stellen Sie sicher, dass Sie die Datumsvariable und die alte quantitative Renditevariable ausschließen). Welche Merkmale sind nützliche Prädiktoren? Berechnen Sie die In-Sample-Genauigkeit und die Fehlerquote. Glauben Sie, dass Sie ein gutes Modell zur Vorhersage der Richtung des Aktienmarktes erstellt haben?</p>
</li>
<li><p>Angenommen, Sie verwenden das von Ihnen erstellte Modell, um die Richtung des Aktienmarktes in den nächsten 25 Jahren vorherzusagen. Berechnen Sie die außerhalb der Stichprobe liegende Genauigkeit und Fehlerquote für die Testdaten. Vergleichen Sie diese Ergebnisse mit den In-Sample-Statistiken. Glauben Sie, dass Ihr Modell gut außerhalb der Stichprobe funktioniert? Interpretieren Sie die Ergebnisse.</p>
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<h3 id="Aufgabe-3.1:">Aufgabe 3.1:<a class="anchor-link" href="#Aufgabe-3.1:"></a></h3><ul>
<li>Erstelle eine neue Variable, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst.</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span> <span class="c1"># to hide the warning message - it's fixed here.</span>
<span class="c1"># Erstelle eine neue Variable, die 1 ist, wenn die Rendite größer als Null ist, und 0 sonst</span>
<span class="c1"># raises a Warning message: A value is trying to be set on a copy of a slice from a DataFrame.</span>
<span class="c1">#train_data['return_positive'] = train_data['ret_next'].apply(lambda x: 1 if x &gt; 0 else 0)</span>
<span class="c1">#test_data['return_positive'] = test_data['ret_next'].apply(lambda x: 1 if x &gt; 0 else 0)</span>
<span class="c1"># .iloc solution to fix the warning:</span>
<span class="n">train_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'return_positive'</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">test_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">'return_positive'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'ret_next'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">train_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th></th>
<th>date</th>
<th>DP</th>
<th>CS</th>
<th>ntis</th>
<th>cay</th>
<th>TS</th>
<th>svar</th>
<th>ret_next</th>
<th>return_positive</th>
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<th>92</th>
<td>1952-Q1</td>
<td>-2.842696</td>
<td>0.005328</td>
<td>0.032094</td>
<td>-0.010595</td>
<td>0.0104</td>
<td>0.002102</td>
<td>0.038275</td>
<td>1</td>
</tr>
<tr>
<th>93</th>
<td>1952-Q2</td>
<td>-2.845711</td>
<td>0.005425</td>
<td>0.027731</td>
<td>0.000055</td>
<td>0.0089</td>
<td>0.001660</td>
<td>-0.004980</td>
<td>0</td>
</tr>
<tr>
<th>94</th>
<td>1952-Q3</td>
<td>-2.828741</td>
<td>0.005521</td>
<td>0.031038</td>
<td>-0.000695</td>
<td>0.0106</td>
<td>0.001076</td>
<td>0.102295</td>
<td>1</td>
</tr>
<tr>
<th>95</th>
<td>1952-Q4</td>
<td>-2.936193</td>
<td>0.005231</td>
<td>0.026535</td>
<td>-0.015950</td>
<td>0.0070</td>
<td>0.001753</td>
<td>-0.035683</td>
<td>0</td>
</tr>
<tr>
<th>96</th>
<td>1953-Q1</td>
<td>-2.886819</td>
<td>0.004354</td>
<td>0.024013</td>
<td>-0.019021</td>
<td>0.0093</td>
<td>0.001574</td>
<td>-0.032102</td>
<td>0</td>
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<h3 id="Aufgabe-3.2:">Aufgabe 3.2:<a class="anchor-link" href="#Aufgabe-3.2:"></a></h3><ul>
<li>Anteil der positiven Aktienrenditen in den Trainingsdaten &amp; Testdaten</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Berechne den Anteil der positiven Aktienrenditen in den Trainingsdaten</span>
<span class="n">positive_proportion_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Anteil der positiven Aktienrenditen in den Trainingsdaten: </span><span class="si">{</span><span class="n">positive_proportion_train</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Berechne den Anteil der positiven Aktienrenditen in den Testdaten</span>
<span class="n">positive_proportion_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Anteil der positiven Aktienrenditen in den Testdaten: </span><span class="si">{</span><span class="n">positive_proportion_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Anteil der positiven Aktienrenditen in den Trainingsdaten: 0.69
Anteil der positiven Aktienrenditen in den Testdaten: 0.73
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<h3 id="Aufgabe-3.3:">Aufgabe 3.3:<a class="anchor-link" href="#Aufgabe-3.3:"></a></h3><ul>
<li>Logistische Regression über die Trainingsdaten, um die Richtung des Aktienmarktes vorherzusagen.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">confusion_matrix</span>
<span class="c1"># Separate features and target variables</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">,</span> <span class="s1">'date'</span><span class="p">,</span> <span class="s1">'ret_next'</span><span class="p">])</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span>
<span class="c1"># Train the logistic regression model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Predict on the training data</span>
<span class="n">y_pred_train</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="c1"># Compute in-sample accuracy and error rate</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_pred_train</span><span class="p">)</span>
<span class="n">error_rate</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracy</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample error rate: </span><span class="si">{</span><span class="n">error_rate</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compute useful predictors</span>
<span class="n">coefficients</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">useful_predictors</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">coefficients</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Useful predictors:"</span><span class="p">,</span> <span class="n">useful_predictors</span><span class="p">)</span> <span class="c1"># Variablen die nicht-null Koeffizienten sind, sind nützlich.</span>
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<pre>In-sample accuracy: 0.69
In-sample error rate: 0.31
Useful predictors: Index(['DP', 'CS', 'ntis', 'cay', 'TS', 'svar'], dtype='object')
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<h3 id="Aufgabe-3.4:">Aufgabe 3.4:<a class="anchor-link" href="#Aufgabe-3.4:"></a></h3><ul>
<li>Äquivalent hierzu werden nun die Testdaten genutzt um Out-of-sample accuracy und error rate zu bestimmen und anschließend zu vergleichen mit den Ergebnissen aus Aufgabe 3.3.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Separate features and target variables for test data</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">,</span> <span class="s1">'date'</span><span class="p">,</span> <span class="s1">'ret_next'</span><span class="p">])</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s1">'return_positive'</span><span class="p">]</span>
<span class="c1"># Predict on the test data</span>
<span class="n">y_pred_test</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Compute out-of-sample accuracy and error rate</span>
<span class="n">accuracy_test</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred_test</span><span class="p">)</span>
<span class="n">error_rate_test</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracy_test</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample accuracy: </span><span class="si">{</span><span class="n">accuracy_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Out-of-sample error rate: </span><span class="si">{</span><span class="n">error_rate_test</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compare with in-sample statistics</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Comparison with in-sample statistics:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"In-sample error rate: </span><span class="si">{</span><span class="n">error_rate</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Out-of-sample accuracy: 0.73
Out-of-sample error rate: 0.27
Comparison with in-sample statistics:
In-sample accuracy: 0.69
In-sample error rate: 0.31
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<h2 id="Anhang-">Anhang <a class="anchor" id="Anhang"></a><a class="anchor-link" href="#Anhang-"></a></h2><p>Der Datensatz enthält die folgenden Variablen:</p>
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<li><strong>ret</strong>: Die vierteljährliche Rendite des US-Aktienmarktes (eine Zahl von 0,01 entspricht einer Rendite von 1% pro Quartal)
date: Das Datum im Format JJJJQ (19941 bedeutet das erste Quartal 1994)</li>
<li><strong>DP</strong>: Das Dividenden-zu-Preis-Verhältnis des Aktienmarktes (eine Bewertungsmessung, ob die Preise im Verhältnis zu den gezahlten Dividenden hoch oder niedrig sind)</li>
<li><strong>CS</strong>: Der Kreditspread definiert als die Differenz der Renditen zwischen hoch bewerteten Unternehmensanleihen (sichere Anlagen) und niedrig bewerteten Unternehmensanleihen (Unternehmen, die möglicherweise bankrott gehen). CS misst die zusätzliche Rendite, die Investoren für Investitionen in riskante Unternehmen im Vergleich zu etablierten Unternehmen mit geringeren Risiken verlangen.</li>
<li><strong>ntis</strong>: Ein Maß für die Aktivität bei der Ausgabe von Unternehmensanleihen (IPOs, Rückkäufe von Aktien,...)</li>
<li><strong>cay</strong>: Ein Maß für das Verhältnis von Vermögen zu Verbrauch (wie viel wird im Verhältnis zum Gesamtvermögen verbraucht)</li>
<li><strong>TS</strong>: Der Term Spread ist die Differenz zwischen der langfristigen Rendite von Staatsanleihen und kurzfristigen Renditen.</li>
<li><strong>svar</strong>: Ein Maß für die Varianz des Aktienmarktes
Für eine vollständige Beschreibung der Daten siehe Welch und Goyal (2007). Google ist auch sehr hilfreich, wenn Sie mehr über die Variablen erfahren möchten.</li>
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<h2 id="Literatur-">Literatur <a class="anchor" id="Literatur"></a><a class="anchor-link" href="#Literatur-"></a></h2><p>Welch, I. and A. Goyal (2007, 03). A Comprehensive Look at The Empirical Performance of Equity
Premium Prediction. The Review of Financial Studies 21 (4), 1455–1508.</p>
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