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FYS-STK4155/doc/LectureNotes/exercisesweek41.html
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lars f817e2c57a Do ex for week 41
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| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
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/*-----------------------------------------------------------------------------
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| Copyright (c) 2014-2017, PhosphorJS Contributors
|
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| Copyright (c) 2014-2017, PhosphorJS Contributors
|
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* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
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/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
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| The full license is in the file LICENSE, distributed with this software.
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* Copyright (c) Jupyter Development Team.
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*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
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|
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| Copyright (c) 2014-2017, PhosphorJS Contributors
|
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| Copyright (c) Jupyter Development Team.
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/* Icons urls */
:root {
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KICA8ZyBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiM0MTQxNDEiPgogICAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiAgPC9nPgoKICA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiBmaWxsPSIjRkZGIj4KICAgIDxjaXJjbGUgY2xhc3M9InN0MiIgY3g9IjUuNSIgY3k9IjE0LjUiIHI9IjEuNSIvPgogICAgPHJlY3QgeD0iMTIiIHk9IjQiIGNsYXNzPSJzdDIiIHdpZHRoPSIxIiBoZWlnaHQ9IjgiLz4KICAgIDxyZWN0IHg9IjguNSIgeT0iNy41IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjg2NiAtMC41IDAuNSAwLjg2NiAtMi4zMjU1IDcuMzIxOSkiIGNsYXNzPSJzdDIiIHdpZHRoPSI4IiBoZWlnaHQ9IjEiLz4KICAgIDxyZWN0IHg9IjEyIiB5PSI0IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjUgLTAuODY2IDAuODY2IDAuNSAtMC42Nzc5IDE0LjgyNTIpIiBjbGFzcz0ic3QyIiB3aWR0aD0iMSIgaGVpZ2h0PSI4Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
0 2px 2px 0 var(--jp-shadow-penumbra-color),
0 1px 5px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
0 4px 5px 0 var(--jp-shadow-penumbra-color),
0 1px 10px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
0 6px 10px 0 var(--jp-shadow-penumbra-color),
0 1px 18px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
0 8px 10px 1px var(--jp-shadow-penumbra-color),
0 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
0 12px 17px 2px var(--jp-shadow-penumbra-color),
0 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
0 16px 24px 2px var(--jp-shadow-penumbra-color),
0 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
0 20px 31px 3px var(--jp-shadow-penumbra-color),
0 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
0 24px 38px 3px var(--jp-shadow-penumbra-color),
0 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-inverse-border-color: var(--md-grey-600);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
'Segoe UI Symbol';
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-900);
--jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
'Segoe UI Emoji', 'Segoe UI Symbol';
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-900);
--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-900);
--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-inverse-border-color);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #a2f;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #a2f;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/*
RTC user specific colors.
These colors are used for the cursor, username in the editor,
and the icon of the user.
*/
--jp-collaborator-color1: #ffad8e;
--jp-collaborator-color2: #dac83d;
--jp-collaborator-color3: #72dd76;
--jp-collaborator-color4: #00e4d0;
--jp-collaborator-color5: #45d4ff;
--jp-collaborator-color6: #e2b1ff;
--jp-collaborator-color7: #ff9de6;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
/* Button colors */
--jp-accept-color-normal: var(--md-blue-700);
--jp-accept-color-hover: var(--md-blue-800);
--jp-accept-color-active: var(--md-blue-900);
--jp-warn-color-normal: var(--md-red-700);
--jp-warn-color-hover: var(--md-red-800);
--jp-warn-color-active: var(--md-red-900);
--jp-reject-color-normal: var(--md-grey-600);
--jp-reject-color-hover: var(--md-grey-700);
--jp-reject-color-active: var(--md-grey-800);
/* File or activity icons and switch semantic variables */
--jp-jupyter-icon-color: #f37626;
--jp-notebook-icon-color: #f37626;
--jp-json-icon-color: var(--md-orange-700);
--jp-console-icon-background-color: var(--md-blue-700);
--jp-console-icon-color: white;
--jp-terminal-icon-background-color: var(--md-grey-800);
--jp-terminal-icon-color: var(--md-grey-200);
--jp-text-editor-icon-color: var(--md-grey-700);
--jp-inspector-icon-color: var(--md-grey-700);
--jp-switch-color: var(--md-grey-400);
--jp-switch-true-position-color: var(--md-orange-900);
}
</style>
<style type="text/css">
/* Force rendering true colors when outputing to pdf */
* {
-webkit-print-color-adjust: exact;
}
/* Misc */
a.anchor-link {
display: none;
}
/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
}
.cm-editor.cm-s-jupyter .highlight pre {
/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
padding: var(--jp-code-padding) 4px;
margin: 0;
font-family: inherit;
font-size: inherit;
line-height: inherit;
color: inherit;
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.jp-OutputArea-output pre {
line-height: inherit;
font-family: inherit;
}
.jp-RenderedText pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
}
/* Hiding the collapser by default */
.jp-Collapser {
display: none;
}
@page {
margin: 0.5in; /* Margin for each printed piece of paper */
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@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
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.jp-Mermaid:not(.jp-RenderedMermaid) {
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.jp-RenderedMermaid {
overflow: auto;
display: flex;
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.jp-RenderedMermaid.jp-mod-warning {
width: auto;
padding: 0.5em;
margin-top: 0.5em;
border: var(--jp-border-width) solid var(--jp-warn-color2);
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.jp-RenderedMermaid-Details > pre {
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.jp-RenderedMermaid-Summary {
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<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=bcb25e64">
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</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
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<h1 id="Exercises-week-41">Exercises week 41<a class="anchor-link" href="#Exercises-week-41"></a></h1><p><strong>Python Code can be found at <a href="https://github.uio.no/larsbog/FYS-STK4155">https://github.uio.no/larsbog/FYS-STK4155</a></strong></p>
<p><strong>October 6-10, 2025</strong></p>
<p>Date: <strong>Deadline is Friday October 10 at midnight</strong></p>
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<h1 id="Overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week<a class="anchor-link" href="#Overarching-aims-of-the-exercises-this-week"></a></h1><p>This week, you will implement the entire feed-forward pass of a neural network! Next week you will compute the gradient of the network by implementing back-propagation manually, and by using autograd which does back-propagation for you (much easier!). Next week, you will also use the gradient to optimize the network with a gradient method! However, there is an optional exercise this week to get started on training the network and getting good results!</p>
<p>We recommend that you do the exercises this week by editing and running this notebook file, as it includes some checks along the way that you have implemented the pieces of the feed-forward pass correctly, and running small parts of the code at a time will be important for understanding the methods.</p>
<p>If you have trouble running a notebook, you can run this notebook in google colab instead (<a href="https://colab.research.google.com/drive/1zKibVQf-iAYaAn2-GlKfgRjHtLnPlBX4#offline=true&amp;sandboxMode=true">https://colab.research.google.com/drive/1zKibVQf-iAYaAn2-GlKfgRjHtLnPlBX4#offline=true&amp;sandboxMode=true</a>), an updated link will be provided on the course discord (you can also send an email to k.h.fredly@fys.uio.no if you encounter any trouble), though we recommend that you set up VSCode and your python environment to run code like this locally.</p>
<p>First, here are some functions you are going to need, don't change this cell. If you are unable to import autograd, just swap in normal numpy until you want to do the final optional exercise.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">autograd.numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span> <span class="c1"># We need to use this numpy wrapper to make automatic differentiation work later</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">datasets</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="c1"># Defining some activation functions</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ReLU</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">z</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">z</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">softmax</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute softmax values for each set of scores in the rows of the matrix z.</span>
<span class="sd"> Used with batched input data."""</span>
<span class="n">e_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="k">return</span> <span class="n">e_z</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">e_z</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">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">softmax_vec</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compute softmax values for each set of scores in the vector z.</span>
<span class="sd"> Use this function when you use the activation function on one vector at a time"""</span>
<span class="n">e_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">z</span><span class="p">))</span>
<span class="k">return</span> <span class="n">e_z</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">e_z</span><span class="p">)</span>
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<h1 id="Exercise-1">Exercise 1<a class="anchor-link" href="#Exercise-1"></a></h1><p>In this exercise you will compute the activation of the first layer. You only need to change the code in the cells right below an exercise, the rest works out of the box. Feel free to make changes and see how stuff works though!</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">2024</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># network input. This is a single input with two features</span>
<span class="n">W1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># first layer weights</span>
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<p><strong>a)</strong> Given the shape of the first layer weight matrix, what is the input shape of the neural network? What is the output shape of the first layer?</p>
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The input shape is (batch_size, 2) and the output shape of the first layer is (batch_size, 4)
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<p><strong>b)</strong> Define the bias of the first layer, <code>b1</code>with the correct shape. (Run the next cell right after the previous to get the random generated values to line up with the test solution below)</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">b1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="c1"># first layer biases</span>
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<p><strong>c)</strong> Compute the intermediary <code>z1</code> for the first layer</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">z1</span> <span class="o">=</span> <span class="n">W1</span> <span class="o">@</span> <span class="n">x</span> <span class="o">+</span> <span class="n">b1</span>
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<p><strong>d)</strong> Compute the activation <code>a1</code> for the first layer using the ReLU activation function defined earlier.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">a1</span> <span class="o">=</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">z1</span><span class="p">)</span>
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<p>Confirm that you got the correct activation with the test below. Make sure that you define <code>b1</code> with the randn function right after you define <code>W1</code>.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">sol1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.60610368</span><span class="p">,</span> <span class="mf">4.0076268</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.56469864</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a1</span><span class="p">,</span> <span class="n">sol1</span><span class="p">))</span>
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<pre>True
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<h1 id="Exercise-2">Exercise 2<a class="anchor-link" href="#Exercise-2"></a></h1><p>Now we will add a layer to the network with an output of length 8 and ReLU activation.</p>
<p><strong>a)</strong> What is the input of the second layer? What is its shape?</p>
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The input of the second layer is the output of layer 1, and its shape is (batch_size, 4)
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<p><strong>b)</strong> Define the weight and bias of the second layer with the right shapes.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">W2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">b2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span>
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<p><strong>c)</strong> Compute the intermediary <code>z2</code> and activation <code>a2</code> for the second layer.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">z2</span> <span class="o">=</span> <span class="n">W2</span> <span class="o">@</span> <span class="n">a1</span> <span class="o">+</span> <span class="n">b2</span>
<span class="n">a2</span> <span class="o">=</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">z2</span><span class="p">)</span>
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<p>Confirm that you got the correct activation shape with the test below.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a2</span><span class="p">)),</span> <span class="mf">2980.9579870417283</span><span class="p">)</span>
<span class="p">)</span> <span class="c1"># This should evaluate to True if a2 has the correct shape :)</span>
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<pre>True
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<h1 id="Exercise-3">Exercise 3<a class="anchor-link" href="#Exercise-3"></a></h1><p>We often want our neural networks to have many layers of varying sizes. To avoid writing very long and error-prone code where we explicitly define and evaluate each layer we should keep all our layers in a single variable which is easy to create and use.</p>
<p><strong>a)</strong> Complete the function below so that it returns a list <code>layers</code> of weight and bias tuples <code>(W, b)</code> for each layer, in order, with the correct shapes that we can use later as our network parameters.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">create_layers</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">):</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">network_input_size</span>
<span class="k">for</span> <span class="n">layer_output_size</span> <span class="ow">in</span> <span class="n">layer_output_sizes</span><span class="p">:</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">layer_output_size</span><span class="p">,</span> <span class="n">i_size</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">layer_output_size</span><span class="p">)</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">layer_output_size</span>
<span class="k">return</span> <span class="n">layers</span>
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<p><strong>b)</strong> Comple the function below so that it evaluates the intermediary <code>z</code> and activation <code>a</code> for each layer, with ReLU actication, and returns the final activation <code>a</code>. This is the complete feed-forward pass, a full neural network!</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">feed_forward_all_relu</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="nb">input</span>
<span class="k">for</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">:</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
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<p><strong>c)</strong> Create a network with input size 8 and layers with output sizes 10, 16, 6, 2. Evaluate it and make sure that you get the correct size vectors along the way.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">input_size</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">layer_output_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">input_size</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="n">create_layers</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">)</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_all_relu</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">predict</span><span class="p">)</span>
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<pre>[5.36337158 0. ]
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<p><strong>d)</strong> Why is a neural network with no activation functions always mathematically equivelent to a neural network with only one layer?</p>
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Because without an activation function, the output of every layer is a linear combination of inputs. And as the linear combination of linear combinations is also a linear combination, the entire network is just a single linear combination of the input.
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<h1 id="Exercise-4---Custom-activation-for-each-layer">Exercise 4 - Custom activation for each layer<a class="anchor-link" href="#Exercise-4---Custom-activation-for-each-layer"></a></h1>
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<p>So far, every layer has used the same activation, ReLU. We often want to use other types of activation however, so we need to update our code to support multiple types of activation functions. Make sure that you have completed every previous exercise before trying this one.</p>
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<p><strong>a)</strong> Complete the <code>feed_forward</code> function which accepts a list of activation functions as an argument, and which evaluates these activation functions at each layer.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">feed_forward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="nb">input</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">activation_func</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">W</span> <span class="o">@</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">activation_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
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<p><strong>b)</strong> You are now given a list with three activation functions, two ReLU and one sigmoid. (Don't call them yet! you can make a list with function names as elements, and then call these elements of the list later. If you add other functions than the ones defined at the start of the notebook, make sure everything is defined using autograd's numpy wrapper, like above, since we want to use automatic differentiation on all of these functions later.)</p>
<p>Evaluate a network with three layers and these activation functions.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">network_input_size</span> <span class="o">=</span> <span class="mi">8</span>
<span class="n">layer_output_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">activation_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">ReLU</span><span class="p">,</span> <span class="n">ReLU</span><span class="p">,</span> <span class="n">sigmoid</span><span class="p">]</span>
<span class="n">layers</span> <span class="o">=</span> <span class="n">create_layers</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">)</span>
<span class="n">feed_forward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
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<pre>array([0.05228192, 1. , 0.59999186, 0.93457593, 0.99786026,
0.99999997])</pre>
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<p><strong>c)</strong> How does the output of the network change if you use sigmoid in the hidden layers and ReLU in the output layer?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">activation_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">sigmoid</span><span class="p">,</span> <span class="n">ReLU</span><span class="p">]</span>
<span class="n">feed_forward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
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<pre>array([0. , 2.72771082, 4.25366266, 0.65139649, 0. ,
0.98143355])</pre>
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<h1 id="Exercise-5---Processing-multiple-inputs-at-once">Exercise 5 - Processing multiple inputs at once<a class="anchor-link" href="#Exercise-5---Processing-multiple-inputs-at-once"></a></h1>
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<p>So far, the feed forward function has taken one input vector as an input. This vector then undergoes a linear transformation and then an element-wise non-linear operation for each layer. This approach of sending one vector in at a time is great for interpreting how the network transforms data with its linear and non-linear operations, but not the best for numerical efficiency. Now, we want to be able to send many inputs through the network at once. This will make the code a bit harder to understand, but it will make it faster, and more compact. It will be worth the trouble.</p>
<p>To process multiple inputs at once, while still performing the same operations, you will only need to flip a couple things around.</p>
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<p><strong>a)</strong> Complete the function <code>create_layers_batch</code> so that the weight matrix is the transpose of what it was when you only sent in one input at a time.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">create_layers_batch</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">):</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">network_input_size</span>
<span class="k">for</span> <span class="n">layer_output_size</span> <span class="ow">in</span> <span class="n">layer_output_sizes</span><span class="p">:</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">i_size</span><span class="p">,</span> <span class="n">layer_output_size</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">layer_output_size</span><span class="p">)</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
<span class="n">i_size</span> <span class="o">=</span> <span class="n">layer_output_size</span>
<span class="k">return</span> <span class="n">layers</span>
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<p><strong>b)</strong> Make a matrix of inputs with the shape (number of features, number of inputs), you choose the number of inputs and features per input. Then complete the function <code>feed_forward_batch</code> so that you can process this matrix of inputs with only one matrix multiplication and one broadcasted vector addition per layer. (Hint: You will only need to swap two variable around from your previous implementation, but remember to test that you get the same results for equivelent inputs!)</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">feed_forward_batch</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">inputs</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="n">activation_func</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="n">W</span> <span class="o">+</span> <span class="n">b</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">activation_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
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<p><strong>c)</strong> Create and evaluate a neural network with 4 inputs and layers with output sizes 12, 10, 3 and activations ReLU, ReLU, softmax.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">network_input_size</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">layer_output_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="n">activation_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">ReLU</span><span class="p">,</span> <span class="n">ReLU</span><span class="p">,</span> <span class="n">softmax</span><span class="p">]</span>
<span class="n">layers</span> <span class="o">=</span> <span class="n">create_layers_batch</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">network_input_size</span><span class="p">)</span>
<span class="n">feed_forward_batch</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[18]:</div>
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<pre>array([[4.04577472e-02, 2.35901721e-04, 9.59306351e-01],
[2.45071088e-03, 2.87778858e-05, 9.97520511e-01],
[6.62736443e-01, 3.36936307e-01, 3.27249462e-04],
...,
[1.12888793e-01, 6.92686503e-04, 8.86418520e-01],
[4.33867835e-03, 6.20348500e-06, 9.95655118e-01],
[9.13896494e-01, 8.07059531e-03, 7.80329107e-02]], shape=(1000, 3))</pre>
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<p>You should use this batched approach moving forward, as it will lead to much more compact code. However, remember that each input is still treated separately, and that you will need to keep in mind the transposed weight matrix and other details when implementing backpropagation.</p>
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<h1 id="Exercise-6---Predicting-on-real-data">Exercise 6 - Predicting on real data<a class="anchor-link" href="#Exercise-6---Predicting-on-real-data"></a></h1>
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<p>You will now evaluate your neural network on the iris data set (<a href="https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html">https://scikit-learn.org/1.5/auto_examples/datasets/plot_iris_dataset.html</a>).</p>
<p>This dataset contains data on 150 flowers of 3 different types which can be separated pretty well using the four features given for each flower, which includes the width and length of their leaves. You are will later train your network to actually make good predictions.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
<span class="n">_</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">scatter</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="n">ax</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="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylabel</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span>
<span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">iris</span><span class="o">.</span><span class="n">target_names</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Classes"</span>
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
<span class="c1"># Since each prediction is a vector with a score for each of the three types of flowers,</span>
<span class="c1"># we need to make each target a vector with a 1 for the correct flower and a 0 for the others.</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
<span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span><span class="w"> </span><span class="nf">accuracy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
<span class="n">one_hot_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">predictions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">prediction</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">predictions</span><span class="p">):</span>
<span class="n">one_hot_predictions</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">prediction</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">one_hot_predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span>
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<pre>['sepal length (cm)',
'sepal width (cm)',
'petal length (cm)',
'petal width (cm)']</pre>
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<p><strong>a)</strong> What should the input size for the network be with this dataset? What should the output size of the last layer be?</p>
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The input of the net should be of size 4 and the output of size 3.
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<p><strong>b)</strong> Create a network with two hidden layers, the first with sigmoid activation and the last with softmax, the first layer should have 8 "nodes", the second has the number of nodes you found in exercise a). Softmax returns a "probability distribution", in the sense that the numbers in the output are positive and add up to 1 and, their magnitude are in some sense relative to their magnitude before going through the softmax function. Remember to use the batched version of the create_layers and feed forward functions.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">input_size</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">layer_output_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="n">layers</span> <span class="o">=</span> <span class="n">create_layers_batch</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">layer_output_sizes</span><span class="p">)</span>
<span class="n">activation_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">ReLU</span><span class="p">,</span> <span class="n">softmax</span><span class="p">]</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span>
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<p><strong>c)</strong> Evaluate your model on the entire iris dataset! For later purposes, we will split the data into train and test sets, and compute gradients on smaller batches of the training data. But for now, evaluate the network on the whole thing at once.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">feed_forward_batch</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
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<p><strong>d)</strong> Compute the accuracy of your model using the accuracy function defined above. Recreate your model a couple times and see how the accuracy changes.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">accuracy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">targets</span><span class="p">))</span>
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<pre>0.11333333333333333
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<h1 id="Exercise-7---Training-on-real-data-(Optional)">Exercise 7 - Training on real data (Optional)<a class="anchor-link" href="#Exercise-7---Training-on-real-data-(Optional)"></a></h1><p>To be able to actually do anything useful with your neural network, you need to train it. For this, we need a cost function and a way to take the gradient of the cost function wrt. the network parameters. The following exercises guide you through taking the gradient using autograd, and updating the network parameters using the gradient. Feel free to implement gradient methods like ADAM if you finish everything.</p>
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<p>Since we are doing a classification task with multiple output classes, we use the cross-entropy loss function, which can evaluate performance on classification tasks. It sees if your prediction is "most certain" on the correct target.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">cross_entropy</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">-</span><span class="n">target</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">predict</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cost</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="n">predict</span> <span class="o">=</span> <span class="n">feed_forward_batch</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">cross_entropy</span><span class="p">(</span><span class="n">predict</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
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<p>To improve our network on whatever prediction task we have given it, we need to use a sensible cost function, take the gradient of that cost function with respect to our network parameters, the weights and biases, and then update the weights and biases using these gradients. To clarify, we need to find and use these</p>
<p>$$
\frac{\partial C}{\partial W}, \frac{\partial C}{\partial b}
$$</p>
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<p>Now we need to compute these gradients. This is pretty hard to do for a neural network, we will use most of next week to do this, but we can also use autograd to just do it for us, which is what we always do in practice. With the code cell below, we create a function which takes all of these gradients for us.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">autograd</span><span class="w"> </span><span class="kn">import</span> <span class="n">grad</span>
<span class="n">gradient_func</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span>
<span class="n">cost</span><span class="p">,</span> <span class="mi">1</span>
<span class="p">)</span> <span class="c1"># Taking the gradient wrt. the second input to the cost function, i.e. the layers</span>
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<p><strong>a)</strong> What shape should the gradient of the cost function wrt. weights and biases be?</p>
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The gradient wrt. weights should have the same shape as the weights, and the gradient wrt. biases should have the same shape as the biases. This is because in the end we will subtract a small multiple of the gradient from the weights and biases to update them, and we can only subtract two arrays of the same shape.
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<p><strong>b)</strong> Use the <code>gradient_func</code> function to take the gradient of the cross entropy wrt. the weights and biases of the network. Check the shapes of what's inside. What does the <code>grad</code> func from autograd actually do?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">layers_grad</span> <span class="o">=</span> <span class="n">gradient_func</span><span class="p">(</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">targets</span>
<span class="p">)</span> <span class="c1"># Don't change this</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">layers_grad</span>
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<pre>[(array([[ -29.5215926 , 0. , -1.25926853, 0. ,
0. , -295.11350004, 230.00061475, 0. ],
[ 10.70435261, 0. , 0.91714136, 0. ,
0. , -214.71193821, 89.39001608, 0. ],
[-129.46931847, 0. , -1.22975204, 0. ,
0. , -89.29053394, 191.02427505, 0. ],
[ -68.01404496, 0. , 0.5141147 , 0. ,
0. , -8.6332998 , 61.15270843, 0. ]]),
array([ 1.82076576e-14, 0.00000000e+00, 2.40779618e-15, 0.00000000e+00,
0.00000000e+00, -6.15980156e+01, 3.29093525e+01, 0.00000000e+00])),
(array([[-62.69553593, -41.76076053, 38.74976712],
[ 0. , 0. , 0. ],
[ 99.97325122, 18.24422461, -78.90479325],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[-12.80588601, 1.7632586 , 10.94015839],
[ 35.13790747, -19.03234894, -13.32782351],
[ 0. , 0. , 0. ]]),
array([-6.66133815e-16, -3.33066907e-16, -3.28626015e-14]))]</pre>
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<p><strong>c)</strong> Finish the <code>train_network</code> function.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">train_network</span><span class="p">(</span>
<span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span>
<span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">layers_grad</span> <span class="o">=</span> <span class="n">gradient_func</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
<span class="k">for</span> <span class="p">(</span><span class="n">W</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="p">(</span><span class="n">W_g</span><span class="p">,</span> <span class="n">b_g</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">layers</span><span class="p">,</span> <span class="n">layers_grad</span><span class="p">):</span>
<span class="n">W</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">W_g</span>
<span class="n">b</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">b_g</span>
<span class="k">yield</span> <span class="n">accuracy</span><span class="p">(</span>
<span class="n">feed_forward_batch</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">),</span> <span class="n">targets</span>
<span class="p">)</span>
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<p><strong>e)</strong> What do we call the gradient method used above?</p>
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We call it gradient descent.
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<p><strong>d)</strong> Train your network and see how the accuracy changes! Make a plot if you want.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">200</span>
<span class="n">accuracies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">epochs</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_network</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">activation_funcs</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)):</span>
<span class="n">accuracies</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">a</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">accuracies</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Epoch"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Accuracy"</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[30]:</div>
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<pre>Text(0, 0.5, 'Accuracy')</pre>
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"/>
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<p><strong>e)</strong> How high of an accuracy is it possible to acheive with a neural network on this dataset, if we use the whole thing as training data?</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">number_of_dpts</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="n">number_of_dof</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">W</span><span class="o">.</span><span class="n">size</span> <span class="o">+</span> <span class="n">b</span><span class="o">.</span><span class="n">size</span> <span class="k">for</span> <span class="n">W</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">layers</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of data points: </span><span class="si">{</span><span class="n">number_of_dpts</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">"Number of degrees of freedom: </span><span class="si">{</span><span class="n">number_of_dof</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Number of data points: 150
Number of degrees of freedom: 67
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Since our network has not more parameters than data points, the problem is still overconstrained and we can not always get perfect accuracy. On the other hand using the entire dataset as training data we will still get a very high accuracy.
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