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Tension-Board-2-Analysis/notebooks/04_feature_engineering.html
Pawel Sarkowicz f3fb9e0c40 html versions of notebooks
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjQiIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiPgogICAgICAgIDxwYXRoIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJNMTcuMTkgOEgyMFYxMEgxNy45MUMxNy45NiAxMC4zMyAxOCAxMC42NiAxOCAxMVYxMkgyMFYxNEgxOC41SDE4VjE0LjAyNzVDMTUuNzUgMTQuMjc2MiAxNCAxNi4xODM3IDE0IDE4LjVDMTQgMTkuMjA4IDE0LjE2MzUgMTkuODc3OSAxNC40NTQ5IDIwLjQ3MzlDMTMuNzA2MyAyMC44MTE3IDEyLjg3NTcgMjEgMTIgMjFDOS43OCAyMSA3Ljg1IDE5Ljc5IDYuODEgMThINFYxNkg2LjA5QzYuMDQgMTUuNjcgNiAxNS4zNCA2IDE1VjE0SDRWMTJINlYxMUM2IDEwLjY2IDYuMDQgMTAuMzMgNi4wOSAxMEg0VjhINi44MUM3LjI2IDcuMjIgNy44OCA2LjU1IDguNjIgNi4wNEw3IDQuNDFMOC40MSAzTDEwLjU5IDUuMTdDMTEuMDQgNS4wNiAxMS41MSA1IDEyIDVDMTIuNDkgNSAxMi45NiA1LjA2IDEzLjQyIDUuMTdMMTUuNTkgM0wxNyA0LjQxTDE1LjM3IDYuMDRDMTYuMTIgNi41NSAxNi43NCA3LjIyIDE3LjE5IDhaTTEwIDE2SDE0VjE0SDEwVjE2Wk0xMCAxMkgxNFYxMEgxMFYxMloiIGZpbGw9IiM2MTYxNjEiLz4KICAgICAgICA8cGF0aCBkPSJNMjIgMTguNUMyMiAyMC40MzMgMjAuNDMzIDIyIDE4LjUgMjJDMTYuNTY3IDIyIDE1IDIwLjQzMyAxNSAxOC41QzE1IDE2LjU2NyAxNi41NjcgMTUgMTguNSAxNUMyMC40MzMgMTUgMjIgMTYuNTY3IDIyIDE4LjVaIiBmaWxsPSIjNjE2MTYxIi8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<h1 id="Tension-Board-2-Mirror:-Feature-Engineering">Tension Board 2 Mirror: Feature Engineering<a class="anchor-link" href="#Tension-Board-2-Mirror:-Feature-Engineering"></a></h1><p>The goal of this notebook is to convert raw climb descriptions into a clean modelling table. Each row of the final table corresponds to a single climb-angle observation, and each column is a numeric feature that may help predict grade.</p>
<h2 id="Modelling-idea">Modelling idea<a class="anchor-link" href="#Modelling-idea"></a></h2><p>A climb's grade should depend on more than just angle. It should also depend on the geometry and sequencing of the holds used. To capture that, this notebook builds features from three sources:</p>
<ol>
<li><p><strong>Wall configuration</strong><br/>
Examples: angle, board geometry, mirrored placements.</p>
</li>
<li><p><strong>Route structure</strong><br/>
Examples: number of holds, spatial spread, height gained, move lengths, left/right balance, and other frame-derived quantities.</p>
</li>
<li><p><strong>Hold difficulty priors</strong><br/>
Examples: average, maximum, and distributional summaries of the empirical hold scores built in notebook 03.</p>
</li>
</ol>
<h2 id="Output">Output<a class="anchor-link" href="#Output"></a></h2><p>The final product is a saved feature matrix that is reused in the predictive modelling and deep learning notebooks.</p>
<h2 id="Notebook-Structure">Notebook Structure<a class="anchor-link" href="#Notebook-Structure"></a></h2><ol>
<li><a href="#setup-and-imports">Setup and Imports</a></li>
<li><a href="#feature-extraction">Feature Extraction</a></li>
<li><a href="#visualizing-key-features">Visualizing Key Features</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ol>
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<h1 id="Setup-and-Imports">Setup and Imports<a class="anchor-link" href="#Setup-and-Imports"></a></h1><p>This section loads the database, auxiliary tables, and the hold-difficulty table produced in notebook 03.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Setup and Imports</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Imports</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.patches</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mpatches</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.spatial</span><span class="w"> </span><span class="kn">import</span> <span class="n">ConvexHull</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.spatial.distance</span><span class="w"> </span><span class="kn">import</span> <span class="n">pdist</span><span class="p">,</span> <span class="n">squareform</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sqlite3</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">re</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">ast</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">PIL</span><span class="w"> </span><span class="kn">import</span> <span class="n">Image</span>
<span class="c1"># Set some display options</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_columns'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_rows'</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># Set style</span>
<span class="n">palette</span><span class="o">=</span><span class="p">[</span><span class="s1">'steelblue'</span><span class="p">,</span> <span class="s1">'coral'</span><span class="p">,</span> <span class="s1">'seagreen'</span><span class="p">]</span> <span class="c1">#(for multi-bar graphs)</span>
<span class="c1"># Set board image for some visual analysis</span>
<span class="n">board_img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../images/tb2_board_12x12_composite.png'</span><span class="p">)</span>
<span class="c1"># Connect to the database</span>
<span class="n">DB_PATH</span><span class="o">=</span><span class="s2">"../data/tb2.db"</span>
<span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">DB_PATH</span><span class="p">)</span>
<span class="c1"># Create output directories</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">'../data/04_climb_features'</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s1">'../images/04_climb_features'</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Query our data from the DB</span>
<span class="sd">==================================</span>
<span class="sd">This time we restrict to where `layout_id=10` for the TB2 Mirror.</span>
<span class="sd">We will also restrict ourselves to an angle of at most 50, since according to our grade vs angle distribution in notebook 01, things start to look a bit weird past 50.</span>
<span class="sd">(Probably a bias towards climbers who can actually climb that steep). We will encode this directly into our query.</span>
<span class="sd">"""</span>
<span class="c1"># Query climbs data</span>
<span class="n">climbs_query</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">SELECT</span>
<span class="s2"> c.uuid,</span>
<span class="s2"> c.name AS climb_name,</span>
<span class="s2"> c.setter_username,</span>
<span class="s2"> c.layout_id AS layout_id,</span>
<span class="s2"> c.description,</span>
<span class="s2"> c.is_nomatch,</span>
<span class="s2"> c.is_listed,</span>
<span class="s2"> l.name AS layout_name,</span>
<span class="s2"> p.name AS board_name,</span>
<span class="s2"> c.frames,</span>
<span class="s2"> cs.angle,</span>
<span class="s2"> cs.display_difficulty,</span>
<span class="s2"> dg.boulder_name AS boulder_grade,</span>
<span class="s2"> cs.ascensionist_count,</span>
<span class="s2"> cs.quality_average,</span>
<span class="s2"> cs.fa_at</span>
<span class="s2">FROM climbs c</span>
<span class="s2">JOIN layouts l ON c.layout_id = l.id</span>
<span class="s2">JOIN products p ON l.product_id = p.id</span>
<span class="s2">JOIN climb_stats cs ON c.uuid = cs.climb_uuid</span>
<span class="s2">JOIN difficulty_grades dg ON ROUND(cs.display_difficulty) = dg.difficulty</span>
<span class="s2">WHERE cs.display_difficulty IS NOT NULL AND c.is_listed=1 AND c.layout_id=10 AND cs.angle &lt;= 50</span>
<span class="s2">"""</span>
<span class="c1"># Query information about placements (and their mirrors)</span>
<span class="n">placements_query</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">SELECT</span>
<span class="s2"> p.id AS placement_id,</span>
<span class="s2"> h.x,</span>
<span class="s2"> h.y,</span>
<span class="s2"> p.default_placement_role_id AS default_role_id,</span>
<span class="s2"> p.set_id AS set_id,</span>
<span class="s2"> s.name AS set_name,</span>
<span class="s2"> p_mirror.id AS mirror_placement_id</span>
<span class="s2">FROM placements p</span>
<span class="s2">JOIN holes h ON p.hole_id = h.id</span>
<span class="s2">JOIN sets s ON p.set_id = s.id</span>
<span class="s2">LEFT JOIN holes h_mirror ON h.mirrored_hole_id = h_mirror.id</span>
<span class="s2">LEFT JOIN placements p_mirror ON p_mirror.hole_id = h_mirror.id AND p_mirror.layout_id = p.layout_id</span>
<span class="s2">WHERE p.layout_id = 10</span>
<span class="s2">"""</span>
<span class="c1"># Load it into a DataFrame</span>
<span class="n">df_climbs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span><span class="n">climbs_query</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
<span class="n">df_placements</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span><span class="n">placements_query</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
<span class="c1"># Load the hold-level difficulty table created in notebook 03</span>
<span class="n">df_hold_difficulty</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'../data/03_hold_difficulty/hold_difficulty_scores.csv'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="s1">'placement_id'</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Difficulty-related columns loaded from Notebook 03:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">([</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="s1">'difficulty'</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()])</span>
<span class="k">assert</span> <span class="s1">'overall_difficulty'</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="s2">"Missing overall_difficulty"</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">placement_coords</span> <span class="o">=</span> <span class="p">{</span>
<span class="n">row</span><span class="p">[</span><span class="s1">'placement_id'</span><span class="p">]:</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'x'</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">'y'</span><span class="p">])</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">df_placements</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span>
<span class="p">}</span>
<span class="n">board_width</span> <span class="o">=</span> <span class="mi">144</span>
<span class="n">board_height</span> <span class="o">=</span> <span class="mi">144</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="o">-</span><span class="mi">68</span><span class="p">,</span> <span class="mi">68</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">144</span>
<span class="c1"># Role definitions (TB2)</span>
<span class="n">ROLE_DEFINITIONS</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'start'</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
<span class="s1">'middle'</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
<span class="s1">'finish'</span><span class="p">:</span> <span class="mi">7</span><span class="p">,</span>
<span class="s1">'foot'</span><span class="p">:</span> <span class="mi">8</span>
<span class="p">}</span>
<span class="n">HAND_ROLE_IDS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]</span>
<span class="n">FOOT_ROLE_IDS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">8</span><span class="p">]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Parse Frame function</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="k">def</span><span class="w"> </span><span class="nf">parse_frames</span><span class="p">(</span><span class="n">frames_str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Parse frames string into list of (placement_id, role_id) tuples.</span>
<span class="sd"> </span>
<span class="sd"> Parameters:</span>
<span class="sd"> -----------</span>
<span class="sd"> frames_str : str</span>
<span class="sd"> Frame string like "p1r5p2r6p3r8"</span>
<span class="sd"> </span>
<span class="sd"> Returns:</span>
<span class="sd"> --------</span>
<span class="sd"> list of tuples: [(placement_id, role_id), ...]</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">frames_str</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[]</span>
<span class="n">matches</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s1">'p(\d+)r(\d+)'</span><span class="p">,</span> <span class="n">frames_str</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[(</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">r</span><span class="p">))</span> <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">matches</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_role_type</span><span class="p">(</span><span class="n">role_id</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Map role_id to role type string."""</span>
<span class="k">for</span> <span class="n">role_type</span><span class="p">,</span> <span class="n">rid</span> <span class="ow">in</span> <span class="n">ROLE_DEFINITIONS</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">role_id</span> <span class="o">==</span> <span class="n">rid</span><span class="p">:</span>
<span class="k">return</span> <span class="n">role_type</span>
<span class="k">return</span> <span class="s1">'unknown'</span>
<span class="c1"># Test</span>
<span class="n">test_frames</span> <span class="o">=</span> <span class="s2">"p1r5p2r6p3r8p4r5"</span>
<span class="n">parsed</span> <span class="o">=</span> <span class="n">parse_frames</span><span class="p">(</span><span class="n">test_frames</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test parse: </span><span class="si">{</span><span class="n">parsed</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<h1 id="Feature-Extraction">Feature Extraction<a class="anchor-link" href="#Feature-Extraction"></a></h1><p>This is the core notebook section. The aim is to translate the raw <code>frames</code> string into a route-level numerical representation suitable for regression or classification models.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Feature Exraction Function</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="k">def</span><span class="w"> </span><span class="nf">extract_features</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">placement_coords</span><span class="p">,</span> <span class="n">df_hold_difficulty</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Extract all features from a single climb row.</span>
<span class="sd"> """</span>
<span class="n">features</span> <span class="o">=</span> <span class="p">{}</span>
<span class="c1"># Parse frames</span>
<span class="n">holds</span> <span class="o">=</span> <span class="n">parse_frames</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'frames'</span><span class="p">])</span>
<span class="n">angle</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">holds</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="c1"># =====================</span>
<span class="c1"># BASIC HOLD EXTRACTION</span>
<span class="c1"># =====================</span>
<span class="n">hold_data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">placement_id</span><span class="p">,</span> <span class="n">role_id</span> <span class="ow">in</span> <span class="n">holds</span><span class="p">:</span>
<span class="n">coords</span> <span class="o">=</span> <span class="n">placement_coords</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">placement_id</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="k">if</span> <span class="n">coords</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">role_type</span> <span class="o">=</span> <span class="n">get_role_type</span><span class="p">(</span><span class="n">role_id</span><span class="p">)</span>
<span class="n">is_hand</span> <span class="o">=</span> <span class="n">role_id</span> <span class="ow">in</span> <span class="n">HAND_ROLE_IDS</span>
<span class="n">is_foot</span> <span class="o">=</span> <span class="n">role_id</span> <span class="ow">in</span> <span class="n">FOOT_ROLE_IDS</span>
<span class="c1"># Get difficulty scores for this hold at this angle</span>
<span class="n">diff_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">role_type</span><span class="si">}</span><span class="s2">_diff_</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span><span class="si">}</span><span class="s2">deg"</span>
<span class="n">hand_diff_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"hand_diff_</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span><span class="si">}</span><span class="s2">deg"</span>
<span class="n">foot_diff_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"foot_diff_</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span><span class="si">}</span><span class="s2">deg"</span>
<span class="n">difficulty</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">placement_id</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
<span class="c1"># Try role-specific first, then aggregate</span>
<span class="k">if</span> <span class="n">diff_key</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">difficulty</span> <span class="o">=</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">placement_id</span><span class="p">,</span> <span class="n">diff_key</span><span class="p">]</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">difficulty</span><span class="p">):</span>
<span class="k">if</span> <span class="n">is_hand</span> <span class="ow">and</span> <span class="n">hand_diff_key</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">difficulty</span> <span class="o">=</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">placement_id</span><span class="p">,</span> <span class="n">hand_diff_key</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">is_foot</span> <span class="ow">and</span> <span class="n">foot_diff_key</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">difficulty</span> <span class="o">=</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">placement_id</span><span class="p">,</span> <span class="n">foot_diff_key</span><span class="p">]</span>
<span class="c1"># Fallback to overall</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">difficulty</span><span class="p">)</span> <span class="ow">and</span> <span class="s1">'overall_difficulty'</span> <span class="ow">in</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">difficulty</span> <span class="o">=</span> <span class="n">df_hold_difficulty</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">placement_id</span><span class="p">,</span> <span class="s1">'overall_difficulty'</span><span class="p">]</span>
<span class="n">hold_data</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'placement_id'</span><span class="p">:</span> <span class="n">placement_id</span><span class="p">,</span>
<span class="s1">'x'</span><span class="p">:</span> <span class="n">coords</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="s1">'y'</span><span class="p">:</span> <span class="n">coords</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
<span class="s1">'role_id'</span><span class="p">:</span> <span class="n">role_id</span><span class="p">,</span>
<span class="s1">'role_type'</span><span class="p">:</span> <span class="n">role_type</span><span class="p">,</span>
<span class="s1">'is_hand'</span><span class="p">:</span> <span class="n">is_hand</span><span class="p">,</span>
<span class="s1">'is_foot'</span><span class="p">:</span> <span class="n">is_foot</span><span class="p">,</span>
<span class="s1">'difficulty'</span><span class="p">:</span> <span class="n">difficulty</span>
<span class="p">})</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">hold_data</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">df_holds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">hold_data</span><span class="p">)</span>
<span class="c1"># Separate by role</span>
<span class="n">hand_holds</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'is_hand'</span><span class="p">]]</span>
<span class="n">foot_holds</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'is_foot'</span><span class="p">]]</span>
<span class="n">start_holds</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'role_type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'start'</span><span class="p">]</span>
<span class="n">finish_holds</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'role_type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'finish'</span><span class="p">]</span>
<span class="n">middle_holds</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'role_type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'middle'</span><span class="p">]</span>
<span class="c1"># =====================</span>
<span class="c1"># 1. ANGLE</span>
<span class="c1"># =====================</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span> <span class="o">=</span> <span class="n">angle</span>
<span class="c1"># =====================</span>
<span class="c1"># 2. BASIC COUNTS</span>
<span class="c1"># =====================</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'foot_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">finish_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'middle_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">middle_holds</span><span class="p">)</span>
<span class="c1"># =====================</span>
<span class="c1"># 3. MATCHING FEATURE</span>
<span class="c1"># =====================</span>
<span class="c1"># A climb is "matching" if you are allowed to match your hands at any hold.</span>
<span class="c1"># There are slight difference in difficulties of matchines vs no matching climbs as per our analysis in 01.</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'is_nomatch'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">row</span><span class="p">[</span><span class="s1">'is_nomatch'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">bool</span><span class="p">(</span><span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="sa">r</span><span class="s1">'\bno\s*match(ing)?\b'</span><span class="p">,</span> <span class="n">row</span><span class="p">[</span><span class="s1">'description'</span><span class="p">],</span> <span class="n">flags</span><span class="o">=</span><span class="n">re</span><span class="o">.</span><span class="n">IGNORECASE</span><span class="p">)))</span>
<span class="c1"># =====================</span>
<span class="c1"># 4. SPATIAL/POSITION</span>
<span class="c1"># =====================</span>
<span class="n">xs</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">ys</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'range_x'</span><span class="p">]</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">xs</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'range_y'</span><span class="p">]</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">ys</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_y'</span><span class="p">]</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">ys</span><span class="p">)</span>
<span class="c1"># =====================</span>
<span class="c1"># 5. HEIGHT FEATURES</span>
<span class="c1"># =====================</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">start_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_min'</span><span class="p">]</span> <span class="o">=</span> <span class="n">start_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_max'</span><span class="p">]</span> <span class="o">=</span> <span class="n">start_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_min'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_max'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">finish_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">finish_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_min'</span><span class="p">]</span> <span class="o">=</span> <span class="n">finish_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_max'</span><span class="p">]</span> <span class="o">=</span> <span class="n">finish_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_min'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_max'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'height_gained'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'max_y'</span><span class="p">]</span> <span class="o">-</span> <span class="n">features</span><span class="p">[</span><span class="s1">'min_y'</span><span class="p">]</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'finish_height'</span><span class="p">))</span> <span class="ow">and</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start_height'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'height_gained_start_finish'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'height_gained_start_finish'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># =====================</span>
<span class="c1"># 6. BBOX FEATURES</span>
<span class="c1"># =====================</span>
<span class="n">bbox_width</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_x'</span><span class="p">]</span>
<span class="n">bbox_height</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_y'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'bbox_area'</span><span class="p">]</span> <span class="o">=</span> <span class="n">bbox_width</span> <span class="o">*</span> <span class="n">bbox_height</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'bbox_aspect_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">bbox_width</span> <span class="o">/</span> <span class="n">bbox_height</span> <span class="k">if</span> <span class="n">bbox_height</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'bbox_normalized_area'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'bbox_area'</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">board_width</span> <span class="o">*</span> <span class="n">board_height</span><span class="p">)</span>
<span class="c1"># =====================</span>
<span class="c1"># 7. HOLD DENSITY</span>
<span class="c1"># =====================</span>
<span class="k">if</span> <span class="n">features</span><span class="p">[</span><span class="s1">'bbox_area'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hold_density'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="n">features</span><span class="p">[</span><span class="s1">'bbox_area'</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hold_density'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'holds_per_vertical_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'range_y'</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># =====================</span>
<span class="c1"># 8. SYMMETRY/BALANCE</span>
<span class="c1"># =====================</span>
<span class="n">center_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_min</span> <span class="o">+</span> <span class="n">x_max</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'left_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">center_x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'right_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">center_x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'left_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'left_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="k">if</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mf">0.5</span>
<span class="c1"># Symmetry score (how balanced left/right)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'symmetry_score'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'left_ratio'</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
<span class="c1"># Hand symmetry</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">hand_left</span> <span class="o">=</span> <span class="p">(</span><span class="n">hand_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">center_x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">hand_right</span> <span class="o">=</span> <span class="p">(</span><span class="n">hand_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">center_x</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_left_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">hand_left</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_symmetry'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">abs</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'hand_left_ratio'</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_left_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_symmetry'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># =====================</span>
<span class="c1"># 9. VERTICAL DISTRIBUTION</span>
<span class="c1"># =====================</span>
<span class="n">y_median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'upper_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">y_median</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'lower_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">y_median</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'upper_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'upper_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span>
<span class="c1"># =====================</span>
<span class="c1"># 10. HAND REACH / SPREAD</span>
<span class="c1"># =====================</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">hand_xs</span> <span class="o">=</span> <span class="n">hand_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">hand_ys</span> <span class="o">=</span> <span class="n">hand_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">hand_distances</span> <span class="o">=</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="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)):</span>
<span class="n">dx</span> <span class="o">=</span> <span class="n">hand_xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">hand_xs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">dy</span> <span class="o">=</span> <span class="n">hand_ys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">hand_ys</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">hand_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">hand_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">hand_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">hand_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">hand_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_spread_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">hand_xs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">hand_xs</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_spread_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">hand_ys</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">hand_ys</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_spread_x'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_spread_y'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># =====================</span>
<span class="c1"># 11. FOOT SPREAD</span>
<span class="c1"># =====================</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">foot_xs</span> <span class="o">=</span> <span class="n">foot_holds</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">foot_ys</span> <span class="o">=</span> <span class="n">foot_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">foot_distances</span> <span class="o">=</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="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)):</span>
<span class="n">dx</span> <span class="o">=</span> <span class="n">foot_xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">foot_xs</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">dy</span> <span class="o">=</span> <span class="n">foot_ys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">foot_ys</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">foot_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_foot_spread'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">foot_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_foot_spread'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">foot_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'foot_spread_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">foot_xs</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">foot_xs</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'foot_spread_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">foot_ys</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">foot_ys</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_foot_spread'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_foot_spread'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'foot_spread_x'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'foot_spread_y'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># =====================</span>
<span class="c1"># 12. HAND-TO-FOOT DISTANCES</span>
<span class="c1"># =====================</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">h2f_distances</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">hand_holds</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">foot_holds</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="n">dx</span> <span class="o">=</span> <span class="n">h</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span> <span class="o">-</span> <span class="n">f</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span>
<span class="n">dy</span> <span class="o">=</span> <span class="n">h</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span> <span class="o">-</span> <span class="n">f</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span>
<span class="n">h2f_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">h2f_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">h2f_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">h2f_distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">h2f_distances</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_to_foot'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># =====================</span>
<span class="c1"># 13. HOLD DIFFICULTY FEATURES</span>
<span class="c1"># =====================</span>
<span class="n">difficulties</span> <span class="o">=</span> <span class="n">df_holds</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hold_difficulty'</span><span class="p">]</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">difficulties</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'median_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_range'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'max_hold_difficulty'</span><span class="p">]</span> <span class="o">-</span> <span class="n">features</span><span class="p">[</span><span class="s1">'min_hold_difficulty'</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'median_hold_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_range'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Hand difficulty</span>
<span class="n">hand_diffs</span> <span class="o">=</span> <span class="n">hand_holds</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">hand_diffs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_difficulty'</span><span class="p">]</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">hand_diffs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">hand_diffs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_hand_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_hand_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_hand_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Foot difficulty</span>
<span class="n">foot_diffs</span> <span class="o">=</span> <span class="n">foot_holds</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">foot_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_foot_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">foot_diffs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_foot_difficulty'</span><span class="p">]</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">foot_diffs</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_foot_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">foot_diffs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_foot_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_foot_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_foot_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Start/Finish difficulty</span>
<span class="n">start_diffs</span> <span class="o">=</span> <span class="n">start_holds</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
<span class="n">finish_diffs</span> <span class="o">=</span> <span class="n">finish_holds</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">finish_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">start_diffs</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">finish_diffs</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">finish_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># =====================</span>
<span class="c1"># 14. ADDITIONAL BASIC FEATURES</span>
<span class="c1"># =====================</span>
<span class="c1"># Hand to foot ratio</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hand_foot_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'hand_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'foot_holds'</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Movement complexity</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'movement_density'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'height_gained'</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Center of mass of holds</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hold_com_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hold_com_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">ys</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="c1"># Weighted difficulty (by y position)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulties</span><span class="p">):</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">(</span><span class="n">ys</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)]</span> <span class="o">-</span> <span class="n">ys</span><span class="o">.</span><span class="n">min</span><span class="p">())</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">ys</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">ys</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'weighted_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">difficulties</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'weighted_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span>
<span class="c1"># =====================================================</span>
<span class="c1"># 15. GEOMETRIC FEATURES</span>
<span class="c1"># =====================================================</span>
<span class="c1"># Convex hull area (2D polygon enclosing all holds)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">])</span>
<span class="n">hull</span> <span class="o">=</span> <span class="n">ConvexHull</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_area'</span><span class="p">]</span> <span class="o">=</span> <span class="n">hull</span><span class="o">.</span><span class="n">volume</span> <span class="c1"># In 2D, volume = area</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_perimeter'</span><span class="p">]</span> <span class="o">=</span> <span class="n">hull</span><span class="o">.</span><span class="n">area</span> <span class="c1"># In 2D, area = perimeter</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hull_area_to_bbox_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_area'</span><span class="p">]</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'bbox_area'</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_area'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_perimeter'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hull_area_to_bbox_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_area'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'convex_hull_perimeter'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'hull_area_to_bbox_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Nearest neighbor distances</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">])</span>
<span class="n">distances</span> <span class="o">=</span> <span class="n">pdist</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_nn_distance'</span><span class="p">]</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">distances</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'min_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_nn_distance'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Clustering coefficient (holds grouped vs spread)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">])</span>
<span class="n">dist_matrix</span> <span class="o">=</span> <span class="n">squareform</span><span class="p">(</span><span class="n">pdist</span><span class="p">(</span><span class="n">points</span><span class="p">))</span>
<span class="c1"># Count neighbors within threshold (e.g., 12 inches)</span>
<span class="n">threshold</span> <span class="o">=</span> <span class="mf">12.0</span>
<span class="n">neighbors_count</span> <span class="o">=</span> <span class="p">(</span><span class="n">dist_matrix</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">sum</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="o">-</span> <span class="mi">1</span> <span class="c1"># Exclude self</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_neighbors_12in'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">neighbors_count</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_neighbors_12in'</span><span class="p">]</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">neighbors_count</span><span class="p">)</span>
<span class="c1"># Clustering: ratio of actual neighbors to max possible</span>
<span class="n">avg_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">neighbors_count</span><span class="p">)</span>
<span class="n">max_possible</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'clustering_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">avg_neighbors</span> <span class="o">/</span> <span class="n">max_possible</span> <span class="k">if</span> <span class="n">max_possible</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_neighbors_12in'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_neighbors_12in'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'clustering_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Path length (greedy nearest-neighbor tour)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="c1"># Sort by y (bottom to top) for approximate path</span>
<span class="n">sorted_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ys</span><span class="p">)</span>
<span class="n">sorted_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="n">xs</span><span class="p">[</span><span class="n">sorted_indices</span><span class="p">],</span> <span class="n">ys</span><span class="p">[</span><span class="n">sorted_indices</span><span class="p">]])</span>
<span class="n">path_length</span> <span class="o">=</span> <span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">sorted_points</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">dx</span> <span class="o">=</span> <span class="n">sorted_points</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">sorted_points</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">dy</span> <span class="o">=</span> <span class="n">sorted_points</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">sorted_points</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">path_length</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'path_length_vertical'</span><span class="p">]</span> <span class="o">=</span> <span class="n">path_length</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'path_efficiency'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'height_gained'</span><span class="p">]</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">path_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'path_length_vertical'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'path_efficiency'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># =====================================================</span>
<span class="c1"># 16. DIFFICULTY-WEIGHTED FEATURES</span>
<span class="c1"># =====================================================</span>
<span class="c1"># Difficulty gradient (finish vs start)</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'finish_difficulty'</span><span class="p">))</span> <span class="ow">and</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start_difficulty'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'finish_difficulty'</span><span class="p">]</span> <span class="o">-</span> <span class="n">features</span><span class="p">[</span><span class="s1">'start_difficulty'</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_gradient'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Difficulty variance by vertical region (split into thirds)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulties</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">y_min_val</span><span class="p">,</span> <span class="n">y_max_val</span> <span class="o">=</span> <span class="n">ys</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">ys</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">y_range</span> <span class="o">=</span> <span class="n">y_max_val</span> <span class="o">-</span> <span class="n">y_min_val</span>
<span class="k">if</span> <span class="n">y_range</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># Split into lower, middle, upper thirds</span>
<span class="n">lower_mask</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">y_min_val</span> <span class="o">+</span> <span class="n">y_range</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">middle_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">ys</span> <span class="o">&gt;</span> <span class="n">y_min_val</span> <span class="o">+</span> <span class="n">y_range</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ys</span> <span class="o">&lt;=</span> <span class="n">y_min_val</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_range</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">upper_mask</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">y_min_val</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_range</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1"># Get difficulties for each region</span>
<span class="n">df_with_diff</span> <span class="o">=</span> <span class="n">df_holds</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'lower'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lower_mask</span>
<span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'middle'</span><span class="p">]</span> <span class="o">=</span> <span class="n">middle_mask</span>
<span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'upper'</span><span class="p">]</span> <span class="o">=</span> <span class="n">upper_mask</span>
<span class="n">lower_diffs</span> <span class="o">=</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'lower'</span><span class="p">]</span> <span class="o">&amp;</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">()][</span><span class="s1">'difficulty'</span><span class="p">]</span>
<span class="n">middle_diffs</span> <span class="o">=</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'middle'</span><span class="p">]</span> <span class="o">&amp;</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">()][</span><span class="s1">'difficulty'</span><span class="p">]</span>
<span class="n">upper_diffs</span> <span class="o">=</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'upper'</span><span class="p">]</span> <span class="o">&amp;</span> <span class="n">df_with_diff</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">()][</span><span class="s1">'difficulty'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'lower_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lower_diffs</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">lower_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'middle_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">middle_diffs</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">middle_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'upper_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">upper_diffs</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">upper_diffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Difficulty progression (upper - lower)</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'lower_region_difficulty'</span><span class="p">])</span> <span class="ow">and</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'upper_region_difficulty'</span><span class="p">]):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_progression'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'upper_region_difficulty'</span><span class="p">]</span> <span class="o">-</span> <span class="n">features</span><span class="p">[</span><span class="s1">'lower_region_difficulty'</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_progression'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'lower_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'middle_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'upper_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_progression'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'lower_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'middle_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'upper_region_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_progression'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Hardest move estimate (max difficulty jump between consecutive holds)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_diffs</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="c1"># Sort hand holds by y position</span>
<span class="n">hand_sorted</span> <span class="o">=</span> <span class="n">hand_holds</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
<span class="n">hand_diff_sorted</span> <span class="o">=</span> <span class="n">hand_sorted</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_diff_sorted</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">difficulty_jumps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">hand_diff_sorted</span><span class="p">))</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_difficulty_jump'</span><span class="p">]</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">difficulty_jumps</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulty_jumps</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_difficulty_jump'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">difficulty_jumps</span><span class="p">)</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">difficulty_jumps</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_difficulty_jump'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_difficulty_jump'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_difficulty_jump'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_difficulty_jump'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Difficulty-weighted reach (combine difficulty with distances)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_holds</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">hand_diffs</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">hand_sorted</span> <span class="o">=</span> <span class="n">hand_holds</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
<span class="n">xs_sorted</span> <span class="o">=</span> <span class="n">hand_sorted</span><span class="p">[</span><span class="s1">'x'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">ys_sorted</span> <span class="o">=</span> <span class="n">hand_sorted</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">diffs_sorted</span> <span class="o">=</span> <span class="n">hand_sorted</span><span class="p">[</span><span class="s1">'difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">hand_diffs</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="o">.</span><span class="n">values</span>
<span class="n">weighted_reach</span> <span class="o">=</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="nb">len</span><span class="p">(</span><span class="n">hand_sorted</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">dx</span> <span class="o">=</span> <span class="n">xs_sorted</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">xs_sorted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">dy</span> <span class="o">=</span> <span class="n">ys_sorted</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ys_sorted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="n">avg_diff</span> <span class="o">=</span> <span class="p">(</span><span class="n">diffs_sorted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">diffs_sorted</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">weighted_reach</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span> <span class="o">*</span> <span class="n">avg_diff</span><span class="p">)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_weighted_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">weighted_reach</span><span class="p">)</span> <span class="k">if</span> <span class="n">weighted_reach</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_weighted_reach'</span><span class="p">]</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">weighted_reach</span><span class="p">)</span> <span class="k">if</span> <span class="n">weighted_reach</span> <span class="k">else</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'difficulty_weighted_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_weighted_reach'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># =====================================================</span>
<span class="c1"># 17. POSITION-NORMALIZED FEATURES</span>
<span class="c1"># =====================================================</span>
<span class="c1"># Normalized positions (0-1 scale)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_x_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'mean_x'</span><span class="p">]</span> <span class="o">-</span> <span class="n">x_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_width</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_y_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'mean_y'</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_x_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'std_x'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_width</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'std_y_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'std_y'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="c1"># Start/finish normalized</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start_height'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'finish_height'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Distance from typical positions (center bottom for start, center top for finish)</span>
<span class="n">typical_start_y</span> <span class="o">=</span> <span class="n">y_min</span> <span class="o">+</span> <span class="n">board_height</span> <span class="o">*</span> <span class="mf">0.15</span>
<span class="n">typical_finish_y</span> <span class="o">=</span> <span class="n">y_min</span> <span class="o">+</span> <span class="n">board_height</span> <span class="o">*</span> <span class="mf">0.85</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start_height'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_offset_from_typical'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">typical_start_y</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'start_offset_from_typical'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'finish_height'</span><span class="p">)):</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_offset_from_typical'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">typical_finish_y</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'finish_offset_from_typical'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Hold positions relative to start</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">start_holds</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">start_y</span> <span class="o">=</span> <span class="n">start_holds</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_y_relative_to_start'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'mean_y'</span><span class="p">]</span> <span class="o">-</span> <span class="n">start_y</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_y_relative_to_start'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'max_y'</span><span class="p">]</span> <span class="o">-</span> <span class="n">start_y</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'mean_y_relative_to_start'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'max_y_relative_to_start'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="c1"># Spread normalized by board</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'spread_x_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_x'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_width</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'spread_y_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_y'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="c1"># Bbox coverage (percentage of board covered)</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'bbox_coverage_x'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_x'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_width</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'bbox_coverage_y'</span><span class="p">]</span> <span class="o">=</span> <span class="n">features</span><span class="p">[</span><span class="s1">'range_y'</span><span class="p">]</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="c1"># Position quartile features</span>
<span class="n">y_quartiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">ys</span><span class="p">,</span> <span class="p">[</span><span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">])</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'y_q25'</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'y_q50'</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'y_q75'</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'y_iqr'</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Holds in each vertical quartile</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'holds_bottom_quartile'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">ys</span> <span class="o">&lt;</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'holds_top_quartile'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">ys</span> <span class="o">&gt;=</span> <span class="n">y_quartiles</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">features</span>
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<h2 id="Sanity-Check-on-One-Example">Sanity Check on One Example<a class="anchor-link" href="#Sanity-Check-on-One-Example"></a></h2><p>Before extracting features for the entire dataset, we inspect one representative climb to confirm that the parsing logic and the computed geometric summaries behave as expected. Let's do the climb "Ooo La La" from notebook two.</p>
<p><img alt="Ooo La La" src="../images/02_hold_stats/Ooo_La_La.png"/></p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">extract_features</span><span class="p">(</span><span class="n">df_climbs</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">10000</span><span class="p">],</span> <span class="n">placement_coords</span><span class="p">,</span> <span class="n">df_placements</span><span class="p">)</span>
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<p>The printed example above is an important checkpoint. If the parsed placements, role counts, or geometric summaries look unreasonable here, then the full feature matrix will inherit those mistakes.</p>
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<h2 id="Extract-Features-or-all-climbs">Extract Features or all climbs<a class="anchor-link" href="#Extract-Features-or-all-climbs"></a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Extract features for all climbs</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span> <span class="c1"># Progess bar. This will take a while.</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Extracting features for </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_climbs</span><span class="p">)</span><span class="si">}</span><span class="s2"> climbs..."</span><span class="p">)</span>
<span class="n">feature_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">df_climbs</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">df_climbs</span><span class="p">)):</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">extract_features</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">placement_coords</span><span class="p">,</span> <span class="n">df_hold_difficulty</span><span class="p">)</span>
<span class="k">if</span> <span class="n">features</span><span class="p">:</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'climb_uuid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'uuid'</span><span class="p">]</span>
<span class="n">features</span><span class="p">[</span><span class="s1">'display_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'display_difficulty'</span><span class="p">]</span>
<span class="n">feature_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="n">df_features</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">feature_list</span><span class="p">)</span>
<span class="n">df_features</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'climb_uuid'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Extracted features for </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span><span class="si">}</span><span class="s2"> climbs"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Feature columns: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">### Feature Table Sample</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_features</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Feature Summary Statistics</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"### Feature Summary</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">summary</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">T</span>
<span class="n">summary</span><span class="p">[</span><span class="s1">'missing'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">summary</span><span class="p">[</span><span class="s1">'missing_pct'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_features</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">summary</span><span class="p">[[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'std'</span><span class="p">,</span> <span class="s1">'min'</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="s1">'missing'</span><span class="p">,</span> <span class="s1">'missing_pct'</span><span class="p">]])</span>
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<h2 id="Correlation-with-Difficulty">Correlation with Difficulty<a class="anchor-link" href="#Correlation-with-Difficulty"></a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Correlation with Difficulty</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">correlations</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">corr</span><span class="p">()[</span><span class="s1">'display_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'display_difficulty'</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="nb">abs</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"### Top 30 Features Correlated with Difficulty</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">correlations</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span><span class="o">.</span><span class="n">to_frame</span><span class="p">(</span><span class="s1">'correlation'</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">### Bottom 10 Features (Least Correlated)</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">correlations</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">to_frame</span><span class="p">(</span><span class="s1">'correlation'</span><span class="p">))</span>
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<h1 id="Visualizing-Key-Features">Visualizing Key Features<a class="anchor-link" href="#Visualizing-Key-Features"></a></h1>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Visualize Key Features</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">))</span>
<span class="n">key_features</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'angle'</span><span class="p">,</span>
<span class="s1">'total_holds'</span><span class="p">,</span>
<span class="s1">'height_gained'</span><span class="p">,</span>
<span class="s1">'mean_hold_difficulty'</span><span class="p">,</span>
<span class="s1">'max_hold_difficulty'</span><span class="p">,</span>
<span class="s1">'mean_hand_reach'</span><span class="p">,</span>
<span class="s1">'hold_density'</span><span class="p">,</span>
<span class="s1">'symmetry_score'</span><span class="p">,</span>
<span class="s1">'is_nomatch'</span><span class="p">,</span>
<span class="s1">'convex_hull_area'</span><span class="p">,</span>
<span class="s1">'difficulty_progression'</span><span class="p">,</span>
<span class="s1">'mean_y_normalized'</span><span class="p">,</span>
<span class="s1">'clustering_ratio'</span><span class="p">,</span>
<span class="s1">'path_efficiency'</span><span class="p">,</span>
<span class="s1">'max_difficulty_jump'</span><span class="p">,</span>
<span class="s1">'difficulty_weighted_reach'</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flat</span><span class="p">,</span> <span class="n">key_features</span><span class="p">):</span>
<span class="k">if</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">df_features</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'display_difficulty'</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Difficulty'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">feature</span><span class="si">}</span><span class="s1"> vs Difficulty'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/04_climb_features/feature_correlations.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Add Interaction Features</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Angle interactions</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'angle_x_holds'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'angle_x_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'angle_squared'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span>
<span class="c1"># Difficulty interactions</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'difficulty_x_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'height_gained'</span><span class="p">]</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'difficulty_x_density'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'hold_density'</span><span class="p">]</span>
<span class="c1"># Complexity features</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'complexity_score'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'total_holds'</span><span class="p">]</span> <span class="o">*</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'mean_hand_reach'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'hold_density'</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># Geometric × difficulty</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'hull_area_x_difficulty'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'convex_hull_area'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'mean_hold_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Added interaction features. Total columns: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Handle Missing Values</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">missing</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">missing_cols</span> <span class="o">=</span> <span class="n">missing</span><span class="p">[</span><span class="n">missing</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"### Columns with Missing Values</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">missing_cols</span><span class="o">.</span><span class="n">to_frame</span><span class="p">(</span><span class="s1">'missing'</span><span class="p">))</span>
<span class="c1"># Fill difficulty NaNs with column mean</span>
<span class="n">difficulty_cols</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df_features</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="s1">'difficulty'</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">difficulty_cols</span><span class="p">:</span>
<span class="k">if</span> <span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
<span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="c1"># Fill start/finish height with min_y/max_y if missing</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="s1">'min_y'</span><span class="p">])</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="s1">'max_y'</span><span class="p">])</span>
<span class="c1"># Fill normalized features</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'start_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'start_height_normalized'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span>
<span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="s1">'start_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="p">)</span>
<span class="n">df_features</span><span class="p">[</span><span class="s1">'finish_height_normalized'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'finish_height_normalized'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span>
<span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="s1">'finish_height'</span><span class="p">]</span> <span class="o">-</span> <span class="n">y_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">board_height</span>
<span class="p">)</span>
<span class="c1"># Fill other NaNs with column means</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df_features</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="k">if</span> <span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
<span class="k">if</span> <span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'float64'</span><span class="p">,</span> <span class="s1">'int64'</span><span class="p">]:</span>
<span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">df_features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="c1"># Check remaining missing</span>
<span class="n">remaining</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Remaining missing values: </span><span class="si">{</span><span class="n">remaining</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">===================================</span>
<span class="sd">Feature Importance Review</span>
<span class="sd">===================================</span>
<span class="sd">"""</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'display_difficulty'</span><span class="p">])</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_features</span><span class="p">[</span><span class="s1">'display_difficulty'</span><span class="p">]</span>
<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">importance</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
<span class="s1">'feature'</span><span class="p">:</span> <span class="n">X</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span>
<span class="s1">'importance'</span><span class="p">:</span> <span class="n">rf</span><span class="o">.</span><span class="n">feature_importances_</span>
<span class="p">})</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'importance'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"### Top 30 Most Important Features (Random Forest)</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">importance</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">30</span><span class="p">))</span>
<span class="c1"># Cross-validation score</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">rf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_absolute_error'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Cross-validated MAE: </span><span class="si">{</span><span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> (+/- </span><span class="si">{</span><span class="n">scores</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
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<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion"></a></h1>
</div>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">============================</span>
<span class="sd">Save Feature Matrix</span>
<span class="sd">============================</span>
<span class="sd">"""</span>
<span class="n">raw_cols</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df_features</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_raw'</span><span class="p">)]</span>
<span class="k">if</span> <span class="n">raw_cols</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Dropping raw columns from final climb feature matrix:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">raw_cols</span><span class="p">)</span>
<span class="n">df_features</span> <span class="o">=</span> <span class="n">df_features</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">raw_cols</span><span class="p">)</span>
<span class="c1"># `climb_features.csv` is the canonical name used by later notebooks.</span>
<span class="n">df_features</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'../data/04_climb_features/climb_features.csv'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Saved feature matrix to:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" - ../data/04_climb_features/climb_features.csv"</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'../data/04_climb_features/feature_list.txt'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df_features</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">col</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Feature list saved to ../data/04_climb_features/feature_list.txt"</span><span class="p">)</span>
</pre></div>
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Final Feature Summary</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"### Feature Engineering Complete</span><span class="se">\n</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">"Total climbs: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_features</span><span class="p">)</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">"Total features: </span><span class="si">{</span><span class="n">df_features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="c1"># Exclude target</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Target: display_difficulty"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Feature matrix shape: </span><span class="si">{</span><span class="n">df_features</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"""</span><span class="se">\n</span><span class="s2">Interpretation:</span>
<span class="s2">- Each row is a climb-angle observation.</span>
<span class="s2">- The target is `display_difficulty`.</span>
<span class="s2">- The predictors combine geometry, hold statistics, and aggregate difficulty information.</span>
<span class="s2">- Hold-difficulty-based features use Bayesian-smoothed hold scores from Notebook 03.</span>
<span class="s2">- The next notebook tests how much predictive signal these engineered features actually contain.</span>
<span class="s2">"""</span><span class="p">)</span>
</pre></div>
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