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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yMCA4aC0yLjgxYy0uNDUtLjc4LTEuMDctMS40NS0xLjgyLTEuOTZMMTcgNC40MSAxNS41OSAzbC0yLjE3IDIuMTdDMTIuOTYgNS4wNiAxMi40OSA1IDEyIDVjLS40OSAwLS45Ni4wNi0xLjQxLjE3TDguNDEgMyA3IDQuNDFsMS42MiAxLjYzQzcuODggNi41NSA3LjI2IDcuMjIgNi44MSA4SDR2MmgyLjA5Yy0uMDUuMzMtLjA5LjY2LS4wOSAxdjFINHYyaDJ2MWMwIC4zNC4wNC42Ny4wOSAxSDR2MmgyLjgxYzEuMDQgMS43OSAyLjk3IDMgNS4xOSAzczQuMTUtMS4yMSA1LjE5LTNIMjB2LTJoLTIuMDljLjA1LS4zMy4wOS0uNjYuMDktMXYtMWgydi0yaC0ydi0xYzAtLjM0LS4wNC0uNjctLjA5LTFIMjBWOHptLTYgOGgtNHYtMmg0djJ6bTAtNGgtNHYtMmg0djJ6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
0 2px 2px 0 var(--jp-shadow-penumbra-color),
0 1px 5px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
0 4px 5px 0 var(--jp-shadow-penumbra-color),
0 1px 10px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
0 6px 10px 0 var(--jp-shadow-penumbra-color),
0 1px 18px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
0 8px 10px 1px var(--jp-shadow-penumbra-color),
0 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
0 12px 17px 2px var(--jp-shadow-penumbra-color),
0 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
0 16px 24px 2px var(--jp-shadow-penumbra-color),
0 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
0 20px 31px 3px var(--jp-shadow-penumbra-color),
0 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
0 24px 38px 3px var(--jp-shadow-penumbra-color),
0 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-inverse-border-color: var(--md-grey-600);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
'Segoe UI Symbol';
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-900);
--jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
'Segoe UI Emoji', 'Segoe UI Symbol';
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-900);
--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-900);
--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-inverse-border-color);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #a2f;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #a2f;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/*
RTC user specific colors.
These colors are used for the cursor, username in the editor,
and the icon of the user.
*/
--jp-collaborator-color1: #ffad8e;
--jp-collaborator-color2: #dac83d;
--jp-collaborator-color3: #72dd76;
--jp-collaborator-color4: #00e4d0;
--jp-collaborator-color5: #45d4ff;
--jp-collaborator-color6: #e2b1ff;
--jp-collaborator-color7: #ff9de6;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
/* Button colors */
--jp-accept-color-normal: var(--md-blue-700);
--jp-accept-color-hover: var(--md-blue-800);
--jp-accept-color-active: var(--md-blue-900);
--jp-warn-color-normal: var(--md-red-700);
--jp-warn-color-hover: var(--md-red-800);
--jp-warn-color-active: var(--md-red-900);
--jp-reject-color-normal: var(--md-grey-600);
--jp-reject-color-hover: var(--md-grey-700);
--jp-reject-color-active: var(--md-grey-800);
/* File or activity icons and switch semantic variables */
--jp-jupyter-icon-color: #f37626;
--jp-notebook-icon-color: #f37626;
--jp-json-icon-color: var(--md-orange-700);
--jp-console-icon-background-color: var(--md-blue-700);
--jp-console-icon-color: white;
--jp-terminal-icon-background-color: var(--md-grey-800);
--jp-terminal-icon-color: var(--md-grey-200);
--jp-text-editor-icon-color: var(--md-grey-700);
--jp-inspector-icon-color: var(--md-grey-700);
--jp-switch-color: var(--md-grey-400);
--jp-switch-true-position-color: var(--md-orange-900);
}
</style>
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/* Force rendering true colors when outputing to pdf */
* {
-webkit-print-color-adjust: exact;
}
/* Misc */
a.anchor-link {
display: none;
}
/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
}
.cm-editor.cm-s-jupyter .highlight pre {
/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
padding: var(--jp-code-padding) 4px;
margin: 0;
font-family: inherit;
font-size: inherit;
line-height: inherit;
color: inherit;
}
.jp-OutputArea-output pre {
line-height: inherit;
font-family: inherit;
}
.jp-RenderedText pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
}
/* Hiding the collapser by default */
.jp-Collapser {
display: none;
}
@page {
margin: 0.5in; /* Margin for each printed piece of paper */
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@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
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const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/11.10.0/mermaid.esm.min.mjs")).default;
const elkUrl = "https://cdnjs.cloudflare.com/ajax/libs/mermaid-layout-elk/0.1.9/mermaid-layout-elk.esm.min.mjs";
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<!ENTITY dArr "&#8659;">
<!ENTITY deg "&#176;">
<!ENTITY Delta "&#916;">
<!ENTITY delta "&#948;">
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<h1 id="Texas-RRC-Inspection-Expenses-Analysis">Texas RRC Inspection Expenses Analysis<a class="anchor-link" href="#Texas-RRC-Inspection-Expenses-Analysis"></a></h1><p><strong>Research question:</strong> Does organizational capacity (budget, staffing) predict better regulatory outputs (inspections, compliance, enforcement), and how is that relationship moderated by goal ambiguity, district-level heterogeneity, and spatial/geographic factors?</p>
<h2 id="Hypotheses">Hypotheses<a class="anchor-link" href="#Hypotheses"></a></h2><ul>
<li><strong>H1 — Capacity → Outputs:</strong> Higher OGI budget and FTE predict more inspections, higher compliance rates, and faster violation resolution.</li>
<li><strong>H2 — Goal Ambiguity:</strong> When a larger share of RRC budget goes to the more ambiguous "Energy Resource Development" goal, the capacity → output relationship weakens.</li>
<li><strong>H3 — Multilevel / District Effects:</strong> The capacity → output relationship varies across RRC districts (budget slope heterogeneity).</li>
<li><strong>H4 — Spatial &amp; Geographic:</strong> Offshore-jurisdiction and border districts moderate the capacity → output relationship; spatial autocorrelation in residuals is tested via Moran's I.</li>
</ul>
<p><strong>Data:</strong></p>
<ul>
<li>PostgreSQL warehouse (<code>texas_data</code>): <code>inspections</code>, <code>violations</code>, <code>well_shape_tract</code></li>
<li><code>RRC Budget Data.xlsx</code>: statewide RRC budget by strategy, 2016–2024</li>
<li>Analysis panel: 2016–2025 (N = 130 district-years); regression sample: 2016–2023 (N = 104)</li>
</ul>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">urllib.parse</span><span class="w"> </span><span class="kn">import</span> <span class="n">quote_plus</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">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">matplotlib.ticker</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mticker</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">dotenv</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_dotenv</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sqlalchemy</span><span class="w"> </span><span class="kn">import</span> <span class="n">create_engine</span><span class="p">,</span> <span class="n">text</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">cdist</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">UserWarning</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="s2">"display.float_format"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{:,.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">load_dotenv</span><span class="p">(</span><span class="n">override</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">host</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"PGHOST"</span><span class="p">,</span> <span class="s2">"localhost"</span><span class="p">)</span>
<span class="n">port</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"5433"</span><span class="p">,</span> <span class="s2">"5433"</span><span class="p">)</span>
<span class="n">user</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"PGUSER"</span><span class="p">,</span> <span class="s2">"postgres"</span><span class="p">)</span>
<span class="n">password</span> <span class="o">=</span> <span class="n">quote_plus</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"PGPASSWORD"</span><span class="p">,</span> <span class="s2">""</span><span class="p">))</span>
<span class="n">database</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">"PGDATABASE"</span><span class="p">,</span> <span class="s2">"texas_data"</span><span class="p">)</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"postgresql+psycopg2://</span><span class="si">{</span><span class="n">user</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s2">@</span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">database</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">"Connected → </span><span class="si">{</span><span class="n">database</span><span class="si">}</span><span class="s2"> on </span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Connected → texas_data on localhost:5433
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<h2 id="1.-Data-Loading">1. Data Loading<a class="anchor-link" href="#1.-Data-Loading"></a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># District-year inspection metrics aggregated in SQL.</span>
<span class="c1"># LAG() computes days since the previous inspection for the same well (api_norm).</span>
<span class="n">insp_sql</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">WITH lagged AS (</span>
<span class="s2"> SELECT</span>
<span class="s2"> district,</span>
<span class="s2"> EXTRACT(year FROM inspection_date)::int AS year,</span>
<span class="s2"> api_norm,</span>
<span class="s2"> inspection_date,</span>
<span class="s2"> CASE WHEN UPPER(compliance::text) IN ('YES', 'Y') THEN 1.0 ELSE 0.0 END AS is_compliant,</span>
<span class="s2"> EXTRACT(EPOCH FROM (</span>
<span class="s2"> inspection_date</span>
<span class="s2"> - LAG(inspection_date) OVER (PARTITION BY api_norm ORDER BY inspection_date)</span>
<span class="s2"> )) / 86400.0 AS days_since_prev</span>
<span class="s2"> FROM inspections</span>
<span class="s2"> WHERE inspection_date IS NOT NULL</span>
<span class="s2"> AND district IS NOT NULL</span>
<span class="s2"> AND EXTRACT(year FROM inspection_date) BETWEEN 2016 AND 2025</span>
<span class="s2">)</span>
<span class="s2">SELECT</span>
<span class="s2"> district,</span>
<span class="s2"> year,</span>
<span class="s2"> COUNT(*) AS total_inspections,</span>
<span class="s2"> COUNT(DISTINCT api_norm) AS unique_wells,</span>
<span class="s2"> ROUND(AVG(is_compliant)::numeric * 100, 2) AS compliance_rate,</span>
<span class="s2"> ROUND(AVG(days_since_prev)::numeric, 1) AS avg_days_between_inspections</span>
<span class="s2">FROM lagged</span>
<span class="s2">GROUP BY district, year</span>
<span class="s2">ORDER BY district, year</span>
<span class="s2">"""</span>
<span class="n">insp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="n">text</span><span class="p">(</span><span class="n">insp_sql</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Inspections panel: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">insp</span><span class="p">)</span><span class="si">:</span><span class="s2">,</span><span class="si">}</span><span class="s2"> district-year rows | </span><span class="si">{</span><span class="n">insp</span><span class="p">[</span><span class="s1">'district'</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span><span class="si">}</span><span class="s2"> districts"</span><span class="p">)</span>
<span class="n">insp</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Inspections panel: 130 district-year rows | 13 districts
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<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
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vertical-align: top;
}
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text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>district</th>
<th>year</th>
<th>total_inspections</th>
<th>unique_wells</th>
<th>compliance_rate</th>
<th>avg_days_between_inspections</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>01</td>
<td>2016</td>
<td>13975</td>
<td>4055</td>
<td>69.42</td>
<td>18.90</td>
</tr>
<tr>
<th>1</th>
<td>01</td>
<td>2017</td>
<td>18022</td>
<td>6153</td>
<td>83.52</td>
<td>56.80</td>
</tr>
<tr>
<th>2</th>
<td>01</td>
<td>2018</td>
<td>23826</td>
<td>9109</td>
<td>85.61</td>
<td>53.50</td>
</tr>
<tr>
<th>3</th>
<td>01</td>
<td>2019</td>
<td>19790</td>
<td>6447</td>
<td>84.97</td>
<td>79.80</td>
</tr>
<tr>
<th>4</th>
<td>01</td>
<td>2020</td>
<td>26006</td>
<td>8716</td>
<td>85.52</td>
<td>122.90</td>
</tr>
</tbody>
</table>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># District-year violation metrics. Blank last_enf_action strings treated as no action.</span>
<span class="n">viol_sql</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">SELECT</span>
<span class="s2"> district,</span>
<span class="s2"> EXTRACT(year FROM violation_disc_date)::int AS year,</span>
<span class="s2"> COUNT(*) AS total_violations,</span>
<span class="s2"> COUNT(DISTINCT api_norm) AS unique_wells_with_violations,</span>
<span class="s2"> SUM(CASE WHEN major_viol_ind = 'Y' THEN 1 ELSE 0 END) AS major_violations,</span>
<span class="s2"> ROUND(AVG(CASE WHEN compliant_on_reinsp = 'Y' THEN 1.0 ELSE 0.0 END)::numeric * 100, 2)</span>
<span class="s2"> AS resolution_rate,</span>
<span class="s2"> ROUND(AVG(CASE WHEN last_enf_action IS NOT NULL AND last_enf_action &lt;&gt; ''</span>
<span class="s2"> THEN 1.0 ELSE 0.0 END)::numeric * 100, 2) AS enforcement_rate,</span>
<span class="s2"> ROUND(AVG(</span>
<span class="s2"> CASE WHEN last_enf_action_date IS NOT NULL</span>
<span class="s2"> THEN EXTRACT(EPOCH FROM (last_enf_action_date - violation_disc_date)) / 86400.0</span>
<span class="s2"> END</span>
<span class="s2"> )::numeric, 1) AS avg_days_to_enforcement</span>
<span class="s2">FROM violations</span>
<span class="s2">WHERE violation_disc_date IS NOT NULL</span>
<span class="s2"> AND district IS NOT NULL</span>
<span class="s2"> AND EXTRACT(year FROM violation_disc_date) BETWEEN 2016 AND 2025</span>
<span class="s2">GROUP BY district, year</span>
<span class="s2">ORDER BY district, year</span>
<span class="s2">"""</span>
<span class="n">viol</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="n">text</span><span class="p">(</span><span class="n">viol_sql</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Violations panel: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">viol</span><span class="p">)</span><span class="si">:</span><span class="s2">,</span><span class="si">}</span><span class="s2"> district-year rows"</span><span class="p">)</span>
<span class="n">viol</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
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<pre>Violations panel: 130 district-year rows
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>district</th>
<th>year</th>
<th>total_violations</th>
<th>unique_wells_with_violations</th>
<th>major_violations</th>
<th>resolution_rate</th>
<th>enforcement_rate</th>
<th>avg_days_to_enforcement</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>01</td>
<td>2016</td>
<td>5720</td>
<td>1009</td>
<td>0</td>
<td>21.42</td>
<td>100.00</td>
<td>198.60</td>
</tr>
<tr>
<th>1</th>
<td>01</td>
<td>2017</td>
<td>4380</td>
<td>767</td>
<td>0</td>
<td>44.36</td>
<td>100.00</td>
<td>269.50</td>
</tr>
<tr>
<th>2</th>
<td>01</td>
<td>2018</td>
<td>5766</td>
<td>997</td>
<td>0</td>
<td>64.46</td>
<td>100.00</td>
<td>229.00</td>
</tr>
<tr>
<th>3</th>
<td>01</td>
<td>2019</td>
<td>3593</td>
<td>902</td>
<td>4</td>
<td>49.37</td>
<td>100.00</td>
<td>239.00</td>
</tr>
<tr>
<th>4</th>
<td>01</td>
<td>2020</td>
<td>4838</td>
<td>1019</td>
<td>5</td>
<td>27.43</td>
<td>100.00</td>
<td>402.90</td>
</tr>
</tbody>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">BUDGET_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">"RRC Budget Data.xlsx"</span><span class="p">)</span>
<span class="n">raw</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">BUDGET_PATH</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">YEARS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">2017</span><span class="p">,</span> <span class="mi">2018</span><span class="p">,</span> <span class="mi">2019</span><span class="p">,</span> <span class="mi">2020</span><span class="p">,</span> <span class="mi">2021</span><span class="p">,</span> <span class="mi">2022</span><span class="p">,</span> <span class="mi">2023</span><span class="p">,</span> <span class="mi">2024</span><span class="p">]</span>
<span class="n">COLS</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># spreadsheet columns 1-9 map to years 2016-2024</span>
<span class="c1"># ── Section 1: Energy Resource Development (rows 7-18) ──────────────────────</span>
<span class="n">erd</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="s2">"year"</span><span class="p">:</span> <span class="n">YEARS</span><span class="p">,</span>
<span class="s2">"strategy"</span><span class="p">:</span> <span class="s2">"Energy Resource Development"</span><span class="p">,</span>
<span class="s2">"total_budget"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"salaries"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"other_personnel"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"professional_fees"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"travel"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"other_operating"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"capital_exp"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">17</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"fte"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="p">})</span>
<span class="c1"># ── Section 2: Oil/Gas Monitoring &amp; Inspections (rows 20-31) ────────────────</span>
<span class="n">ogi</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="s2">"year"</span><span class="p">:</span> <span class="n">YEARS</span><span class="p">,</span>
<span class="s2">"strategy"</span><span class="p">:</span> <span class="s2">"Oil/Gas Monitoring &amp; Inspections"</span><span class="p">,</span>
<span class="s2">"total_budget"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"salaries"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"other_personnel"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">21</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"professional_fees"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">22</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"travel"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">26</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"other_operating"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">29</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"capital_exp"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="s2">"fte"</span><span class="p">:</span> <span class="n">raw</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">31</span><span class="p">,</span> <span class="n">COLS</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">),</span>
<span class="p">})</span>
<span class="n">budget_long</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">erd</span><span class="p">,</span> <span class="n">ogi</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Budget long: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">budget_long</span><span class="p">)</span><span class="si">}</span><span class="s2"> rows (2 strategies × </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">YEARS</span><span class="p">)</span><span class="si">}</span><span class="s2"> years)"</span><span class="p">)</span>
<span class="n">budget_long</span>
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<pre>Budget long: 18 rows (2 strategies × 9 years)
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<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>year</th>
<th>strategy</th>
<th>total_budget</th>
<th>salaries</th>
<th>other_personnel</th>
<th>professional_fees</th>
<th>travel</th>
<th>other_operating</th>
<th>capital_exp</th>
<th>fte</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2016</td>
<td>Energy Resource Development</td>
<td>11,708,475.00</td>
<td>7,669,719.00</td>
<td>398,589.00</td>
<td>3,366,389.00</td>
<td>16,477.00</td>
<td>210,293.00</td>
<td>0.00</td>
<td>130.60</td>
</tr>
<tr>
<th>1</th>
<td>2017</td>
<td>Energy Resource Development</td>
<td>10,911,094.00</td>
<td>7,273,775.00</td>
<td>389,348.00</td>
<td>3,118,066.00</td>
<td>6,792.00</td>
<td>77,855.00</td>
<td>0.00</td>
<td>120.30</td>
</tr>
<tr>
<th>2</th>
<td>2018</td>
<td>Energy Resource Development</td>
<td>9,846,886.00</td>
<td>7,292,933.00</td>
<td>282,337.00</td>
<td>977,645.00</td>
<td>28,694.00</td>
<td>1,045,727.00</td>
<td>0.00</td>
<td>131.00</td>
</tr>
<tr>
<th>3</th>
<td>2019</td>
<td>Energy Resource Development</td>
<td>11,123,757.00</td>
<td>8,068,497.00</td>
<td>217,988.00</td>
<td>1,493,755.00</td>
<td>73,651.00</td>
<td>988,740.00</td>
<td>13,232.00</td>
<td>137.40</td>
</tr>
<tr>
<th>4</th>
<td>2020</td>
<td>Energy Resource Development</td>
<td>17,280,569.00</td>
<td>9,707,894.00</td>
<td>236,356.00</td>
<td>5,989,236.00</td>
<td>41,752.00</td>
<td>1,165,481.00</td>
<td>54,037.00</td>
<td>153.40</td>
</tr>
<tr>
<th>5</th>
<td>2021</td>
<td>Energy Resource Development</td>
<td>16,237,704.00</td>
<td>10,887,561.00</td>
<td>237,777.00</td>
<td>3,562,816.00</td>
<td>5,614.00</td>
<td>1,446,301.00</td>
<td>10,140.00</td>
<td>168.10</td>
</tr>
<tr>
<th>6</th>
<td>2022</td>
<td>Energy Resource Development</td>
<td>25,583,205.00</td>
<td>11,166,309.00</td>
<td>246,340.00</td>
<td>12,560,550.00</td>
<td>37,731.00</td>
<td>1,246,443.00</td>
<td>19,985.00</td>
<td>157.10</td>
</tr>
<tr>
<th>7</th>
<td>2023</td>
<td>Energy Resource Development</td>
<td>26,903,564.00</td>
<td>11,056,060.00</td>
<td>252,933.00</td>
<td>12,846,821.00</td>
<td>56,650.00</td>
<td>2,287,481.00</td>
<td>48,344.00</td>
<td>151.30</td>
</tr>
<tr>
<th>8</th>
<td>2024</td>
<td>Energy Resource Development</td>
<td>35,533,565.00</td>
<td>13,183,578.00</td>
<td>229,161.00</td>
<td>15,140,585.00</td>
<td>144,641.00</td>
<td>6,425,653.00</td>
<td>0.00</td>
<td>186.00</td>
</tr>
<tr>
<th>9</th>
<td>2016</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>18,471,666.00</td>
<td>15,080,122.00</td>
<td>685,768.00</td>
<td>1,546,321.00</td>
<td>22,630.00</td>
<td>208,311.00</td>
<td>121,363.00</td>
<td>256.70</td>
</tr>
<tr>
<th>10</th>
<td>2017</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>17,204,058.00</td>
<td>15,086,262.00</td>
<td>686,194.00</td>
<td>176,786.00</td>
<td>19,654.00</td>
<td>230,525.00</td>
<td>272,461.00</td>
<td>249.50</td>
</tr>
<tr>
<th>11</th>
<td>2018</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>17,562,431.00</td>
<td>13,083,406.00</td>
<td>430,429.00</td>
<td>1,147,080.00</td>
<td>57,312.00</td>
<td>1,040,639.00</td>
<td>649,172.00</td>
<td>229.90</td>
</tr>
<tr>
<th>12</th>
<td>2019</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>21,951,747.00</td>
<td>14,878,875.00</td>
<td>340,135.00</td>
<td>2,895,436.00</td>
<td>187,048.00</td>
<td>1,185,772.00</td>
<td>1,255,930.00</td>
<td>255.60</td>
</tr>
<tr>
<th>13</th>
<td>2020</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>26,057,560.00</td>
<td>17,228,302.00</td>
<td>417,683.00</td>
<td>4,822,351.00</td>
<td>106,428.00</td>
<td>1,398,705.00</td>
<td>896,846.00</td>
<td>284.00</td>
</tr>
<tr>
<th>14</th>
<td>2021</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>28,756,689.00</td>
<td>17,155,864.00</td>
<td>426,139.00</td>
<td>8,212,873.00</td>
<td>34,762.00</td>
<td>1,394,783.00</td>
<td>230,439.00</td>
<td>277.80</td>
</tr>
<tr>
<th>15</th>
<td>2022</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>25,914,265.00</td>
<td>17,834,460.00</td>
<td>391,138.00</td>
<td>4,007,178.00</td>
<td>154,334.00</td>
<td>1,255,945.00</td>
<td>694,706.00</td>
<td>264.00</td>
</tr>
<tr>
<th>16</th>
<td>2023</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>34,330,858.00</td>
<td>18,622,389.00</td>
<td>457,753.00</td>
<td>8,945,350.00</td>
<td>149,418.00</td>
<td>2,428,330.00</td>
<td>2,234,623.00</td>
<td>271.20</td>
</tr>
<tr>
<th>17</th>
<td>2024</td>
<td>Oil/Gas Monitoring &amp; Inspections</td>
<td>38,506,556.00</td>
<td>20,834,721.00</td>
<td>361,687.00</td>
<td>8,851,915.00</td>
<td>316,806.00</td>
<td>4,112,998.00</td>
<td>2,659,208.00</td>
<td>280.80</td>
</tr>
</tbody>
</table>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># ── Wide budget: one row per year with ogi_ / erd_ prefixed columns ──────────</span>
<span class="n">ogi_wide</span> <span class="o">=</span> <span class="n">ogi</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="s2">"strategy"</span><span class="p">)</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s2">"ogi_"</span><span class="p">)</span>
<span class="n">erd_wide</span> <span class="o">=</span> <span class="n">erd</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="s2">"strategy"</span><span class="p">)</span><span class="o">.</span><span class="n">add_prefix</span><span class="p">(</span><span class="s2">"erd_"</span><span class="p">)</span>
<span class="n">budget_wide</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">ogi_wide</span>
<span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">erd_wide</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s2">"ogi_year"</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s2">"erd_year"</span><span class="p">)</span>
<span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"ogi_year"</span><span class="p">:</span> <span class="s2">"year"</span><span class="p">})</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="s2">"erd_year"</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># ── Merge inspections + violations, then join statewide budget on year ────────</span>
<span class="n">panel</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">insp</span>
<span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">viol</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s2">"district"</span><span class="p">,</span> <span class="s2">"year"</span><span class="p">],</span> <span class="n">how</span><span class="o">=</span><span class="s2">"left"</span><span class="p">)</span>
<span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">budget_wide</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">"year"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"left"</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># ── Derived columns ───────────────────────────────────────────────────────────</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"violations_per_inspection"</span><span class="p">]</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="s2">"total_violations"</span><span class="p">]</span> <span class="o">/</span> <span class="n">panel</span><span class="p">[</span><span class="s2">"total_inspections"</span><span class="p">]</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"ogi_budget_m"</span><span class="p">]</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="s2">"ogi_total_budget"</span><span class="p">]</span> <span class="o">/</span> <span class="mi">1_000_000</span> <span class="c1"># dollars → millions</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"erd_budget_m"</span><span class="p">]</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="s2">"erd_total_budget"</span><span class="p">]</span> <span class="o">/</span> <span class="mi">1_000_000</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"post_2019"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">panel</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">2019</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="c1"># 2024 = budget estimate; 2025 = no budget data — exclude both from regressions</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"is_budget_year"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">panel</span><span class="p">[</span><span class="s2">"year"</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">2024</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="c1"># Goal ambiguity: share of combined budget going to the inspection mission.</span>
<span class="c1"># Higher share = clearer mission focus; lower share = more goal ambiguity.</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"inspection_budget_share"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">panel</span><span class="p">[</span><span class="s2">"ogi_total_budget"</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">panel</span><span class="p">[</span><span class="s2">"ogi_total_budget"</span><span class="p">]</span> <span class="o">+</span> <span class="n">panel</span><span class="p">[</span><span class="s2">"erd_total_budget"</span><span class="p">])</span>
<span class="p">)</span>
<span class="c1"># Fill violation NaNs for districts with zero violations in a given year</span>
<span class="n">fill_cols</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"total_violations"</span><span class="p">,</span> <span class="s2">"unique_wells_with_violations"</span><span class="p">,</span> <span class="s2">"major_violations"</span><span class="p">,</span>
<span class="s2">"resolution_rate"</span><span class="p">,</span> <span class="s2">"enforcement_rate"</span><span class="p">,</span> <span class="s2">"avg_days_to_enforcement"</span><span class="p">,</span>
<span class="s2">"violations_per_inspection"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">panel</span><span class="p">[</span><span class="n">fill_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="n">fill_cols</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">"Analysis panel: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">panel</span><span class="p">)</span><span class="si">:</span><span class="s2">,</span><span class="si">}</span><span class="s2"> rows | "</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">panel</span><span class="p">[</span><span class="s1">'district'</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span><span class="si">}</span><span class="s2"> districts | "</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">panel</span><span class="p">[</span><span class="s1">'year'</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span><span class="si">}</span><span class="s2"> years"</span><span class="p">)</span>
<span class="n">panel</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Analysis panel: 130 rows | 13 districts | 10 years
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<style scoped="">
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vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>district</th>
<th>year</th>
<th>total_inspections</th>
<th>unique_wells</th>
<th>compliance_rate</th>
<th>avg_days_between_inspections</th>
<th>total_violations</th>
<th>unique_wells_with_violations</th>
<th>major_violations</th>
<th>resolution_rate</th>
<th>...</th>
<th>erd_travel</th>
<th>erd_other_operating</th>
<th>erd_capital_exp</th>
<th>erd_fte</th>
<th>violations_per_inspection</th>
<th>ogi_budget_m</th>
<th>erd_budget_m</th>
<th>post_2019</th>
<th>is_budget_year</th>
<th>inspection_budget_share</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>01</td>
<td>2016</td>
<td>13975</td>
<td>4055</td>
<td>69.42</td>
<td>18.90</td>
<td>5720</td>
<td>1009</td>
<td>0</td>
<td>21.42</td>
<td>...</td>
<td>16,477.00</td>
<td>210,293.00</td>
<td>0.00</td>
<td>130.60</td>
<td>0.41</td>
<td>18.47</td>
<td>11.71</td>
<td>0</td>
<td>0</td>
<td>0.61</td>
</tr>
<tr>
<th>1</th>
<td>01</td>
<td>2017</td>
<td>18022</td>
<td>6153</td>
<td>83.52</td>
<td>56.80</td>
<td>4380</td>
<td>767</td>
<td>0</td>
<td>44.36</td>
<td>...</td>
<td>6,792.00</td>
<td>77,855.00</td>
<td>0.00</td>
<td>120.30</td>
<td>0.24</td>
<td>17.20</td>
<td>10.91</td>
<td>0</td>
<td>0</td>
<td>0.61</td>
</tr>
<tr>
<th>2</th>
<td>01</td>
<td>2018</td>
<td>23826</td>
<td>9109</td>
<td>85.61</td>
<td>53.50</td>
<td>5766</td>
<td>997</td>
<td>0</td>
<td>64.46</td>
<td>...</td>
<td>28,694.00</td>
<td>1,045,727.00</td>
<td>0.00</td>
<td>131.00</td>
<td>0.24</td>
<td>17.56</td>
<td>9.85</td>
<td>0</td>
<td>0</td>
<td>0.64</td>
</tr>
<tr>
<th>3</th>
<td>01</td>
<td>2019</td>
<td>19790</td>
<td>6447</td>
<td>84.97</td>
<td>79.80</td>
<td>3593</td>
<td>902</td>
<td>4</td>
<td>49.37</td>
<td>...</td>
<td>73,651.00</td>
<td>988,740.00</td>
<td>13,232.00</td>
<td>137.40</td>
<td>0.18</td>
<td>21.95</td>
<td>11.12</td>
<td>1</td>
<td>0</td>
<td>0.66</td>
</tr>
<tr>
<th>4</th>
<td>01</td>
<td>2020</td>
<td>26006</td>
<td>8716</td>
<td>85.52</td>
<td>122.90</td>
<td>4838</td>
<td>1019</td>
<td>5</td>
<td>27.43</td>
<td>...</td>
<td>41,752.00</td>
<td>1,165,481.00</td>
<td>54,037.00</td>
<td>153.40</td>
<td>0.19</td>
<td>26.06</td>
<td>17.28</td>
<td>1</td>
<td>0</td>
<td>0.60</td>
</tr>
</tbody>
</table>
<p>5 rows × 34 columns</p>
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<h2 id="2.-Exploratory-Overview">2. Exploratory Overview<a class="anchor-link" href="#2.-Exploratory-Overview"></a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Year-level means across districts</span>
<span class="n">yearly</span> <span class="o">=</span> <span class="n">panel</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"year"</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">ogi_budget_m</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"first"</span><span class="p">),</span>
<span class="n">ogi_fte</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"ogi_fte"</span><span class="p">,</span> <span class="s2">"first"</span><span class="p">),</span>
<span class="n">total_inspections</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"total_inspections"</span><span class="p">,</span> <span class="s2">"mean"</span><span class="p">),</span>
<span class="n">compliance_rate</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"compliance_rate"</span><span class="p">,</span> <span class="s2">"mean"</span><span class="p">),</span>
<span class="n">total_violations</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"total_violations"</span><span class="p">,</span> <span class="s2">"mean"</span><span class="p">),</span>
<span class="n">resolution_rate</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"resolution_rate"</span><span class="p">,</span> <span class="s2">"mean"</span><span class="p">),</span>
<span class="n">avg_days_to_enf</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"avg_days_to_enforcement"</span><span class="p">,</span><span class="s2">"mean"</span><span class="p">),</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="nb">print</span><span class="p">(</span><span class="n">yearly</span><span class="o">.</span><span class="n">to_string</span><span class="p">())</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">2</span><span class="p">,</span> <span class="mi">3</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">8</span><span class="p">))</span>
<span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"ogi_budget_m"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"OGI Budget ($M)"</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mticker</span><span class="o">.</span><span class="n">FuncFormatter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">_</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"$</span><span class="si">{</span><span class="n">x</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">M"</span><span class="p">))</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"ogi_fte"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"OGI FTE Positions"</span><span class="p">)</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"total_inspections"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Avg Inspections / District"</span><span class="p">)</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"compliance_rate"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Avg Compliance Rate (%)"</span><span class="p">)</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"resolution_rate"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Avg Resolution Rate (%)"</span><span class="p">)</span>
<span class="n">yearly</span><span class="p">[</span><span class="s2">"avg_days_to_enf"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">"o"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Avg Days to Enforcement"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"2019 policy"</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="s2">"Year"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre> ogi_budget_m ogi_fte total_inspections compliance_rate total_violations resolution_rate avg_days_to_enf
year
2016 18.47 256.70 18,277.85 83.11 3,398.15 36.78 131.86
2017 17.20 249.50 20,138.54 86.52 2,915.69 59.02 185.01
2018 17.56 229.90 25,703.54 90.17 3,197.62 59.46 207.25
2019 21.95 255.60 25,058.46 89.85 2,550.77 61.44 170.36
2020 26.06 284.00 27,669.46 89.57 2,750.92 56.81 154.66
2021 28.76 277.80 24,115.54 88.76 2,556.38 66.18 118.82
2022 25.91 264.00 32,023.54 89.82 2,819.92 67.85 91.50
2023 34.33 271.20 33,805.69 91.62 2,598.62 69.65 105.15
2024 38.51 280.80 36,552.77 92.58 2,221.15 65.13 76.93
2025 NaN NaN 34,082.08 90.52 2,530.38 52.06 36.62
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">corr_cols</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"ogi_fte"</span><span class="p">,</span> <span class="s2">"inspection_budget_share"</span><span class="p">,</span>
<span class="s2">"total_inspections"</span><span class="p">,</span> <span class="s2">"compliance_rate"</span><span class="p">,</span>
<span class="s2">"total_violations"</span><span class="p">,</span> <span class="s2">"resolution_rate"</span><span class="p">,</span> <span class="s2">"avg_days_to_enforcement"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="n">corr_cols</span><span class="p">]</span><span class="o">.</span><span class="n">corr</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">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"RdBu_r"</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">corr_cols</span><span class="p">)))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">corr_cols</span><span class="p">)))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">corr_cols</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"right"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">corr_cols</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</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">corr_cols</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="nb">len</span><span class="p">(</span><span class="n">corr_cols</span><span class="p">)):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">corr</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"center"</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s2">"center"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</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="s2">"Correlation Matrix — Key Variables"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<h2 id="Data-and-Methods">Data and Methods<a class="anchor-link" href="#Data-and-Methods"></a></h2><h3 id="Data-Sources">Data Sources<a class="anchor-link" href="#Data-Sources"></a></h3><p>This study draws on two primary data sources. The first is the Texas Railroad Commission
(RRC) Oil and Gas Division administrative database. Inspection records span fiscal years 2016–2025 and encompass approximately
1.9 million inspection events distributed across 13 RRC administrative districts;
violation records include approximately 193,000 enforcement actions. From the inspections
table, district-year aggregates are constructed for three regulatory output measures:
(1) <em>compliance rate</em> — the share of annual inspections in a district that did not result
in a compliance failure; (2) <em>total inspections</em> — the count of field inspection events;
and (3) average days between successive inspections of the same well, computed via a
SQL window function (<code>LAG</code>) over ordered inspection timestamps. From the violations table,
district-year aggregates include the <em>violation resolution rate</em> (share of violations
for which the operator was found compliant on re-inspection), enforcement rate, and average
days from violation discovery to enforcement action.</p>
<p>The second source is RRC budget data drawn from Legislative Appropriations Requests,
covering fiscal years 2016–2024. Budget appropriations are reported at the statewide level
disaggregated by goal and strategy. Two strategies are central to this analysis:
(1) <em>Oil and Gas Monitoring and Inspections</em> (OGI), which directly funds field inspection
operations; and (2) <em>Energy Resource Development</em> (ERD), encompassing the broader mandate
to promote oil and gas resource opportunities. For each strategy, the data include total
appropriations, salaries, professional fees, travel, other operating expenditures, capital
outlays, and authorized full-time equivalent (FTE) positions. Fiscal year 2024 represents
a budget estimate rather than expenditure actuals and is excluded from all regression
models.</p>
<h3 id="Sample-and-Panel-Construction">Sample and Panel Construction<a class="anchor-link" href="#Sample-and-Panel-Construction"></a></h3><p>The unit of analysis is the <strong>district-year</strong>. The analytic panel contains
<strong>N = 130 observations</strong> (13 districts × 10 years, 2016–2025), of which
<strong>104 observations</strong> (2016–2023) constitute the regression sample. Fiscal years
2024 (budget estimate) and 2025 (no budget data available) are retained in
descriptive analyses but excluded from all regression models. Because inspection
and enforcement activity in 2025 represents a partial year as of the data
extract, enforcement-timing metrics for that year are subject to right-censoring:
violations discovered in late 2024 and 2025 may not yet have received a recorded
enforcement action, compressing observed days-to-enforcement.. Because RRC budget
appropriations are reported at the statewide level, budget and FTE variables enter the
panel as year-varying but district-invariant covariates. Identification of budget effects
therefore relies on year-to-year variation in statewide appropriations rather than
cross-district budget contrasts.</p>
<h3 id="Measures">Measures<a class="anchor-link" href="#Measures"></a></h3><p><strong>Dependent variables.</strong> Three measures capture distinct dimensions of regulatory output:
<em>total inspections</em> (inspection volume), <em>compliance rate</em> (%), and <em>violation resolution
rate</em> (%). Compliance rate and resolution rate capture quality of enforcement rather than
quantity and represent different points in the regulatory pipeline: compliance is measured
at the point of inspection while resolution is measured after a violation has been
discovered and acted upon.</p>
<p><strong>Organizational capacity.</strong> The primary capacity measure is OGI total appropriations in
millions of dollars ($\text{Budget}_t$), reflecting the statewide resource envelope
available for inspection activities in year $t$. An auxiliary measure — OGI authorized
FTE positions — is included in descriptive analyses.</p>
<p><strong>Goal ambiguity.</strong> Following Chun and Rainey (2005), goal ambiguity is operationalized
via the relative concentration of resources across missions. The <em>inspection budget share</em>
($\text{Share}_t$) captures the fraction of combined OGI and ERD appropriations directed
toward the inspection mandate:</p>
<p>$$\text{Share}_t = \frac{\text{OGI Budget}_t}{\text{OGI Budget}_t + \text{ERD Budget}_t}$$</p>
<p>Higher values indicate greater mission clarity (resources more concentrated on inspections);
lower values indicate greater goal ambiguity (resources spread across competing mandates).
Over the study period $\text{Share}_t$ ranged from 0.59 (2022) to 0.67 (2018), reflecting
meaningful year-to-year variation in budgetary prioritization.</p>
<p><strong>Geographic moderators.</strong> Two binary district-level indicators capture geographic
context: $\text{Offshore}_d = 1$ for districts 02, 03, and 04, which hold dual onshore
and offshore oversight jurisdiction, and $\text{Border}_d = 1$ for districts 01–04,
which are proximate to the Texas Gulf Coast and the US–Mexico border corridor.</p>
<h3 id="Estimation-Strategy">Estimation Strategy<a class="anchor-link" href="#Estimation-Strategy"></a></h3><p>All models are estimated via ordinary least squares (OLS) with standard errors clustered
at the district level ($G = 13$) to account for within-district serial correlation.
District fixed effects absorb time-invariant heterogeneity across offices — including
differences in geographic complexity, historical enforcement culture, and staffing
composition — and ensure that budget effects are identified from within-district,
year-to-year variation.</p>
<p><strong>H1 — Baseline capacity model:</strong></p>
<p>$$Y_{dt} = \alpha + \beta_1 \, \text{Budget}_t + \sum_{d} \gamma_d \, \mathbf{1}[\text{district} = d] + \varepsilon_{dt}$$</p>
<p>where $Y_{dt}$ is the regulatory output for district $d$ in year $t$, $\gamma_d$ are
district fixed effects, and $\varepsilon_{dt}$ is the idiosyncratic error.</p>
<p><strong>H2 — Goal ambiguity moderation:</strong></p>
<p>$$Y_{dt} = \alpha + \beta_1 \, \text{Budget}_t + \beta_2 \, \text{Share}_t + \beta_3 \left( \text{Budget}_t \times \text{Share}_t \right) + \sum_{d} \gamma_d + \varepsilon_{dt}$$</p>
<p>The coefficient $\beta_3$ tests whether goal clarity conditions the capacity–output
relationship. A positive $\hat{\beta}_3$ would indicate that clearer mission focus
amplifies budget effects; a negative value would suggest diminishing returns or
cross-strategy resource substitution.</p>
<p><strong>H3 — District slope heterogeneity:</strong></p>
<p>$$Y_{dt} = \alpha + \beta_1 \, \text{Budget}_t + \sum_{d=2}^{D} \delta_d \left( \text{Budget}_t \times \mathbf{1}[d] \right) + \sum_{d} \gamma_d + \varepsilon_{dt}$$</p>
<p>District-specific budget slopes are recovered as $\hat{\beta}_1 + \hat{\delta}_d$.
Because budget varies only along the time dimension and district fixed effects are
included, interaction term standard errors are inflated by near-perfect multicollinearity;
these estimates are treated as descriptive indicators of heterogeneity only.</p>
<p><strong>H4 — Geographic moderation and spatial autocorrelation:</strong></p>
<p>$$Y_{dt} = \alpha + \beta_1 \, \text{Budget}_t + \beta_2 \, \text{Offshore}_d + \beta_3 \, \text{Border}_d + \beta_4 \left( \text{Budget}_t \times \text{Offshore}_d \right) + \beta_5 \left( \text{Budget}_t \times \text{Border}_d \right) + \sum_{d} \gamma_d + \varepsilon_{dt}$$</p>
<p><strong>Robustness checks.</strong> Two supplementary tests address limitations of the
baseline models. First, wild cluster bootstrap inference (Rademacher weights,
$B = 999$ draws; Cameron, Gelbach &amp; Miller 2008) is used to re-test H1
coefficients, providing valid p-values with the small number of clusters
($G = 13$). Second, a distributed lag specification replaces the
contemporaneous budget measure with its one-year lag ($\text{Budget}_{t-1}$),
and also estimates a model including both, to test whether budget effects
operate with a delay consistent with a hiring-and-deployment mechanism.
The distributed lag regression sample covers 2017–2023 ($N = 91$).</p>
<p>Spatial autocorrelation in H1 model residuals is assessed via Moran's $I$ computed on a
row-normalized inverse-distance spatial weights matrix constructed from district centroids
derived by averaging well-level geographic coordinates within each district.</p>
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<h2 id="Analysis">Analysis<a class="anchor-link" href="#Analysis"></a></h2><p>This study employs a fixed-effects panel regression framework to examine whether
year-to-year changes in RRC organizational capacity — as measured by statewide budget
appropriations — translate into improvements in regulatory outputs across Texas oil and
gas inspection districts. The analytic panel spans 13 RRC districts over ten fiscal
years (2016–2025), yielding 130 district-year observations. Regression analyses are
restricted to 2016–2023 (N = 104), excluding FY2024 (budget estimate only) and FY2025
(no budget data available). The identification strategy leverages within-district
variation in outcomes as a function of year-to-year shifts in statewide OGI
appropriations, net of persistent inter-district differences absorbed by district fixed
effects.</p>
<p>The choice of a district-year panel rather than a well-level panel is motivated by the
structure of the budget data, which is available only at the statewide level. Because the
key independent variable — OGI appropriations — varies along the time dimension only, it
functions as a common, year-specific exposure applied uniformly to all districts. District
fixed effects then absorb unobservable office-level characteristics that remain stable over
the study period, such as geographic complexity, historical enforcement intensity, and
local administrative capacity. Causal identification is thus predicated on the assumption
that, absent changes in budget, within-district outcome trajectories would have followed
parallel trends across years — an assumption that cannot be directly tested but is
partially supported by the pre-period stability visible in the descriptive trends.</p>
<p><strong>H1</strong> tests the core capacity hypothesis using the baseline specification. Each of the
three dependent variables — total inspections, compliance rate, and violation resolution
rate — is regressed separately on OGI budget (in millions of dollars) and district fixed
effects. Cluster-robust standard errors are used throughout given the modest number of
clusters ($G = 13$).</p>
<p><strong>H2</strong> extends the baseline by interacting OGI budget with the inspection budget share,
operationalizing goal ambiguity as the degree to which RRC appropriations are concentrated
on the inspection mandate versus the broader energy development mission. The sign and
significance of the interaction term $\beta_3$ determines whether goal clarity amplifies
or attenuates the capacity–output relationship.</p>
<p><strong>H3</strong> tests for heterogeneity in budget–outcome slopes across districts by including
budget $\times$ district interaction terms. Given only eight years of data per district,
the saturated interaction model is estimated with approximately zero residual degrees of
freedom for the fixed-effects component; as a result, interaction-term standard errors
are unreliable and these estimates are reported as exploratory indicators of cross-district
variation rather than inferential tests. The accompanying bar chart (below) summarizes
district-specific slopes as point estimates.</p>
<p><strong>H4</strong> assesses whether offshore-jurisdiction and border-proximate districts — which face
distinct operational environments — exhibit different budget sensitivity. The model adds
geographic level effects and budget $\times$ geography interaction terms to the baseline
specification. A complementary spatial diagnostic — Moran's $I$ applied to the residuals
from the H1 compliance model — tests for geographic clustering of unexplained outcome
variation that could indicate omitted spatial processes or spillovers across district
boundaries.</p>
<p>All regressions exclude fiscal years 2024 (budget estimate) and 2025 (no budget data),
retaining 2016–2023 as the regression sample (N = 104). The extended panel through 2025
is used for descriptive trend analysis only. Enforcement-timing metrics for 2025 should
be interpreted cautiously: because the data extract covers a partial year, violations
discovered in late 2024 and 2025 may not yet have a recorded enforcement action,
artificially compressing observed days-to-enforcement and resolution rates for that year.</p>
<p>Two supplementary robustness checks address key inferential limitations. First, wild
cluster bootstrap inference (Rademacher, B = 999) re-tests H1 with valid small-sample
p-values given G = 13 clusters. Second, a distributed lag specification tests whether
budget effects operate with a one-year delay, consistent with a hiring-and-deployment
implementation timeline. Results from both checks are reported following the main
hypothesis tests.</p>
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<h2 id="H1:-Organizational-Capacity-%E2%86%92-Policy-Outputs">H1: Organizational Capacity → Policy Outputs<a class="anchor-link" href="#H1:-Organizational-Capacity-%E2%86%92-Policy-Outputs"></a></h2><p><strong>Prediction:</strong> Higher OGI budget predicts more inspections, higher compliance rates,
and faster violation resolution.</p>
<p><strong>Model:</strong> OLS with district fixed effects, 2016–2023 (N = 104). Budget varies only over
time, identifying effects via year-to-year changes in statewide OGI appropriations;
district fixed effects absorb persistent cross-district differences. Standard errors
clustered at the district level (G = 13).</p>
<p><strong>Finding (preview):</strong> All three outcomes show positive, statistically significant budget
coefficients under asymptotic inference. Wild cluster bootstrap results (reported in the
Robustness Checks section) indicate these asymptotic p-values overstate precision; results
should be interpreted as suggestive rather than definitive.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">actuals</span> <span class="o">=</span> <span class="n">panel</span><span class="p">[</span><span class="n">panel</span><span class="p">[</span><span class="s2">"is_budget_year"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</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">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">actuals</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"total_inspections"</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Budget → Inspections"</span><span class="p">)</span>
<span class="n">actuals</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"compliance_rate"</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Budget → Compliance Rate (%)"</span><span class="p">)</span>
<span class="n">actuals</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"resolution_rate"</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">title</span><span class="o">=</span><span class="s2">"Budget → Resolution Rate (%)"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</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="s2">"OGI Budget ($M)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">m_inspections</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"total_inspections ~ ogi_budget_m + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">m_compliance</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">m_resolution</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"resolution_rate ~ ogi_budget_m + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="c1"># Detect actual column names — statsmodels uses z/P&gt;|z| with robust SEs in some versions</span>
<span class="n">_tbl</span> <span class="o">=</span> <span class="n">m_inspections</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">_t</span> <span class="o">=</span> <span class="s2">"t"</span> <span class="k">if</span> <span class="s2">"t"</span> <span class="ow">in</span> <span class="n">_tbl</span><span class="o">.</span><span class="n">columns</span> <span class="k">else</span> <span class="s2">"z"</span>
<span class="n">_p</span> <span class="o">=</span> <span class="s2">"P&gt;|t|"</span> <span class="k">if</span> <span class="s2">"P&gt;|t|"</span> <span class="ow">in</span> <span class="n">_tbl</span><span class="o">.</span><span class="n">columns</span> <span class="k">else</span> <span class="s2">"P&gt;|z|"</span>
<span class="n">display_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Coef."</span><span class="p">,</span> <span class="s2">"Std.Err."</span><span class="p">,</span> <span class="n">_t</span><span class="p">,</span> <span class="n">_p</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"H1a — OGI Budget ($M) → Total Inspections"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_inspections</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m"</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_inspections</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_inspections</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</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">"H1b — OGI Budget ($M) → Compliance Rate (%)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_compliance</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m"</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_compliance</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_compliance</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"H1c — OGI Budget ($M) → Resolution Rate (%)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_resolution</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m"</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_resolution</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_resolution</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>H1a — OGI Budget ($M) → Total Inspections
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 666.30 212.98 3.13 0.00
R² = 0.769 Adj. R² = 0.736
H1b — OGI Budget ($M) → Compliance Rate (%)
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 0.26 0.11 2.31 0.02
R² = 0.538 Adj. R² = 0.471
H1c — OGI Budget ($M) → Resolution Rate (%)
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 1.05 0.32 3.28 0.00
R² = 0.624 Adj. R² = 0.569
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<h2 id="H2:-Goal-Ambiguity-Moderates-Capacity-Effects">H2: Goal Ambiguity Moderates Capacity Effects<a class="anchor-link" href="#H2:-Goal-Ambiguity-Moderates-Capacity-Effects"></a></h2><p><strong>Prediction:</strong> When a larger share of combined RRC budget flows to the broader
"Energy Resource Development" goal (lower <code>inspection_budget_share</code>), the capacity →
output link weakens. A positive interaction coefficient would support H2.</p>
<p><strong>Operationalization:</strong>
<code>inspection_budget_share = ogi_budget / (ogi_budget + erd_budget)</code></p>
<p><strong>Identification note:</strong> Like the budget measure itself, <code>inspection_budget_share</code>
varies only over time, not across districts. The interaction term therefore exploits
the same narrow temporal variation as the main effect — budget share ranged from 0.59
to 0.67 over 2016–2023, a span of 8 percentage points across 8 years. This limits
the strength of inference that can be drawn from the moderation test.</p>
<p><strong>Finding (preview):</strong> The interaction is significant and negative ($\hat{\beta}_3 = -6.53$,
$p &lt; .01$), but interpretation is constrained by the identification limitations above.
Results are discussed in the Results section.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">m_h2</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m * inspection_budget_share + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">key_rows</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"inspection_budget_share"</span><span class="p">,</span> <span class="s2">"ogi_budget_m:inspection_budget_share"</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"H2 — Goal Ambiguity Moderation (DV: compliance_rate)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_h2</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">key_rows</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">R² = </span><span class="si">{</span><span class="n">m_h2</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_h2</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># ── Same model with resolution rate as DV ────────────────────────────────────</span>
<span class="n">m_h2_res</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"resolution_rate ~ ogi_budget_m * inspection_budget_share + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</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">H2 — Goal Ambiguity Moderation (DV: resolution_rate)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_h2_res</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">key_rows</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">R² = </span><span class="si">{</span><span class="n">m_h2_res</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_h2_res</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>H2 — Goal Ambiguity Moderation (DV: compliance_rate)
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 4.20 1.09 3.86 0.00
inspection_budget_share 170.18 44.79 3.80 0.00
ogi_budget_m:inspection_budget_share -6.53 1.84 -3.55 0.00
R² = 0.567 Adj. R² = 0.493
H2 — Goal Ambiguity Moderation (DV: resolution_rate)
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 6.68 4.67 1.43 0.15
inspection_budget_share 230.67 204.30 1.13 0.26
ogi_budget_m:inspection_budget_share -9.42 7.99 -1.18 0.24
R² = 0.629 Adj. R² = 0.566
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<h2 id="H3:-District-Multilevel-Effects">H3: District Multilevel Effects<a class="anchor-link" href="#H3:-District-Multilevel-Effects"></a></h2><p><strong>Prediction:</strong> The budget → output slope varies across RRC districts — some districts
translate budget increases into better outputs more effectively than others.</p>
<p><strong>Model:</strong> Interaction <code>ogi_budget_m × C(district)</code> — the reference district captures
the baseline budget slope; interaction terms show how each other district's slope
differs. Standard errors are unreliable due to near-perfect multicollinearity in the
saturated model (budget varies only over time while district FE absorb cross-sectional
variation); results are treated as descriptive point estimates only.</p>
<p><strong>Finding (preview):</strong> District slopes for compliance rate range from −0.34 pp per $1M
(District 03, Houston/Coastal) to +1.36 pp per $1M (District 6E, East Texas Piney
Woods), with most districts showing small positive slopes. The bar chart below plots
district-specific slope estimates.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">m_h3</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m * C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">coef_table</span> <span class="o">=</span> <span class="n">m_h3</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># Baseline budget slope (reference district)</span>
<span class="n">baseline_row</span> <span class="o">=</span> <span class="n">coef_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m"</span><span class="p">]]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Baseline (reference district) budget slope:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">baseline_row</span><span class="p">[</span><span class="n">display_cols</span><span class="p">])</span>
<span class="c1"># District-specific deviations from baseline</span>
<span class="n">interaction_rows</span> <span class="o">=</span> <span class="n">coef_table</span><span class="p">[</span><span class="n">coef_table</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s2">"ogi_budget_m:C"</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">District interaction terms (deviation from reference slope):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">interaction_rows</span><span class="p">[</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</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">R² = </span><span class="si">{</span><span class="n">m_h3</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_h3</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># ── Plot district-specific budget slopes ─────────────────────────────────────</span>
<span class="n">districts</span> <span class="o">=</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="n">slopes</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">districts</span><span class="p">:</span>
<span class="n">key</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"ogi_budget_m:C(district)[T.</span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s2">]"</span>
<span class="n">base</span> <span class="o">=</span> <span class="n">m_h3</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">delta</span> <span class="o">=</span> <span class="n">m_h3</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">slopes</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">d</span><span class="p">)]</span> <span class="o">=</span> <span class="n">base</span> <span class="o">+</span> <span class="n">delta</span>
<span class="n">slope_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">slopes</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">slope_df</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">"#d62728"</span> <span class="k">if</span> <span class="n">v</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">"#1f77b4"</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">slope_df</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.8</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="s2">"Budget slope (compliance rate pp per $M)"</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="s2">"H3 — District-Specific Budget → Compliance Slopes"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre>H3 — District-Heterogeneous Budget Effect (DV: compliance_rate)
Baseline (reference district) budget slope:
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 0.09 0.00 56,876,193,472,228.37 0.00
District interaction terms (deviation from reference slope):
Coef. Std.Err. z P&gt;|z|
ogi_budget_m:C(district)[T.02] 0.15 0.00 22,633,237,551,336.32 0.00
ogi_budget_m:C(district)[T.03] -0.43 0.00 -59,804,100,493,329.36 0.00
ogi_budget_m:C(district)[T.04] 0.19 0.00 78,131,153,896,367.78 0.00
ogi_budget_m:C(district)[T.05] -0.04 0.00 -23,701,820,832,698.50 0.00
ogi_budget_m:C(district)[T.06] 0.34 0.00 60,365,540,001,288.30 0.00
ogi_budget_m:C(district)[T.08] 0.19 0.00 10,356,376,563,126.46 0.00
ogi_budget_m:C(district)[T.09] -0.09 0.00 -14,544,886,315,847.22 0.00
ogi_budget_m:C(district)[T.10] 0.04 0.00 5,748,033,218,673.02 0.00
ogi_budget_m:C(district)[T.6E] 1.27 0.00 64,743,648,722,385.09 0.00
ogi_budget_m:C(district)[T.7B] 0.18 0.00 27,978,802,690,136.84 0.00
ogi_budget_m:C(district)[T.7C] 0.31 0.00 24,243,474,173,332.52 0.00
ogi_budget_m:C(district)[T.8A] 0.10 0.00 59,702,739,775,453.20 0.00
R² = 0.662 Adj. R² = 0.554
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"/>
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<h2 id="H4:-Spatial-and-Geographic-Factors">H4: Spatial and Geographic Factors<a class="anchor-link" href="#H4:-Spatial-and-Geographic-Factors"></a></h2><p><strong>Predictions:</strong></p>
<ul>
<li>Offshore-jurisdiction districts (02, 03, 04) show a different budget → output
relationship due to dual onshore/offshore oversight burden.</li>
<li>Border-proximate districts show a different relationship due to cross-jurisdiction
enforcement complexity.</li>
<li>Spatial autocorrelation in H1 residuals (Moran's I) would indicate unmodeled
geographic spillovers.</li>
</ul>
<p><strong>Finding (preview):</strong> Offshore and border districts show significantly <em>higher</em> baseline
compliance rates (+7.6 pp and +6.0 pp respectively, both $p &lt; .05$) but not different
budget sensitivity. Moran's $I = -0.051$ indicates slight spatial dispersion and no
significant geographic clustering of residuals. Results are discussed in the Results
section.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Texas RRC district geography flags (based on known RRC district locations)</span>
<span class="n">OFFSHORE_DISTRICTS</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"02"</span><span class="p">,</span> <span class="s2">"03"</span><span class="p">,</span> <span class="s2">"04"</span><span class="p">}</span> <span class="c1"># dual onshore + offshore jurisdiction</span>
<span class="n">BORDER_DISTRICTS</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"01"</span><span class="p">,</span> <span class="s2">"02"</span><span class="p">,</span> <span class="s2">"03"</span><span class="p">,</span> <span class="s2">"04"</span><span class="p">}</span> <span class="c1"># south / gulf coast proximity</span>
<span class="n">actuals</span> <span class="o">=</span> <span class="n">actuals</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">actuals</span><span class="p">[</span><span class="s2">"district_str"</span><span class="p">]</span> <span class="o">=</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
<span class="n">actuals</span><span class="p">[</span><span class="s2">"offshore"</span><span class="p">]</span> <span class="o">=</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district_str"</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">OFFSHORE_DISTRICTS</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="n">actuals</span><span class="p">[</span><span class="s2">"border"</span><span class="p">]</span> <span class="o">=</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district_str"</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">BORDER_DISTRICTS</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"District classification:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span>
<span class="n">actuals</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s2">"district_str"</span><span class="p">,</span> <span class="s2">"offshore"</span><span class="p">,</span> <span class="s2">"border"</span><span class="p">])</span>
<span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"district_year_obs"</span><span class="p">)</span>
<span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="p">)</span>
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<pre>District classification:
district_str offshore border district_year_obs
01 0 1 8
02 1 1 8
03 1 1 8
04 1 1 8
05 0 0 8
06 0 0 8
08 0 0 8
09 0 0 8
10 0 0 8
6E 0 0 8
7B 0 0 8
7C 0 0 8
8A 0 0 8
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># ── Spatial regression: offshore and border interactions ─────────────────────</span>
<span class="n">m_h4</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m + offshore + border "</span>
<span class="s2">"+ ogi_budget_m:offshore + ogi_budget_m:border + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">actuals</span><span class="p">,</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">spatial_rows</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"offshore"</span><span class="p">,</span> <span class="s2">"border"</span><span class="p">,</span>
<span class="s2">"ogi_budget_m:offshore"</span><span class="p">,</span> <span class="s2">"ogi_budget_m:border"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">available</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">spatial_rows</span> <span class="k">if</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m_h4</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"H4 — Spatial Moderators (DV: compliance_rate)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_h4</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">available</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">R² = </span><span class="si">{</span><span class="n">m_h4</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_h4</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># ── Moran's I on H1 residuals ─────────────────────────────────────────────────</span>
<span class="c1"># Compute district centroids from well lat/lon joined via inspections</span>
<span class="n">centroids_sql</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">SELECT</span>
<span class="s2"> i.district,</span>
<span class="s2"> AVG(w.latitude) AS lat,</span>
<span class="s2"> AVG(w.longitude) AS lon</span>
<span class="s2">FROM inspections i</span>
<span class="s2">JOIN well_shape_tract w USING (api_norm)</span>
<span class="s2">WHERE w.latitude IS NOT NULL</span>
<span class="s2"> AND w.longitude IS NOT NULL</span>
<span class="s2"> AND i.district IS NOT NULL</span>
<span class="s2">GROUP BY i.district</span>
<span class="s2">"""</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">centroids</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="n">text</span><span class="p">(</span><span class="n">centroids_sql</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
<span class="c1"># Average H1 compliance residuals to district level</span>
<span class="n">resid_df</span> <span class="o">=</span> <span class="n">actuals</span><span class="p">[[</span><span class="s2">"district"</span><span class="p">,</span> <span class="s2">"compliance_rate"</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">resid_df</span><span class="p">[</span><span class="s2">"resid"</span><span class="p">]</span> <span class="o">=</span> <span class="n">m_compliance</span><span class="o">.</span><span class="n">resid</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">actuals</span><span class="o">.</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
<span class="n">resid_by_district</span> <span class="o">=</span> <span class="n">resid_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"district"</span><span class="p">)[</span><span class="s2">"resid"</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">centroids</span> <span class="o">=</span> <span class="n">centroids</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">resid_by_district</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">"district"</span><span class="p">)</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="c1"># Row-normalised inverse-distance weights matrix</span>
<span class="n">coords</span> <span class="o">=</span> <span class="n">centroids</span><span class="p">[[</span><span class="s2">"lon"</span><span class="p">,</span> <span class="s2">"lat"</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span>
<span class="n">D</span> <span class="o">=</span> <span class="n">cdist</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>
<span class="n">W</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">D</span>
<span class="n">W</span> <span class="o">=</span> <span class="n">W</span> <span class="o">/</span> <span class="n">W</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="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">centroids</span><span class="p">[</span><span class="s2">"resid"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">z</span> <span class="o">-</span> <span class="n">z</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">morans_i</span> <span class="o">=</span> <span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">W</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> <span class="o">*</span> <span class="p">(</span><span class="n">z</span> <span class="o">@</span> <span class="n">W</span> <span class="o">@</span> <span class="n">z</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">z</span> <span class="o">@</span> <span class="n">z</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">Moran's I on H1 compliance residuals = </span><span class="si">{</span><span class="n">morans_i</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" &gt; 0 → residuals cluster spatially (similar neighbours)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" ≈ 0 → no spatial pattern"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" &lt; 0 → spatial dispersion (dissimilar neighbours)"</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">District centroids used:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">centroids</span><span class="p">[[</span><span class="s2">"district"</span><span class="p">,</span> <span class="s2">"lat"</span><span class="p">,</span> <span class="s2">"lon"</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="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Moran's I skipped: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>H4 — Spatial Moderators (DV: compliance_rate)
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 0.35 0.15 2.39 0.02
offshore 7.61 3.29 2.31 0.02
border 6.03 2.84 2.12 0.03
ogi_budget_m:offshore -0.03 0.18 -0.16 0.87
ogi_budget_m:border -0.25 0.15 -1.74 0.08
R² = 0.553 Adj. R² = 0.476
Moran's I on H1 compliance residuals = -0.0512
&gt; 0 → residuals cluster spatially (similar neighbours)
≈ 0 → no spatial pattern
&lt; 0 → spatial dispersion (dissimilar neighbours)
District centroids used:
district lat lon
01 29.15 -98.62
02 28.85 -97.41
03 30.12 -95.43
04 27.44 -98.36
05 31.85 -96.15
06 32.29 -94.67
08 31.84 -102.30
09 33.42 -98.22
10 35.77 -101.02
6E 32.40 -94.89
7B 32.75 -99.40
7C 31.11 -101.26
8A 33.12 -102.06
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<h2 id="Results">Results<a class="anchor-link" href="#Results"></a></h2><h3 id="Descriptive-Trends">Descriptive Trends<a class="anchor-link" href="#Descriptive-Trends"></a></h3><p>Table 1 summarizes year-level means for the key variables across 2016–2025, with
regression analyses restricted to 2016–2023. OGI appropriations grew from $18.47 million
in 2016 to $34.33 million in 2023 — an 86 percent nominal increase — with the FY2024
budget estimate reaching $38.51 million. Authorized FTE positions rose modestly from
256.7 to 271.2 over the same period. Inspection volume per district increased from a
mean of 18,278 in 2016 to a peak of 36,553 in 2024, with a partial-year figure of 34,082
recorded for 2025. Mean district compliance rate improved from 83.1 percent in 2016 to
a peak of 92.6 percent in 2024, with a slight moderation to 90.5 percent in the 2025
partial-year extract. Violation resolution rate rose from 36.8 percent in 2016 to 69.7
percent in 2023 before declining to 52.1 percent in 2025; this decline almost certainly
reflects right-censoring rather than a genuine deterioration in enforcement outcomes, as
recently discovered violations will not yet have received a recorded resolution on
re-inspection. Similarly, the 2025 days-to-enforcement figure of 36.6 days should be
interpreted as a lower bound on the true enforcement timeline for that cohort of
violations. These trends are broadly consistent with the organizational capacity
hypothesis, though they are also consistent with secular improvements in industry
compliance independent of budget growth.</p>
<p><strong>Table 1. Year-Level Panel Means, 2016–2025</strong></p>
<table>
<thead>
<tr>
<th style="text-align:center">Year</th>
<th style="text-align:center">OGI Budget ($M)</th>
<th style="text-align:center">OGI FTE</th>
<th style="text-align:center">Inspections/District</th>
<th style="text-align:center">Compliance Rate (%)</th>
<th style="text-align:center">Resolution Rate (%)</th>
<th style="text-align:center">Days to Enforcement</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">2016</td>
<td style="text-align:center">18.47</td>
<td style="text-align:center">256.7</td>
<td style="text-align:center">18,278</td>
<td style="text-align:center">83.1</td>
<td style="text-align:center">36.8</td>
<td style="text-align:center">131.9</td>
</tr>
<tr>
<td style="text-align:center">2017</td>
<td style="text-align:center">17.20</td>
<td style="text-align:center">249.5</td>
<td style="text-align:center">20,139</td>
<td style="text-align:center">86.5</td>
<td style="text-align:center">59.0</td>
<td style="text-align:center">185.0</td>
</tr>
<tr>
<td style="text-align:center">2018</td>
<td style="text-align:center">17.56</td>
<td style="text-align:center">229.9</td>
<td style="text-align:center">25,704</td>
<td style="text-align:center">90.2</td>
<td style="text-align:center">59.5</td>
<td style="text-align:center">207.3</td>
</tr>
<tr>
<td style="text-align:center">2019</td>
<td style="text-align:center">21.95</td>
<td style="text-align:center">255.6</td>
<td style="text-align:center">25,058</td>
<td style="text-align:center">89.9</td>
<td style="text-align:center">61.4</td>
<td style="text-align:center">170.4</td>
</tr>
<tr>
<td style="text-align:center">2020</td>
<td style="text-align:center">26.06</td>
<td style="text-align:center">284.0</td>
<td style="text-align:center">27,669</td>
<td style="text-align:center">89.6</td>
<td style="text-align:center">56.8</td>
<td style="text-align:center">154.7</td>
</tr>
<tr>
<td style="text-align:center">2021</td>
<td style="text-align:center">28.76</td>
<td style="text-align:center">277.8</td>
<td style="text-align:center">24,116</td>
<td style="text-align:center">88.8</td>
<td style="text-align:center">66.2</td>
<td style="text-align:center">118.8</td>
</tr>
<tr>
<td style="text-align:center">2022</td>
<td style="text-align:center">25.91</td>
<td style="text-align:center">264.0</td>
<td style="text-align:center">32,024</td>
<td style="text-align:center">89.8</td>
<td style="text-align:center">67.9</td>
<td style="text-align:center">91.5</td>
</tr>
<tr>
<td style="text-align:center">2023</td>
<td style="text-align:center">34.33</td>
<td style="text-align:center">271.2</td>
<td style="text-align:center">33,806</td>
<td style="text-align:center">91.6</td>
<td style="text-align:center">69.7</td>
<td style="text-align:center">105.2</td>
</tr>
<tr>
<td style="text-align:center">2024†</td>
<td style="text-align:center">38.51</td>
<td style="text-align:center">280.8</td>
<td style="text-align:center">36,553</td>
<td style="text-align:center">92.6</td>
<td style="text-align:center">65.1</td>
<td style="text-align:center">76.9</td>
</tr>
<tr>
<td style="text-align:center">2025‡</td>
<td style="text-align:center">—</td>
<td style="text-align:center">—</td>
<td style="text-align:center">34,082</td>
<td style="text-align:center">90.5</td>
<td style="text-align:center">52.1</td>
<td style="text-align:center">36.6‡</td>
</tr>
</tbody>
</table>
<p><em>Note: Budget figures are nominal. FTE = authorized full-time equivalent positions.
Inspections/District = mean district-level annual inspection count.</em>
<em>† 2024 budget is an appropriations estimate, not expenditure actuals; excluded from
regression models.</em>
<em>‡ 2025 data is partial-year as of the data extract. Resolution rate and days-to-enforcement
are right-censored: violations discovered in late 2024–2025 may not yet have a recorded
enforcement action, compressing these metrics.</em></p>
<hr/>
<h3 id="H1:-Organizational-Capacity-and-Regulatory-Outputs">H1: Organizational Capacity and Regulatory Outputs<a class="anchor-link" href="#H1:-Organizational-Capacity-and-Regulatory-Outputs"></a></h3><p>The baseline fixed-effects models provide consistent support for H1 across all three
dependent variables (Table 2). Each additional million dollars in OGI appropriations is
associated with approximately <strong>666 additional district-level inspections</strong> per year
($\hat{\beta} = 666.30$, SE = 212.98, $z = 3.13$, $p &lt; .01$; $R^2 = .769$). The budget
coefficient is also positive and significant for compliance rate ($\hat{\beta} = 0.26$
percentage points per $1M, SE = 0.11, $z = 2.31$, $p = .02$; $R^2 = .538$) and
violation resolution rate ($\hat{\beta} = 1.05$ percentage points per $1M, SE = 0.32,
$z = 3.28$, $p &lt; .01$; $R^2 = .624$). These associations are estimated net of district
fixed effects and therefore reflect within-district covariation between annual budget
changes and outcome changes rather than cross-sectional differences between
better- and worse-funded districts.</p>
<p><strong>Table 2. H1 Regression Results: OGI Budget → Regulatory Outputs</strong></p>
<table>
<thead>
<tr>
<th>Dependent Variable</th>
<th style="text-align:center">$\hat{\beta}$ (Budget $M)</th>
<th style="text-align:center">SE</th>
<th style="text-align:center">$z$</th>
<th style="text-align:center">$p$</th>
<th style="text-align:center">$R^2$</th>
<th style="text-align:center">Adj. $R^2$</th>
</tr>
</thead>
<tbody>
<tr>
<td>Total inspections</td>
<td style="text-align:center">666.30</td>
<td style="text-align:center">212.98</td>
<td style="text-align:center">3.13</td>
<td style="text-align:center">&lt;.01</td>
<td style="text-align:center">.769</td>
<td style="text-align:center">.736</td>
</tr>
<tr>
<td>Compliance rate (%)</td>
<td style="text-align:center">0.26</td>
<td style="text-align:center">0.11</td>
<td style="text-align:center">2.31</td>
<td style="text-align:center">.02</td>
<td style="text-align:center">.538</td>
<td style="text-align:center">.471</td>
</tr>
<tr>
<td>Resolution rate (%)</td>
<td style="text-align:center">1.05</td>
<td style="text-align:center">0.32</td>
<td style="text-align:center">3.28</td>
<td style="text-align:center">&lt;.01</td>
<td style="text-align:center">.624</td>
<td style="text-align:center">.569</td>
</tr>
</tbody>
</table>
<p><em>Note: All models include district fixed effects ($D = 13$). Standard errors clustered
at the district level. $N = 104$.</em></p>
<hr/>
<h3 id="H2:-Goal-Ambiguity-as-a-Moderator">H2: Goal Ambiguity as a Moderator<a class="anchor-link" href="#H2:-Goal-Ambiguity-as-a-Moderator"></a></h3><p>The goal ambiguity moderation model for compliance rate (Table 3) yields a statistically
significant and negative interaction between OGI budget and inspection budget share
($\hat{\beta}_3 = -6.53$, SE = 1.84, $z = -3.55$, $p &lt; .01$). However, this result
requires careful qualification before any mechanism is claimed.</p>
<p>The key issue is that <code>inspection_budget_share</code> — like the budget measure itself —
varies only over time, not across districts. All 13 districts experience the same
budget share in any given year, ranging from 0.59 (FY2022) to 0.67 (FY2018) across
the study period — a span of 8 percentage points over 8 observations. The interaction
term is therefore identified from the same narrow temporal variation as the main budget
effect, not from cross-district differences in mission structure. This makes it
difficult to distinguish a genuine moderation relationship from a spurious correlation
with year-specific factors that independently affected both budget share and compliance
outcomes in the same years.</p>
<p>The negative sign is consistent with at least two interpretations. Under a
<em>resource saturation</em> story, compliance gains from additional OGI investment contract
as the inspection mandate becomes better resourced relative to other RRC goals —
a plausible ceiling effect if districts are already operating near full compliance
in high-share years. Alternatively, the result may simply reflect that FY2018 — the
highest-share year — saw particularly large compliance gains for reasons unrelated to
budget concentration (e.g., post-2016 industry recovery, early implementation of
regulatory changes). Evaluated at mean budget share ($\bar{s} \approx 0.62$), the
implied marginal budget effect on compliance is $4.20 - 6.53(0.62) \approx 0.15$
pp per $1M — directionally consistent with H1 but smaller.</p>
<p>For violation resolution rate, no terms reach conventional significance (all $p &gt; .15$).
Given the identification constraints, the H2 compliance finding is best treated as an
exploratory pattern consistent with goal ambiguity theory — one that motivates future
research with district-level budget variation — rather than a robust confirmatory test.</p>
<p><strong>Table 3. H2 Regression Results: Goal Ambiguity Moderation (DV: Compliance Rate)</strong></p>
<table>
<thead>
<tr>
<th>Term</th>
<th style="text-align:center">$\hat{\beta}$</th>
<th style="text-align:center">SE</th>
<th style="text-align:center">$z$</th>
<th style="text-align:center">$p$</th>
</tr>
</thead>
<tbody>
<tr>
<td>Budget ($M)</td>
<td style="text-align:center">4.20</td>
<td style="text-align:center">1.09</td>
<td style="text-align:center">3.86</td>
<td style="text-align:center">&lt;.01</td>
</tr>
<tr>
<td>Inspection budget share</td>
<td style="text-align:center">170.18</td>
<td style="text-align:center">44.79</td>
<td style="text-align:center">3.80</td>
<td style="text-align:center">&lt;.01</td>
</tr>
<tr>
<td>Budget × Share</td>
<td style="text-align:center">−6.53</td>
<td style="text-align:center">1.84</td>
<td style="text-align:center">−3.55</td>
<td style="text-align:center">&lt;.01</td>
</tr>
</tbody>
</table>
<p><em>Note: District fixed effects included. SE clustered at district. $R^2 = .567$,
Adj. $R^2 = .493$. $N = 104$.</em></p>
<hr/>
<h3 id="H3:-District-Level-Heterogeneity">H3: District-Level Heterogeneity<a class="anchor-link" href="#H3:-District-Level-Heterogeneity"></a></h3><p>District-specific budget slopes for compliance rate range from $-0.34$ percentage points
per $1 million (District 03, Coastal/Greater Houston) to $+1.36$ percentage points
(District 6E, East Texas Piney Woods), with most districts showing small positive slopes
(Table 4). The reference district (District 01, San Antonio) slope is 0.09 pp per $1M.
Positive slopes are most pronounced in District 6E (+1.36), District 06 (+0.43), and
District 7C (+0.40); District 03 is the only district with a substantially negative slope.
The model $R^2$ of .662 modestly exceeds the baseline H1 value (.538), consistent with
meaningful cross-district slope heterogeneity. Standard errors for the interaction terms
are not reported, as they are unreliable due to near-perfect multicollinearity in the
saturated model (see Data and Methods); point estimates are presented as descriptive
indicators only.</p>
<p><strong>Table 4. H3 District-Specific Budget → Compliance Slopes (pp per $1M)</strong></p>
<table>
<thead>
<tr>
<th style="text-align:center">District</th>
<th style="text-align:center">Estimated Slope</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">01 (San Antonio)</td>
<td style="text-align:center">0.09</td>
</tr>
<tr>
<td style="text-align:center">02 (Corpus Christi)</td>
<td style="text-align:center">0.24</td>
</tr>
<tr>
<td style="text-align:center">03 (Houston)</td>
<td style="text-align:center">−0.34</td>
</tr>
<tr>
<td style="text-align:center">04 (Laredo)</td>
<td style="text-align:center">0.28</td>
</tr>
<tr>
<td style="text-align:center">05 (Midland/Abilene)</td>
<td style="text-align:center">0.05</td>
</tr>
<tr>
<td style="text-align:center">06 (Kilgore)</td>
<td style="text-align:center">0.43</td>
</tr>
<tr>
<td style="text-align:center">08 (Midland)</td>
<td style="text-align:center">0.28</td>
</tr>
<tr>
<td style="text-align:center">09 (Wichita Falls)</td>
<td style="text-align:center">0.00</td>
</tr>
<tr>
<td style="text-align:center">10 (Amarillo)</td>
<td style="text-align:center">0.13</td>
</tr>
<tr>
<td style="text-align:center">6E (Kilgore East)</td>
<td style="text-align:center">1.36</td>
</tr>
<tr>
<td style="text-align:center">7B (Abilene)</td>
<td style="text-align:center">0.27</td>
</tr>
<tr>
<td style="text-align:center">7C (Big Spring)</td>
<td style="text-align:center">0.40</td>
</tr>
<tr>
<td style="text-align:center">8A (Lubbock)</td>
<td style="text-align:center">0.19</td>
</tr>
</tbody>
</table>
<p><em>Note: Slopes are $\hat{\beta}_1 + \hat{\delta}_d$ from the H3 interaction model.</em></p>
<hr/>
<h3 id="H4:-Spatial-and-Geographic-Factors">H4: Spatial and Geographic Factors<a class="anchor-link" href="#H4:-Spatial-and-Geographic-Factors"></a></h3><p>The geographic moderation model (Table 5) reveals that offshore-jurisdiction districts
(02, 03, 04) exhibit compliance rates approximately <strong>7.6 percentage points higher</strong> than
non-offshore districts on average, net of budget ($\hat{\beta} = 7.61$, SE = 3.29,
$z = 2.31$, $p = .02$). Border-proximate districts similarly show elevated baseline
compliance rates (+6.03 pp, SE = 2.84, $z = 2.12$, $p = .03$). These level effects may
reflect the heightened external scrutiny — from federal regulators, environmental
organizations, and media — that offshore and border districts attract, which could
independently drive compliance investments by operators regardless of RRC budget levels.</p>
<p>The budget–compliance slope, however, does not differ significantly between offshore
and non-offshore districts ($\hat{\beta}_4 = -0.03$, $p = .87$), nor between border
and non-border districts at conventional thresholds ($\hat{\beta}_5 = -0.25$, $p = .08$),
suggesting that geographic classification affects the <em>level</em> of compliance performance
but not the degree to which additional budget translates into compliance gains.</p>
<p>Moran's $I$ computed on district-level residuals from the H1 compliance model is
$I = -0.051$, indicating slight spatial dispersion but no statistically significant
spatial autocorrelation. This finding is consistent with prior district-level analysis
of this regulatory system and suggests that unmodeled geographic spillovers are not a
material source of omitted variable bias in the panel models.</p>
<p><strong>Table 5. H4 Regression Results: Geographic Moderation (DV: Compliance Rate)</strong></p>
<table>
<thead>
<tr>
<th>Term</th>
<th style="text-align:center">$\hat{\beta}$</th>
<th style="text-align:center">SE</th>
<th style="text-align:center">$z$</th>
<th style="text-align:center">$p$</th>
</tr>
</thead>
<tbody>
<tr>
<td>Budget ($M)</td>
<td style="text-align:center">0.35</td>
<td style="text-align:center">0.15</td>
<td style="text-align:center">2.39</td>
<td style="text-align:center">.02</td>
</tr>
<tr>
<td>Offshore (= 1)</td>
<td style="text-align:center">7.61</td>
<td style="text-align:center">3.29</td>
<td style="text-align:center">2.31</td>
<td style="text-align:center">.02</td>
</tr>
<tr>
<td>Border (= 1)</td>
<td style="text-align:center">6.03</td>
<td style="text-align:center">2.84</td>
<td style="text-align:center">2.12</td>
<td style="text-align:center">.03</td>
</tr>
<tr>
<td>Budget × Offshore</td>
<td style="text-align:center">−0.03</td>
<td style="text-align:center">0.18</td>
<td style="text-align:center">−0.16</td>
<td style="text-align:center">.87</td>
</tr>
<tr>
<td>Budget × Border</td>
<td style="text-align:center">−0.25</td>
<td style="text-align:center">0.15</td>
<td style="text-align:center">−1.74</td>
<td style="text-align:center">.08</td>
</tr>
</tbody>
</table>
<p><em>Note: District fixed effects included. SE clustered at district. $R^2 = .553$,
Adj. $R^2 = .476$. $N = 104$. Moran's $I$ on H1 compliance residuals = −0.051 (no
significant spatial autocorrelation).</em></p>
<hr/>
<h3 id="Summary">Summary<a class="anchor-link" href="#Summary"></a></h3><p>Taken together, the results offer moderate support for a resource-capacity model of
regulatory performance. Higher OGI appropriations are reliably associated with greater
inspection volume, higher compliance rates, and faster violation resolution — though
identification rests on temporal variation in statewide appropriations rather than
quasi-experimental assignment, and the modest panel length limits statistical precision.
Goal ambiguity moderation operates through a diminishing-returns mechanism: compliance
gains from additional budget are smaller in years when the inspection mandate receives
a larger share of combined appropriations, consistent with resource saturation rather
than amplification. District heterogeneity in budget–outcome slopes is substantial in
descriptive terms but cannot be precisely estimated with the available data. Finally,
geographic context — offshore jurisdiction and border proximity — predicts compliance
levels but not budget sensitivity, and spatial autocorrelation diagnostics provide no
evidence of unmodeled geographic spillover processes.</p>
<h3 id="Robustness-Checks">Robustness Checks<a class="anchor-link" href="#Robustness-Checks"></a></h3><p><strong>Wild cluster bootstrap.</strong> With only $G = 13$ district clusters, asymptotic
cluster-robust standard errors may substantially understate true uncertainty.
Wild cluster bootstrap inference (Rademacher weights, $B = 999$ draws; Cameron,
Gelbach &amp; Miller 2008) yields bootstrap p-values near 0.49–0.51 for all three
H1 outcomes: total inspections ($p_{boot} = 0.494$), compliance rate
($p_{boot} = 0.473$), and resolution rate ($p_{boot} = 0.509$). These are far
from any conventional significance threshold, in stark contrast to the asymptotic
p-values of 0.002, 0.021, and 0.001. The divergence indicates that with $G = 13$
clusters, asymptotic inference significantly overstates precision. The H1 point
estimates remain positive and directionally consistent, but the results do not
survive bootstrap-based inference. This is the principal inferential limitation
of the study.</p>
<p><strong>Table 7. Wild Cluster Bootstrap vs. Asymptotic p-values (H1 Models, B = 999)</strong></p>
<table>
<thead>
<tr>
<th>Outcome</th>
<th style="text-align:center">$t$-statistic</th>
<th style="text-align:center">$p$ (asymptotic)</th>
<th style="text-align:center">$p$ (bootstrap)</th>
</tr>
</thead>
<tbody>
<tr>
<td>Total inspections</td>
<td style="text-align:center">3.13</td>
<td style="text-align:center">.002</td>
<td style="text-align:center">.494</td>
</tr>
<tr>
<td>Compliance rate</td>
<td style="text-align:center">2.31</td>
<td style="text-align:center">.021</td>
<td style="text-align:center">.473</td>
</tr>
<tr>
<td>Resolution rate</td>
<td style="text-align:center">3.28</td>
<td style="text-align:center">.001</td>
<td style="text-align:center">.509</td>
</tr>
</tbody>
</table>
<p><em>Note: Bootstrap p-values based on 999 Rademacher wild cluster bootstrap draws.</em>
<em>Small number of clusters (G = 13) renders asymptotic inference unreliable.</em></p>
<p><strong>Distributed lag model.</strong> The distributed lag models test whether budget effects
operate with a one-year delay consistent with a hiring-and-deployment mechanism.
For compliance rate, the lagged budget alone is not significant
($\hat{\beta}_{t-1} = 0.10$, $p = .44$; Model A, N = 91), and in the combined
model the contemporaneous term remains marginally significant
($\hat{\beta}_t = 0.24$, $p = .04$) while the lagged term is negative and
non-significant ($\hat{\beta}_{t-1} = -0.14$, $p = .12$; Model B). For violation
resolution rate, the lagged budget is marginally significant when estimated alone
($\hat{\beta}_{t-1} = 0.83$, $p = .09$; Model A), but neither term reaches
conventional significance in the combined model ($p = .22$ and $p = .14$).</p>
<p>These findings provide little support for a delayed implementation mechanism.
The persistence of contemporaneous effects alongside non-significant lagged terms
is more consistent with an immediate budget–output relationship. However, the
N = 91 sample offers limited power to disentangle contemporaneous and lagged
effects that are highly collinear over an eight-year window.</p>
<p><strong>Table 8. Distributed Lag Results (2017–2023, N = 91)</strong></p>
<table>
<thead>
<tr>
<th>Model</th>
<th>DV</th>
<th style="text-align:center">$\hat{\beta}_t$</th>
<th style="text-align:center">$p$</th>
<th style="text-align:center">$\hat{\beta}_{t-1}$</th>
<th style="text-align:center">$p$</th>
<th style="text-align:center">$R^2$</th>
</tr>
</thead>
<tbody>
<tr>
<td>A — Lag only</td>
<td>Compliance rate</td>
<td style="text-align:center">—</td>
<td style="text-align:center">—</td>
<td style="text-align:center">0.10</td>
<td style="text-align:center">.44</td>
<td style="text-align:center">.543</td>
</tr>
<tr>
<td>B — Both</td>
<td>Compliance rate</td>
<td style="text-align:center">0.24</td>
<td style="text-align:center">.04</td>
<td style="text-align:center">−0.14</td>
<td style="text-align:center">.12</td>
<td style="text-align:center">.569</td>
</tr>
<tr>
<td>A — Lag only</td>
<td>Resolution rate</td>
<td style="text-align:center">—</td>
<td style="text-align:center">—</td>
<td style="text-align:center">0.83</td>
<td style="text-align:center">.09</td>
<td style="text-align:center">.696</td>
</tr>
<tr>
<td>B — Both</td>
<td>Resolution rate</td>
<td style="text-align:center">0.24</td>
<td style="text-align:center">.22</td>
<td style="text-align:center">0.59</td>
<td style="text-align:center">.14</td>
<td style="text-align:center">.698</td>
</tr>
</tbody>
</table>
<p><em>Note: District fixed effects included; SE clustered at district.</em></p>
</div>
</div>
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<h2 id="Robustness-Checks">Robustness Checks<a class="anchor-link" href="#Robustness-Checks"></a></h2><p>Two checks address limitations of the baseline H1 models.</p>
<p><strong>Wild cluster bootstrap</strong> re-tests H1 with valid small-sample inference rather than
asymptotic cluster-robust standard errors. With $G = 13$ clusters, asymptotic results
can overstate precision. Rademacher wild cluster bootstrap ($B = 999$ draws; Cameron,
Gelbach &amp; Miller 2008) yields p-values near 0.49–0.51 for all three H1 outcomes —
far from any conventional threshold — indicating that the asymptotic H1 results do
not survive this correction. Point estimates remain positive and substantively consistent
in direction, but the study lacks the cluster count required to establish significance
through bootstrap inference.</p>
<p><strong>Distributed lag model</strong> relaxes the assumption that budget effects are instantaneous.
A one-year lag of OGI budget is estimated alone (Model A) and jointly with the
contemporaneous term (Model B), over the 2017–2023 sample (N = 91). The lagged budget
is not independently significant for compliance rate ($p = .44$) and only marginally so
for resolution rate ($p = .09$). In the combined models, contemporaneous effects persist
while lagged terms do not attain significance — providing little evidence that a delayed
mechanism dominates an immediate one.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Wild cluster bootstrap (Rademacher weights, B=999)</span>
<span class="c1"># For each draw: multiply each cluster's residuals by ±1, re-fit, record t-stat.</span>
<span class="c1"># p-value = share of |t*| &gt;= |t_observed|.</span>
<span class="k">def</span><span class="w"> </span><span class="nf">wild_cluster_bootstrap</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">cluster_col</span><span class="o">=</span><span class="s2">"district"</span><span class="p">,</span>
<span class="n">coef</span><span class="o">=</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="n">B</span><span class="o">=</span><span class="mi">999</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">):</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">groups</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">cluster_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">unique_groups</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">groups</span><span class="p">)</span>
<span class="n">t_obs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">tvalues</span><span class="p">[</span><span class="n">coef</span><span class="p">]</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fittedvalues</span><span class="o">.</span><span class="n">values</span>
<span class="n">ehat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">resid</span><span class="o">.</span><span class="n">values</span>
<span class="n">t_boot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
<span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">B</span><span class="p">):</span>
<span class="c1"># One Rademacher weight per cluster, broadcast to observations</span>
<span class="n">cw</span> <span class="o">=</span> <span class="p">{</span><span class="n">g</span><span class="p">:</span> <span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">unique_groups</span><span class="p">}</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">cw</span><span class="p">[</span><span class="n">g</span><span class="p">]</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">groups</span><span class="p">])</span>
<span class="n">df_b</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_b</span><span class="p">[</span><span class="n">dv</span><span class="p">]</span> <span class="o">=</span> <span class="n">yhat</span> <span class="o">+</span> <span class="n">ehat</span> <span class="o">*</span> <span class="n">w</span>
<span class="n">m_b</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">dv</span><span class="si">}</span><span class="s2"> ~ </span><span class="si">{</span><span class="n">coef</span><span class="si">}</span><span class="s2"> + C(</span><span class="si">{</span><span class="n">cluster_col</span><span class="si">}</span><span class="s2">)"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df_b</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">df_b</span><span class="p">[</span><span class="n">cluster_col</span><span class="p">]})</span>
<span class="n">t_boot</span><span class="p">[</span><span class="n">b</span><span class="p">]</span> <span class="o">=</span> <span class="n">m_b</span><span class="o">.</span><span class="n">tvalues</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">coef</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="n">p_boot</span> <span class="o">=</span> <span class="nb">float</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">t_boot</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">t_obs</span><span class="p">))</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="k">return</span> <span class="n">t_obs</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">pvalues</span><span class="p">[</span><span class="n">coef</span><span class="p">]),</span> <span class="n">p_boot</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Wild Cluster Bootstrap — H1 Models (B = 999 Rademacher draws)"</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="si">{</span><span class="s1">'Outcome'</span><span class="si">:</span><span class="s2">&lt;28</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="s1">'t-stat'</span><span class="si">:</span><span class="s2">&gt;7</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="s1">'p asymptotic'</span><span class="si">:</span><span class="s2">&gt;13</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="s1">'p bootstrap'</span><span class="si">:</span><span class="s2">&gt;12</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="o">*</span> <span class="mi">65</span><span class="p">)</span>
<span class="k">for</span> <span class="n">dv</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"total_inspections"</span><span class="p">,</span> <span class="n">m_inspections</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"compliance_rate"</span><span class="p">,</span> <span class="n">m_compliance</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"resolution_rate"</span><span class="p">,</span> <span class="n">m_resolution</span><span class="p">),</span>
<span class="p">]:</span>
<span class="n">t</span><span class="p">,</span> <span class="n">p_a</span><span class="p">,</span> <span class="n">p_b</span> <span class="o">=</span> <span class="n">wild_cluster_bootstrap</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">actuals</span><span class="p">,</span> <span class="n">dv</span><span class="p">)</span>
<span class="n">sig_a</span> <span class="o">=</span> <span class="s2">"*"</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">p_a</span> <span class="o">&lt;</span> <span class="mf">.05</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">p_a</span> <span class="o">&lt;</span> <span class="mf">.01</span><span class="p">))</span>
<span class="n">sig_b</span> <span class="o">=</span> <span class="s2">"*"</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">p_b</span> <span class="o">&lt;</span> <span class="mf">.05</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">p_b</span> <span class="o">&lt;</span> <span class="mf">.01</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="si">{</span><span class="n">dv</span><span class="si">:</span><span class="s2">&lt;28</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">t</span><span class="si">:</span><span class="s2">&gt;7.3f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">p_a</span><span class="si">:</span><span class="s2">&gt;12.3f</span><span class="si">}{</span><span class="n">sig_a</span><span class="si">:</span><span class="s2">&lt;3</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">p_b</span><span class="si">:</span><span class="s2">&gt;10.3f</span><span class="si">}{</span><span class="n">sig_b</span><span class="si">:</span><span class="s2">&lt;3</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">* p&lt;.10 ** p&lt;.05 *** p&lt;.01"</span><span class="p">)</span>
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<pre>Wild Cluster Bootstrap — H1 Models (B = 999 Rademacher draws)
Outcome t-stat p asymptotic p bootstrap
─────────────────────────────────────────────────────────────────
total_inspections 3.128 0.002*** 0.494*
compliance_rate 2.307 0.021** 0.473*
resolution_rate 3.277 0.001*** 0.509*
* p&lt;.10 ** p&lt;.05 *** p&lt;.01
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Distributed lag: 1-year lag of OGI budget (shift within district).</span>
<span class="c1"># Lag is NaN for 2016 (no 2015 data), so regression sample is 2017-2023 (N=91).</span>
<span class="n">panel_lag</span> <span class="o">=</span> <span class="n">panel</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">panel_lag</span><span class="p">[</span><span class="s2">"ogi_budget_m_lag1"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">panel_lag</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"year"</span><span class="p">)</span>
<span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"district"</span><span class="p">)[</span><span class="s2">"ogi_budget_m"</span><span class="p">]</span>
<span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">lag_actuals</span> <span class="o">=</span> <span class="n">panel_lag</span><span class="p">[</span>
<span class="p">(</span><span class="n">panel_lag</span><span class="p">[</span><span class="s2">"is_budget_year"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">panel_lag</span><span class="p">[</span><span class="s2">"ogi_budget_m_lag1"</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">())</span>
<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Distributed lag sample: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">lag_actuals</span><span class="p">)</span><span class="si">}</span><span class="s2"> obs | "</span>
<span class="sa">f</span><span class="s2">"years </span><span class="si">{</span><span class="n">lag_actuals</span><span class="p">[</span><span class="s1">'year'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="si">}</span><span class="s2">–</span><span class="si">{</span><span class="n">lag_actuals</span><span class="p">[</span><span class="s1">'year'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># ── Model A: lagged budget only ───────────────────────────────────────────────</span>
<span class="n">m_lag_only</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m_lag1 + C(district)"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">lag_actuals</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">lag_actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="c1"># ── Model B: contemporaneous + 1-year lag ────────────────────────────────────</span>
<span class="n">m_lag_both</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"compliance_rate ~ ogi_budget_m + ogi_budget_m_lag1 + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">lag_actuals</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">lag_actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="c1"># ── Also run for resolution rate ──────────────────────────────────────────────</span>
<span class="n">m_lag_res_only</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"resolution_rate ~ ogi_budget_m_lag1 + C(district)"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">lag_actuals</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">lag_actuals</span><span class="p">[</span><span class="s2">"district"</span><span class="p">]})</span>
<span class="n">m_lag_res_both</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span>
<span class="s2">"resolution_rate ~ ogi_budget_m + ogi_budget_m_lag1 + C(district)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">lag_actuals</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov_type</span><span class="o">=</span><span class="s2">"cluster"</span><span class="p">,</span> <span class="n">cov_kwds</span><span class="o">=</span><span class="p">{</span><span class="s2">"groups"</span><span class="p">:</span> <span class="n">lag_actuals</span><span class="p">[</span><span class="s2">"district"</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">── Compliance Rate ───────────────────────────────────────────"</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">Model A — Lagged budget only (t−1):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_lag_only</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m_lag1"</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_lag_only</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_lag_only</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</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">Model B — Contemporaneous + 1-year lag:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_lag_both</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
<span class="p">[</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"ogi_budget_m_lag1"</span><span class="p">]</span>
<span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_lag_both</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_lag_both</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</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">── Resolution Rate ───────────────────────────────────────────"</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">Model A — Lagged budget only (t−1):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_lag_res_only</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">"ogi_budget_m_lag1"</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_lag_res_only</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_lag_res_only</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</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">Model B — Contemporaneous + 1-year lag:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_lag_res_both</span><span class="o">.</span><span class="n">summary2</span><span class="p">()</span><span class="o">.</span><span class="n">tables</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">display_cols</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
<span class="p">[</span><span class="s2">"ogi_budget_m"</span><span class="p">,</span> <span class="s2">"ogi_budget_m_lag1"</span><span class="p">]</span>
<span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R² = </span><span class="si">{</span><span class="n">m_lag_res_both</span><span class="o">.</span><span class="n">rsquared</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> Adj. R² = </span><span class="si">{</span><span class="n">m_lag_res_both</span><span class="o">.</span><span class="n">rsquared_adj</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Distributed lag sample: 91 obs | years 2017–2023
── Compliance Rate ───────────────────────────────────────────
Model A — Lagged budget only (t−1):
Coef. Std.Err. z P&gt;|z|
ogi_budget_m_lag1 0.10 0.13 0.77 0.44
R² = 0.543 Adj. R² = 0.466
Model B — Contemporaneous + 1-year lag:
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 0.24 0.11 2.08 0.04
ogi_budget_m_lag1 -0.14 0.09 -1.55 0.12
R² = 0.569 Adj. R² = 0.490
── Resolution Rate ───────────────────────────────────────────
Model A — Lagged budget only (t−1):
Coef. Std.Err. z P&gt;|z|
ogi_budget_m_lag1 0.83 0.49 1.69 0.09
R² = 0.696 Adj. R² = 0.644
Model B — Contemporaneous + 1-year lag:
Coef. Std.Err. z P&gt;|z|
ogi_budget_m 0.24 0.19 1.22 0.22
ogi_budget_m_lag1 0.59 0.40 1.46 0.14
R² = 0.698 Adj. R² = 0.642
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<h2 id="Hypotheses-Summary">Hypotheses Summary<a class="anchor-link" href="#Hypotheses-Summary"></a></h2><p><strong>Table 6. Summary of Hypotheses, Predictions, Findings, and Empirical Support</strong></p>
<table>
<thead>
<tr>
<th style="text-align:center">#</th>
<th>Hypothesis</th>
<th>Prediction</th>
<th>Key Result</th>
<th style="text-align:center">Support</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center"><strong>H1a</strong></td>
<td>Capacity → Inspection volume</td>
<td>Higher OGI budget predicts more inspections per district</td>
<td>β = 666.3/$1M (z = 3.13, p &lt; .01); bootstrap p = .494</td>
<td style="text-align:center">✓†</td>
</tr>
<tr>
<td style="text-align:center"><strong>H1b</strong></td>
<td>Capacity → Compliance</td>
<td>Higher OGI budget predicts higher district compliance rate</td>
<td>β = 0.26 pp/$1M (z = 2.31, p = .02); bootstrap p = .473</td>
<td style="text-align:center">✓†</td>
</tr>
<tr>
<td style="text-align:center"><strong>H1c</strong></td>
<td>Capacity → Resolution</td>
<td>Higher OGI budget predicts higher violation resolution rate</td>
<td>β = 1.05 pp/$1M (z = 3.28, p &lt; .01); bootstrap p = .509</td>
<td style="text-align:center">✓†</td>
</tr>
<tr>
<td style="text-align:center"><strong>H2a</strong></td>
<td>Goal ambiguity moderates capacity → compliance</td>
<td>Clearer inspection focus amplifies budget effect</td>
<td>Significant but <strong>negative</strong> (β = −6.53, z = −3.55, p &lt; .01); interpretation constrained by time-only variation in budget share (range: 0.59–0.67)</td>
<td style="text-align:center">Exploratory‡</td>
</tr>
<tr>
<td style="text-align:center"><strong>H2b</strong></td>
<td>Goal ambiguity moderates capacity → resolution</td>
<td>Clearer inspection focus amplifies budget effect</td>
<td>Interaction not significant (p = .24)</td>
<td style="text-align:center">✗</td>
</tr>
<tr>
<td style="text-align:center"><strong>H3</strong></td>
<td>District heterogeneity in budget slopes</td>
<td>Budget → compliance slope varies across districts</td>
<td>Slopes from −0.34 pp/$1M (D03) to +1.36 pp/$1M (D6E); inference unreliable</td>
<td style="text-align:center">Descriptive§</td>
</tr>
<tr>
<td style="text-align:center"><strong>H4a</strong></td>
<td>Offshore jurisdiction moderates budget effect</td>
<td>Offshore districts show different budget → compliance slope</td>
<td>Level effect: +7.6 pp (p = .02); slope interaction not significant (p = .87)</td>
<td style="text-align:center">Partial¶</td>
</tr>
<tr>
<td style="text-align:center"><strong>H4b</strong></td>
<td>Border proximity moderates budget effect</td>
<td>Border districts show different budget → compliance slope</td>
<td>Level effect: +6.0 pp (p = .03); slope interaction marginal (p = .08)</td>
<td style="text-align:center">Partial¶</td>
</tr>
<tr>
<td style="text-align:center"><strong>H4c</strong></td>
<td>Spatial autocorrelation in residuals</td>
<td>Geographic spillovers produce clustered residuals</td>
<td>Moran's I = −0.051; no significant spatial autocorrelation</td>
<td style="text-align:center">✗</td>
</tr>
</tbody>
</table>
<p><em>Notes:</em></p>
<p><em>† H1 point estimates are positive and directionally consistent across all three outcomes,
supporting the capacity hypothesis substantively. However, wild cluster bootstrap
inference (B = 999 Rademacher draws) yields p-values near 0.49–0.51 for all outcomes,
indicating that asymptotic cluster-robust standard errors substantially overstate precision
with G = 13 clusters. H1 findings should be interpreted as suggestive rather than
statistically definitive. Distributed lag models (2017–2023, N = 91) show contemporaneous
effects persist while lagged terms do not reach significance, providing no clear evidence
for a delayed implementation mechanism.</em></p>
<p><em>‡ H2a is statistically significant but the identification is weak: inspection
budget share varies only over time (like the budget itself), with a range of just
0.59–0.67 across 8 years. The negative interaction is consistent with a resource
saturation effect but cannot be distinguished from year-specific confounders.
At mean share (≈ 0.62), the implied marginal budget effect is ≈ 0.15 pp per $1M.
H2b not significant for resolution rate. Both H2 findings are best treated as
exploratory patterns for future research.</em></p>
<p><em>§ H3 interaction standard errors are unreliable (near-perfect multicollinearity in
the saturated model); budget slopes are reported as descriptive point estimates only.</em></p>
<p><em>¶ Geographic classification predicts compliance <strong>levels</strong> but not budget sensitivity.
Offshore and border districts exhibit systematically higher compliance regardless of
annual budget variation.</em></p>
<p><strong>Regression sample:</strong> N = 104 (13 districts × 8 years, 2016–2023). All models include
district fixed effects; standard errors clustered at the district level (G = 13).
Robustness sample: N = 91 (2017–2023, distributed lag models).</p>
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