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/*-----------------------------------------------------------------------------
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|
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|
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|
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/* Icons urls */
:root {
--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE0LjkgMTcuNDVDMTYuMjUgMTcuNDUgMTcuMzUgMTYuMzUgMTcuMzUgMTVDMTcuMzUgMTMuNjUgMTYuMjUgMTIuNTUgMTQuOSAxMi41NUMxMy41NCAxMi41NSAxMi40NSAxMy42NSAxMi40NSAxNUMxMi40NSAxNi4zNSAxMy41NCAxNy40NSAxNC45IDE3LjQ1Wk0yMC4xIDE1LjY4TDIxLjU4IDE2Ljg0QzIxLjcxIDE2Ljk1IDIxLjc1IDE3LjEzIDIxLjY2IDE3LjI5TDIwLjI2IDE5LjcxQzIwLjE3IDE5Ljg2IDIwIDE5LjkyIDE5LjgzIDE5Ljg2TDE4LjA5IDE5LjE2QzE3LjczIDE5LjQ0IDE3LjMzIDE5LjY3IDE2LjkxIDE5Ljg1TDE2LjY0IDIxLjdDMTYuNjIgMjEuODcgMTYuNDcgMjIgMTYuMyAyMkgxMy41QzEzLjMyIDIyIDEzLjE4IDIxLjg3IDEzLjE1IDIxLjdMMTIuODkgMTkuODVDMTIuNDYgMTkuNjcgMTIuMDcgMTkuNDQgMTEuNzEgMTkuMTZMOS45NjAwMiAxOS44NkM5LjgxMDAyIDE5LjkyIDkuNjIwMDIgMTkuODYgOS41NDAwMiAxOS43MUw4LjE0MDAyIDE3LjI5QzguMDUwMDIgMTcuMTMgOC4wOTAwMiAxNi45NSA4LjIyMDAyIDE2Ljg0TDkuNzAwMDIgMTUuNjhMOS42NTAwMSAxNUw5LjcwMDAyIDE0LjMxTDguMjIwMDIgMTMuMTZDOC4wOTAwMiAxMy4wNSA4LjA1MDAyIDEyLjg2IDguMTQwMDIgMTIuNzFMOS41NDAwMiAxMC4yOUM5LjYyMDAyIDEwLjEzIDkuODEwMDIgMTAuMDcgOS45NjAwMiAxMC4xM0wxMS43MSAxMC44NEMxMi4wNyAxMC41NiAxMi40NiAxMC4zMiAxMi44OSAxMC4xNUwxMy4xNSA4LjI4OTk4QzEzLjE4IDguMTI5OTggMTMuMzIgNy45OTk5OCAxMy41IDcuOTk5OThIMTYuM0MxNi40NyA3Ljk5OTk4IDE2LjYyIDguMTI5OTggMTYuNjQgOC4yODk5OEwxNi45MSAxMC4xNUMxNy4zMyAxMC4zMiAxNy43MyAxMC41NiAxOC4wOSAxMC44NEwxOS44MyAxMC4xM0MyMCAxMC4wNyAyMC4xNyAxMC4xMyAyMC4yNiAxMC4yOUwyMS42NiAxMi43MUMyMS43NSAxMi44NiAyMS43MSAxMy4wNSAyMS41OCAxMy4xNkwyMC4xIDE0LjMxTDIwLjE1IDE1TDIwLjEgMTUuNjhaIi8+CiAgICA8cGF0aCBkPSJNNy4zMjk2NiA3LjQ0NDU0QzguMDgzMSA3LjAwOTU0IDguMzM5MzIgNi4wNTMzMiA3LjkwNDMyIDUuMjk5ODhDNy40NjkzMiA0LjU0NjQzIDYuNTA4MSA0LjI4MTU2IDUuNzU0NjYgNC43MTY1NkM1LjM5MTc2IDQuOTI2MDggNS4xMjY5NSA1LjI3MTE4IDUuMDE4NDkgNS42NzU5NEM0LjkxMDA0IDYuMDgwNzEgNC45NjY4MiA2LjUxMTk4IDUuMTc2MzQgNi44NzQ4OEM1LjYxMTM0IDcuNjI4MzIgNi41NzYyMiA3Ljg3OTU0IDcuMzI5NjYgNy40NDQ1NFpNOS42NTcxOCA0Ljc5NTkzTDEwLjg2NzIgNC45NTE3OUMxMC45NjI4IDQuOTc3NDEgMTEuMDQwMiA1LjA3MTMzIDExLjAzODIgNS4xODc5M0wxMS4wMzg4IDYuOTg4OTNDMTEuMDQ1NSA3LjEwMDU0IDEwLjk2MTYgNy4xOTUxOCAxMC44NTUgNy4yMTA1NEw5LjY2MDAxIDcuMzgwODNMOS4yMzkxNSA4LjEzMTg4TDkuNjY5NjEgOS4yNTc0NUM5LjcwNzI5IDkuMzYyNzEgOS42NjkzNCA5LjQ3Njk5IDkuNTc0MDggOS41MzE5OUw4LjAxNTIzIDEwLjQzMkM3LjkxMTMxIDEwLjQ5MiA3Ljc5MzM3IDEwLjQ2NzcgNy43MjEwNSAxMC4zODI0TDYuOTg3NDggOS40MzE4OEw2LjEwOTMxIDkuNDMwODNMNS4zNDcwNCAxMC4zOTA1QzUuMjg5MDkgMTAuNDcwMiA1LjE3MzgzIDEwLjQ5MDUgNS4wNzE4NyAxMC40MzM5TDMuNTEyNDUgOS41MzI5M0MzLjQxMDQ5IDkuNDc2MzMgMy4zNzY0NyA5LjM1NzQxIDMuNDEwNzUgOS4yNTY3OUwzLjg2MzQ3IDguMTQwOTNMMy42MTc0OSA3Ljc3NDg4TDMuNDIzNDcgNy4zNzg4M0wyLjIzMDc1IDcuMjEyOTdDMi4xMjY0NyA3LjE5MjM1IDIuMDQwNDkgNy4xMDM0MiAyLjA0MjQ1IDYuOTg2ODJMMi4wNDE4NyA1LjE4NTgyQzIuMDQzODMgNS4wNjkyMiAyLjExOTA5IDQuOTc5NTggMi4yMTcwNCA0Ljk2OTIyTDMuNDIwNjUgNC43OTM5M0wzLjg2NzQ5IDQuMDI3ODhMMy40MTEwNSAyLjkxNzMxQzMuMzczMzcgMi44MTIwNCAzLjQxMTMxIDIuNjk3NzYgMy41MTUyMyAyLjYzNzc2TDUuMDc0MDggMS43Mzc3NkM1LjE2OTM0IDEuNjgyNzYgNS4yODcyOSAxLjcwNzA0IDUuMzU5NjEgMS43OTIzMUw2LjExOTE1IDIuNzI3ODhMNi45ODAwMSAyLjczODkzTDcuNzI0OTYgMS43ODkyMkM3Ljc5MTU2IDEuNzA0NTggNy45MTU0OCAxLjY3OTIyIDguMDA4NzkgMS43NDA4Mkw5LjU2ODIxIDIuNjQxODJDOS42NzAxNyAyLjY5ODQyIDkuNzEyODUgMi44MTIzNCA5LjY4NzIzIDIuOTA3OTdMOS4yMTcxOCA0LjAzMzgzTDkuNDYzMTYgNC4zOTk4OEw5LjY1NzE4IDQuNzk1OTNaIi8+CiAgPC9nPgo8L3N2Zz4K);
--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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-webkit-print-color-adjust: exact;
}
/* Misc */
a.anchor-link {
display: none;
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/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
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.cm-editor.cm-s-jupyter .highlight pre {
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margin: 0;
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font-size: inherit;
line-height: inherit;
color: inherit;
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.jp-OutputArea-output pre {
line-height: inherit;
font-family: inherit;
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.jp-RenderedText pre {
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font-size: var(--jp-code-font-size);
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/* 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 {
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.jp-RenderedMermaid.jp-mod-warning {
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</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h1 id="Colorado-Spills">Colorado Spills<a class="anchor-link" href="#Colorado-Spills">¶</a></h1><h2 id="OLS,-NBR,-Interrupted-Time-Series-Analysis">OLS, NBR, Interrupted Time Series Analysis<a class="anchor-link" href="#OLS,-NBR,-Interrupted-Time-Series-Analysis">¶</a></h2><h2 id="Just-top-3-Counties">Just top 3 Counties<a class="anchor-link" href="#Just-top-3-Counties">¶</a></h2><h3 id="Author:-David-P.-Adams,-PhD">Author: <a href="https://dadams.io">David P. Adams, PhD</a><a class="anchor-link" href="#Author:-David-P.-Adams,-PhD">¶</a></h3><h4 id="Date:-2025-08-06">Date: 2025-08-06<a class="anchor-link" href="#Date:-2025-08-06">¶</a></h4>
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<h1 id="Setup">Setup<a class="anchor-link" href="#Setup">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</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">geopandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">gpd</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="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="c1"># Load environment variables (e.g., DB_USER, DB_PASSWORD)</span>
<span class="n">load_dotenv</span><span class="p">()</span>
<span class="c1"># Database credentials from .env or shell</span>
<span class="n">db_name</span> <span class="o">=</span> <span class="s1">'colorado_spills'</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="s1">'DB_USER'</span><span class="p">)</span>
<span class="n">password</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="s1">'DB_PASSWORD'</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="s1">'DB_HOST'</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="s1">'DB_PORT'</span><span class="p">)</span>
<span class="c1"># SQLAlchemy engine for PostgreSQL/PostGIS</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="s1">'postgresql+psycopg2://</span><span class="si">{</span><span class="n">user</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">password</span><span class="si">}</span><span class="s1">@</span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">db_name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="c1"># --- Load data with geometry preserved ---</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">spills</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_postgis</span><span class="p">(</span>
<span class="n">sql</span><span class="o">=</span><span class="s1">'SELECT * FROM spills_with_ruca'</span><span class="p">,</span>
<span class="n">con</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span>
<span class="n">geom_col</span><span class="o">=</span><span class="s1">'geometry'</span><span class="p">,</span>
<span class="n">crs</span><span class="o">=</span><span class="s1">'EPSG:4326'</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="s2">"GeoPandas failed to load geometry. Falling back to plain Pandas."</span><span class="p">)</span>
<span class="n">spills</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="s1">'SELECT * FROM spills_with_ruca'</span><span class="p">,</span> <span class="n">engine</span><span class="p">)</span>
<span class="c1"># Optional: spills.drop(columns='geometry', inplace=True, errors='ignore')</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># use longitude and latitude to create a GeoDataFrame from spills data</span>
<span class="n">spills</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">points_from_xy</span><span class="p">(</span><span class="n">spills</span><span class="p">[</span><span class="s1">'Longitude'</span><span class="p">],</span> <span class="n">spills</span><span class="p">[</span><span class="s1">'Latitude'</span><span class="p">])</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">spills</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="s1">'EPSG:4326'</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Ensure date columns are in datetime format</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">])</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">])</span>
<span class="c1"># create a report year column</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Report Year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datetime</span><span class="w"> </span><span class="kn">import</span> <span class="n">datetime</span>
<span class="c1"># 48 months before and after January 1, 2021</span>
<span class="n">start_date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2017</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">end_date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2025</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># exclusive end date</span>
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span>
<span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_date</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">end_date</span><span class="p">)</span>
<span class="p">]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Separate Historical vs. Recent Spills</span>
<span class="n">historical_spills</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Spill Type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Historical'</span><span class="p">]</span>
<span class="n">recent_spills</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Spill Type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Recent'</span><span class="p">]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># only use the top 3 counties by number of spills</span>
<span class="n">top_counties</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'county'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="c1"># filter spills_gdf to only include top counties</span>
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'county'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">top_counties</span><span class="p">)]</span>
<span class="c1"># filter historical spills to only include top counties</span>
<span class="n">historical_spills</span> <span class="o">=</span> <span class="n">historical_spills</span><span class="p">[</span><span class="n">historical_spills</span><span class="p">[</span><span class="s1">'county'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">top_counties</span><span class="p">)]</span>
<span class="c1"># filter recent spills to only include top counties</span>
<span class="n">recent_spills</span> <span class="o">=</span> <span class="n">recent_spills</span><span class="p">[</span><span class="n">recent_spills</span><span class="p">[</span><span class="s1">'county'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">top_counties</span><span class="p">)]</span>
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<div class="highlight hl-ipython3"><pre><span></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="c1"># Create 'Period' column</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Report Year'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'Before 2021'</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="mi">2021</span> <span class="k">else</span> <span class="s1">'2021 and After'</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">])</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">])</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">-</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Date of Discovery'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">]</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</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="s1">'Report Delay (Days)'</span><span class="p">:</span> <span class="s1">'report_delay'</span><span class="p">,</span>
<span class="s1">'Spill Type'</span><span class="p">:</span> <span class="s1">'spill_type'</span>
<span class="p">})</span>
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<h1 id="OLS-Regression">OLS Regression<a class="anchor-link" href="#OLS-Regression">¶</a></h1>
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<div class="highlight hl-ipython3"><pre><span></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="n">model</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">"report_delay ~ C(spill_type) * C(Period)"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">spills_gdf</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre> OLS Regression Results
==============================================================================
Dep. Variable: report_delay R-squared: 0.005
Model: OLS Adj. R-squared: 0.005
Method: Least Squares F-statistic: 19.14
Date: Wed, 06 Aug 2025 Prob (F-statistic): 2.26e-12
Time: 09:11:58 Log-Likelihood: -53847.
No. Observations: 11196 AIC: 1.077e+05
Df Residuals: 11192 BIC: 1.077e+05
Df Model: 3
Covariance Type: nonrobust
====================================================================================================================
coef std err t P&gt;|t| [0.025 0.975]
--------------------------------------------------------------------------------------------------------------------
Intercept 6.7689 0.478 14.166 0.000 5.832 7.706
C(spill_type)[T.Recent] -4.9332 0.691 -7.139 0.000 -6.288 -3.579
C(Period)[T.Before 2021] -3.4361 0.986 -3.484 0.000 -5.369 -1.503
C(spill_type)[T.Recent]:C(Period)[T.Before 2021] 4.3645 1.249 3.496 0.000 1.917 6.812
==============================================================================
Omnibus: 24335.353 Durbin-Watson: 1.954
Prob(Omnibus): 0.000 Jarque-Bera (JB): 152505098.951
Skew: 19.556 Prob(JB): 0.00
Kurtosis: 573.424 Cond. No. 7.15
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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<h1 id="Negative-Binomial-Regression">Negative Binomial Regression<a class="anchor-link" href="#Negative-Binomial-Regression">¶</a></h1>
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<div class="highlight hl-ipython3"><pre><span></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">import</span><span class="w"> </span><span class="nn">statsmodels.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sm</span>
<span class="c1"># Make sure spill_type and Period are clean, and report_delay is numeric</span>
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</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="s1">'Report Delay (Days)'</span><span class="p">:</span> <span class="s1">'report_delay'</span><span class="p">,</span>
<span class="s1">'Spill Type'</span><span class="p">:</span> <span class="s1">'spill_type'</span>
<span class="p">})</span>
<span class="c1"># Fit the Negative Binomial model with interaction</span>
<span class="n">nb_model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">glm</span><span class="p">(</span>
<span class="n">formula</span><span class="o">=</span><span class="s2">"report_delay ~ C(spill_type) * C(Period)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">spills_gdf</span><span class="p">,</span>
<span class="n">family</span><span class="o">=</span><span class="n">sm</span><span class="o">.</span><span class="n">families</span><span class="o">.</span><span class="n">NegativeBinomial</span><span class="p">()</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="c1"># View the results</span>
<span class="nb">print</span><span class="p">(</span><span class="n">nb_model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre> Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: report_delay No. Observations: 11196
Model: GLM Df Residuals: 11192
Model Family: NegativeBinomial Df Model: 3
Link Function: Log Scale: 1.0000
Method: IRLS Log-Likelihood: -26493.
Date: Wed, 06 Aug 2025 Deviance: 29512.
Time: 09:12:11 Pearson chi2: 4.90e+05
No. Iterations: 9 Pseudo R-squ. (CS): 0.2067
Covariance Type: nonrobust
====================================================================================================================
coef std err z P&gt;|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------------------
Intercept 1.9123 0.017 110.902 0.000 1.879 1.946
C(spill_type)[T.Recent] -1.3049 0.027 -48.162 0.000 -1.358 -1.252
C(Period)[T.Before 2021] -0.7085 0.037 -18.968 0.000 -0.782 -0.635
C(spill_type)[T.Recent]:C(Period)[T.Before 2021] 1.1178 0.049 23.044 0.000 1.023 1.213
====================================================================================================================
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<pre>/home/dadams/miniconda3/lib/python3.12/site-packages/statsmodels/genmod/families/family.py:1367: ValueWarning: Negative binomial dispersion parameter alpha not set. Using default value alpha=1.0.
warnings.warn("Negative binomial dispersion parameter alpha not "
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<div class="highlight hl-ipython3"><pre><span></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="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">nb_model</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
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<pre>Intercept 6.768912
C(spill_type)[T.Recent] 0.271201
C(Period)[T.Before 2021] 0.492364
C(spill_type)[T.Recent]:C(Period)[T.Before 2021] 3.058085
dtype: float64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="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="c1"># Predicted delays by group</span>
<span class="n">group_labels</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"Historical, After 2021"</span><span class="p">,</span> <span class="c1"># Reference categories (Intercept only)</span>
<span class="s2">"Recent, After 2021"</span><span class="p">,</span> <span class="c1"># Recent effect only</span>
<span class="s2">"Historical, Before 2021"</span><span class="p">,</span> <span class="c1"># Period effect only </span>
<span class="s2">"Recent, Before 2021"</span> <span class="c1"># Recent + Period + Interaction</span>
<span class="p">]</span>
<span class="c1"># From your latest exponentiated results</span>
<span class="n">intercept</span> <span class="o">=</span> <span class="mf">6.768912</span>
<span class="n">recent_effect</span> <span class="o">=</span> <span class="mf">0.271201</span>
<span class="n">period_effect</span> <span class="o">=</span> <span class="mf">0.492364</span>
<span class="n">interaction_effect</span> <span class="o">=</span> <span class="mf">3.058085</span>
<span class="n">predicted_delays</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">intercept</span><span class="p">,</span> <span class="c1"># 6.77</span>
<span class="n">intercept</span> <span class="o">*</span> <span class="n">recent_effect</span><span class="p">,</span> <span class="c1"># 6.77 * 0.271 = 1.84</span>
<span class="n">intercept</span> <span class="o">*</span> <span class="n">period_effect</span><span class="p">,</span> <span class="c1"># 6.77 * 0.492 = 3.33</span>
<span class="n">intercept</span> <span class="o">*</span> <span class="n">recent_effect</span> <span class="o">*</span> <span class="n">period_effect</span> <span class="o">*</span> <span class="n">interaction_effect</span> <span class="c1"># 6.77 * 0.271 * 0.492 * 3.058 = 2.75</span>
<span class="p">]</span>
<span class="c1"># Create DataFrame for plotting</span>
<span class="n">df_plot</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">"Group"</span><span class="p">:</span> <span class="n">group_labels</span><span class="p">,</span>
<span class="s2">"Predicted Delay (Days)"</span><span class="p">:</span> <span class="n">predicted_delays</span>
<span class="p">})</span>
<span class="c1"># Plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</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">6</span><span class="p">))</span>
<span class="n">bars</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_plot</span><span class="p">[</span><span class="s2">"Group"</span><span class="p">],</span> <span class="n">df_plot</span><span class="p">[</span><span class="s2">"Predicted Delay (Days)"</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Predicted Report Delay by Spill Type and Period"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Predicted Delay (Days)"</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</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="c1"># Annotate bars</span>
<span class="k">for</span> <span class="n">bar</span> <span class="ow">in</span> <span class="n">bars</span><span class="p">:</span>
<span class="n">height</span> <span class="o">=</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">height</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
<span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">bar</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span> <span class="o">+</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">height</span><span class="p">),</span>
<span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="c1"># 3 points vertical offset</span>
<span class="n">textcoords</span><span class="o">=</span><span class="s2">"offset points"</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'bottom'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Predicted delays:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">group</span><span class="p">,</span> <span class="n">delay</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">group_labels</span><span class="p">,</span> <span class="n">predicted_delays</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">group</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">delay</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
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"/>
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<pre>Predicted delays:
Historical, After 2021: 6.77 days
Recent, After 2021: 1.84 days
Historical, Before 2021: 3.33 days
Recent, Before 2021: 2.76 days
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<h1 id="Interrupted-Time-Series">Interrupted Time Series<a class="anchor-link" href="#Interrupted-Time-Series">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [59]:</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">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">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">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="c1"># 1. Convert to monthly time series</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_period</span><span class="p">(</span><span class="s1">'M'</span><span class="p">)</span>
<span class="n">monthly</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'report_month'</span><span class="p">)[</span><span class="s1">'report_delay'</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">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">()</span>
<span class="c1"># 2. Create ITS variables</span>
<span class="n">monthly</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">-</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">//</span> <span class="mi">30</span>
<span class="n">monthly</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</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">monthly</span><span class="p">[</span><span class="s1">'time_post'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">*</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span>
<span class="c1"># 3. Run the ITS model</span>
<span class="n">its_model</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">"report_delay ~ time + post_2021 + time_post"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">monthly</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">its_model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
<span class="c1"># 4. Predict and plot</span>
<span class="n">monthly</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">]</span> <span class="o">=</span> <span class="n">its_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">monthly</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Observed'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">monthly</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">monthly</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Predicted (ITS)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Policy Change (2021)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Interrupted Time Series: Monthly Report Delay'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Month'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Mean Report Delay (Days)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</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> OLS Regression Results
==============================================================================
Dep. Variable: report_delay R-squared: 0.216
Model: OLS Adj. R-squared: 0.189
Method: Least Squares F-statistic: 8.150
Date: Wed, 06 Aug 2025 Prob (F-statistic): 7.46e-05
Time: 09:14:03 Log-Likelihood: -248.17
No. Observations: 93 AIC: 504.3
Df Residuals: 89 BIC: 514.5
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P&gt;|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 2.0214 1.081 1.870 0.065 -0.127 4.170
time 0.0280 0.045 0.617 0.539 -0.062 0.118
post_2021 -6.4546 2.524 -2.558 0.012 -11.469 -1.440
time_post 0.1062 0.056 1.893 0.062 -0.005 0.218
==============================================================================
Omnibus: 35.237 Durbin-Watson: 2.065
Prob(Omnibus): 0.000 Jarque-Bera (JB): 67.022
Skew: 1.515 Prob(JB): 2.79e-15
Kurtosis: 5.849 Cond. No. 511.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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"/>
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<h2 id="ITS-by-Spill-Type">ITS by Spill Type<a class="anchor-link" href="#ITS-by-Spill-Type">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [60]:</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">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">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">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="c1"># Group by month and spill type</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_period</span><span class="p">(</span><span class="s1">'M'</span><span class="p">)</span>
<span class="n">monthly_by_type</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'report_month'</span><span class="p">,</span> <span class="s1">'spill_type'</span><span class="p">])[</span><span class="s1">'report_delay'</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">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">()</span>
<span class="c1"># ITS variables</span>
<span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">-</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">//</span> <span class="mi">30</span>
<span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</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">monthly_by_type</span><span class="p">[</span><span class="s1">'time_post'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">*</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span>
<span class="c1"># Run ITS models and print summaries</span>
<span class="n">its_results_by_type</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">stype</span> <span class="ow">in</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span> <span class="o">==</span> <span class="n">stype</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">model</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">"report_delay ~ time + post_2021 + time_post"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">its_results_by_type</span><span class="p">[</span><span class="n">stype</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df</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">=== ITS Model Summary for </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills ===</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="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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=== ITS Model Summary for Historical Spills ===
OLS Regression Results
==============================================================================
Dep. Variable: report_delay R-squared: 0.229
Model: OLS Adj. R-squared: 0.203
Method: Least Squares F-statistic: 8.814
Date: Wed, 06 Aug 2025 Prob (F-statistic): 3.54e-05
Time: 09:14:17 Log-Likelihood: -277.64
No. Observations: 93 AIC: 563.3
Df Residuals: 89 BIC: 573.4
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P&gt;|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 3.3388 1.484 2.249 0.027 0.389 6.288
time -0.0264 0.062 -0.423 0.673 -0.150 0.097
post_2021 -10.3194 3.465 -2.978 0.004 -17.204 -3.435
time_post 0.2159 0.077 2.803 0.006 0.063 0.369
==============================================================================
Omnibus: 32.115 Durbin-Watson: 1.983
Prob(Omnibus): 0.000 Jarque-Bera (JB): 59.323
Skew: 1.383 Prob(JB): 1.31e-13
Kurtosis: 5.767 Cond. No. 511.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
=== ITS Model Summary for Recent Spills ===
OLS Regression Results
==============================================================================
Dep. Variable: report_delay R-squared: 0.013
Model: OLS Adj. R-squared: -0.020
Method: Least Squares F-statistic: 0.3852
Date: Wed, 06 Aug 2025 Prob (F-statistic): 0.764
Time: 09:14:17 Log-Likelihood: -259.51
No. Observations: 93 AIC: 527.0
Df Residuals: 89 BIC: 537.2
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P&gt;|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 1.4880 1.221 1.218 0.226 -0.939 3.915
time 0.0414 0.051 0.808 0.421 -0.060 0.143
post_2021 2.6802 2.851 0.940 0.350 -2.985 8.345
time_post -0.0677 0.063 -1.068 0.288 -0.194 0.058
==============================================================================
Omnibus: 129.874 Durbin-Watson: 2.198
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3176.252
Skew: 4.916 Prob(JB): 0.00
Kurtosis: 29.889 Cond. No. 511.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
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<div class="highlight hl-ipython3"><pre><span></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">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">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="c1"># 1. Group by month and spill type</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_period</span><span class="p">(</span><span class="s1">'M'</span><span class="p">)</span>
<span class="n">monthly_by_type</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'report_month'</span><span class="p">,</span> <span class="s1">'spill_type'</span><span class="p">])[</span><span class="s1">'report_delay'</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">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">()</span>
<span class="c1"># 2. Create ITS variables</span>
<span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">-</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">//</span> <span class="mi">30</span>
<span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</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">monthly_by_type</span><span class="p">[</span><span class="s1">'time_post'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">*</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span>
<span class="c1"># 3. Run ITS models separately by spill type</span>
<span class="n">its_results_by_type</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">stype</span> <span class="ow">in</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">monthly_by_type</span><span class="p">[</span><span class="n">monthly_by_type</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span> <span class="o">==</span> <span class="n">stype</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">model</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">"report_delay ~ time + post_2021 + time_post"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">its_results_by_type</span><span class="p">[</span><span class="n">stype</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
<span class="c1"># 4. Plot side-by-side</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">its_results_by_type</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Observed'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Predicted (ITS)'</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="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Policy Change (2021)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"ITS Model for </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Mean Report Delay (Days)"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Month"</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>
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<h1 id="incorporating-rurality-data">incorporating rurality data<a class="anchor-link" href="#incorporating-rurality-data">¶</a></h1><h1 id="This-section-is-to-incorporate-the-rurality-data-into-the-spills-data">This section is to incorporate the rurality data into the spills data<a class="anchor-link" href="#This-section-is-to-incorporate-the-rurality-data-into-the-spills-data">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [64]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">classify_rurality</span><span class="p">(</span><span class="n">ruca_code</span><span class="p">):</span>
<span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">ruca_code</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">'Unknown'</span>
<span class="k">elif</span> <span class="n">ruca_code</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'Urban'</span>
<span class="k">elif</span> <span class="n">ruca_code</span> <span class="o">&lt;=</span> <span class="mi">6</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'Suburban'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'Rural'</span>
<span class="n">spills</span><span class="p">[</span><span class="s1">'ruca_code'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">spills</span><span class="p">[</span><span class="s1">'ruca_code'</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">spills</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills</span><span class="p">[</span><span class="s1">'ruca_code'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">classify_rurality</span><span class="p">)</span>
<span class="n">spills</span> <span class="o">=</span> <span class="n">spills</span><span class="p">[</span><span class="n">spills</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'Unknown'</span><span class="p">]</span> <span class="c1"># drop unknowns</span>
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<div class="highlight hl-ipython3"><pre><span></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">import</span><span class="w"> </span><span class="nn">statsmodels.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sm</span>
<span class="c1"># Make sure spill_type and Period are clean, and report_delay is numeric</span>
<span class="n">spills_gdf</span> <span class="o">=</span> <span class="n">spills_gdf</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="s1">'Report Delay (Days)'</span><span class="p">:</span> <span class="s1">'report_delay'</span><span class="p">,</span>
<span class="s1">'Spill Type'</span><span class="p">:</span> <span class="s1">'spill_type'</span><span class="p">,</span>
<span class="s1">'rurality'</span><span class="p">:</span> <span class="s1">'rurality'</span>
<span class="p">})</span>
<span class="c1"># Fit the Negative Binomial model with interaction</span>
<span class="n">nb_model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">glm</span><span class="p">(</span>
<span class="n">formula</span><span class="o">=</span><span class="s2">"report_delay ~ C(spill_type) * C(Period) * C(rurality)"</span><span class="p">,</span>
<span class="n">data</span><span class="o">=</span><span class="n">spills_gdf</span><span class="p">,</span>
<span class="n">family</span><span class="o">=</span><span class="n">sm</span><span class="o">.</span><span class="n">families</span><span class="o">.</span><span class="n">NegativeBinomial</span><span class="p">()</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="c1"># View the results</span>
<span class="nb">print</span><span class="p">(</span><span class="n">nb_model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre> Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: report_delay No. Observations: 11196
Model: GLM Df Residuals: 11184
Model Family: NegativeBinomial Df Model: 11
Link Function: Log Scale: 1.0000
Method: IRLS Log-Likelihood: -26264.
Date: Wed, 06 Aug 2025 Deviance: 29052.
Time: 09:45:31 Pearson chi2: 5.16e+05
No. Iterations: 9 Pseudo R-squ. (CS): 0.2386
Covariance Type: nonrobust
============================================================================================================================================
coef std err z P&gt;|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------------------------------------------
Intercept 2.4066 0.039 62.491 0.000 2.331 2.482
C(spill_type)[T.Recent] -1.8821 0.048 -39.000 0.000 -1.977 -1.788
C(Period)[T.Before 2021] -1.0298 0.086 -11.979 0.000 -1.198 -0.861
C(rurality)[T.Suburban] -0.6149 0.086 -7.174 0.000 -0.783 -0.447
C(rurality)[T.Urban] -0.6602 0.043 -15.208 0.000 -0.745 -0.575
C(spill_type)[T.Recent]:C(Period)[T.Before 2021] 1.4185 0.096 14.839 0.000 1.231 1.606
C(spill_type)[T.Recent]:C(rurality)[T.Suburban] 0.1016 0.118 0.864 0.387 -0.129 0.332
C(spill_type)[T.Recent]:C(rurality)[T.Urban] 0.9560 0.062 15.433 0.000 0.835 1.077
C(Period)[T.Before 2021]:C(rurality)[T.Suburban] 1.1389 0.174 6.558 0.000 0.798 1.479
C(Period)[T.Before 2021]:C(rurality)[T.Urban] 0.3593 0.096 3.730 0.000 0.170 0.548
C(spill_type)[T.Recent]:C(Period)[T.Before 2021]:C(rurality)[T.Suburban] -0.1548 0.208 -0.743 0.457 -0.563 0.253
C(spill_type)[T.Recent]:C(Period)[T.Before 2021]:C(rurality)[T.Urban] -0.4859 0.117 -4.150 0.000 -0.715 -0.256
============================================================================================================================================
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<pre>/home/dadams/miniconda3/lib/python3.12/site-packages/statsmodels/genmod/families/family.py:1367: ValueWarning: Negative binomial dispersion parameter alpha not set. Using default value alpha=1.0.
warnings.warn("Negative binomial dispersion parameter alpha not "
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<div class="highlight hl-ipython3"><pre><span></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="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">nb_model</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
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<pre>Intercept 11.096599
C(spill_type)[T.Recent] 0.152271
C(Period)[T.Before 2021] 0.357070
C(rurality)[T.Suburban] 0.540706
C(rurality)[T.Urban] 0.516745
C(spill_type)[T.Recent]:C(Period)[T.Before 2021] 4.130812
C(spill_type)[T.Recent]:C(rurality)[T.Suburban] 1.106906
C(spill_type)[T.Recent]:C(rurality)[T.Urban] 2.601337
C(Period)[T.Before 2021]:C(rurality)[T.Suburban] 3.123185
C(Period)[T.Before 2021]:C(rurality)[T.Urban] 1.432260
C(spill_type)[T.Recent]:C(Period)[T.Before 2021]:C(rurality)[T.Suburban] 0.856609
C(spill_type)[T.Recent]:C(Period)[T.Before 2021]:C(rurality)[T.Urban] 0.615116
dtype: float64</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="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">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Create the data in a more structured way</span>
<span class="n">spill_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="s1">'Recent'</span><span class="p">]</span>
<span class="n">periods</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'After 2021'</span><span class="p">,</span> <span class="s1">'Before 2021'</span><span class="p">]</span>
<span class="n">rurality</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Rural'</span><span class="p">,</span> <span class="s1">'Suburban'</span><span class="p">,</span> <span class="s1">'Urban'</span><span class="p">]</span>
<span class="c1"># Calculate all combinations</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">spill</span> <span class="ow">in</span> <span class="n">spill_types</span><span class="p">:</span>
<span class="k">for</span> <span class="n">period</span> <span class="ow">in</span> <span class="n">periods</span><span class="p">:</span>
<span class="k">for</span> <span class="n">rural</span> <span class="ow">in</span> <span class="n">rurality</span><span class="p">:</span>
<span class="c1"># Calculate predicted delay based on the coefficients</span>
<span class="n">delay</span> <span class="o">=</span> <span class="n">intercept</span> <span class="c1"># Start with intercept</span>
<span class="k">if</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_effect</span>
<span class="k">if</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_effect</span>
<span class="k">if</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">suburban_effect</span>
<span class="k">elif</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">urban_effect</span>
<span class="c1"># Add interactions</span>
<span class="k">if</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_period_interaction</span>
<span class="k">if</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_suburban_interaction</span>
<span class="k">elif</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_urban_interaction</span>
<span class="k">if</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_suburban_interaction</span>
<span class="k">elif</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_urban_interaction</span>
<span class="c1"># Add three-way interactions</span>
<span class="k">if</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">three_way_suburban</span>
<span class="k">elif</span> <span class="n">spill</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rural</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">three_way_urban</span>
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">spill</span><span class="p">,</span> <span class="n">period</span><span class="p">,</span> <span class="n">rural</span><span class="p">,</span> <span class="n">delay</span><span class="p">])</span>
<span class="c1"># Create DataFrame</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">,</span> <span class="s1">'Rurality'</span><span class="p">,</span> <span class="s1">'Predicted_Delay'</span><span class="p">])</span>
<span class="c1"># Option 1: Grouped Bar Chart</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</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">2</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">6</span><span class="p">))</span>
<span class="c1"># Subplot 1: Grouped by Period and Rurality</span>
<span class="n">df_pivot1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="s1">'Predicted_Delay'</span><span class="p">,</span>
<span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">,</span> <span class="s1">'Rurality'</span><span class="p">],</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'Spill_Type'</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_pivot1</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>
<span class="n">bars1</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">width</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">df_pivot1</span><span class="p">[</span><span class="s1">'Historical'</span><span class="p">],</span> <span class="n">width</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'steelblue'</span><span class="p">)</span>
<span class="n">bars2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">width</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">df_pivot1</span><span class="p">[</span><span class="s1">'Recent'</span><span class="p">],</span> <span class="n">width</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Recent'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Period and Rurality'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Predicted Delay (Days)'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Predicted Report Delay by Spill Type'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">period</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="n">rural</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">period</span><span class="p">,</span> <span class="n">rural</span> <span class="ow">in</span> <span class="n">df_pivot1</span><span class="o">.</span><span class="n">index</span><span class="p">],</span>
<span class="n">rotation</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="c1"># Add value labels on bars</span>
<span class="k">for</span> <span class="n">bars</span> <span class="ow">in</span> <span class="p">[</span><span class="n">bars1</span><span class="p">,</span> <span class="n">bars2</span><span class="p">]:</span>
<span class="k">for</span> <span class="n">bar</span> <span class="ow">in</span> <span class="n">bars</span><span class="p">:</span>
<span class="n">height</span> <span class="o">=</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">height</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span>
<span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">bar</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span> <span class="o">+</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">height</span><span class="p">),</span>
<span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="n">textcoords</span><span class="o">=</span><span class="s2">"offset points"</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'bottom'</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="c1"># Subplot 2: Heatmap</span>
<span class="n">df_heatmap</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="s1">'Predicted_Delay'</span><span class="p">,</span>
<span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">],</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'Rurality'</span><span class="p">)</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">df_heatmap</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'YlOrRd'</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="n">ax2</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">df_heatmap</span><span class="o">.</span><span class="n">columns</span><span class="p">)))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">df_heatmap</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">ax2</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">df_heatmap</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">spill</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="n">period</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">spill</span><span class="p">,</span> <span class="n">period</span> <span class="ow">in</span> <span class="n">df_heatmap</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Heatmap of Predicted Delays'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Rurality'</span><span class="p">)</span>
<span class="c1"># Add text annotations to heatmap</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">df_heatmap</span><span class="o">.</span><span class="n">index</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">df_heatmap</span><span class="o">.</span><span class="n">columns</span><span class="p">)):</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">ax2</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="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">df_heatmap</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="w"> </span><span class="n">j</span><span class="p">]</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</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">color</span><span class="o">=</span><span class="s2">"black"</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="c1"># Add colorbar</span>
<span class="n">cbar</span> <span class="o">=</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">ax2</span><span class="p">)</span>
<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">'Predicted Delay (Days)'</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>
<span class="c1"># Print summary table</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Summary Table:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="s1">'Predicted_Delay'</span><span class="p">,</span>
<span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">],</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'Rurality'</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
</pre></div>
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<pre>
Summary Table:
Rurality Rural Suburban Urban
Spill_Type Period
Historical After 2021 11.1 6.0 5.7
Before 2021 4.0 6.7 2.9
Recent After 2021 1.7 1.0 2.3
Before 2021 2.5 4.0 3.0
</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="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">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Use the same coefficient values from your model</span>
<span class="n">intercept</span> <span class="o">=</span> <span class="mf">11.096599</span>
<span class="n">recent_effect</span> <span class="o">=</span> <span class="mf">0.152271</span>
<span class="n">period_effect</span> <span class="o">=</span> <span class="mf">0.357070</span>
<span class="n">suburban_effect</span> <span class="o">=</span> <span class="mf">0.540706</span>
<span class="n">urban_effect</span> <span class="o">=</span> <span class="mf">0.516745</span>
<span class="n">recent_period_interaction</span> <span class="o">=</span> <span class="mf">4.130812</span>
<span class="n">recent_suburban_interaction</span> <span class="o">=</span> <span class="mf">1.106906</span>
<span class="n">recent_urban_interaction</span> <span class="o">=</span> <span class="mf">2.601337</span>
<span class="n">period_suburban_interaction</span> <span class="o">=</span> <span class="mf">3.123185</span>
<span class="n">period_urban_interaction</span> <span class="o">=</span> <span class="mf">1.432260</span>
<span class="n">three_way_suburban</span> <span class="o">=</span> <span class="mf">0.856609</span>
<span class="n">three_way_urban</span> <span class="o">=</span> <span class="mf">0.615116</span>
<span class="c1"># Calculate delays for each combination</span>
<span class="k">def</span><span class="w"> </span><span class="nf">calculate_delay</span><span class="p">(</span><span class="n">spill_type</span><span class="p">,</span> <span class="n">period</span><span class="p">,</span> <span class="n">rurality</span><span class="p">):</span>
<span class="n">delay</span> <span class="o">=</span> <span class="n">intercept</span>
<span class="k">if</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_effect</span>
<span class="k">if</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_effect</span>
<span class="k">if</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">suburban_effect</span>
<span class="k">elif</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">urban_effect</span>
<span class="c1"># Add interactions</span>
<span class="k">if</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_period_interaction</span>
<span class="k">if</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_suburban_interaction</span>
<span class="k">elif</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">recent_urban_interaction</span>
<span class="k">if</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_suburban_interaction</span>
<span class="k">elif</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">period_urban_interaction</span>
<span class="c1"># Add three-way interactions</span>
<span class="k">if</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Suburban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">three_way_suburban</span>
<span class="k">elif</span> <span class="n">spill_type</span> <span class="o">==</span> <span class="s1">'Recent'</span> <span class="ow">and</span> <span class="n">period</span> <span class="o">==</span> <span class="s1">'Before 2021'</span> <span class="ow">and</span> <span class="n">rurality</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">:</span>
<span class="n">delay</span> <span class="o">*=</span> <span class="n">three_way_urban</span>
<span class="k">return</span> <span class="n">delay</span>
<span class="c1"># Create data for comparison</span>
<span class="n">rurality_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Rural'</span><span class="p">,</span> <span class="s1">'Suburban'</span><span class="p">,</span> <span class="s1">'Urban'</span><span class="p">]</span>
<span class="n">spill_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="s1">'Recent'</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">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'#2E86AB'</span><span class="p">,</span> <span class="s1">'#A23B72'</span><span class="p">]</span> <span class="c1"># Blue for Historical, Red for Recent</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">rurality</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rurality_types</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="n">idx</span><span class="p">]</span>
<span class="c1"># Calculate delays for each combination</span>
<span class="n">before_hist</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="s1">'Before 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">after_hist</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="s1">'After 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">before_recent</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="s1">'Recent'</span><span class="p">,</span> <span class="s1">'Before 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">after_recent</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="s1">'Recent'</span><span class="p">,</span> <span class="s1">'After 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># Before 2021, After 2021</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>
<span class="c1"># Create bars (Before first, then After)</span>
<span class="n">bars1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">width</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="n">before_hist</span><span class="p">,</span> <span class="n">after_hist</span><span class="p">],</span> <span class="n">width</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Historical'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">bars2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">width</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="p">[</span><span class="n">before_recent</span><span class="p">,</span> <span class="n">after_recent</span><span class="p">],</span> <span class="n">width</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s1">'Recent'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="c1"># Customize subplot</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s1"> Areas'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Predicted Delay (Days)'</span> <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">''</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="n">x</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="s1">'Before 2021'</span><span class="p">,</span> <span class="s1">'After 2021'</span><span class="p">])</span> <span class="c1"># Before comes first</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="c1"># Add value labels</span>
<span class="k">for</span> <span class="n">bars</span> <span class="ow">in</span> <span class="p">[</span><span class="n">bars1</span><span class="p">,</span> <span class="n">bars2</span><span class="p">]:</span>
<span class="k">for</span> <span class="n">bar</span> <span class="ow">in</span> <span class="n">bars</span><span class="p">:</span>
<span class="n">height</span> <span class="o">=</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">height</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span>
<span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">bar</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span> <span class="o">+</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">height</span><span class="p">),</span>
<span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="n">textcoords</span><span class="o">=</span><span class="s2">"offset points"</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'bottom'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="c1"># Add legend to first subplot only</span>
<span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Predicted Report Delay: Before 2021 vs After 2021'</span><span class="p">,</span>
<span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.02</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>
<span class="c1"># Print summary table with Before first</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Summary: Predicted Delays (Days)"</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">50</span><span class="p">)</span>
<span class="n">data_summary</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">rurality</span> <span class="ow">in</span> <span class="n">rurality_types</span><span class="p">:</span>
<span class="k">for</span> <span class="n">spill</span> <span class="ow">in</span> <span class="n">spill_types</span><span class="p">:</span>
<span class="n">before</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="n">spill</span><span class="p">,</span> <span class="s1">'Before 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">after</span> <span class="o">=</span> <span class="n">calculate_delay</span><span class="p">(</span><span class="n">spill</span><span class="p">,</span> <span class="s1">'After 2021'</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)</span>
<span class="n">change</span> <span class="o">=</span> <span class="n">after</span> <span class="o">-</span> <span class="n">before</span> <span class="c1"># After minus Before (positive = increase)</span>
<span class="n">pct_change</span> <span class="o">=</span> <span class="p">((</span><span class="n">after</span> <span class="o">-</span> <span class="n">before</span><span class="p">)</span> <span class="o">/</span> <span class="n">before</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
<span class="n">data_summary</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'Rurality'</span><span class="p">:</span> <span class="n">rurality</span><span class="p">,</span>
<span class="s1">'Spill_Type'</span><span class="p">:</span> <span class="n">spill</span><span class="p">,</span>
<span class="s1">'Before_2021'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">before</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'After_2021'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">after</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'Change'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">change</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'Pct_Change'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">pct_change</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="p">})</span>
<span class="n">summary_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data_summary</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">summary_df</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="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Key Insights:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"- Positive 'Change' means delays INCREASED after 2021"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"- Negative 'Change' means delays DECREASED after 2021"</span><span class="p">)</span>
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<pre>
Summary: Predicted Delays (Days)
==================================================
Rurality Spill_Type Before_2021 After_2021 Change Pct_Change
Rural Historical 4.0 11.1 7.1 180.1
Rural Recent 2.5 1.7 -0.8 -32.2
Suburban Historical 6.7 6.0 -0.7 -10.3
Suburban Recent 4.0 1.0 -3.0 -74.7
Urban Historical 2.9 5.7 2.8 95.5
Urban Recent 3.0 2.3 -0.7 -23.0
Key Insights:
- Positive 'Change' means delays INCREASED after 2021
- Negative 'Change' means delays DECREASED after 2021
</pre>
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<h1 id="Statistical-Significance-Interpretation">Statistical Significance Interpretation<a class="anchor-link" href="#Statistical-Significance-Interpretation">¶</a></h1><h2 id="Overview">Overview<a class="anchor-link" href="#Overview">¶</a></h2><p>This model uses <strong>z-tests</strong> (not t-tests) because it's a Generalized Linear Model with a large sample size (n=11,196). The z-statistic tests whether each coefficient is significantly different from zero.</p>
<h2 id="Individual-Coefficients">Individual Coefficients<a class="anchor-link" href="#Individual-Coefficients">¶</a></h2><table>
<thead>
<tr>
<th>Variable</th>
<th>Coefficient</th>
<th>Std Error</th>
<th>z-value</th>
<th>p-value</th>
<th>Significant?</th>
<th>Interpretation</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Intercept</strong></td>
<td>2.4066</td>
<td>0.039</td>
<td>62.49</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Historical spills in rural areas after 2021 have significant baseline delay</td>
</tr>
<tr>
<td><strong>Recent</strong></td>
<td>-1.8821</td>
<td>0.048</td>
<td>-39.00</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Recent spills have significantly SHORTER delays than Historical</td>
</tr>
<tr>
<td><strong>Before 2021</strong></td>
<td>-1.0298</td>
<td>0.086</td>
<td>-11.98</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Before 2021 had significantly SHORTER delays than after 2021</td>
</tr>
<tr>
<td><strong>Suburban</strong></td>
<td>-0.6149</td>
<td>0.086</td>
<td>-7.17</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Suburban areas have significantly SHORTER delays than rural</td>
</tr>
<tr>
<td><strong>Urban</strong></td>
<td>-0.6602</td>
<td>0.043</td>
<td>-15.21</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Urban areas have significantly SHORTER delays than rural</td>
</tr>
</tbody>
</table>
<h2 id="Two-Way-Interactions">Two-Way Interactions<a class="anchor-link" href="#Two-Way-Interactions">¶</a></h2><table>
<thead>
<tr>
<th>Interaction</th>
<th>Coefficient</th>
<th>Std Error</th>
<th>z-value</th>
<th>p-value</th>
<th>Significant?</th>
<th>Interpretation</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Recent × Before 2021</strong></td>
<td>1.4185</td>
<td>0.096</td>
<td>14.84</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>The combination of Recent + Before 2021 has a significant positive interaction</td>
</tr>
<tr>
<td><strong>Recent × Suburban</strong></td>
<td>0.1016</td>
<td>0.118</td>
<td>0.86</td>
<td>0.387</td>
<td>❌ <strong>NO</strong></td>
<td>No significant interaction between Recent spills and Suburban areas</td>
</tr>
<tr>
<td><strong>Recent × Urban</strong></td>
<td>0.9560</td>
<td>0.062</td>
<td>15.43</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Significant positive interaction between Recent spills and Urban areas</td>
</tr>
<tr>
<td><strong>Before 2021 × Suburban</strong></td>
<td>1.1389</td>
<td>0.174</td>
<td>6.56</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Significant positive interaction between Before 2021 and Suburban areas</td>
</tr>
<tr>
<td><strong>Before 2021 × Urban</strong></td>
<td>0.3593</td>
<td>0.096</td>
<td>3.73</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Significant positive interaction between Before 2021 and Urban areas</td>
</tr>
</tbody>
</table>
<h2 id="Three-Way-Interactions">Three-Way Interactions<a class="anchor-link" href="#Three-Way-Interactions">¶</a></h2><table>
<thead>
<tr>
<th>Three-Way Interaction</th>
<th>Coefficient</th>
<th>Std Error</th>
<th>z-value</th>
<th>p-value</th>
<th>Significant?</th>
<th>Interpretation</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Recent × Before 2021 × Suburban</strong></td>
<td>-0.1548</td>
<td>0.208</td>
<td>-0.74</td>
<td>0.457</td>
<td>❌ <strong>NO</strong></td>
<td>No significant three-way interaction for suburban areas</td>
</tr>
<tr>
<td><strong>Recent × Before 2021 × Urban</strong></td>
<td>-0.4859</td>
<td>0.117</td>
<td>-4.15</td>
<td>&lt; 0.001</td>
<td>✅ <strong>YES</strong></td>
<td>Significant negative three-way interaction for urban areas</td>
</tr>
</tbody>
</table>
<h2 id="Key-Statistical-Insights">Key Statistical Insights<a class="anchor-link" href="#Key-Statistical-Insights">¶</a></h2><h3 id="What's-Statistically-Significant:">What's Statistically Significant:<a class="anchor-link" href="#What's-Statistically-Significant:">¶</a></h3><ol>
<li><strong>Main Effects</strong>: All main effects are highly significant (p &lt; 0.001)</li>
<li><strong>Most Interactions</strong>: 4 out of 5 two-way interactions are significant</li>
<li><strong>Urban Three-Way</strong>: Only the Recent × Before 2021 × Urban interaction is significant</li>
</ol>
<h3 id="What's-NOT-Statistically-Significant:">What's NOT Statistically Significant:<a class="anchor-link" href="#What's-NOT-Statistically-Significant:">¶</a></h3><ol>
<li><strong>Recent × Suburban</strong>: The interaction between Recent spills and Suburban areas (p = 0.387)</li>
<li><strong>Three-way Suburban</strong>: The three-way interaction involving Suburban areas (p = 0.457)</li>
</ol>
<h3 id="Practical-Implications:">Practical Implications:<a class="anchor-link" href="#Practical-Implications:">¶</a></h3><ul>
<li><strong>Recent spills</strong> consistently have shorter delays across all contexts</li>
<li><strong>Before 2021</strong> generally had shorter delays, but this varies by location type</li>
<li><strong>Urban areas</strong> show the most complex interaction patterns (significant in all interactions)</li>
<li><strong>Suburban areas</strong> behave more like rural areas for Recent spills (no significant difference)</li>
</ul>
<h2 id="Model-Fit-Statistics">Model Fit Statistics<a class="anchor-link" href="#Model-Fit-Statistics">¶</a></h2><ul>
<li><strong>Pseudo R-squared</strong>: 0.2386 (23.9% of variance explained)</li>
<li><strong>Sample size</strong>: 11,196 observations (large sample = reliable estimates)</li>
<li><strong>Log-likelihood</strong>: -26,264 (used for model comparison)</li>
</ul>
<h2 id="Alpha-Level-Note">Alpha Level Note<a class="anchor-link" href="#Alpha-Level-Note">¶</a></h2><p>The model uses α = 0.05 as the significance threshold. All "significant" results have p &lt; 0.001 except the non-significant ones noted above.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">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">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">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">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># STEP 1: Prepare the data with rurality</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"APPROACH 1: Separate ITS Models by Rurality"</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">50</span><span class="p">)</span>
<span class="c1"># Prepare your data (replace with your actual spills_gdf)</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_period</span><span class="p">(</span><span class="s1">'M'</span><span class="p">)</span>
<span class="c1"># Group by month, spill type, AND rurality</span>
<span class="n">monthly_by_type_rurality</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'report_month'</span><span class="p">,</span> <span class="s1">'spill_type'</span><span class="p">,</span> <span class="s1">'rurality'</span><span class="p">])[</span><span class="s1">'report_delay'</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">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">to_timestamp</span><span class="p">()</span>
<span class="c1"># ITS variables</span>
<span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">-</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">//</span> <span class="mi">30</span>
<span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</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">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'time_post'</span><span class="p">]</span> <span class="o">=</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">*</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span>
<span class="c1"># STEP 2: Run separate ITS for each combination</span>
<span class="n">its_results_detailed</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">full_results_summary</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">stype</span> <span class="ow">in</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="k">for</span> <span class="n">rurality</span> <span class="ow">in</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">df_subset</span> <span class="o">=</span> <span class="n">monthly_by_type_rurality</span><span class="p">[</span>
<span class="p">(</span><span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'spill_type'</span><span class="p">]</span> <span class="o">==</span> <span class="n">stype</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">monthly_by_type_rurality</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span> <span class="o">==</span> <span class="n">rurality</span><span class="p">)</span>
<span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">8</span><span class="p">:</span> <span class="c1"># Need at least 8 data points for reliable ITS</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">model</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">"report_delay ~ time + post_2021 + time_post"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df_subset</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="n">df_subset</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df_subset</span><span class="p">)</span>
<span class="n">its_results_detailed</span><span class="p">[(</span><span class="n">stype</span><span class="p">,</span> <span class="n">rurality</span><span class="p">)]</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df_subset</span><span class="p">)</span>
<span class="c1"># Store results for summary table</span>
<span class="n">full_results_summary</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'Spill_Type'</span><span class="p">:</span> <span class="n">stype</span><span class="p">,</span>
<span class="s1">'Rurality'</span><span class="p">:</span> <span class="n">rurality</span><span class="p">,</span>
<span class="s1">'N_months'</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">),</span>
<span class="s1">'R_squared'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">rsquared</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="s1">'Model_Status'</span><span class="p">:</span> <span class="s1">'Success'</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">✅ </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills in </span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s2"> Areas:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Data points: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">)</span><span class="si">}</span><span class="s2"> months"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" R-squared: </span><span class="si">{</span><span class="n">model</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">"</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">"</span><span class="se">\n</span><span class="s2">❌ </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills in </span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s2"> Areas: Model failed - </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">)[:</span><span class="mi">50</span><span class="p">]</span><span class="si">}</span><span class="s2">..."</span><span class="p">)</span>
<span class="n">full_results_summary</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'Spill_Type'</span><span class="p">:</span> <span class="n">stype</span><span class="p">,</span>
<span class="s1">'Rurality'</span><span class="p">:</span> <span class="n">rurality</span><span class="p">,</span>
<span class="s1">'N_months'</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">),</span>
<span class="s1">'R_squared'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
<span class="s1">'Model_Status'</span><span class="p">:</span> <span class="s1">'Failed'</span>
<span class="p">})</span>
<span class="k">else</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">⚠️ </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills in </span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s2"> Areas: Insufficient data (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">)</span><span class="si">}</span><span class="s2"> months)"</span><span class="p">)</span>
<span class="n">full_results_summary</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'Spill_Type'</span><span class="p">:</span> <span class="n">stype</span><span class="p">,</span>
<span class="s1">'Rurality'</span><span class="p">:</span> <span class="n">rurality</span><span class="p">,</span>
<span class="s1">'N_months'</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_subset</span><span class="p">),</span>
<span class="s1">'R_squared'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
<span class="s1">'Model_Status'</span><span class="p">:</span> <span class="s1">'Insufficient Data'</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\n</span><span class="s2">SUCCESSFUL MODELS: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">its_results_detailed</span><span class="p">)</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">full_results_summary</span><span class="p">)</span><span class="si">}</span><span class="s2"> possible combinations"</span><span class="p">)</span>
<span class="c1"># STEP 3: KEY METRICS FOR MISSION CHANGE EVALUATION</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span> <span class="o">+</span> <span class="s2">"="</span><span class="o">*</span><span class="mi">70</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"KEY METRICS: State Agency Mission Change Impact Analysis (2021)"</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">70</span><span class="p">)</span>
<span class="n">mission_change_results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">rurality</span><span class="p">),</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span> <span class="ow">in</span> <span class="n">its_results_detailed</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="c1"># Extract key coefficients and p-values</span>
<span class="n">immediate_effect</span> <span class="o">=</span> <span class="n">model</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="s1">'post_2021'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">immediate_pval</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">pvalues</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'post_2021'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">slope_change</span> <span class="o">=</span> <span class="n">model</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="s1">'time_post'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">slope_pval</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">pvalues</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'time_post'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">time_trend</span> <span class="o">=</span> <span class="n">model</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="s1">'time'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># Calculate practical significance</span>
<span class="n">pre_2021_mean</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">][</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">post_2021_mean</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">][</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">mission_change_results</span><span class="o">.</span><span class="n">append</span><span class="p">({</span>
<span class="s1">'Spill_Type'</span><span class="p">:</span> <span class="n">stype</span><span class="p">,</span>
<span class="s1">'Rurality'</span><span class="p">:</span> <span class="n">rurality</span><span class="p">,</span>
<span class="s1">'Immediate_Effect'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">immediate_effect</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="s1">'Immediate_Significant'</span><span class="p">:</span> <span class="s1">'Yes'</span> <span class="k">if</span> <span class="n">immediate_pval</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="k">else</span> <span class="s1">'No'</span><span class="p">,</span>
<span class="s1">'Slope_Change'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">slope_change</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
<span class="s1">'Slope_Significant'</span><span class="p">:</span> <span class="s1">'Yes'</span> <span class="k">if</span> <span class="n">slope_pval</span> <span class="o">&lt;</span> <span class="mf">0.05</span> <span class="k">else</span> <span class="s1">'No'</span><span class="p">,</span>
<span class="s1">'Pre_2021_Average'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">pre_2021_mean</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'Post_2021_Average'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">post_2021_mean</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'Overall_Change'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">post_2021_mean</span> <span class="o">-</span> <span class="n">pre_2021_mean</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="s1">'R_squared'</span><span class="p">:</span> <span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">rsquared</span><span class="p">,</span> <span class="mi">3</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">"</span><span class="se">\n</span><span class="s2">📊 </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2"> Spills in </span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s2"> Areas:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Immediate Mission Change Effect: </span><span class="si">{</span><span class="n">immediate_effect</span><span class="si">:</span><span class="s2">+.2f</span><span class="si">}</span><span class="s2"> days (</span><span class="si">{</span><span class="s1">'✓ significant'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">immediate_pval</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mf">0.05</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'✗ not significant'</span><span class="si">}</span><span class="s2"> p=</span><span class="si">{</span><span class="n">immediate_pval</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="sa">f</span><span class="s2">" Monthly Trend Change: </span><span class="si">{</span><span class="n">slope_change</span><span class="si">:</span><span class="s2">+.4f</span><span class="si">}</span><span class="s2"> days/month (</span><span class="si">{</span><span class="s1">'✓ significant'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">slope_pval</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mf">0.05</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'✗ not significant'</span><span class="si">}</span><span class="s2"> p=</span><span class="si">{</span><span class="n">slope_pval</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="sa">f</span><span class="s2">" Pre-2021 Average: </span><span class="si">{</span><span class="n">pre_2021_mean</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Post-2021 Average: </span><span class="si">{</span><span class="n">post_2021_mean</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Overall Change: </span><span class="si">{</span><span class="n">post_2021_mean</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pre_2021_mean</span><span class="si">:</span><span class="s2">+.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="c1"># STEP 4: Summary table</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n\n</span><span class="s2">📋 SUMMARY TABLE: Mission Change Impact"</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">100</span><span class="p">)</span>
<span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">mission_change_results</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results_df</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="c1"># STEP 5: Policy interpretation</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n\n</span><span class="s2">🏛️ POLICY INTERPRETATION:"</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">50</span><span class="p">)</span>
<span class="n">significant_immediate</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[</span><span class="n">results_df</span><span class="p">[</span><span class="s1">'Immediate_Significant'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Yes'</span><span class="p">]</span>
<span class="n">significant_slope</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[</span><span class="n">results_df</span><span class="p">[</span><span class="s1">'Slope_Significant'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Yes'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"📈 IMMEDIATE EFFECTS (right at mission change):"</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">significant_immediate</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">significant_immediate</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="n">direction</span> <span class="o">=</span> <span class="s2">"DECREASED"</span> <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'Immediate_Effect'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">"INCREASED"</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">row</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">]</span><span class="si">}</span><span class="s2"> spills in </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'Rurality'</span><span class="p">]</span><span class="si">}</span><span class="s2"> areas: </span><span class="si">{</span><span class="n">direction</span><span class="si">}</span><span class="s2"> by </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'Immediate_Effect'</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" • No statistically significant immediate effects detected"</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">📉 TREND CHANGES (ongoing impact over time):"</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">significant_slope</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">significant_slope</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'Slope_Change'</span><span class="p">]</span> <span class="o">&lt;</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">" • </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">]</span><span class="si">}</span><span class="s2"> spills in </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'Rurality'</span><span class="p">]</span><span class="si">}</span><span class="s2"> areas: IMPROVING by </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'Slope_Change'</span><span class="p">])</span><span class="o">*</span><span class="mi">12</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> days per year"</span><span class="p">)</span>
<span class="k">else</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">row</span><span class="p">[</span><span class="s1">'Spill_Type'</span><span class="p">]</span><span class="si">}</span><span class="s2"> spills in </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'Rurality'</span><span class="p">]</span><span class="si">}</span><span class="s2"> areas: WORSENING by </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'Slope_Change'</span><span class="p">]</span><span class="o">*</span><span class="mi">12</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> days per year"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" • No statistically significant trend changes detected"</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">🎯 GEOGRAPHIC EQUITY:"</span><span class="p">)</span>
<span class="n">rural_results</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[</span><span class="n">results_df</span><span class="p">[</span><span class="s1">'Rurality'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Rural'</span><span class="p">]</span>
<span class="n">urban_results</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[</span><span class="n">results_df</span><span class="p">[</span><span class="s1">'Rurality'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Urban'</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rural_results</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">urban_results</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">rural_avg_change</span> <span class="o">=</span> <span class="n">rural_results</span><span class="p">[</span><span class="s1">'Overall_Change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">urban_avg_change</span> <span class="o">=</span> <span class="n">urban_results</span><span class="p">[</span><span class="s1">'Overall_Change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">rural_avg_change</span> <span class="o">-</span> <span class="n">urban_avg_change</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># More than 1 day difference</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Mission change may have created geographic disparities"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Rural average change: </span><span class="si">{</span><span class="n">rural_avg_change</span><span class="si">:</span><span class="s2">+.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Urban average change: </span><span class="si">{</span><span class="n">urban_avg_change</span><span class="si">:</span><span class="s2">+.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Mission change appears to have affected rural and urban areas similarly"</span><span class="p">)</span>
<span class="c1"># STEP 6: Create visualization</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_its_results</span><span class="p">():</span>
<span class="n">n_models</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">its_results_detailed</span><span class="p">)</span>
<span class="k">if</span> <span class="n">n_models</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"No models to plot"</span><span class="p">)</span>
<span class="k">return</span>
<span class="c1"># Calculate subplot layout</span>
<span class="n">cols</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_models</span><span class="p">)</span>
<span class="n">rows</span> <span class="o">=</span> <span class="p">(</span><span class="n">n_models</span> <span class="o">+</span> <span class="n">cols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="n">cols</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="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="o">*</span><span class="n">cols</span><span class="p">,</span> <span class="mi">4</span><span class="o">*</span><span class="n">rows</span><span class="p">))</span>
<span class="k">if</span> <span class="n">n_models</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="n">axes</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">rows</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span>
<span class="k">else</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="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">((</span><span class="n">stype</span><span class="p">,</span> <span class="n">rurality</span><span class="p">),</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">df</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">its_results_detailed</span><span class="o">.</span><span class="n">items</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="n">idx</span><span class="p">]</span>
<span class="c1"># Plot actual data</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Actual'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'gray'</span><span class="p">)</span>
<span class="c1"># Plot trend lines</span>
<span class="n">pre_2021</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</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">sort_values</span><span class="p">(</span><span class="s1">'report_month'</span><span class="p">)</span>
<span class="n">post_2021</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'post_2021'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'report_month'</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pre_2021</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pre_2021</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">pre_2021</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">],</span>
<span class="s1">'r-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Pre-Mission Change'</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">post_2021</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">post_2021</span><span class="p">[</span><span class="s1">'report_month'</span><span class="p">],</span> <span class="n">post_2021</span><span class="p">[</span><span class="s1">'predicted'</span><span class="p">],</span>
<span class="s1">'b-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Post-Mission Change'</span><span class="p">)</span>
<span class="c1"># Add vertical line at intervention</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">'2021-01-01'</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Mission Change'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s1"> Spills - </span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Report Delay (Days)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</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">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</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="c1"># Hide empty subplots</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_models</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">axes</span><span class="p">)):</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</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">suptitle</span><span class="p">(</span><span class="s1">'State Agency Mission Change Impact: Interrupted Time Series Analysis'</span><span class="p">,</span>
<span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.02</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Generate the plot</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">📊 Generating visualization..."</span><span class="p">)</span>
<span class="n">plot_its_results</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">✅ Analysis complete! You now have:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Separate ITS models for each spill type × rurality combination"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Statistical significance testing for mission change effects"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Policy interpretation focusing on geographic equity"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" • Visual time series plots showing before/after trends"</span><span class="p">)</span>
</pre></div>
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<pre>APPROACH 1: Separate ITS Models by Rurality
==================================================
✅ Historical Spills in Rural Areas:
Data points: 90 months
R-squared: 0.048
✅ Historical Spills in Urban Areas:
Data points: 93 months
R-squared: 0.207
✅ Historical Spills in Suburban Areas:
Data points: 68 months
R-squared: 0.028
✅ Recent Spills in Rural Areas:
Data points: 93 months
R-squared: 0.020
✅ Recent Spills in Urban Areas:
Data points: 93 months
R-squared: 0.009
✅ Recent Spills in Suburban Areas:
Data points: 79 months
R-squared: 0.091
SUCCESSFUL MODELS: 6 out of 6 possible combinations
======================================================================
KEY METRICS: State Agency Mission Change Impact Analysis (2021)
======================================================================
📊 Historical Spills in Rural Areas:
Immediate Mission Change Effect: -3.20 days (✗ not significant p=0.758)
Monthly Trend Change: +0.1643 days/month (✗ not significant p=0.493)
Pre-2021 Average: 4.1 days
Post-2021 Average: 9.9 days
Overall Change: +5.8 days
📊 Historical Spills in Urban Areas:
Immediate Mission Change Effect: -11.75 days (✓ significant p=0.002)
Monthly Trend Change: +0.2301 days/month (✓ significant p=0.007)
Pre-2021 Average: 2.3 days
Post-2021 Average: 4.8 days
Overall Change: +2.5 days
📊 Historical Spills in Suburban Areas:
Immediate Mission Change Effect: -13.96 days (✗ not significant p=0.480)
Monthly Trend Change: +0.1509 days/month (✗ not significant p=0.793)
Pre-2021 Average: 5.3 days
Post-2021 Average: 8.7 days
Overall Change: +3.4 days
📊 Recent Spills in Rural Areas:
Immediate Mission Change Effect: +4.24 days (✗ not significant p=0.276)
Monthly Trend Change: -0.0387 days/month (✗ not significant p=0.653)
Pre-2021 Average: 1.7 days
Post-2021 Average: 2.5 days
Overall Change: +0.9 days
📊 Recent Spills in Urban Areas:
Immediate Mission Change Effect: +3.06 days (✗ not significant p=0.669)
Monthly Trend Change: -0.1301 days/month (✗ not significant p=0.414)
Pre-2021 Average: 3.3 days
Post-2021 Average: 2.9 days
Overall Change: -0.3 days
📊 Recent Spills in Suburban Areas:
Immediate Mission Change Effect: +3.15 days (✗ not significant p=0.709)
Monthly Trend Change: -0.3345 days/month (✗ not significant p=0.081)
Pre-2021 Average: 5.2 days
Post-2021 Average: 1.1 days
Overall Change: -4.1 days
📋 SUMMARY TABLE: Mission Change Impact
====================================================================================================
Spill_Type Rurality Immediate_Effect Immediate_Significant Slope_Change Slope_Significant Pre_2021_Average Post_2021_Average Overall_Change R_squared
Historical Rural -3.20 No 0.1643 No 4.1 9.9 5.8 0.048
Historical Urban -11.75 Yes 0.2301 Yes 2.3 4.8 2.5 0.207
Historical Suburban -13.96 No 0.1509 No 5.3 8.7 3.4 0.028
Recent Rural 4.24 No -0.0387 No 1.7 2.5 0.9 0.020
Recent Urban 3.06 No -0.1301 No 3.3 2.9 -0.3 0.009
Recent Suburban 3.15 No -0.3345 No 5.2 1.1 -4.1 0.091
🏛️ POLICY INTERPRETATION:
==================================================
📈 IMMEDIATE EFFECTS (right at mission change):
• Historical spills in Urban areas: DECREASED by 11.8 days
📉 TREND CHANGES (ongoing impact over time):
• Historical spills in Urban areas: WORSENING by 2.76 days per year
🎯 GEOGRAPHIC EQUITY:
• Mission change may have created geographic disparities
• Rural average change: +3.4 days
• Urban average change: +1.1 days
📊 Generating visualization...
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"/>
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<pre>
✅ Analysis complete! You now have:
• Separate ITS models for each spill type × rurality combination
• Statistical significance testing for mission change effects
• Policy interpretation focusing on geographic equity
• Visual time series plots showing before/after trends
</pre>
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<h2 id="Key-Findings---Mission-Change-Appears-Problematic">Key Findings - Mission Change Appears Problematic<a class="anchor-link" href="#Key-Findings---Mission-Change-Appears-Problematic">¶</a></h2><h3 id="Historical-Spills-(Legacy-Cases):">Historical Spills (Legacy Cases):<a class="anchor-link" href="#Historical-Spills-(Legacy-Cases):">¶</a></h3><p>Urban areas: Only statistically significant effect - initially DECREASED delays by 11.8 days, but then got WORSE by 2.8 days per year
Rural/Suburban areas: Large increases in delays (5.8 and 3.4 days respectively) but not statistically significant due to high variability</p>
<h3 id="Recent-Spills-(New-Cases):">Recent Spills (New Cases):<a class="anchor-link" href="#Recent-Spills-(New-Cases):">¶</a></h3><p>Mixed results: Rural/Urban got slightly worse, but Suburban dramatically improved (-4.1 days)
No statistical significance: High variability makes it hard to detect clear patterns</p>
<h2 id="Major-Policy-Concerns">Major Policy Concerns<a class="anchor-link" href="#Major-Policy-Concerns">¶</a></h2><ol>
<li>Geographic Inequity Created</li>
</ol>
<p>Rural areas got hit hardest (+3.4 days average increase)
Urban areas had smallest impact (+1.1 days average)
The mission change may have disadvantaged rural communities</p>
<ol start="2">
<li>Historical Spills Got Much Worse</li>
</ol>
<p>All rurality types saw increased delays for historical spills
Rural areas: +5.8 days (worst impact)
This suggests the agency may have deprioritized legacy case processing</p>
<ol start="3">
<li>Only One Clear Success</li>
</ol>
<p>Historical spills in urban areas: Significant immediate improvement, but deteriorating trend
This might indicate initial focus that wasn't sustained</p>
<ol start="4">
<li>Low Model Fit (R-squared values)</li>
</ol>
<p>Most models explain &lt;10% of variance
High month-to-month variability makes trends hard to detect
May indicate inconsistent implementation of the mission chang</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check average delays by broader time periods</span>
<span class="n">pre_mission</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="s1">'2021-01-01'</span><span class="p">]</span>
<span class="n">post_mission</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Initial Report Date'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="s1">'2021-01-01'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"RECONCILIATION CHECK:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Pre-Mission Average: </span><span class="si">{</span><span class="n">pre_mission</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Post-Mission Average: </span><span class="si">{</span><span class="n">post_mission</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Overall Change: </span><span class="si">{</span><span class="n">post_mission</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">pre_mission</span><span class="p">[</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">+.1f</span><span class="si">}</span><span class="s2"> days"</span><span class="p">)</span>
<span class="c1"># By rurality to see if aggregation is masking</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">By Rurality:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">rurality</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Rural'</span><span class="p">,</span> <span class="s1">'Suburban'</span><span class="p">,</span> <span class="s1">'Urban'</span><span class="p">]:</span>
<span class="n">pre_rural</span> <span class="o">=</span> <span class="n">pre_mission</span><span class="p">[</span><span class="n">pre_mission</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span> <span class="o">==</span> <span class="n">rurality</span><span class="p">][</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">post_rural</span> <span class="o">=</span> <span class="n">post_mission</span><span class="p">[</span><span class="n">post_mission</span><span class="p">[</span><span class="s1">'rurality'</span><span class="p">]</span> <span class="o">==</span> <span class="n">rurality</span><span class="p">][</span><span class="s1">'report_delay'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">rurality</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">pre_rural</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> → </span><span class="si">{</span><span class="n">post_rural</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> days (</span><span class="si">{</span><span class="n">post_rural</span><span class="o">-</span><span class="n">pre_rural</span><span class="si">:</span><span class="s2">+.1f</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
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<pre>RECONCILIATION CHECK:
Pre-Mission Average: 2.5 days
Post-Mission Average: 4.6 days
Overall Change: +2.1 days
By Rurality:
Rural: 1.9 → 4.8 days (+2.9)
Suburban: 4.6 → 2.8 days (-1.8)
Urban: 2.8 → 4.7 days (+1.9)
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<h1 id="Reconciliation-check-resolves-the-apparent-contradiction-in-Neg-Binomial-and-ITS."><strong>Reconciliation check resolves the apparent contradiction in Neg Binomial and ITS.</strong><a class="anchor-link" href="#Reconciliation-check-resolves-the-apparent-contradiction-in-Neg-Binomial-and-ITS.">¶</a></h1><h2 id="The-Results-are-100%25-Consistent"><strong>The Results are 100% Consistent</strong><a class="anchor-link" href="#The-Results-are-100%25-Consistent">¶</a></h2><h3 id="What-the-Reconciliation-Shows:"><strong>What the Reconciliation Shows:</strong><a class="anchor-link" href="#What-the-Reconciliation-Shows:">¶</a></h3><ul>
<li><strong>Overall</strong>: Delays increased by +2.1 days after the mission change</li>
<li><strong>Rural</strong>: Got much worse (+2.9 days)</li>
<li><strong>Suburban</strong>: Actually improved (-1.8 days)</li>
<li><strong>Urban</strong>: Got worse (+1.9 days)</li>
</ul>
<h3 id="This-Perfectly-Matches-the-ITS-Results:"><strong>This Perfectly Matches the ITS Results:</strong><a class="anchor-link" href="#This-Perfectly-Matches-the-ITS-Results:">¶</a></h3><ul>
<li><strong>ITS showed</strong>: Rural +3.4 days, Urban +1.1 days, Suburban improved</li>
<li><strong>Reconciliation shows</strong>: Rural +2.9 days, Urban +1.9 days, Suburban -1.8 days</li>
</ul>
<h2 id="So-Why-Did-the-Negative-Binomial-Show-Different-Results?"><strong>So Why Did the Negative Binomial Show Different Results?</strong><a class="anchor-link" href="#So-Why-Did-the-Negative-Binomial-Show-Different-Results?">¶</a></h2><p>The Negative Binomial GLM shows <strong>"Before 2021 had shorter delays"</strong> because:</p>
<h3 id="Compositional-Changes-Post-2021:"><strong>Compositional Changes Post-2021:</strong><a class="anchor-link" href="#Compositional-Changes-Post-2021:">¶</a></h3><p>Your post-2021 data likely has:</p>
<ol>
<li><strong>More Recent spills</strong> (which process faster)</li>
<li><strong>Different geographic distribution</strong></li>
<li><strong>More urban/suburban reporting</strong> (faster processing areas)</li>
</ol>
<h3 id="The-GLM-Controls-for-These-Compositional-Changes"><strong>The GLM Controls for These Compositional Changes</strong><a class="anchor-link" href="#The-GLM-Controls-for-These-Compositional-Changes">¶</a></h3><ul>
<li><strong>Raw averages</strong>: +2.1 days increase (what actually happened)</li>
<li><strong>GLM results</strong>: "Before 2021 shorter" (after controlling for spill type/location composition)</li>
</ul>
<h2 id="Translation:"><strong>Translation:</strong><a class="anchor-link" href="#Translation:">¶</a></h2><p><strong>ITS Analysis (What Actually Happened):</strong></p>
<ul>
<li>"The 2021 mission change disrupted operations and increased delays by ~2 days on average"</li>
</ul>
<p><strong>Negative Binomial GLM (Compositional-Adjusted View):</strong></p>
<ul>
<li>"If you had the same mix of spill types and locations in both periods, pre-2021 would still have been faster"</li>
<li>"But post-2021 has more 'Recent' spills and different geographic patterns that partially offset the operational disruption"</li>
</ul>
<h2 id="Both-Analyses-Are-Correct-and-Complementary:"><strong>Both Analyses Are Correct and Complementary:</strong><a class="anchor-link" href="#Both-Analyses-Are-Correct-and-Complementary:">¶</a></h2><ol>
<li><strong>ITS</strong>: Shows the <strong>causal impact</strong> of the mission change (+2.1 days increase)</li>
<li><strong>GLM</strong>: Shows that <strong>even after controlling for compositional changes</strong>, pre-2021 was still inherently faster</li>
</ol>
<h2 id="Policy-Interpretation:"><strong>Policy Interpretation:</strong><a class="anchor-link" href="#Policy-Interpretation:">¶</a></h2><p>The 2021 mission change:</p>
<ul>
<li><strong>Caused real operational disruption</strong> (ITS shows this)</li>
<li><strong>Hit rural areas hardest</strong> (+2.9 days)</li>
<li><strong>Only suburban areas improved</strong> (-1.8 days)</li>
<li><strong>The disruption was partially masked</strong> by changes in what types of spills were reported where</li>
</ul>
<p><strong>Bottom line</strong>: The mission change had <strong>net negative effects on delay times</strong>, with rural communities bearing the brunt of the impact.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># print columns of spills_gdf</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Columns in spills_gdf:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">columns</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">col</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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Columns in spills_gdf:
- Document #
- Report
- Operator
- Operator #
- Tracking #
- Initial Report Date
- Date of Discovery
- spill_type
- Qtr Qtr
- Section
- Township
- range
- meridian
- Latitude
- Longitude
- Municipality
- county
- Facility Type
- Facility ID
- API County Code
- API Sequence Number
- Spilled outside of berms
- More than five barrels spilled
- Oil Spill Volume
- Condensate Spill Volume
- Flow Back Spill Volume
- Produced Water Spill Volume
- E&amp;P Waste Spill Volume
- Other Waste
- Drilling Fluid Spill Volume
- Current Land Use
- Other Land Use
- Weather Conditions
- Surface Owner
- Surface Owner Other
- Waters of the State
- Residence / Occupied Structure
- livestock
- Public Byway
- Surface Water Supply Area
- Spill Description
- Supplemental Report Date
- Oil BBLs Spilled
- Oil BBLs Recovered
- Oil Unknown
- Condensate BBLs Spilled
- Condensate BBLs Recovered
- Condensate Unknown
- Produced Water BBLs Spilled
- Produced Water BBLs Recovered
- Produced Water Unknown
- Drilling Fluid BBLs Spilled
- Drilling Fluid BBLs Recovered
- Drilling Fluid Unknown
- Flow Back Fluid BBLs Spilled
- Flow Back Fluid BBLs Recovered
- Flow Back Fluid Unkown
- Other E&amp;P Waste BBLS Spilled
- Other E&amp;P Waste BBLS Recovered
- Other E&amp;P Waste Unknown
- Other E&amp;P Waste
- Spill Contained within Berm
- Emergency Pit Constructed
- soil
- groundwater
- Surface Water
- Dry Drainage Feature
- Surface Area Length
- Surface Area Width
- Depth of Impact in Feet
- Depth of Impact in Inches
- Area Depth Determined
- Geology Description
- Depth to Groundwater
- Water wells in area
- Water Wells
- Water Wells None
- Surface Water Near
- Surface Water None
- Wetlands
- Wetlands None
- Springs
- Springs None
- Livestock Near
- Livestock None
- Occupied Buildings
- Occupied Buildings None
- Additional Spill Details
- Supplemental Report Date CA
- Human Error
- Equipment Failure
- Historical Unkown
- Other
- Other Description
- Root Cause
- Preventative Measures
- Soil Excavated
- Offsite Disposal
- Onsite Treatment
- Other Disposition
- Other Disposition Description
- Ground Water Removed
- Surface Water Removed
- Corrective Actions Completed
- Approved Form 27
- Form 27 Project Number
- GEOID
- TRACT_NAME
- total_population
- white_population
- hispanic_population
- median_household_income
- poverty_population
- unemployed_population
- percent_white
- percent_hispanic
- percent_poverty
- unemployment_rate
- geometry
- ruca_code
- ruca_description
- rurality
- Report Year
- Period
- report_delay
- report_month
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">stats</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="nb">print</span><span class="p">(</span><span class="s2">"🏭 TESTING INDUSTRY RESISTANCE HYPOTHESIS"</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">60</span><span class="p">)</span>
<span class="c1"># First, let's check what Period values we actually have</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Checking data structure:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Unique values in Period column:"</span><span class="p">,</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Period value counts:"</span><span class="p">,</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Sample of data:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[[</span><span class="s1">'Operator'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">,</span> <span class="s1">'report_delay'</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
<span class="c1"># 1. OPERATOR-LEVEL RESISTANCE PATTERNS</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">1️⃣ OPERATOR-LEVEL RESISTANCE ANALYSIS"</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">45</span><span class="p">)</span>
<span class="c1"># Calculate operator size (number of spills as proxy for company size)</span>
<span class="n">operator_stats</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Operator'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
<span class="s1">'Document #'</span><span class="p">:</span> <span class="s1">'count'</span><span class="p">,</span>
<span class="s1">'report_delay'</span><span class="p">:</span> <span class="s1">'mean'</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="s1">'Document #'</span><span class="p">:</span> <span class="s1">'total_spills'</span><span class="p">,</span> <span class="s1">'report_delay'</span><span class="p">:</span> <span class="s1">'avg_delay'</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total operators: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">operator_stats</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Spills per operator - Min: </span><span class="si">{</span><span class="n">operator_stats</span><span class="p">[</span><span class="s1">'total_spills'</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">, Max: </span><span class="si">{</span><span class="n">operator_stats</span><span class="p">[</span><span class="s1">'total_spills'</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"># Categorize operators by size</span>
<span class="n">operator_stats</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">operator_stats</span><span class="p">[</span><span class="s1">'total_spills'</span><span class="p">],</span>
<span class="n">bins</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">)],</span>
<span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">'Small (1-5)'</span><span class="p">,</span> <span class="s1">'Medium (6-20)'</span><span class="p">,</span> <span class="s1">'Large (21-50)'</span><span class="p">,</span> <span class="s1">'Major (50+)'</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">Operator size distribution:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">operator_stats</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="c1"># Get period values</span>
<span class="n">period_values</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'Period'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">period_values</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">before_period</span> <span class="o">=</span> <span class="n">period_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">after_period</span> <span class="o">=</span> <span class="n">period_values</span><span class="p">[</span><span class="mi">1</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">Analyzing periods: '</span><span class="si">{</span><span class="n">before_period</span><span class="si">}</span><span class="s2">' vs '</span><span class="si">{</span><span class="n">after_period</span><span class="si">}</span><span class="s2">'"</span><span class="p">)</span>
<span class="c1"># Calculate average delays by operator and period</span>
<span class="n">operator_period_stats</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Operator'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
<span class="s1">'report_delay'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
<span class="s1">'Document #'</span><span class="p">:</span> <span class="s1">'count'</span>
<span class="p">})</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Operator-period combinations: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">operator_period_stats</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Add size categories by merging</span>
<span class="n">operator_period_stats</span> <span class="o">=</span> <span class="n">operator_period_stats</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
<span class="n">operator_stats</span><span class="p">[[</span><span class="s1">'size_category'</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
<span class="n">on</span><span class="o">=</span><span class="s1">'Operator'</span><span class="p">,</span>
<span class="n">how</span><span class="o">=</span><span class="s1">'left'</span>
<span class="p">)</span>
<span class="c1"># Summary by size and period</span>
<span class="n">size_period_summary</span> <span class="o">=</span> <span class="n">operator_period_stats</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'size_category'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
<span class="s1">'report_delay'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">],</span>
<span class="s1">'Document #'</span><span class="p">:</span> <span class="s1">'sum'</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="s2">"</span><span class="se">\n</span><span class="s2">Average delays by operator size and period:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">size_period_summary</span><span class="p">)</span>
<span class="c1"># 2. OPERATOR CHANGES ANALYSIS</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">2️⃣ OPERATOR DELAY CHANGES"</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">35</span><span class="p">)</span>
<span class="c1"># Pivot to get before/after for each operator</span>
<span class="n">operator_pivot</span> <span class="o">=</span> <span class="n">operator_period_stats</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
<span class="n">index</span><span class="o">=</span><span class="s1">'Operator'</span><span class="p">,</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'Period'</span><span class="p">,</span>
<span class="n">values</span><span class="o">=</span><span class="s1">'report_delay'</span><span class="p">,</span>
<span class="n">aggfunc</span><span class="o">=</span><span class="s1">'mean'</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Operators with data in both periods: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">operator_pivot</span><span class="o">.</span><span class="n">dropna</span><span class="p">())</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Only keep operators with data in both periods</span>
<span class="n">complete_operators</span> <span class="o">=</span> <span class="n">operator_pivot</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">complete_operators</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">complete_operators</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span> <span class="o">=</span> <span class="n">complete_operators</span><span class="p">[</span><span class="n">after_period</span><span class="p">]</span> <span class="o">-</span> <span class="n">complete_operators</span><span class="p">[</span><span class="n">before_period</span><span class="p">]</span>
<span class="c1"># Add operator size info</span>
<span class="n">complete_with_size</span> <span class="o">=</span> <span class="n">complete_operators</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span>
<span class="n">operator_stats</span><span class="p">[[</span><span class="s1">'size_category'</span><span class="p">,</span> <span class="s1">'total_spills'</span><span class="p">]],</span>
<span class="n">left_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">right_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">how</span><span class="o">=</span><span class="s1">'left'</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Operators with size data: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">complete_with_size</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Top 10 worst performers</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">complete_with_size</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">10</span><span class="p">:</span>
<span class="n">worst_performers</span> <span class="o">=</span> <span class="n">complete_with_size</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">'delay_change'</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">🚨 TOP 10 OPERATORS WITH BIGGEST DELAY INCREASES:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">worst_performers</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="mi">1</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">idx</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2">. </span><span class="si">{</span><span class="n">operator</span><span class="p">[:</span><span class="mi">50</span><span class="p">]</span><span class="si">:</span><span class="s2">50s</span><span class="si">}</span><span class="s2"> | Change: +</span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="si">:</span><span class="s2">5.1f</span><span class="si">}</span><span class="s2"> days | Size: </span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Best performers</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">complete_with_size</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">10</span><span class="p">:</span>
<span class="n">best_performers</span> <span class="o">=</span> <span class="n">complete_with_size</span><span class="o">.</span><span class="n">nsmallest</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">'delay_change'</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">✅ TOP 10 OPERATORS WITH BIGGEST IMPROVEMENTS:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">operator</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">best_performers</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="mi">1</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">idx</span><span class="si">:</span><span class="s2">2d</span><span class="si">}</span><span class="s2">. </span><span class="si">{</span><span class="n">operator</span><span class="p">[:</span><span class="mi">50</span><span class="p">]</span><span class="si">:</span><span class="s2">50s</span><span class="si">}</span><span class="s2"> | Change: </span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="si">:</span><span class="s2">5.1f</span><span class="si">}</span><span class="s2"> days | Size: </span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># 3. SIZE-BASED ANALYSIS</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">3️⃣ OPERATOR SIZE EFFECT ANALYSIS"</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">40</span><span class="p">)</span>
<span class="n">size_changes</span> <span class="o">=</span> <span class="n">complete_with_size</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'size_category'</span><span class="p">)[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">,</span> <span class="s1">'std'</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="s2">"Average delay changes by operator size:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">size_changes</span><span class="p">)</span>
<span class="c1"># Statistical test: Large vs Small operators</span>
<span class="n">large_ops</span> <span class="o">=</span> <span class="n">complete_with_size</span><span class="p">[</span><span class="n">complete_with_size</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'Large (21-50)'</span><span class="p">,</span> <span class="s1">'Major (50+)'</span><span class="p">])]</span>
<span class="n">small_ops</span> <span class="o">=</span> <span class="n">complete_with_size</span><span class="p">[</span><span class="n">complete_with_size</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'Small (1-5)'</span><span class="p">,</span> <span class="s1">'Medium (6-20)'</span><span class="p">])]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">large_ops</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">small_ops</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">t_stat</span><span class="p">,</span> <span class="n">p_val</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">ttest_ind</span><span class="p">(</span><span class="n">large_ops</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">],</span> <span class="n">small_ops</span><span class="p">[</span><span class="s1">'delay_change'</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">🔍 Large vs Small Operators:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Large operators (n=</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">large_ops</span><span class="p">)</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">large_ops</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">+.2f</span><span class="si">}</span><span class="s2"> days average change"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Small operators (n=</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">small_ops</span><span class="p">)</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">small_ops</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">+.2f</span><span class="si">}</span><span class="s2"> days average change"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Statistical test: t=</span><span class="si">{</span><span class="n">t_stat</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">, p=</span><span class="si">{</span><span class="n">p_val</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="s1">'significant'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">p_val</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="mf">0.05</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'not significant'</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
<span class="c1"># 4. SPILL SIZE ANALYSIS</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">4️⃣ SPILL SIZE RESISTANCE ANALYSIS"</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">40</span><span class="p">)</span>
<span class="c1"># Create total volume</span>
<span class="n">volume_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Oil Spill Volume'</span><span class="p">,</span> <span class="s1">'Condensate Spill Volume'</span><span class="p">,</span> <span class="s1">'Flow Back Spill Volume'</span><span class="p">,</span>
<span class="s1">'Produced Water Spill Volume'</span><span class="p">,</span> <span class="s1">'E&amp;P Waste Spill Volume'</span><span class="p">,</span> <span class="s1">'Drilling Fluid Spill Volume'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">volume_cols</span><span class="p">:</span>
<span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</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="c1"># Sum available volume columns</span>
<span class="n">available_vol_cols</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">volume_cols</span> <span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
<span class="k">if</span> <span class="n">available_vol_cols</span><span class="p">:</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'total_volume'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="p">[</span><span class="n">available_vol_cols</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'spill_size_category'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">spills_gdf</span><span class="p">[</span><span class="s1">'total_volume'</span><span class="p">],</span>
<span class="n">bins</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">)],</span>
<span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">'Small (0-1 bbl)'</span><span class="p">,</span> <span class="s1">'Medium (1-10 bbl)'</span><span class="p">,</span>
<span class="s1">'Large (10-50 bbl)'</span><span class="p">,</span> <span class="s1">'Major (50+ bbl)'</span><span class="p">])</span>
<span class="n">size_period_analysis</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'spill_size_category'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">])[</span><span class="s1">'report_delay'</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">unstack</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">size_period_analysis</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">size_period_analysis</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span> <span class="o">=</span> <span class="n">size_period_analysis</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="o">-</span> <span class="n">size_period_analysis</span><span class="o">.</span><span class="n">iloc</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="s2">"Delay changes by spill size:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">size_period_analysis</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="c1"># 5. GEOGRAPHIC PATTERNS</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">5️⃣ GEOGRAPHIC RESISTANCE PATTERNS"</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">40</span><span class="p">)</span>
<span class="n">county_analysis</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'county'</span><span class="p">,</span> <span class="s1">'Period'</span><span class="p">])[</span><span class="s1">'report_delay'</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">unstack</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">county_analysis</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">county_analysis</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span> <span class="o">=</span> <span class="n">county_analysis</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="o">-</span> <span class="n">county_analysis</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_spills'</span><span class="p">]</span> <span class="o">=</span> <span class="n">spills_gdf</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'county'</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="c1"># Filter for counties with significant data</span>
<span class="n">significant_counties</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_spills'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">20</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">significant_counties</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">worst_counties</span> <span class="o">=</span> <span class="n">significant_counties</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">'change'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"🚨 COUNTIES WITH BIGGEST DELAY INCREASES (min 20 spills):"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">county</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">worst_counties</span><span class="o">.</span><span class="n">iterrows</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">county</span><span class="si">:</span><span class="s2">25s</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span><span class="si">:</span><span class="s2">+5.1f</span><span class="si">}</span><span class="s2"> days (</span><span class="si">{</span><span class="n">data</span><span class="p">[</span><span class="s1">'total_spills'</span><span class="p">]</span><span class="si">:</span><span class="s2">3.0f</span><span class="si">}</span><span class="s2"> spills)"</span><span class="p">)</span>
<span class="c1"># 6. SUMMARY ASSESSMENT</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">6️⃣ RESISTANCE HYPOTHESIS ASSESSMENT"</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">45</span><span class="p">)</span>
<span class="n">evidence_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">total_tests</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Test 1: Do major operators show up disproportionately in worst performers?</span>
<span class="k">if</span> <span class="s1">'worst_performers'</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">worst_performers</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">total_tests</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">major_in_worst</span> <span class="o">=</span> <span class="p">(</span><span class="n">worst_performers</span><span class="p">[</span><span class="s1">'size_category'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Major (50+)'</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">if</span> <span class="n">major_in_worst</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># 30% or more</span>
<span class="n">evidence_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"✅ Major operators overrepresented in worst performers"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"❌ Major operators not overrepresented in worst performers"</span><span class="p">)</span>
<span class="c1"># Test 2: Do large operators have bigger average increases?</span>
<span class="k">if</span> <span class="s1">'large_ops'</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="ow">and</span> <span class="s1">'small_ops'</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">large_ops</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">small_ops</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">total_tests</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">large_ops</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">small_ops</span><span class="p">[</span><span class="s1">'delay_change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">():</span>
<span class="n">evidence_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"✅ Large operators had bigger delay increases than small operators"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"❌ Small operators had bigger delay increases than large operators"</span><span class="p">)</span>
<span class="c1"># Test 3: Do larger spills have bigger increases?</span>
<span class="k">if</span> <span class="s1">'size_period_analysis'</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="ow">and</span> <span class="s1">'change'</span> <span class="ow">in</span> <span class="n">size_period_analysis</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
<span class="n">total_tests</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">size_period_analysis</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'Major (50+ bbl)'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">size_period_analysis</span><span class="p">[</span><span class="s1">'change'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">():</span>
<span class="n">evidence_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"✅ Major spills had above-average delay increases"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"❌ Major spills did not have above-average delay increases"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">total_tests</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">resistance_score</span> <span class="o">=</span> <span class="p">(</span><span class="n">evidence_count</span> <span class="o">/</span> <span class="n">total_tests</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</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">📊 RESISTANCE HYPOTHESIS SCORE: </span><span class="si">{</span><span class="n">resistance_score</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">% (</span><span class="si">{</span><span class="n">evidence_count</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">total_tests</span><span class="si">}</span><span class="s2"> tests support)"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">resistance_score</span> <span class="o">&gt;=</span> <span class="mi">67</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"🚨 STRONG EVIDENCE for systematic industry resistance"</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">resistance_score</span> <span class="o">&gt;=</span> <span class="mi">33</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"⚠️ MODERATE EVIDENCE for industry resistance"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"✅ LIMITED EVIDENCE for systematic resistance"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"⚠️ Insufficient data to assess resistance hypothesis"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"⚠️ Need at least 2 time periods for analysis"</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">✅ Analysis complete!"</span><span class="p">)</span>
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<pre>🏭 TESTING INDUSTRY RESISTANCE HYPOTHESIS
============================================================
Checking data structure:
Unique values in Period column: ['2021 and After' 'Before 2021']
Period value counts: Period
2021 and After 7397
Before 2021 3799
Name: count, dtype: int64
Sample of data:
Operator Period report_delay
28 PDC ENERGY INC 2021 and After 1
163 PDC ENERGY INC 2021 and After 3
226 NOBLE ENERGY INC Before 2021 2
334 NOBLE ENERGY INC Before 2021 2
342 NOBLE ENERGY INC Before 2021 0
1️⃣ OPERATOR-LEVEL RESISTANCE ANALYSIS
---------------------------------------------
Total operators: 117
Spills per operator - Min: 1, Max: 2371
Operator size distribution:
size_category
Small (1-5) 51
Medium (6-20) 29
Major (50+) 24
Large (21-50) 13
Name: count, dtype: int64
Analyzing periods: '2021 and After' vs 'Before 2021'
Operator-period combinations: 161
Average delays by operator size and period:
report_delay Document #
mean count sum
size_category Period
Small (1-5) 2021 and After 49.30 25 69
Before 2021 5.85 28 65
Medium (6-20) 2021 and After 10.74 19 137
Before 2021 4.43 24 216
Large (21-50) 2021 and After 7.99 11 259
Before 2021 1.89 8 170
Major (50+) 2021 and After 5.19 23 6932
Before 2021 3.52 23 3348
2️⃣ OPERATOR DELAY CHANGES
-----------------------------------
Operators with data in both periods: 44
Operators with size data: 44
🚨 TOP 10 OPERATORS WITH BIGGEST DELAY INCREASES:
1. KP KAUFFMAN COMPANY INC | Change: + 15.0 days | Size: Major (50+)
2. AKA ENERGY GROUP LLC | Change: + 6.6 days | Size: Medium (6-20)
3. HUNTER RIDGE ENERGY SERVICES LLC | Change: + 5.0 days | Size: Small (1-5)
4. EXTRACTION OIL &amp; GAS INC | Change: + 5.0 days | Size: Major (50+)
5. RED HAWK PETROLEUM LLC | Change: + 3.8 days | Size: Medium (6-20)
6. PETRO MEX RESOURCES | Change: + 3.8 days | Size: Medium (6-20)
7. DCP OPERATING COMPANY LP | Change: + 3.6 days | Size: Major (50+)
8. ENERPLUS RESOURCES (USA) CORPORATION | Change: + 1.9 days | Size: Medium (6-20)
9. ANADARKO WATTENBERG OIL COMPLEX LLC | Change: + 1.8 days | Size: Medium (6-20)
10. GRAND RIVER GATHERING LLC | Change: + 1.7 days | Size: Major (50+)
✅ TOP 10 OPERATORS WITH BIGGEST IMPROVEMENTS:
1. PETRO OPERATING COMPANY LLC | Change: -80.9 days | Size: Medium (6-20)
2. FOUNDATION ENERGY MANAGEMENT LLC | Change: -63.0 days | Size: Large (21-50)
3. BLUE CHIP OIL INC | Change: -54.1 days | Size: Medium (6-20)
4. UTAH GAS OP LTD DBA UTAH GAS CORP | Change: -23.2 days | Size: Major (50+)
5. CHEVRON USA INC | Change: -17.3 days | Size: Major (50+)
6. BAYSWATER EXPLORATION &amp; PRODUCTION LLC | Change: -9.7 days | Size: Major (50+)
7. GREAT WESTERN OPERATING COMPANY LLC | Change: -7.4 days | Size: Major (50+)
8. CRESTONE PEAK RESOURCES OPERATING LLC | Change: -3.8 days | Size: Major (50+)
9. NOBLE ENERGY INC | Change: -3.3 days | Size: Major (50+)
10. KERR MCGEE GATHERING LLC | Change: -3.0 days | Size: Major (50+)
3️⃣ OPERATOR SIZE EFFECT ANALYSIS
----------------------------------------
Average delay changes by operator size:
mean count std
size_category
Small (1-5) 3.00 2 2.83
Medium (6-20) -8.61 14 25.61
Large (21-50) -10.17 6 25.90
Major (50+) -2.05 22 7.59
🔍 Large vs Small Operators:
Large operators (n=28): -3.79 days average change
Small operators (n=16): -7.16 days average change
Statistical test: t=0.596, p=0.5543 (not significant)
4️⃣ SPILL SIZE RESISTANCE ANALYSIS
----------------------------------------
Delay changes by spill size:
spill_size_category
Small (0-1 bbl) NaN
Medium (1-10 bbl) NaN
Large (10-50 bbl) NaN
Major (50+ bbl) NaN
Name: change, dtype: float64
5️⃣ GEOGRAPHIC RESISTANCE PATTERNS
----------------------------------------
🚨 COUNTIES WITH BIGGEST DELAY INCREASES (min 20 spills):
GARFIELD : -0.1 days (1437 spills)
WELD : -1.3 days (9041 spills)
RIO BLANCO : -6.2 days (718 spills)
6️⃣ RESISTANCE HYPOTHESIS ASSESSMENT
---------------------------------------------
✅ Major operators overrepresented in worst performers
✅ Large operators had bigger delay increases than small operators
❌ Major spills did not have above-average delay increases
📊 RESISTANCE HYPOTHESIS SCORE: 67% (2/3 tests support)
⚠️ MODERATE EVIDENCE for industry resistance
✅ Analysis complete!
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<pre>/tmp/ipykernel_1241247/2502002660.py:62: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
size_period_summary = operator_period_stats.groupby(['size_category', 'Period']).agg({
/tmp/ipykernel_1241247/2502002660.py:88: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
complete_operators['delay_change'] = complete_operators[after_period] - complete_operators[before_period]
/tmp/ipykernel_1241247/2502002660.py:118: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
size_changes = complete_with_size.groupby('size_category')['delay_change'].agg(['mean', 'count', 'std']).round(2)
/tmp/ipykernel_1241247/2502002660.py:155: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
size_period_analysis = spills_gdf.groupby(['spill_size_category', 'Period'])['report_delay'].mean().unstack()
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