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Tension-Board-2-Analysis/notebooks/01_data_overview_and_climbing_statistics.html
Pawel Sarkowicz f3fb9e0c40 html versions of notebooks
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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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),
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<h1 id="Tension-Board-2-/-Tension-Board-1:-Data-Overview-and-Climbing-Statistics">Tension Board 2 / Tension Board 1: Data Overview and Climbing Statistics<a class="anchor-link" href="#Tension-Board-2-/-Tension-Board-1:-Data-Overview-and-Climbing-Statistics">¶</a></h1><h2 id="Purpose">Purpose<a class="anchor-link" href="#Purpose">¶</a></h2><p>This notebook establishes the basic statistical landscape of the dataset before we move into hold-level analysis and predictive modelling. The main goals are:</p>
<ol>
<li>to understand the size and scope of the data,</li>
<li>to compare layouts, boards, and angles at a high level,</li>
<li>to identify broad trends in grade, popularity, and quality,</li>
<li>to create a clean descriptive baseline for the later modelling notebooks.</li>
</ol>
<p>Throughout, I treat each climb-angle entry as a separate observation unless explicitly noted otherwise. That matters because some climbs appear at multiple angles, so a unique climb count and a climb-angle count are not always the same thing.</p>
<h2 id="Outputs">Outputs<a class="anchor-link" href="#Outputs">¶</a></h2><p>This notebook produces summary tables and exploratory plots that motivate the later notebooks on:</p>
<ul>
<li>hold usage,</li>
<li>hold difficulty,</li>
<li>feature engineering,</li>
<li>predictive modelling,</li>
<li>and deep learning.</li>
</ul>
<h2 id="Notebook-Structure">Notebook Structure<a class="anchor-link" href="#Notebook-Structure">¶</a></h2><ol>
<li><a href="#setup-and-imports">Setup and Imports</a></li>
<li><a href="#popularity-and-temporal-trends">Popularity and Temporal Trends</a></li>
<li><a href="#climbing-statistics-grades-angles-quality-and-matching">Climbing Statistics</a></li>
<li><a href="#prolific-statistics">Prolific Statistics</a></li>
<li><a href="#conclusion">Conclusion</a></li>
</ol>
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<h2 id="Setup-and-Imports">Setup and Imports<a class="anchor-link" href="#Setup-and-Imports">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Setup and imports</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Imports</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sqlite3</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.patches</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mpatches</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sqlite3</span>
<span class="c1"># Set some display options</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_columns'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.max_rows'</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="c1"># Set style</span>
<span class="n">palette</span><span class="o">=</span><span class="p">[</span><span class="s1">'steelblue'</span><span class="p">,</span> <span class="s1">'coral'</span><span class="p">,</span> <span class="s1">'seagreen'</span><span class="p">]</span> <span class="c1">#(for multi-bar graphs)</span>
<span class="c1"># Connect to the database</span>
<span class="n">DB_PATH</span><span class="o">=</span><span class="s2">"../data/tb2.db"</span>
<span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">DB_PATH</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">### Query our data from the DB</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Query climb data</span>
<span class="n">climbs_query</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">SELECT</span>
<span class="s2"> c.uuid,</span>
<span class="s2"> c.name AS climb_name,</span>
<span class="s2"> c.setter_username,</span>
<span class="s2"> c.layout_id AS layout_id,</span>
<span class="s2"> c.description,</span>
<span class="s2"> c.is_nomatch,</span>
<span class="s2"> c.is_listed,</span>
<span class="s2"> l.name AS layout_name,</span>
<span class="s2"> p.name AS board_name,</span>
<span class="s2"> c.frames,</span>
<span class="s2"> cs.angle,</span>
<span class="s2"> cs.display_difficulty,</span>
<span class="s2"> dg.boulder_name AS boulder_grade,</span>
<span class="s2"> cs.ascensionist_count,</span>
<span class="s2"> cs.quality_average,</span>
<span class="s2"> cs.fa_at</span>
<span class="s2"> </span>
<span class="s2">FROM climbs c</span>
<span class="s2">JOIN layouts l ON c.layout_id = l.id</span>
<span class="s2">JOIN products p ON l.product_id = p.id</span>
<span class="s2">JOIN climb_stats cs ON c.uuid = cs.climb_uuid</span>
<span class="s2">JOIN difficulty_grades dg ON ROUND(cs.display_difficulty) = dg.difficulty</span>
<span class="s2">WHERE cs.display_difficulty IS NOT NULL;</span>
<span class="s2">"""</span>
<span class="c1"># Load it into a DataFrame</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span><span class="n">climbs_query</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
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<p>The above query will allow us to gather basically anything we need to in order to analyze climbing statistics. We leave out information about climging holds and things like this, because they will be analyzed in a different notebook. Let's see what our DataFrame looks like.</p>
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<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>uuid</th>
<th>climb_name</th>
<th>setter_username</th>
<th>layout_id</th>
<th>description</th>
<th>is_nomatch</th>
<th>is_listed</th>
<th>layout_name</th>
<th>board_name</th>
<th>frames</th>
<th>angle</th>
<th>display_difficulty</th>
<th>boulder_grade</th>
<th>ascensionist_count</th>
<th>quality_average</th>
<th>fa_at</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0004edf6aeac9618d96a3b949cd9a724</td>
<td>Jumping Giraffe</td>
<td>david.p.kunz</td>
<td>9</td>
<td></td>
<td>0</td>
<td>1</td>
<td>Original Layout</td>
<td>Tension Board</td>
<td>p42r1p71r4p76r1p89r2p91r3p104r2p111r2p232r2</td>
<td>40</td>
<td>24.0000</td>
<td>7b/V8</td>
<td>2</td>
<td>3.00000</td>
<td>2020-03-23 23:52:37</td>
</tr>
<tr>
<th>1</th>
<td>00072fbd8c22711ef3532a5017c1a5c2</td>
<td>Albatross</td>
<td>free.and.independent</td>
<td>9</td>
<td>No Matching</td>
<td>1</td>
<td>1</td>
<td>Original Layout</td>
<td>Tension Board</td>
<td>p31r2p49r2p52r2p53r2p87r3p92r1p99r2p118r2p120r...</td>
<td>25</td>
<td>19.2500</td>
<td>6b+/V4</td>
<td>4</td>
<td>3.00000</td>
<td>2019-10-05 01:55:14</td>
</tr>
<tr>
<th>2</th>
<td>0008d8af4649234054bea434aaeabaab</td>
<td>crossup</td>
<td>judemandudeman</td>
<td>9</td>
<td></td>
<td>0</td>
<td>1</td>
<td>Original Layout</td>
<td>Tension Board</td>
<td>p22r2p58r2p76r1p83r3p166r2p228r2p280r4</td>
<td>45</td>
<td>20.0000</td>
<td>6c/V5</td>
<td>2</td>
<td>2.00000</td>
<td>2018-01-30 03:18:13</td>
</tr>
<tr>
<th>3</th>
<td>000eb831d3a1e92ea8fdec2518fd77d3</td>
<td>For the love of Tension</td>
<td>willrossiter</td>
<td>9</td>
<td>No matching</td>
<td>1</td>
<td>1</td>
<td>Original Layout</td>
<td>Tension Board</td>
<td>p86r3p95r1p131r2p151r2p171r1p173r2p187r2p261r4...</td>
<td>20</td>
<td>18.0000</td>
<td>6b/V4</td>
<td>1</td>
<td>3.00000</td>
<td>2019-03-15 15:46:06</td>
</tr>
<tr>
<th>4</th>
<td>000eb831d3a1e92ea8fdec2518fd77d3</td>
<td>For the love of Tension</td>
<td>willrossiter</td>
<td>9</td>
<td>No matching</td>
<td>1</td>
<td>1</td>
<td>Original Layout</td>
<td>Tension Board</td>
<td>p86r3p95r1p131r2p151r2p171r1p173r2p187r2p261r4...</td>
<td>40</td>
<td>23.0000</td>
<td>7a+/V7</td>
<td>1</td>
<td>3.00000</td>
<td>2021-06-27 22:41:10</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>147041</th>
<td>0B714869B80248D1B698E449EE885AB0</td>
<td>Loch Ness Monster</td>
<td>bennykuttler1285</td>
<td>10</td>
<td>No matching</td>
<td>1</td>
<td>1</td>
<td>Tension Board 2 Mirror</td>
<td>Tension Board 2</td>
<td>p333r6p344r6p367r8p379r8p380r5p500r8p570r6p579...</td>
<td>45</td>
<td>16.0000</td>
<td>6a/V3</td>
<td>3468</td>
<td>2.98991</td>
<td>2022-12-28 17:42:57</td>
</tr>
<tr>
<th>147042</th>
<td>71C4F8D564D045EFA1C9F26BB949E040</td>
<td>$2 lap dance 💃🕺</td>
<td>nelldell</td>
<td>10</td>
<td>No Match</td>
<td>1</td>
<td>1</td>
<td>Tension Board 2 Mirror</td>
<td>Tension Board 2</td>
<td>p311r6p320r5p366r8p372r7p445r5p462r6p468r8p570r6</td>
<td>40</td>
<td>20.0000</td>
<td>6c/V5</td>
<td>3388</td>
<td>2.98022</td>
<td>2022-12-07 00:30:56</td>
</tr>
<tr>
<th>147043</th>
<td>B998C0712A2240E8AC858CB72E9115D5</td>
<td>Aftermath</td>
<td>tensionclimbing</td>
<td>10</td>
<td></td>
<td>0</td>
<td>1</td>
<td>Tension Board 2 Mirror</td>
<td>Tension Board 2</td>
<td>p350r8p370r8p464r6p569r5p589r7p685r5p718r6p767r8</td>
<td>40</td>
<td>23.0000</td>
<td>7a+/V7</td>
<td>3</td>
<td>3.00000</td>
<td>2023-07-04 20:16:10</td>
</tr>
<tr>
<th>147044</th>
<td>C67E01D16E2940C2AFA76B436F9541D5</td>
<td>curly fries</td>
<td>sidpintobean</td>
<td>10</td>
<td>no matching</td>
<td>1</td>
<td>1</td>
<td>Tension Board 2 Mirror</td>
<td>Tension Board 2</td>
<td>p485r8p497r8p692r6p716r6p722r6p726r6p740r6p753...</td>
<td>40</td>
<td>20.1111</td>
<td>6c/V5</td>
<td>18</td>
<td>2.88889</td>
<td>2024-03-22 19:39:34</td>
</tr>
<tr>
<th>147045</th>
<td>397494C43D6A47A1BE9DB5FA7A9351A9</td>
<td>duck</td>
<td>trevordoesinfactclimb</td>
<td>10</td>
<td>No match</td>
<td>1</td>
<td>1</td>
<td>Tension Board 2 Mirror</td>
<td>Tension Board 2</td>
<td>p322r6p385r6p486r5p499r8p504r8p557r7p594r6</td>
<td>45</td>
<td>28.1000</td>
<td>8a/V11</td>
<td>10</td>
<td>3.00000</td>
<td>2025-01-16 17:54:22</td>
</tr>
</tbody>
</table>
<p>147046 rows × 16 columns</p>
</div>
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<h1 id="Popularity-and-Temporal-Trends">Popularity and Temporal Trends<a class="anchor-link" href="#Popularity-and-Temporal-Trends">¶</a></h1><h2 id="Popularity-of-Tension-Board">Popularity of Tension Board<a class="anchor-link" href="#Popularity-of-Tension-Board">¶</a></h2><p>Since we do not have access to user data, we will examine the popular of the Tension Boards by counting first ascents and unique setters by year. Often it's the case that the first ascensionist is the also the setter of the climb, but not always. None the less, we group up first ascensionists by year, with an extra tidbit about how many unique setters there were.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Popular of tension board by year. </span>
<span class="sd">First ascents by year + unique setters by year</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Convert df['fa_at'] to datetime format. (For some reason, it does not register as such)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'fa_at'</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">df</span><span class="p">[</span><span class="s1">'fa_at'</span><span class="p">])</span>
<span class="c1"># Add a new column for the year</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'fa_year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fa_at'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span>
<span class="c1"># Make a new DataFrame with year, first_ascents, and unique_setters</span>
<span class="n">df_growth</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'fa_year'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">first_ascents</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="n">unique_setters</span><span class="o">=</span><span class="p">(</span><span class="s1">'setter_username'</span><span class="p">,</span> <span class="s1">'nunique'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># Disregard the year 2026 since the data only goes one month in. </span>
<span class="n">df_growth</span> <span class="o">=</span> <span class="n">df_growth</span><span class="p">[</span><span class="n">df_growth</span><span class="p">[</span><span class="s1">'fa_year'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">2026</span><span class="p">]</span>
<span class="c1">## Plot</span>
<span class="c1"># Dual index plotting</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="c1"># Bar chart for first ascents</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_growth</span><span class="p">[</span><span class="s1">'fa_year'</span><span class="p">],</span> <span class="n">df_growth</span><span class="p">[</span><span class="s1">'first_ascents'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'First Ascents'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'coral'</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">'Year'</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">'First Ascents'</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">'TB First Ascents &amp; Unique Setters over Time'</span><span class="p">)</span>
<span class="c1">#ax1.tick_params(axis='y')</span>
<span class="c1"># Line chart for unique setters (secondary axis)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df_growth</span><span class="p">[</span><span class="s1">'fa_year'</span><span class="p">],</span> <span class="n">df_growth</span><span class="p">[</span><span class="s1">'unique_setters'</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">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">'Unique Setters'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Unique Setters'</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">ax2</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">'y'</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">'steelblue'</span><span class="p">)</span>
<span class="c1"># Other stuff</span>
<span class="n">fig</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 left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.15</span><span class="p">,</span><span class="mf">0.85</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">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/first_ascents_by_year.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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"/>
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<h2 id="Seasonal-analysis">Seasonal analysis<a class="anchor-link" href="#Seasonal-analysis">¶</a></h2><p>Next, we examine when the Tension board is most popular. Again, we will work with what we have and use first ascent data. We will plot first ascents by month, combing all years. We exclude the year 2026 because this can skew the analysis as some of the month of January has data (and clearly, 2026 is when the TB2 is the most popular, so this can actually add quite a bit bias).</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Season analysis: first ascents by month</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># First let us add a column for the month to our data</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'fa_month'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fa_at'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">month</span>
<span class="c1"># Filter to years &lt; 2026 since the data only goes one month in</span>
<span class="n">df_filter</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">'fa_year'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">2026</span><span class="p">]</span>
<span class="c1"># Make a new DataFrame with month and first ascents</span>
<span class="n">df_season</span> <span class="o">=</span> <span class="n">df_filter</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'fa_month'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">first_ascents</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># We also add a column for the month name. </span>
<span class="n">month_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Jan'</span><span class="p">,</span> <span class="s1">'Feb'</span><span class="p">,</span> <span class="s1">'Mar'</span><span class="p">,</span> <span class="s1">'Apr'</span><span class="p">,</span> <span class="s1">'May'</span><span class="p">,</span> <span class="s1">'Jun'</span><span class="p">,</span>
<span class="s1">'Jul'</span><span class="p">,</span> <span class="s1">'Aug'</span><span class="p">,</span> <span class="s1">'Sep'</span><span class="p">,</span> <span class="s1">'Oct'</span><span class="p">,</span> <span class="s1">'Nov'</span><span class="p">,</span> <span class="s1">'Dec'</span><span class="p">]</span>
<span class="n">df_season</span><span class="p">[</span><span class="s1">'fa_month_name'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_season</span><span class="p">[</span><span class="s1">'fa_month'</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="n">month_names</span><span class="p">[</span><span class="n">x</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># Plot the data</span>
<span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_season</span><span class="p">[</span><span class="s1">'fa_month_name'</span><span class="p">],</span> <span class="n">df_season</span><span class="p">[</span><span class="s1">'first_ascents'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'coral'</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="s1">'First Ascents by Month (All Years Combined)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Month'</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">'Total First Ascents'</span><span class="p">)</span>
<span class="c1"># Save the file</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/first_ascents_by_month.png'</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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zfdcccd+vPPP2/YJwAUFsI/ANymqlWrpvr16zu8buTq06115TrsDRs2KCIiQhMnTlTdunV15513Kioq6pauVc26Fr9z5846ffq0Tp8+LbvdrgcffFDLly83T2/Py/Kzru+93o0Cs54o8Pjjj8vNzc3hNWHCBBmGke1xZaVLl3Z4n3W9c16C/Pvvv69XX31Vq1atUrNmzeTv769OnTrpf//7X562T3BwcI7Drj5Fu06dOnrooYfMa+bXrFmjQ4cO5fvReD4+Purevbvmzp2rOXPmmJdg5OTkyZPZ9hFJZlC79hTyW9mGuc0jaz43mkfW+Jwuf8i6vOTxxx8397/09HR17NhR33//vX7++ec895cXWet99Q3+svbJ+++/P9s+uWzZModT5Hv27Klp06bp6aef1jfffKPt27drx44duvPOO3PcDjn9jZYtW6bevXtr9uzZCg8Pl7+/v5566iklJSXl2ndAQIC8vb0VHx+fp/XM+vvnto9cu3/4+/tnq3N3d882PGu75XQfi2s/K66uripdunSeLrG50f6Z9TcaNmxYtr/RoEGDJMn8O508eTLXz21uPD098/3DIAAUJO72DwB/IzndEbxcuXJmWP/ll1/0ySefKDo6WmlpaeZ9AfIjJSVFy5cvl64En5wsWbLE/Mf1jZZ/5513SpKOHDmS63XJAQEBkqSpU6fm+qSDq4/Y3SofHx+NGTNGY8aM0bFjx8yzADp06JCnYJlTIEtKSlLFihUdhkVFRalr167atWuXpk2bpsqVK6tly5b57rdfv36aPXu2du/ercWLF+daV7p0aSUmJmYbnnWUPWs7FwdZvVz7o87VN5bs0qVLjtPOnTtXEydOLJT+Pvvss1x/bNGVz8uaNWs0evRojRgxwhyempqabd2y5PQ5DggI0JQpUzRlyhQdPnxYq1ev1ogRI3T8+PFcb6bn4uKi5s2b6+uvv87TkziywnRiYmK22qNHjxbI/pGUlKS77rrLfH/58mWdPHkyxx+N8iur35EjR+a6r2TdTLJ06dK5fm5zk5ycXKw+MwDAkX8AgKly5cr6xz/+oRo1amjXrl3m8Lwcic2yZMkSXbx4UW+++aa+++67bK+AgIBcT73OafmtWrWSi4uLpk+fnusyGzdurDvuuEP79+/PdjZE1utmHruWl/UOCgpSnz599MQTT+jgwYO6cOHCDed7bQDfsmWL/vjjD4c7luvKmRNly5bV0KFDtX79eg0aNOimHukWHh6ufv36qXPnzurcuXOudc2bN9f+/fsd/va6comJzWbL9eZ613MzZwPkRdap4r/99pvD8G+++UZHjhzR888/n+P+d9999+mjjz7K0w0yb0VERIRcXV3122+/5bpP6kqQNwwj2532Z8+erYyMjJtadtmyZfXCCy+oZcuW2f6W1xo5cqQMw9CAAQOUlpaWbXx6erq++OILSdIjjzwiXbm55tV27NihAwcO5HgTyVt17Wflk08+0eXLl7N9Vm5GlSpVVKlSJf3000+5/o2yLklp1qyZNmzYYJ4toCs3TFy2bFmO8z569KguXbpU7B5DCODvjSP/APA3tnv3br3wwgvq2rWrKlWqJHd3d3377bfavXu3w1HIGjVqaOnSpVq2bJnuueceeXp6ms81v9acOXNUqlQpDRs2LMdTsp966ilNmjRJP/30k2w22w2XX758eb322mt68803dfHiRfPxfPv379dff/2lMWPGqGTJkpo6dap69+6tU6dO6fHHH1dgYKBOnDihn376SSdOnLjujwe5qVGjhjZu3KgvvvhCZcqUka+vr6pUqaIGDRqoffv2qlmzpkqVKqUDBw5o4cKFCg8Pl7e39w3nu3PnTj399NPq2rWrEhISNGrUKN11113m2RBZXFxc9Pzzz+vVV1+Vj49Prtfq58W1j0XMyUsvvaSPPvpI7dq109ixY1WuXDl9+eWX+uCDD/Tcc8+Z17PnR9Z+MmHCBLVp00YuLi6qWbPmTf0Yc7W7775b99xzj3744QdFRUWZw+fMmSNXV1e99tprDteVZxk4cKCioqL05Zdf6tFHH72lHq6nfPnyGjt2rEaNGqXff/9drVu3VqlSpXTs2DFt377dPHvEz89PDz/8sN555x0FBASofPny2rRpk+bMmWPer+BGUlJS1KxZM/Xs2VNVq1aVr6+vduzYoZiYmFyPaGcJDw/X9OnTNWjQINWrV0/PPfec7rvvPqWnp+vHH3/Uhx9+qLCwMHXo0EFVqlTRM888o6lTp6pEiRJq06aNDh06pNdff12hoaEOT8twlhUrVsjV1VUtW7bUvn379Prrr6tWrVrq1q2bU+Y/c+ZMtWnTRhEREerTp4/uuusunTp1SgcOHNCuXbvMe2784x//0OrVq/XII4/ojTfekLe3t/79739nu5dIlqxHCt7MD2YAUGCK+o6DAID8ybrj944dO3Icf727/Z84ccKh9tixY0afPn2MqlWrGj4+PkbJkiWNmjVrGpMnT3a4S/ahQ4eMVq1aGb6+voakXO+E/tNPPxmSrnvX8Z9//tmQZAwePDjPyzcMw/joo4+M+++/3/D09DRKlixp1KlTx2EdDcMwNm3aZLRr187w9/c33NzcjLvuusto166d8emnn95wW+R01/e4uDijcePGhre3tyHJvKP4iBEjjPr16xulSpUyPDw8jHvuucd46aWXjL/++ivX9b56GWvXrjUiIyONO+64w/Dy8jLatm1r/O9//8txmkOHDhmSjGefffa6875abut4rZyeZvDHH38YPXv2NEqXLm24ubkZVapUMd555x3zrufGVfvYO++8k22ekozRo0eb71NTU42nn37auPPOOw2bzeawjSUZzz//fLZ5lCtXLsenLFzr9ddfN0qVKmVcunTJMAzDOHHihOHu7n7dJzEkJycbXl5e5pMhnHG3/+uty6pVq4xmzZoZfn5+hoeHh1GuXDnj8ccfN9avX2/WHDlyxHjssceMUqVKGb6+vkbr1q2NvXv3ZtsOuX32L126ZDz77LNGzZo1DT8/P8PLy8uoUqWKMXr0aOP8+fN56j8uLs7o3bu3UbZsWcPd3d3w8fEx6tSpY7zxxhvG8ePHzbqMjAxjwoQJRuXKlQ03NzcjICDAePLJJ42EhASH+TVp0sS47777si2nXLlyRrt27W64/bL24djYWKNDhw5GyZIlDV9fX+OJJ54wjh07lm1ZOd3tPy/7p3Hle6tbt25GYGCg4ebmZgQHBxuPPPKIMWPGDIe677//3mjYsKHh4eFhBAcHG6+88orx4Ycf5ni3/8jISKNGjRo5bmsAKCo249oHLgMAgGJj6tSpioqK0t69e3XfffcVdTvFytGjR1WhQgV99NFHud51HShsZ86cUUhIiCZPnqwBAwYUdTsAYCL8AwBQDP3444+Kj4/XwIED1bhxY61ataqoWyqWXn31VX399deKi4tTiRLcyghFb8yYMVq2bJl27959w8evAkBh4hsJAIBiqHPnzkpKStJDDz10U09d+Lv4xz/+IW9vb/3555+5Pg0CKEx+fn6aP38+wR9AscORfwAAAAAALI7z4wAAAAAAsDjCPwAAAAAAFkf4BwAAAADA4rgTiRNlZmbq6NGj8vX1lc1mK+p2AAAAAAAWZxiGzp49q5CQkOs++Ybw70RHjx7lTsMAAAAAgEKXkJCgu+++O9fxhH8n8vX1la5sdD8/v6JuBwAAAABgcWfOnFFoaKiZR3ND+HeirFP9/fz8CP8AAAAAgEJzo0vPueEfAAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFica1E3AAAAAAC4zUR3LuoOCl70yqLuwKk48g8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4oo0/P/nP/9Rhw4dFBISIpvNplWrVuVaO3DgQNlsNk2ZMsVheGpqqgYPHqyAgAD5+PioY8eOOnLkiENNcnKyIiMjZbfbZbfbFRkZqdOnTzvUHD58WB06dJCPj48CAgIUFRWltLQ0J68xAAAAAACFr0jD//nz51WrVi1NmzbtunWrVq3Stm3bFBISkm3ckCFDtHLlSi1dulSbN2/WuXPn1L59e2VkZJg1PXv2VFxcnGJiYhQTE6O4uDhFRkaa4zMyMtSuXTudP39emzdv1tKlS7V8+XINHTrUyWsMAAAAAEDhcy3Khbdp00Zt2rS5bs2ff/6pF154Qd98843atWvnMC4lJUVz5szRwoUL1aJFC0nSokWLFBoaqvXr1ysiIkIHDhxQTEyMfvjhBzVo0ECSNGvWLIWHh+vgwYOqUqWK1q5dq/379yshIcH8geG9995Tnz599Pbbb8vPz6/AtgEAAAAAAAWtWF/zn5mZqcjISL3yyiu67777so2PjY1Venq6WrVqZQ4LCQlRWFiYtmzZIknaunWr7Ha7GfwlqWHDhrLb7Q41YWFhDmcWREREKDU1VbGxsbn2l5qaqjNnzji8AAAAAAAobop1+J8wYYJcXV0VFRWV4/ikpCS5u7urVKlSDsODgoKUlJRk1gQGBmabNjAw0KEmKCjIYXypUqXk7u5u1uRk/Pjx5n0E7Ha7QkNDb2o9AQAAAAAoSMU2/MfGxupf//qX5s+fL5vNlq9pDcNwmCan6W+m5lojR45USkqK+UpISMhXnwAAAAAAFIZiG/7/+9//6vjx4ypbtqxcXV3l6uqqP/74Q0OHDlX58uUlScHBwUpLS1NycrLDtMePHzeP5AcHB+vYsWPZ5n/ixAmHmmuP8CcnJys9PT3bGQFX8/DwkJ+fn8MLAAAAAIDiptiG/8jISO3evVtxcXHmKyQkRK+88oq++eYbSVK9evXk5uamdevWmdMlJiZq7969atSokSQpPDxcKSkp2r59u1mzbds2paSkONTs3btXiYmJZs3atWvl4eGhevXqFeJaAwAAAADgfEV6t/9z587p119/Nd/Hx8crLi5O/v7+Klu2rEqXLu1Q7+bmpuDgYFWpUkWSZLfb1b9/fw0dOlSlS5eWv7+/hg0bpho1aph3/69WrZpat26tAQMGaObMmZKkZ555Ru3btzfn06pVK1WvXl2RkZF65513dOrUKQ0bNkwDBgzgaD4AAAAA4LZXpEf+d+7cqTp16qhOnTqSpJdffll16tTRG2+8ked5TJ48WZ06dVK3bt3UuHFjeXt764svvpCLi4tZs3jxYtWoUUOtWrVSq1atVLNmTS1cuNAc7+Lioi+//FKenp5q3LixunXrpk6dOundd9918hoDAAAAAFD4bIZhGEXdhFWcOXNGdrtdKSkpnDEAAAAAwLqiOxd1BwUvemVRd5Anec2hxfaafwAAAAAA4ByEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAW51rUDaCIcHdOAAAAAPjb4Mg/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDiCP8AAAAAAFgc4R8AAAAAAIsj/AMAAAAAYHGEfwAAAAAALM61qBsAAAAAgGInunNRd1A4olcWdQcoJBz5BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4lyLugEAt5nozkXdQeGIXlnUHQAAAABOw5F/AAAAAAAsrkjD/3/+8x916NBBISEhstlsWrVqlTkuPT1dr776qmrUqCEfHx+FhIToqaee0tGjRx3mkZqaqsGDBysgIEA+Pj7q2LGjjhw54lCTnJysyMhI2e122e12RUZG6vTp0w41hw8fVocOHeTj46OAgABFRUUpLS2tgLcAAAAAAAAFr0jD//nz51WrVi1NmzYt27gLFy5o165dev3117Vr1y6tWLFCv/zyizp27OhQN2TIEK1cuVJLly7V5s2bde7cObVv314ZGRlmTc+ePRUXF6eYmBjFxMQoLi5OkZGR5viMjAy1a9dO58+f1+bNm7V06VItX75cQ4cOLeAtAAAAAABAwSvSa/7btGmjNm3a5DjObrdr3bp1DsOmTp2qBx54QIcPH1bZsmWVkpKiOXPmaOHChWrRooUkadGiRQoNDdX69esVERGhAwcOKCYmRj/88IMaNGggSZo1a5bCw8N18OBBValSRWvXrtX+/fuVkJCgkJAQSdJ7772nPn366O2335afn1+BbwsAAICbwr1YAAB5cFtd85+SkiKbzaY77rhDkhQbG6v09HS1atXKrAkJCVFYWJi2bNkiSdq6davsdrsZ/CWpYcOGstvtDjVhYWFm8JekiIgIpaamKjY2Ntd+UlNTdebMGYcXAAAAAADFzW0T/i9duqQRI0aoZ8+e5pH4pKQkubu7q1SpUg61QUFBSkpKMmsCAwOzzS8wMNChJigoyGF8qVKl5O7ubtbkZPz48eZ9BOx2u0JDQ52yrgAAAAAAONNtEf7T09PVo0cPZWZm6oMPPrhhvWEYstls5vur//tWaq41cuRIpaSkmK+EhIQ8rhEAAAAAAIWn2If/9PR0devWTfHx8Vq3bp3D9ffBwcFKS0tTcnKywzTHjx83j+QHBwfr2LFj2eZ74sQJh5prj/AnJycrPT092xkBV/Pw8JCfn5/DCwAAAACA4qZYh/+s4P+///1P69evV+nSpR3G16tXT25ubg43BkxMTNTevXvVqFEjSVJ4eLhSUlK0fft2s2bbtm1KSUlxqNm7d68SExPNmrVr18rDw0P16tUrhDUFAAAAAKDgFOnd/s+dO6dff/3VfB8fH6+4uDj5+/srJCREjz/+uHbt2qU1a9YoIyPDPDrv7+8vd3d32e129e/fX0OHDlXp0qXl7++vYcOGqUaNGubd/6tVq6bWrVtrwIABmjlzpiTpmWeeUfv27VWlShVJUqtWrVS9enVFRkbqnXfe0alTpzRs2DANGDCAo/kAAAAAgNtekYb/nTt3qlmzZub7l19+WZLUu3dvRUdHa/Xq1ZKk2rVrO0z33XffqWnTppKkyZMny9XVVd26ddPFixfVvHlzzZ8/Xy4uLmb94sWLFRUVZT4VoGPHjpo2bZo53sXFRV9++aUGDRqkxo0by8vLSz179tS7775bwFsAAAAAKEJ/h0dF8phIQCrq8N+0aVMZhpHr+OuNy+Lp6ampU6dq6tSpudb4+/tr0aJF151P2bJltWbNmhsuDwAAAACA202xvuYfAAAAAADcOsI/AAAAAAAWV6Sn/QPFFte/AQAAALAQjvwDAAAAAGBxhH8AAAAAACyO0/4BAABgXVzKBwASR/4BAAAAALA+wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA416JuAAAA4IaiOxd1BwUvemVRdwAAsDCO/AMAAAAAYHGEfwAAAAAALI7wDwAAAACAxRH+AQAAAACwOMI/AAAAAAAWR/gHAAAAAMDieNQfAADFAY+yAwAABYgj/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABbnWtQNAIClRHcu6g4KXvTKou4AAAAA+cSRfwAAAAAALI7wDwAAAACAxRVp+P/Pf/6jDh06KCQkRDabTatWrXIYbxiGoqOjFRISIi8vLzVt2lT79u1zqElNTdXgwYMVEBAgHx8fdezYUUeOHHGoSU5OVmRkpOx2u+x2uyIjI3X69GmHmsOHD6tDhw7y8fFRQECAoqKilJaWVoBrDwAAAABA4SjSa/7Pnz+vWrVqqW/fvnrssceyjZ84caImTZqk+fPnq3LlynrrrbfUsmVLHTx4UL6+vpKkIUOG6IsvvtDSpUtVunRpDR06VO3bt1dsbKxcXFwkST179tSRI0cUExMjSXrmmWcUGRmpL774QpKUkZGhdu3a6c4779TmzZt18uRJ9e7dW4ZhaOrUqYW6TQDA0rgnAgAAQJEo0vDfpk0btWnTJsdxhmFoypQpGjVqlLp06SJJWrBggYKCgrRkyRINHDhQKSkpmjNnjhYuXKgWLVpIkhYtWqTQ0FCtX79eEREROnDggGJiYvTDDz+oQYMGkqRZs2YpPDxcBw8eVJUqVbR27Vrt379fCQkJCgkJkSS999576tOnj95++235+fkV2jYBAAAAAMDZiu01//Hx8UpKSlKrVq3MYR4eHmrSpIm2bNkiSYqNjVV6erpDTUhIiMLCwsyarVu3ym63m8Ffkho2bCi73e5QExYWZgZ/SYqIiFBqaqpiY2Nz7TE1NVVnzpxxeAEAAAAAUNwU2/CflJQkSQoKCnIYHhQUZI5LSkqSu7u7SpUqdd2awMDAbPMPDAx0qLl2OaVKlZK7u7tZk5Px48eb9xGw2+0KDQ296fUFAAAAAKCgFNvwn8Vmszm8Nwwj27BrXVuTU/3N1Fxr5MiRSklJMV8JCQk3XB8AAAAAAApbsQ3/wcHB0lVnAGQ5fvy4eZQ+ODhYaWlpSk5Ovm7NsWPHss3/xIkTDjXXLic5OVnp6enZzgi4moeHh/z8/BxeAAAAAAAUN8U2/FeoUEHBwcFat26dOSwtLU2bNm1So0aNJEn16tWTm5ubQ01iYqL27t1r1oSHhyslJUXbt283a7Zt26aUlBSHmr179yoxMdGsWbt2rTw8PFSvXr1CWV8AAAAAAArKLd/tPyMjQ3v27FG5cuWyXXt/I+fOndOvv/5qvo+Pj1dcXJz8/f1VtmxZDRkyROPGjVOlSpVUqVIljRs3Tt7e3urZs6ckyW63q3///ho6dKhKly4tf39/DRs2TDVq1DDv/l+tWjW1bt1aAwYM0MyZM6Urj/pr3769qlSpIklq1aqVqlevrsjISL3zzjs6deqUhg0bpgEDBnA0HwAAAABw28v3kf8hQ4Zozpw50pXg36RJE9WtW1ehoaHauHFjvua1c+dO1alTR3Xq1JEkvfzyy6pTp47eeOMNSdLw4cM1ZMgQDRo0SPXr19eff/6ptWvXytfX15zH5MmT1alTJ3Xr1k2NGzeWt7e3vvjiC7m4uJg1ixcvVo0aNdSqVSu1atVKNWvW1MKFC83xLi4u+vLLL+Xp6anGjRurW7du6tSpk9599938bh4AAAAAAIqdfB/5/+yzz/Tkk09Kkr744gvFx8fr559/1kcffaRRo0bp+++/z/O8mjZtKsMwch1vs9kUHR2t6OjoXGs8PT01depUTZ06Ndcaf39/LVq06Lq9lC1bVmvWrMlj5wAAAAAA3D7yfeT/r7/+Mm/G99VXX6lr166qXLmy+vfvrz179hREjwAAAAAA4BbkO/wHBQVp//79ysjIUExMjHlt/YULFxxOtQcAAAAAAMVDvk/779u3r7p166YyZcrIZrOpZcuW0pU76FetWrUgegQAAAAAALcg3+E/OjpaYWFhSkhIUNeuXeXh4SFduWneiBEjCqJHAAAAAABwC/Id/j/66CN1797dDP1ZnnjiCS1dutSZvQEAAAAAACfI9zX/ffv2VUpKSrbhZ8+eVd++fZ3VFwAAAAAAcJJ8h3/DMGSz2bINP3LkiOx2u7P6AgAAAAAATpLn0/7r1Kkjm80mm82m5s2by9X1/ybNyMhQfHy8WrduXVB9AgAAAACAm5Tn8N+pUydJUlxcnCIiIlSyZElznLu7u8qXL6/HHnusYLoEAAAAAAA3Lc/hf/To0ZKk8uXLq3v37vL09CzIvgAAAAAAgJPk+27/vXv3liSlpaXp+PHjyszMdBhftmxZ53UHAAAAAABuWb7D///+9z/169dPW7ZscRiedSPAjIwMZ/YHAAAAAABuUb7Df58+feTq6qo1a9aoTJkyOd75HwAAAAAAFB/5Dv9xcXGKjY1V1apVC6YjAAAAAADgVCXyO0H16tX1119/FUw3AAAAAADA6fId/idMmKDhw4dr48aNOnnypM6cOePwAgAAAAAAxUu+T/tv0aKFJKl58+YOw7nhHwAAAAAAxVO+w/93331XMJ0AAAAAAIACke/w36RJk4LpBAAAAAAAFIh8X/MvSf/973/15JNPqlGjRvrzzz8lSQsXLtTmzZud3R8AAAAAALhF+Q7/y5cvV0REhLy8vLRr1y6lpqZKks6ePatx48YVRI8AAAAAAOAW5Dv8v/XWW5oxY4ZmzZolNzc3c3ijRo20a9cuZ/cHAAAAAABuUb7D/8GDB/Xwww9nG+7n56fTp087qy8AAAAAAOAk+Q7/ZcqU0a+//ppt+ObNm3XPPfc4qy8AAAAAAOAk+Q7/AwcO1Isvvqht27bJZrPp6NGjWrx4sYYNG6ZBgwYVTJcAAAAAAOCm5ftRf8OHD1dKSoqaNWumS5cu6eGHH5aHh4eGDRumF154oWC6BAAAAAAANy3f4V+S3n77bY0aNUr79+9XZmamqlevrpIlSzq/OwAAAAAAcMvyHf5TUlKUkZEhf39/1a9f3xx+6tQpubq6ys/Pz9k9AgAAAACAW5Dva/579OihpUuXZhv+ySefqEePHs7qCwAAAAAAOEm+w/+2bdvUrFmzbMObNm2qbdu2OasvAAAAAADgJPkO/6mpqbp8+XK24enp6bp48aKz+gIAAAAAAE6S7/B///3368MPP8w2fMaMGapXr56z+gIAAAAAAE6S7xv+vf3222rRooV++uknNW/eXJK0YcMG7dixQ2vXri2IHgEAAAAAwC3I95H/xo0ba+vWrQoNDdUnn3yiL774QhUrVtTu3bv10EMPFUyXAAAAAADgpuX7yL8k1a5dW4sXL3Z+NwAAAAAAwOnyfeR/165d2rNnj/n+888/V6dOnfTaa68pLS3N2f0BAAAAAIBblO/wP3DgQP3yyy+SpN9//13du3eXt7e3Pv30Uw0fPrwgegQAAAAAALcg3+H/l19+Ue3atSVJn376qZo0aaIlS5Zo/vz5Wr58eUH0CAAAAAAAbkG+w79hGMrMzJQkrV+/Xm3btpUkhYaG6q+//nJ+hwAAAAAA4JbkO/zXr19fb731lhYuXKhNmzapXbt2kqT4+HgFBQUVRI8AAAAAAOAW5Dv8T5kyRbt27dILL7ygUaNGqWLFipKkzz77TI0aNSqIHgEAAAAAwC3I96P+atas6XC3/yzvvPOOXF1v6smBAAAAAACgAOX7yH9O9u/fr1GjRumuu+5yxuwAAAAAAIAT3XT4P3funGbPnq3w8HDVrFlT27Zt04gRI5zbHQAAAAAAuGX5Pk9/8+bNmj17tpYvX64KFSpo//792rRpkxo3blwwHQIAAAAAgFuS5yP/EydOVNWqVdWjRw/deeed2rx5s3bv3i2bzaZSpUoVbJcAAAAAAOCm5fnI/2uvvaZXX31VY8eOlYuLS8F2BQAAAAAAnCbPR/7Hjh2rTz/9VBUqVNCrr76qvXv3FmxnAAAAAADAKfIc/l977TX98ssvWrhwoZKSktSwYUPVqlVLhmEoOTm5QJq7fPmy/vGPf6hChQry8vLSPffco7FjxyozM9OsMQxD0dHRCgkJkZeXl5o2bap9+/Y5zCc1NVWDBw9WQECAfHx81LFjRx05csShJjk5WZGRkbLb7bLb7YqMjNTp06cLZL0AAAAAAChM+b7bf5MmTbRgwQIlJibqueeeU7169dSkSRM1atRIkyZNcmpzEyZM0IwZMzRt2jQdOHBAEydO1DvvvKOpU6eaNRMnTtSkSZM0bdo07dixQ8HBwWrZsqXOnj1r1gwZMkQrV67U0qVLtXnzZp07d07t27dXRkaGWdOzZ0/FxcUpJiZGMTExiouLU2RkpFPXBwAAAACAonDTj/rz9fXVs88+q23btunHH3/UAw88oH/+859ObW7r1q169NFH1a5dO5UvX16PP/64WrVqpZ07d0pXjvpPmTJFo0aNUpcuXRQWFqYFCxbowoULWrJkiSQpJSVFc+bM0XvvvacWLVqoTp06WrRokfbs2aP169dLkg4cOKCYmBjz0YXh4eGaNWuW1qxZo4MHDzp1nQAAAAAAKGw3Hf6vVqNGDU2ZMkV//vmnM2ZnevDBB7Vhwwb98ssvkqSffvpJmzdvVtu2bSVJ8fHxSkpKUqtWrcxpPDw81KRJE23ZskWSFBsbq/T0dIeakJAQhYWFmTVbt26V3W5XgwYNzJqGDRvKbrebNTlJTU3VmTNnHF4AAAAAABQ3eb7bf164ubk5c3Z69dVXlZKSoqpVq8rFxUUZGRl6++239cQTT0iSkpKSJElBQUEO0wUFBemPP/4wa9zd3bM9jjAoKMicPikpSYGBgdmWHxgYaNbkZPz48RozZowT1hQAAAAAgILjlCP/BWXZsmVatGiRlixZol27dmnBggV69913tWDBAoc6m83m8N4wjGzDrnVtTU71N5rPyJEjlZKSYr4SEhLysXYAAAAAABQOpx75d7ZXXnlFI0aMUI8ePaQrlxf88ccfGj9+vHr37q3g4GDpypH7MmXKmNMdP37cPBsgODhYaWlpSk5Odjj6f/z4cTVq1MisOXbsWLblnzhxIttZBVfz8PCQh4eHE9cYAAAAAADnK9ZH/i9cuKASJRxbdHFxMR/1V6FCBQUHB2vdunXm+LS0NG3atMkM9vXq1ZObm5tDTWJiovbu3WvWhIeHKyUlRdu3bzdrtm3bppSUFLMGAAAAAIDbVb6P/Lu4uCgxMTHbNfInT55UYGCgw+PzblWHDh309ttvq2zZsrrvvvv0448/atKkSerXr5905VT9IUOGaNy4capUqZIqVaqkcePGydvbWz179pQk2e129e/fX0OHDlXp0qXl7++vYcOGqUaNGmrRooUkqVq1amrdurUGDBigmTNnSpKeeeYZtW/fXlWqVHHa+gAAAAAAUBTyHf4Nw8hxeGpqqtzd3Z3Rk2nq1Kl6/fXXNWjQIB0/flwhISEaOHCg3njjDbNm+PDhunjxogYNGqTk5GQ1aNBAa9eula+vr1kzefJkubq6qlu3brp48aKaN2+u+fPny8XFxaxZvHixoqKizKcCdOzYUdOmTXPq+gAAAAAAUBTyHP7ff/996crR9tmzZ6tkyZLmuIyMDP3nP/9R1apVndqcr6+vpkyZoilTpuRaY7PZFB0drejo6FxrPD09NXXqVE2dOjXXGn9/fy1atOiWewYAAAAAoLjJc/ifPHmydOXI/4wZMxyOmru7u6t8+fKaMWNGwXQJAAAAAABuWp7Df3x8vCSpWbNmWrFihcOd8wEAAAAAQPGV77v9f/fddw7BPyMjQ3FxcUpOTnZ2bwAAAAAAwAnyHf6HDBmiOXPmSFeC/8MPP6y6desqNDRUGzduLIgeAQAAAADALch3+P/0009Vq1YtSdIXX3yhQ4cO6eeff9aQIUM0atSogugRAAAAAADcgnyH/5MnTyo4OFiS9NVXX6lr166qXLmy+vfvrz179hREjwAAAAAA4BbkO/wHBQVp//79ysjIUExMjFq0aCFJunDhgsMTAAAAAAAAQPGQ57v9Z+nbt6+6deumMmXKyGazqWXLlpKkbdu2qWrVqgXRIwAAAAAAuAX5Dv/R0dGqUaOGDh8+rK5du8rDw0OS5OLiohEjRhREjwAAAAAA4BbkK/ynp6erVatWmjlzph577DGHcb1793Z2bwAAAAAAwAnydc2/m5ub9u7dK5vNVnAdAQAAAAAAp8r3Df+eeuopzZkzp2C6AQAAAAAATpfva/7T0tI0e/ZsrVu3TvXr15ePj4/D+EmTJjmzPwAAAAAAcIvyHf737t2runXrSpJ++eUXh3FcDgAAAAAAQPGT7/D/3XffFUwnAAAAAACgQOT7mn8AAAAAAHB7ydOR/y5dumj+/Pny8/NTly5drlu7YsUKZ/UGAAAAAACcIE/h3263m9fz2+32gu4JAAAAAAA4UZ7C/7x58/Ttt9/q4Ycf1rx58wq+KwAAAAAA4DR5vua/ZcuWOnXqlPm+YcOG+vPPPwuqLwAAAAAA4CR5Dv+GYTi837dvn1JTUwuiJwAAAAAA4ETc7R8AAAAAAIvLc/i32WzmTf9yeg8AAAAAAIqnPN3wT1dO+2/evLlcXf//JBcuXFCHDh3k7u7uULdr1y7ndwkAAAAAAG5ansP/6NGjHd4/+uijBdEPAAAAAABwspsO/wAAAAAA4PbADf8AAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAi8vT3f7ff//9PM8wKirqVvoBAAAAAABOlqfwP3ny5DzNzGazEf4BAAAAAChm8hT+4+PjC74TAAAAAABQILjmHwAAAAAAi8vTkf9rHTlyRKtXr9bhw4eVlpbmMG7SpEnO6g0AAAAAADhBvsP/hg0b1LFjR1WoUEEHDx5UWFiYDh06JMMwVLdu3YLpEgAAAAAA3LR8n/Y/cuRIDR06VHv37pWnp6eWL1+uhIQENWnSRF27di2YLgEAAAAAwE3Ld/g/cOCAevfuLUlydXXVxYsXVbJkSY0dO1YTJkwoiB4BAAAAAMAtyHf49/HxUWpqqiQpJCREv/32mznur7/+cm53AAAAAADgluX7mv+GDRvq+++/V/Xq1dWuXTsNHTpUe/bs0YoVK9SwYcOC6RIAAAAAANy0fIf/SZMm6dy5c5Kk6OhonTt3TsuWLVPFihU1efLkgugRAAAAAADcgnyH/3vuucf8b29vb33wwQfO7gkAAAAAADhRvq/5v+eee3Ty5Mlsw0+fPu3wwwAAAAAAACge8h3+Dx06pIyMjGzDU1NT9eeffzqrLwAAAAAA4CR5Pu1/9erV5n9/8803stvt5vuMjAxt2LBB5cuXd36HAAAAAADgluQ5/Hfq1EmSZLPZ1Lt3b4dxbm5uKl++vN577z3ndwgAAAAAAG5JnsN/ZmamJKlChQrasWOHAgICCrIvAAAAAADgJPm+5j8+Pr5Qg/+ff/6pJ598UqVLl5a3t7dq166t2NhYc7xhGIqOjlZISIi8vLzUtGlT7du3z2EeqampGjx4sAICAuTj46OOHTvqyJEjDjXJycmKjIyU3W6X3W5XZGSkTp8+XWjrCQAAAABAQcl3+JekTZs2qUOHDqpYsaIqVaqkjh076r///a/Tm0tOTlbjxo3l5uamr7/+Wvv379d7772nO+64w6yZOHGiJk2apGnTpmnHjh0KDg5Wy5YtdfbsWbNmyJAhWrlypZYuXarNmzfr3Llzat++vcONC3v27Km4uDjFxMQoJiZGcXFxioyMdPo6AQAAAABQ2PJ82n+WRYsWqW/fvurSpYuioqJkGIa2bNmi5s2ba/78+erZs6fTmpswYYJCQ0M1b948c9jVNxU0DENTpkzRqFGj1KVLF0nSggULFBQUpCVLlmjgwIFKSUnRnDlztHDhQrVo0cJch9DQUK1fv14RERE6cOCAYmJi9MMPP6hBgwaSpFmzZik8PFwHDx5UlSpVnLZOAAAAAAAUtnwf+X/77bc1ceJELVu2TFFRUXrxxRe1bNky/fOf/9Sbb77p1OZWr16t+vXrq2vXrgoMDFSdOnU0a9Ysc3x8fLySkpLUqlUrc5iHh4eaNGmiLVu2SJJiY2OVnp7uUBMSEqKwsDCzZuvWrbLb7Wbwl6SGDRvKbrebNTlJTU3VmTNnHF4AAAAAABQ3+Q7/v//+uzp06JBteMeOHRUfH++svsxlTZ8+XZUqVdI333yjZ599VlFRUfroo48kSUlJSZKkoKAgh+mCgoLMcUlJSXJ3d1epUqWuWxMYGJht+YGBgWZNTsaPH2/eI8Butys0NNQJaw0AAAAAgHPlO/yHhoZqw4YN2YZv2LDB6eE3MzNTdevW1bhx41SnTh0NHDhQAwYM0PTp0x3qbDabw3vDMLINu9a1NTnV32g+I0eOVEpKivlKSEjIx9oBAAAAAFA48nzNf79+/fSvf/1LQ4cOVVRUlOLi4tSoUSPZbDZt3rxZ8+fP17/+9S+nNlemTBlVr17dYVi1atW0fPlySVJwcLB05ch9mTJlzJrjx4+bZwMEBwcrLS1NycnJDkf/jx8/rkaNGpk1x44dy7b8EydOZDur4GoeHh7y8PC45fUEAAAAAKAg5fnI/4IFC3Tx4kU999xzWrp0qfbs2aMhQ4boxRdf1N69e7Vs2TINHDjQqc01btxYBw8edBj2yy+/qFy5cpKkChUqKDg4WOvWrTPHp6WladOmTWawr1evntzc3BxqEhMTtXfvXrMmPDxcKSkp2r59u1mzbds2paSkmDUAAAAAANyu8nzk3zAM8787d+6szp07F1RPppdeekmNGjXSuHHj1K1bN23fvl0ffvihPvzwQ+nKqfpDhgzRuHHjVKlSJVWqVEnjxo2Tt7e3+dQBu92u/v37a+jQoSpdurT8/f01bNgw1ahRw7z7f7Vq1dS6dWsNGDBAM2fOlCQ988wzat++PXf6BwAAAADc9vL1qL8bXUfvbPfff79WrlypkSNHauzYsapQoYKmTJmiXr16mTXDhw/XxYsXNWjQICUnJ6tBgwZau3atfH19zZrJkyfL1dVV3bp108WLF83HErq4uJg1ixcvVlRUlPlUgI4dO2ratGmFur4AAAAAABSEfIX/ypUr3/AHgFOnTt1qTw7at2+v9u3b5zreZrMpOjpa0dHRudZ4enpq6tSpmjp1aq41/v7+WrRo0S33CwAAAABAcZOv8D9mzBjZ7faC6wYAAAAAADhdvsJ/jx49FBgYWHDdAAAAAAAAp8vz3f4L+3p/AAAAAADgHHkO/1ff7R8AAAAAANw+8nzaf2ZmZsF2AgAAAAAACkSej/wDAAAAAIDbE+EfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxt1X4Hz9+vGw2m4YMGWIOMwxD0dHRCgkJkZeXl5o2bap9+/Y5TJeamqrBgwcrICBAPj4+6tixo44cOeJQk5ycrMjISNntdtntdkVGRur06dOFtm4AAAAAABSU2yb879ixQx9++KFq1qzpMHzixImaNGmSpk2bph07dig4OFgtW7bU2bNnzZohQ4Zo5cqVWrp0qTZv3qxz586pffv2ysjIMGt69uypuLg4xcTEKCYmRnFxcYqMjCzUdQQAAAAAoCDcFuH/3Llz6tWrl2bNmqVSpUqZww3D0JQpUzRq1Ch16dJFYWFhWrBggS5cuKAlS5ZIklJSUjRnzhy99957atGiherUqaNFixZpz549Wr9+vSTpwIEDiomJ0ezZsxUeHq7w8HDNmjVLa9as0cGDB4tsvQEAAAAAcIbbIvw///zzateunVq0aOEwPD4+XklJSWrVqpU5zMPDQ02aNNGWLVskSbGxsUpPT3eoCQkJUVhYmFmzdetW2e12NWjQwKxp2LCh7Ha7WZOT1NRUnTlzxuEFAAAAAEBx41rUDdzI0qVLtWvXLu3YsSPbuKSkJElSUFCQw/CgoCD98ccfZo27u7vDGQNZNVnTJyUlKTAwMNv8AwMDzZqcjB8/XmPGjLnJNQMAAAAAoHAU6yP/CQkJevHFF7Vo0SJ5enrmWmez2RzeG4aRbdi1rq3Jqf5G8xk5cqRSUlLMV0JCwg3WCAAAAACAwlesw39sbKyOHz+uevXqydXVVa6urtq0aZPef/99ubq6mkf8rz06f/z4cXNccHCw0tLSlJycfN2aY8eOZVv+iRMnsp1VcDUPDw/5+fk5vAAAAAAAKG6Kdfhv3ry59uzZo7i4OPNVv3599erVS3FxcbrnnnsUHBysdevWmdOkpaVp06ZNatSokSSpXr16cnNzc6hJTEzU3r17zZrw8HClpKRo+/btZs22bduUkpJi1gAAAAAAcLsq1tf8+/r6KiwszGGYj4+PSpcubQ4fMmSIxo0bp0qVKqlSpUoaN26cvL291bNnT0mS3W5X//79NXToUJUuXVr+/v4aNmyYatSoYd5AsFq1amrdurUGDBigmTNnSpKeeeYZtW/fXlWqVCn09QYAAAAAwJmKdfjPi+HDh+vixYsaNGiQkpOT1aBBA61du1a+vr5mzeTJk+Xq6qpu3brp4sWLat68uebPny8XFxezZvHixYqKijKfCtCxY0dNmzatSNYJAAAAAABnuu3C/8aNGx3e22w2RUdHKzo6OtdpPD09NXXqVE2dOjXXGn9/fy1atMipvQIAAAAAUBwU62v+AQAAAADArSP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwAAAABgcYR/AAAAAAAsjvAPAAAAAIDFEf4BAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyvW4X/8+PG6//775evrq8DAQHXq1EkHDx50qDEMQ9HR0QoJCZGXl5eaNm2qffv2OdSkpqZq8ODBCggIkI+Pjzp27KgjR4441CQnJysyMlJ2u112u12RkZE6ffp0oawnAAAAAAAFqViH/02bNun555/XDz/8oHXr1uny5ctq1aqVzp8/b9ZMnDhRkyZN0rRp07Rjxw4FBwerZcuWOnv2rFkzZMgQrVy5UkuXLtXmzZt17tw5tW/fXhkZGWZNz549FRcXp5iYGMXExCguLk6RkZGFvs4AAAAAADiba1E3cD0xMTEO7+fNm6fAwEDFxsbq4YcflmEYmjJlikaNGqUuXbpIkhYsWKCgoCAtWbJEAwcOVEpKiubMmaOFCxeqRYsWkqRFixYpNDRU69evV0REhA4cOKCYmBj98MMPatCggSRp1qxZCg8P18GDB1WlSpUc+0tNTVVqaqr5/syZMwW4NQAAAAAAuDnF+sj/tVJSUiRJ/v7+kqT4+HglJSWpVatWZo2Hh4eaNGmiLVu2SJJiY2OVnp7uUBMSEqKwsDCzZuvWrbLb7Wbwl6SGDRvKbrebNTkZP368eZmA3W5XaGhoAaw1AAAAAAC35rYJ/4Zh6OWXX9aDDz6osLAwSVJSUpIkKSgoyKE2KCjIHJeUlCR3d3eVKlXqujWBgYHZlhkYGGjW5GTkyJFKSUkxXwkJCU5YUwAAAAAAnKtYn/Z/tRdeeEG7d+/W5s2bs42z2WwO7w3DyDbsWtfW5FR/o/l4eHjIw8Mjj2sAAAAAAEDRuC2O/A8ePFirV6/Wd999p7vvvtscHhwcLF11BkCW48ePm2cDBAcHKy0tTcnJydetOXbsWLblnjhxIttZBQAAAAAA3G6Kdfg3DEMvvPCCVqxYoW+//VYVKlRwGF+hQgUFBwdr3bp15rC0tDRt2rRJjRo1kiTVq1dPbm5uDjWJiYnau3evWRMeHq6UlBRt377drNm2bZtSUlLMGgAAAAAAblfF+rT/559/XkuWLNHnn38uX19f8wi/3W6Xl5eXbDabhgwZonHjxqlSpUqqVKmSxo0bJ29vb/Xs2dOs7d+/v4YOHarSpUvL399fw4YNU40aNcy7/1erVk2tW7fWgAEDNHPmTEnSM888o/bt2+d6p38AAAAAAG4XxTr8T58+XZLUtGlTh+Hz5s1Tnz59JEnDhw/XxYsXNWjQICUnJ6tBgwZau3atfH19zfrJkyfL1dVV3bp108WLF9W8eXPNnz9fLi4uZs3ixYsVFRVlPhWgY8eOmjZtWiGtKQAAAAAABadYh3/DMG5YY7PZFB0drejo6FxrPD09NXXqVE2dOjXXGn9/fy1atOimewUAAAAAoLgq1tf8AwAAAACAW0f4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4BwAAAADA4gj/AAAAAABYHOEfAAAAAACLI/wDAAAAAGBxhH8AAAAAACyO8A8AAAAAgMUR/gEAAAAAsDjC/zU++OADVahQQZ6enqpXr57++9//FnVLAAAAAADcEsL/VZYtW6YhQ4Zo1KhR+vHHH/XQQw+pTZs2Onz4cFG3BgAAAADATSP8X2XSpEnq37+/nn76aVWrVk1TpkxRaGiopk+fXtStAQAAAABw01yLuoHiIi0tTbGxsRoxYoTD8FatWmnLli05TpOamqrU1FTzfUpKiiTpzJkzBdytE6SmF3UHBe9W/g5sn9z9HbaN2D7XxWfr+th3cse+c33sO9fH9skdn63rY9+5PrZP7m6HXHdV/jQM47p1NuNGFX8TR48e1V133aXvv/9ejRo1MoePGzdOCxYs0MGDB7NNEx0drTFjxhRypwAAAAAAOEpISNDdd9+d63iO/F/DZrM5vDcMI9uwLCNHjtTLL79svs/MzNSpU6dUunTpXKf5uzpz5oxCQ0OVkJAgPz+/om6nWGHbXB/b5/rYPrlj21wf2yd3bJvrY/tcH9snd2yb62P7XB/bJ3eGYejs2bMKCQm5bh3h/4qAgAC5uLgoKSnJYfjx48cVFBSU4zQeHh7y8PBwGHbHHXcUaJ+3Oz8/Pz6suWDbXB/b5/rYPrlj21wf2yd3bJvrY/tcH9snd2yb62P7XB/bJ2d2u/2GNdzw7wp3d3fVq1dP69atcxi+bt06h8sAAAAAAAC43XDk/yovv/yyIiMjVb9+fYWHh+vDDz/U4cOH9eyzzxZ1awAAAAAA3DTC/1W6d++ukydPauzYsUpMTFRYWJi++uorlStXrqhbu+15eHho9OjR2S6TANvmRtg+18f2yR3b5vrYPrlj21wf2+f62D65Y9tcH9vn+tg+t467/QMAAAAAYHFc8w8AAAAAgMUR/gEAAAAAsDjCPwAAAAAAFkf4B4ohm82mVatWFXUbAICr8N0MALidEf5xy/r06aNOnToVdRvFTp8+fWSz2bK9fv3116JurchlbZucHqM5aNAg2Ww29enTp0h6K262bNkiFxcXtW7duqhbKXLsN3nH93LesJ2y4zsnd8ePH9fAgQNVtmxZeXh4KDg4WBEREdq6dWtRt1ZsJCQkqH///goJCZG7u7vKlSunF198USdPnszT9Bs3bpTNZtPp06cLvNfCkvX/rn/+858Ow1etWiWbzVZkfRUXV/972c3NTUFBQWrZsqXmzp2rzMzMom7Pcgj/QAFq3bq1EhMTHV4VKlQo6raKhdDQUC1dulQXL140h126dEkff/yxypYte0vzTk9Pd0KHxcPcuXM1ePBgbd68WYcPH76leWVkZNz2/yMtyP0GgHO/c6zmscce008//aQFCxbol19+0erVq9W0aVOdOnWqqFsrFn7//XfVr19fv/zyiz7++GP9+uuvmjFjhjZs2KDw8PC/9Xby9PTUhAkTlJycXNStFEtZ/14+dOiQvv76azVr1kwvvvii2rdvr8uXLxd1e5ZC+IdTxcTE6MEHH9Qdd9yh0qVLq3379vrtt9/M8YcOHZLNZtOKFSvUrFkzeXt7q1atWpb91TzryMDVLxcXF33xxReqV6+ePD09dc8992jMmDHZvtwSExPVpk0beXl5qUKFCvr000+LbD0KQt26dVW2bFmtWLHCHLZixQqFhoaqTp065rC87lOffPKJmjZtKk9PTy1atKjQ16cgnD9/Xp988omee+45tW/fXvPnzzfHZR0d+fLLL1WrVi15enqqQYMG2rNnj1kzf/583XHHHVqzZo2qV68uDw8P/fHHH0W0Ns7hrP3mkUce0QsvvOAw75MnT8rDw0PffvttIa1N4ShfvrymTJniMKx27dqKjo4239tsNs2ePVudO3eWt7e3KlWqpNWrVxdBt0UnL9vJ6q73nZP1fXK1nI5cvvXWWwoMDJSvr6+efvppjRgxQrVr1y60dSgop0+f1ubNmzVhwgQ1a9ZM5cqV0wMPPKCRI0eqXbt2kqSUlBQ988wzCgwMlJ+fnx555BH99NNP5jyio6NVu3ZtzZw5U6GhofL29lbXrl0tc5T7+eefl7u7u9auXasmTZqobNmyatOmjdavX68///xTo0aNkiSlpqZq+PDhCg0NlYeHhypVqqQ5c+bo0KFDatasmSSpVKlSljqbq0WLFgoODtb48eNzrVm+fLnuu+8+eXh4qHz58nrvvffMcSNHjlTDhg2zTVOzZk2NHj26wPouLFn/Xr7rrrtUt25dvfbaa/r888/19ddfm99DN/p8SdLq1atVv359eXp6KiAgQF26dCmiNSq+CP9wqvPnz+vll1/Wjh07tGHDBpUoUUKdO3fOdrRx1KhRGjZsmOLi4lS5cmU98cQTf5tf9r755hs9+eSTioqK0v79+zVz5kzNnz9fb7/9tkPd66+/bh5lePLJJ/XEE0/owIEDRdZ3Qejbt6/mzZtnvp87d6769evnUJPXferVV19VVFSUDhw4oIiIiEJbh4K0bNkyValSRVWqVNGTTz6pefPmyTAMh5pXXnlF7777rnbs2KHAwEB17NjR4cyHCxcuaPz48Zo9e7b27dunwMDAIlgT53LGfvP0009ryZIlSk1NNadZvHixQkJCzH98/t2MGTNG3bp10+7du9W2bVv16tXrb32k7u8oL98517N48WK9/fbbmjBhgmJjY1W2bFlNnz69QHsuLCVLllTJkiW1atUqh++NLIZhqF27dkpKStJXX32l2NhY1a1bV82bN3f4HP3666/65JNP9MUXXygmJkZxcXF6/vnnC3ltnO/UqVP65ptvNGjQIHl5eTmMCw4OVq9evbRs2TIZhqGnnnpKS5cu1fvvv68DBw5oxowZKlmypEJDQ7V8+XJJ0sGDB5WYmKh//etfRbRGzuXi4qJx48Zp6tSpOnLkSLbxsbGx6tatm3r06KE9e/YoOjpar7/+uhl8e/XqpW3btjn8iL1v3z7t2bNHvXr1KtR1KSyPPPKIatWqpRUrVuTp8/Xll1+qS5cuateunX788Udt2LBB9evXL+rVKH4M4Bb17t3bePTRR3Mcd/z4cUOSsWfPHsMwDCM+Pt6QZMyePdus2bdvnyHJOHDgQKH1XBh69+5tuLi4GD4+Pubr8ccfNx566CFj3LhxDrULFy40ypQpY76XZDz77LMONQ0aNDCee+65Quu/IGXtMydOnDA8PDyM+Ph449ChQ4anp6dx4sQJ49FHHzV69+6d47S57VNTpkwp5LUoeI0aNTLXKz093QgICDDWrVtnGIZhfPfdd4YkY+nSpWb9yZMnDS8vL2PZsmWGYRjGvHnzDElGXFxcEa2Bczlzv7l06ZLh7+9vbivDMIzatWsb0dHRhbY+Benq7+Vy5coZkydPdhhfq1YtY/To0eZ7ScY//vEP8/25c+cMm81mfP3114XYdeG7me20cuXKQu+zsFzvO2fevHmG3W53qF+5cqVx9T8lGzRoYDz//PMONY0bNzZq1apVKP0XtM8++8woVaqU4enpaTRq1MgYOXKk8dNPPxmGYRgbNmww/Pz8jEuXLjlMc++99xozZ840DMMwRo8ebbi4uBgJCQnm+K+//tooUaKEkZiYWMhr41w//PDDdT8fkyZNMiQZ27ZtMySZ+9W1sv7flpycXMAdF56rv2caNmxo9OvXzzCu+fz07NnTaNmypcN0r7zyilG9enXzfc2aNY2xY8ea70eOHGncf//9hbQWBed6OaJ79+5GtWrV8vT5Cg8PN3r16lUoPd/OOPIPp/rtt9/Us2dP3XPPPfLz8zOvb7/2usGaNWua/12mTBnpyo10rKZZs2aKi4szX++//75iY2M1duxY8yhCyZIlNWDAACUmJurChQvmtOHh4Q7zCg8Pt9yR/4CAALVr104LFizQvHnz1K5dOwUEBDjU5HWfstqvuwcPHtT27dvVo0cPSZKrq6u6d++uuXPnOtRdvZ/4+/urSpUqDvuJu7u7w+fNCpyx33h4eOjJJ580t2dcXJx++ukny5xiejOu3k98fHzk6+trye9l5Cyv3zk3mscDDzzgMOza97ezxx57TEePHtXq1asVERGhjRs3qm7dupo/f75iY2N17tw5lS5d2uH/7/Hx8Q5Ha8uWLau7777bfB8eHq7MzEwdPHiwiNaqcGSdQRIfHy8XFxc1adKkqFsqEhMmTNCCBQu0f/9+h+EHDhxQ48aNHYY1btxY//vf/5SRkSFdOfq/ePFi6cr2/Pjjjy171D+LYRiy2Wx5+nzFxcWpefPmRd1yseda1A3AWjp06KDQ0FDNmjVLISEhyszMVFhYmNLS0hzq3NzczP/Oul7wdr8RWU58fHxUsWJFh2GZmZkaM2ZMjtcheXp6Xnd+VrwrbL9+/cxrr//9739nG5/XfcrHx6fQei4Mc+bM0eXLl3XXXXeZwwzDkJub2w1vGHT1fuLl5cV+k8t+8/TTT6t27do6cuSI5s6dq+bNm6tcuXKFuh6FoUSJEtlO3c7ppphXfy/ryn5kxe/l3OR1O1nVjb5z8rp9rv2+yc9lA7cDT09PtWzZUi1bttQbb7yhp59+WqNHj9agQYNUpkwZbdy4Mds0194r4WpZ2+t2/56uWLGibDab9u/fn+MTNH7++WeVKlVK3t7eRdJfcfHwww8rIiJCr732msOPzVkh92rXfnZ69uypESNGaNeuXbp48aISEhLMH+us6sCBA6pQoYIyMzNv+Pm69nIT5IzwD6c5efKkDhw4oJkzZ+qhhx6SJG3evLmo2yp26tatq4MHD2b7UeBaP/zwg5566imH91ff0MwqWrdubQaya6/V/7vuU5cvX9ZHH32k9957T61atXIY99hjj2nx4sUKCwuTruwXWXe5T05O1i+//KKqVasWSd+FyRn7TY0aNVS/fn3NmjVLS5Ys0dSpUwup+8J15513KjEx0Xx/5swZxcfHF2lPxdHfeTvl5Tvn3nvv1dmzZ3X+/Hnzx9a4uDiH2ipVqmj79u2KjIw0h+3cubOQ1qJoVK9eXatWrVLdunWVlJQkV1dXlS9fPtf6w4cP6+jRowoJCZEkbd26VSVKlFDlypULsWvnK126tFq2bKkPPvhAL730kkMQS0pK0uLFi/XUU0+pRo0ayszM1KZNm9SiRYts83F3d5euPJ3Gqv75z3+qdu3aDn/z6tWrZ/v/1JYtW1S5cmW5uLhIku6++249/PDDWrx4sS5evKgWLVooKCio0PsvLN9++6327Nmjl156SXffffcNP181a9bUhg0b1Ldv30Lv9XZC+IfTlCpVSqVLl9aHH36oMmXK6PDhwxoxYkRRt1XsvPHGG2rfvr1CQ0PVtWtXlShRQrt379aePXv01ltvmXWffvqp6tevrwcffFCLFy/W9u3bNWfOnCLtvSC4uLiYp6ln/Q8uy991n1qzZo2Sk5PVv39/2e12h3GPP/645syZo8mTJ0uSxo4dq9KlSysoKEijRo1SQEDA3+K55c7ab55++mm98MIL8vb2VufOnQul98L2yCOPaP78+erQoYNKlSql119/Pds2w997O+XlO2fDhg3y9vbWa6+9psGDB2v79u0OTwOQpMGDB2vAgAGqX7++GjVqpGXLlmn37t265557CnmNnO/kyZPq2rWr+vXrp5o1a8rX11c7d+7UxIkT9eijj6pFixYKDw9Xp06dNGHCBFWpUkVHjx7VV199pU6dOpmXpnl6eqp379569913debMGUVFRalbt24KDg4u6lW8ZdOmTVOjRo0UERGht956SxUqVNC+ffv0yiuv6K677tLbb78tf39/9e7dW/369dP777+vWrVq6Y8//tDx48fVrVs3lStXTjabTWvWrFHbtm3l5eWlkiVLFvWqOVWNGjXUq1cvhx+chw4dqvvvv19vvvmmunfvrq1bt2ratGn64IMPHKbt1auXoqOjlZaWZv47wApSU1OVlJSkjIwMHTt2TDExMRo/frzat2+vp556SiVKlLjh52v06NFq3ry57r33XvXo0UOXL1/W119/reHDhxf16hUrXPOPW5aZmSlXV1eVKFFCS5cuVWxsrMLCwvTSSy/pnXfeKer2ip2IiAitWbNG69at0/3336+GDRtq0qRJ2U43HjNmjJYuXaqaNWtqwYIFWrx4sapXr15kfRckPz8/+fn5ZRv+d92n5syZoxYtWmT7R7iuHIWLi4vTrl27pCtHEF588UXVq1dPiYmJWr16tXnkxOqcsd888cQTcnV1Vc+ePW942c3tJOt7WVceEfXwww+rffv2atu2rTp16qR77723qFssFthO/19evnMOHTqkRYsW6auvvlKNGjX08ccfZ3sMYq9evTRy5EgNGzZMdevWVXx8vPr06WOJz1bJkiXVoEEDTZ48WQ8//LDCwsL0+uuva8CAAZo2bZpsNpu++uorPfzww+rXr58qV66sHj166NChQw5HZytWrKguXbqobdu2atWqlcLCwrIFvNtVpUqVtHPnTt17773q3r277r33Xj3zzDNq1qyZtm7dKn9/f0nS9OnT9fjjj2vQoEGqWrWqBgwYoPPnz0uS7rrrLo0ZM0YjRoxQUFBQtkeyWsWbb77pcFp/3bp19cknn2jp0qUKCwvTG2+8obFjx2a7D03Xrl118uRJXbhwwVI/9MfExKhMmTIqX768Wrdure+++07vv/++Pv/8c7m4uOTp89W0aVN9+umnWr16tWrXrq1HHnlE27ZtK+pVK3ZshtUuxkKha926tSpWrKhp06YVdSvA38bGjRvVrFkzJScnX/d6UlxfQkKCypcvrx07dqhu3bpF3Y7T8L2cN2yngteyZUsFBwdr4cKFRd1KkYuOjtaqVauyXS4BAIWF0/5x05KTk7VlyxZt3LhRzz77bFG3AwB5lp6ersTERI0YMUINGza0TPDnezlv2E4F48KFC5oxY4YiIiLk4uKijz/+WOvXr9e6deuKujUAAOEft6Jfv37asWOHhg4dqkcffbSo2wGAPPv+++/VrFkzVa5cWZ999llRt+M0fC/nDdupYGSdmvvWW28pNTVVVapU0fLly3O8sRsAoPBx2j8AAAAAABbHDf8AAAAAALA4wj8AAAAAABZH+AcAAAAAwOII/wAAAAAAWBzhHwAAAAAAiyP8AwCA25bNZtOqVauKug0AAIo9wj8AAMi3Pn36yGaz6dlnn802btCgQbLZbOrTp4/TlhcdHa3atWs7bX4AAPzdEP4BAMBNCQ0N1dKlS3Xx4kVz2KVLl/Txxx+rbNmyRdobAABwRPgHAAA3pW7duipbtqxWrFhhDluxYoVCQ0NVp04dc1hqaqqioqIUGBgoT09PPfjgg9qxY4c5fuPGjbLZbNqwYYPq168vb29vNWrUSAcPHpQkzZ8/X2PGjNFPP/0km80mm82m+fPnm9P/9ddf6ty5s7y9vVWpUiWtXr260LYBAAC3C8I/AAC4aX379tW8efPM93PnzlW/fv0caoYPH67ly5drwYIF2rVrlypWrKiIiAidOnXKoW7UqFF67733tHPnTrm6uprz6d69u4YOHar77rtPiYmJSkxMVPfu3c3pxowZo27dumn37t1q27atevXqlW3eAAD83RH+AQDATYuMjNTmzZt16NAh/fHHH/r+++/15JNPmuPPnz+v6dOn65133lGbNm1UvXp1zZo1S15eXpozZ47DvN5++201adJE1atX14gRI7RlyxZdunRJXl5eKlmypFxdXRUcHKzg4GB5eXmZ0/Xp00dPPPGEKlasqHHjxun8+fPavn17oW4HAACKO9eibgAAANy+AgIC1K5dOy1YsECGYahdu3YKCAgwx//2229KT09X48aNzWFubm564IEHdODAAYd51axZ0/zvMmXKSJKOHz9+w/sHXD2dj4+PfH19dfz4caesHwAAVkH4BwAAt6Rfv3564YUXJEn//ve/HcYZhiFdeSTftcOvHebm5mb+d9a4zMzMGy7/6umyps3LdAAA/J1w2j8AALglrVu3VlpamtLS0hQREeEwrmLFinJ3d9fmzZvNYenp6dq5c6eqVauW52W4u7srIyPDqX0DAPB3wpF/AABwS1xcXMxT+F1cXBzG+fj46LnnntMrr7wif39/lS1bVhMnTtSFCxfUv3//PC+jfPnyio+PV1xcnO6++275+vrKw8PD6esCAIBVEf4BAMAt8/Pzy3XcP//5T2VmZioyMlJnz55V/fr19c0336hUqVJ5nv9jjz2mFStWqFmzZjp9+rTmzZunPn36OKl7AACsz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<p>This should be what we expect: that the winter months (Dec/Jan) see the most traffic. This is probably when the outdoor climbers are hitting the boards because they're stuck inside. The warmer months see the least number of first ascents since the strong climbers are probably outdoors.</p>
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<h2 id="Day-of-Week-Analysis">Day of Week Analysis<a class="anchor-link" href="#Day-of-Week-Analysis">¶</a></h2><p>We can plot the number of first ascents by day of week. Removing the 2026 data shouldn't make a difference here, so we opt to keep it.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Day of Week analysis</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Let us add a column in our DataFrame for the day of the week.</span>
<span class="c1"># Note that df.dt.day_of_week will have Monday be 0 and Sunday be 6. </span>
<span class="n">df</span><span class="p">[</span><span class="s1">'fa_day_of_week'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fa_at'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">day_of_week</span>
<span class="c1"># Make a new DataFrame with month and first ascents</span>
<span class="n">df_days</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'fa_day_of_week'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">first_ascents</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># We also add a column for the month name. </span>
<span class="n">day_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Mon'</span><span class="p">,</span> <span class="s1">'Tues'</span><span class="p">,</span> <span class="s1">'Wed'</span><span class="p">,</span> <span class="s1">'Thurs'</span><span class="p">,</span> <span class="s1">'Fri'</span><span class="p">,</span> <span class="s1">'Sat'</span><span class="p">,</span> <span class="s1">'Sun'</span><span class="p">]</span>
<span class="n">df_days</span><span class="p">[</span><span class="s1">'fa_day_name'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_days</span><span class="p">[</span><span class="s1">'fa_day_of_week'</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="n">day_names</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="c1"># Plot the data</span>
<span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_days</span><span class="p">[</span><span class="s1">'fa_day_name'</span><span class="p">],</span> <span class="n">df_days</span><span class="p">[</span><span class="s1">'first_ascents'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'coral'</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="s1">'First Ascents by Day of Week (All Years Combined)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Day'</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">'Total First Ascents'</span><span class="p">)</span>
<span class="c1"># Save the file</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/first_ascents_by_day_of_week.png'</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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<p>Interesting, Tuesday and Wednesday have the most traffic, while Monday is the least popular.</p>
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<h2 id="Time-of-Day-Analysis">Time of Day Analysis<a class="anchor-link" href="#Time-of-Day-Analysis">¶</a></h2><p>We can even do a time of day analysis. Again, we will keep the 2026 data since it shouldn't affect much. It is not entirely clear that makes sense to look at this, as we don't know if the time of first ascent is recorded in local time of the climber or local time of the server. These boards are all over the world, so this may add quite a bit of variance.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Time of Day analysis</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Let us add a column in our DataFrame for the day of the week.</span>
<span class="c1"># Note that df.dt.day_of_week will have Monday be 0 and Sunday be 6. </span>
<span class="n">df</span><span class="p">[</span><span class="s1">'fa_hour'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'fa_at'</span><span class="p">]</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">hour</span>
<span class="c1"># Make a new DataFrame with month and first ascents</span>
<span class="n">df_hour</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'fa_hour'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">first_ascents</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># We also add a column for the month name. </span>
<span class="c1">#df_time['fa_day_name'] = df_time['fa_day_of_week'].apply(lambda x: day_names[x])</span>
<span class="c1"># Plot the data</span>
<span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_hour</span><span class="p">[</span><span class="s1">'fa_hour'</span><span class="p">],</span> <span class="n">df_hour</span><span class="p">[</span><span class="s1">'first_ascents'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'coral'</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="s1">'First Ascents by Hour (All Years Combined)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Hour'</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">'Total First Ascents'</span><span class="p">)</span>
<span class="c1"># Save the file</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/first_ascents_by_hour.png'</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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<h1 id="Climbing-Statistics:-Grades,-Angles,-Quality,-and-Matching">Climbing Statistics: Grades, Angles, Quality, and Matching<a class="anchor-link" href="#Climbing-Statistics:-Grades,-Angles,-Quality,-and-Matching">¶</a></h1><p>We will visualize the climbing grade distribution. Recall that we have the following table of grades (with some other unlisted grades).</p>
<table>
<thead>
<tr>
<th>difficulty</th>
<th>boulder_name</th>
<th>route_name</th>
</tr>
</thead>
<tbody>
<tr>
<td>10</td>
<td>4a/V0</td>
<td>5b/5.9</td>
</tr>
<tr>
<td>11</td>
<td>4b/V0</td>
<td>5c/5.10a</td>
</tr>
<tr>
<td>12</td>
<td>4c/V0</td>
<td>6a/5.10b</td>
</tr>
<tr>
<td>13</td>
<td>5a/V1</td>
<td>6a+/5.10c</td>
</tr>
<tr>
<td>14</td>
<td>5b/V1</td>
<td>6b/5.10d</td>
</tr>
<tr>
<td>15</td>
<td>5c/V2</td>
<td>6b+/5.11a</td>
</tr>
<tr>
<td>16</td>
<td>6a/V3</td>
<td>6c/5.11b</td>
</tr>
<tr>
<td>17</td>
<td>6a+/V3</td>
<td>6c+/5.11c</td>
</tr>
<tr>
<td>18</td>
<td>6b/V4</td>
<td>7a/5.11d</td>
</tr>
<tr>
<td>19</td>
<td>6b+/V4</td>
<td>7a+/5.12a</td>
</tr>
<tr>
<td>20</td>
<td>6c/V5</td>
<td>7b/5.12b</td>
</tr>
<tr>
<td>21</td>
<td>6c+/V5</td>
<td>7b+/5.12c</td>
</tr>
<tr>
<td>22</td>
<td>7a/V6</td>
<td>7c/5.12d</td>
</tr>
<tr>
<td>23</td>
<td>7a+/V7</td>
<td>7c+/5.13a</td>
</tr>
<tr>
<td>24</td>
<td>7b/V8</td>
<td>8a/5.13b</td>
</tr>
<tr>
<td>25</td>
<td>7b+/V8</td>
<td>8a+/5.13c</td>
</tr>
<tr>
<td>26</td>
<td>7c/V9</td>
<td>8b/5.13d</td>
</tr>
<tr>
<td>27</td>
<td>7c+/V10</td>
<td>8b+/5.14a</td>
</tr>
<tr>
<td>28</td>
<td>8a/V11</td>
<td>8c/5.14b</td>
</tr>
<tr>
<td>29</td>
<td>8a+/V12</td>
<td>8c+/5.14c</td>
</tr>
<tr>
<td>30</td>
<td>8b/V13</td>
<td>9a/5.14d</td>
</tr>
<tr>
<td>31</td>
<td>8b+/V14</td>
<td>9a+/5.15a</td>
</tr>
<tr>
<td>32</td>
<td>8c/V15</td>
<td>9b/5.15b</td>
</tr>
<tr>
<td>33</td>
<td>8c+/V16</td>
<td>9b+/5.15c</td>
</tr>
</tbody>
</table>
<p>We will use the actual difficulty in our work, and then unpack translations into boulder_name as we see fit.</p>
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<h2 id="Grade-distribution">Grade distribution<a class="anchor-link" href="#Grade-distribution">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Difficulty distribution by layout (with total)</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">grade_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'boulder_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">grade_order</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'boulder_grade'</span><span class="p">)[</span><span class="s1">'display_difficulty'</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">sort_values</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="n">grade_counts</span> <span class="o">=</span> <span class="n">grade_counts</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">grade_order</span><span class="p">)</span>
<span class="c1"># Prepare data in long format</span>
<span class="n">df_long</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'boulder_grade'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="c1"># Calculate total for background</span>
<span class="n">df_total</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'boulder_grade'</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="n">df_total</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'All Layouts'</span>
<span class="c1"># Reindex to correct grade order</span>
<span class="n">df_long</span><span class="p">[</span><span class="s1">'grade_order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_long</span><span class="p">[</span><span class="s1">'boulder_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
<span class="p">{</span><span class="n">g</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">grade_order</span><span class="p">)}</span>
<span class="p">)</span>
<span class="n">df_long</span> <span class="o">=</span> <span class="n">df_long</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'grade_order'</span><span class="p">)</span>
<span class="n">df_total</span><span class="p">[</span><span class="s1">'grade_order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_total</span><span class="p">[</span><span class="s1">'boulder_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
<span class="p">{</span><span class="n">g</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">grade_order</span><span class="p">)}</span>
<span class="p">)</span>
<span class="n">df_total</span> <span class="o">=</span> <span class="n">df_total</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'grade_order'</span><span class="p">)</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="c1"># Plot "All Layouts" behind (light gray)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_total</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'boulder_grade'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">grade_order</span>
<span class="p">)</span>
<span class="c1"># Plot individual layouts (grouped) in front</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_long</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'boulder_grade'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="s1">'layout_name'</span><span class="p">,</span>
<span class="n">palette</span><span class="o">=</span><span class="p">[</span><span class="s1">'steelblue'</span><span class="p">,</span> <span class="s1">'coral'</span><span class="p">,</span> <span class="s1">'seagreen'</span><span class="p">],</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">grade_order</span>
<span class="p">)</span>
<span class="c1"># Create custom legend with "All Layouts" included</span>
<span class="n">handles</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
<span class="n">all_layouts_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'All Layouts'</span><span class="p">)</span>
<span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">all_layouts_patch</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">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Layout'</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">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Grade'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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">'Number of Climbs'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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="s1">'Difficulty Distribution by Board Layout'</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">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="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">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/difficulty_distribution_by_layout_with_total.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>As a climber in North America, I tend to just use the V-grade and not look at the French grade. So let us group the V-grades together and show the distribution like that. We'll usually just stick the boulder_grade (e.g., 5c/V2) instead of grouping the V-grades though.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">V-Grade distribution by layout (with total)</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Let's add a v_grade column and v_grade_counts</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'v_grade'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'boulder_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'/'</span><span class="p">)</span><span class="o">.</span><span class="n">str</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">v_grade_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'v_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">v_grade_order</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'v_grade'</span><span class="p">)[</span><span class="s1">'display_difficulty'</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">sort_values</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="n">v_grade_counts</span> <span class="o">=</span> <span class="n">grade_counts</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">v_grade_order</span><span class="p">)</span>
<span class="c1"># Prepare data in long format</span>
<span class="n">df_long</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'v_grade'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="c1"># Calculate total for background</span>
<span class="n">df_total</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'v_grade'</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="n">df_total</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'All Layouts'</span>
<span class="c1"># Reindex to correct grade order</span>
<span class="n">df_long</span><span class="p">[</span><span class="s1">'v_grade_order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_long</span><span class="p">[</span><span class="s1">'v_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
<span class="p">{</span><span class="n">g</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">v_grade_order</span><span class="p">)}</span>
<span class="p">)</span>
<span class="n">df_long</span> <span class="o">=</span> <span class="n">df_long</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'v_grade_order'</span><span class="p">)</span>
<span class="n">df_total</span><span class="p">[</span><span class="s1">'v_grade_order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_total</span><span class="p">[</span><span class="s1">'v_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
<span class="p">{</span><span class="n">g</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">v_grade_order</span><span class="p">)}</span>
<span class="p">)</span>
<span class="n">df_total</span> <span class="o">=</span> <span class="n">df_total</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'v_grade_order'</span><span class="p">)</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="c1"># Plot "All Layouts" behind (light gray)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_total</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'v_grade'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">v_grade_order</span>
<span class="p">)</span>
<span class="c1"># Plot individual layouts (grouped) in front</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_long</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'v_grade'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="s1">'layout_name'</span><span class="p">,</span>
<span class="n">palette</span><span class="o">=</span><span class="n">palette</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">v_grade_order</span>
<span class="p">)</span>
<span class="c1"># Create legend with "All Layouts" included</span>
<span class="n">handles</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
<span class="n">all_layouts_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'All Layouts'</span><span class="p">)</span>
<span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">all_layouts_patch</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">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Layout'</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">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Grade'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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">'Number of Climbs'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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="s1">'Difficulty Distribution by Board Layout'</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">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="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">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/v_grade_distribution_by_layout_with_total.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
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aDUAAAAAACArcCWcIttpQZbxI7M1sv+S57Zf7Mj22ytXLmy2EPYov3vf/+LsB9nTr7Zke2mt2rVqmIPAerF8SJb8s2O4242li9fXuwhbNGWLFkSKaUoKSkp9lA2mlJjK1ZVVRWxxgHDL7tsyDUbNScQNfk6oWhYy5Yti1j9Cy/k2+Dee++9WLFiRZSUlMg2A6+99lr85je/iRUrVhR7KFukadOmxZVXXhmzZ88u9lC2SFOmTImOHTvG008/vUW82djcLFy4MN55551455135JuBmvcV3l9kb8GCBfHGG2/E22+/HRERjRo1kncDqjk/c57W8D744IOYN29evP/++xERUVJSYt9tQPPmzYupU6fGyy+/HKtWrZJvA3v11Vfjrrvuilh93HWMaFjPPfdcnHPOOTF//vxiD2WLNGXKlPjGN74RDz30ULGH0iCUGlupKVOmxCmnnBJ9+/aNQYMGxeOPP+6NXQN6880346mnnopwkpaJ119/PS6++OIYPHhwXHHFFTFt2jRv5BrQ1KlT46tf/WocdthhMXDgwHj++eejUSO/LhrKm2++Gbvuumv07ds3VqxY4WS4gb344ovRqVOnmDNnTpSXl0f4UK1Bvfzyy9GrV69YvHhxzJkzJ0K+DeqFF16IQw45JN577734zW9+E1VVVfJtQJMnT44jjzwyPv/5z8eee+4Zd999d7GHtEV55ZVXYsiQITFw4MD48pe/HOPHj/f+IiMvvfRSHH744dG3b9844ogj4ogjjogZM2bIu4FMmzYtLrroopgzZ47ztAY2ZcqUGDRoUHzuc5+Lo446Kn74wx8WPnhn47388stx1FFHxbHHHhuDBg2Ks846S74NaMmSJXHAAQfEaaedFj/5yU8iFBsN6sUXX4wePXpE8+bNo1WrVrWecz688aZMmRK9e/eOXXbZJbp27VrrubzOwvcp1Vbo9ddfj969e0fr1q2ja9eu0aJFi+jfv39ceumlMXPmzGIPL/dee+21OOCAA+KEE06Ixx57LEKx0aAmT54cvXr1itmzZ8fSpUtjwoQJcd5558W8efOcrDWAmnzbtm0bhx56aOywww4xYsSIWLp0aYSTiQbx/vvvxyc+8YmYPn16HHLIIYVig4330ksvRa9eveKCCy6Ic889d63nveHYOJWVlXHiiSfGV77ylRgzZkx06dIlYvU+zcZ78cUXo2fPnnHOOefE1VdfHX/4wx+iqqrKOUQDee211+Kwww6Lww8/PMaMGRMXXHBBfOc734mFCxdG+P220aZOnRoHH3xwtGrVKrp16xYtW7aMI488Mr73ve/FjBkzij28LcqMGTPi85//fBx55JHx61//OioqKmLZsmVx4IEHxt/+9rdiDy/33njjjTjkkEPitttui8suuyzmzp3rQ8sGMnXq1Dj00EPjk5/8ZFx22WXRqVOn+P3vfx9PP/10sYe2RXjllVeiT58+cdhhh8Wdd94ZZ5xxRkyYMCHeeuutwjr2443TvHnz6NGjR3zzm9+MUaNGxXXXXRdhplyDeOmll6J3795x/vnnx+jRoyNWz/qsuYJESUlJbj943xy8//77ccEFF8Tpp58eo0ePjg4dOsS0adNi4sSJUVVVFaWlpcUeYv0ktjoXX3xxOvLII2st++Uvf5nKy8vT+eefn+bNm1e0seXd7NmzU//+/VO/fv3Sl770pdS5c+f05z//ufD8qlWrijq+vHvnnXfSvvvumy666KLCsvvvvz917Ngxvfzyy0Ud25bgzTffTHvvvXe6+OKLC8tuvfXWdMopp6QVK1akhQsXFnV8W4KVK1emyZMnpwMPPDD94x//SJ06dUq9e/cuPD9t2rSiji/PXnnllbTNNtuk73znOymtPt7+/Oc/TyNGjEiXXHJJevHFF4s9xNx7+umn00EHHZRWrFiRVq5cmYYMGZL69euXdt999/TjH/84vfXWW8UeYm4999xzqbS0NF1yySUppZTef//9tOuuu6YRI0YUe2hbhJUrV6azzjornXTSSYVlL7/8cho4cGCaMWNGmjZtWqquri7qGPNsxYoV6dRTT03f/OY3C8uqqqpS7969U+PGjdPIkSPTokWLijrGLcmjjz6aPvOZz6TKysrCsv/973/pi1/8YmrTpk2aNGlSSinZp+th8eLFadCgQenEE09MV1xxRerVq1f6xje+kebMmZPS6mMJ9TNv3rzUt2/fNHTo0MKyqqqq1KlTpzRy5Miijm1LUFlZmbp3717rvGHOnDmpT58+6e9//3t65pln0vLly1OyH2+0o446Kv34xz9O1113XWrWrFn6yU9+klJK6a9//WtasGBBsYeXS++9914qKSlJp556akqr38cNHz48HXHEEal3797pW9/6VqqqqkrJ/ltvCxcuTF27dk1PPfVUqq6uTsccc0zq0qVLat26ddprr73SY489lstsfTV0KzJnzpxIKUVlZWU0adIkYvUUo1WrVsXpp58ed9xxR4wZMybuueeeCN9Y22Dz58+PGTNmRGlpaYwcOTKGDx8e3bp1ixEjRsQjjzxS7OHl3sKFC2PatGnRvn37GDJkSOFbJieccEKUlJTESy+9VOwh5tqCBQviP//5T3Tr1i3OOeecwvLp06fHhAkTokePHtGjRw/Hh3qaO3duYUZG586do3379tGuXbv45S9/GbNmzYrDDz+8MI3Zt9433MKFC2PixImxYsWK6NSpU8yfPz8OPfTQ+NWvfhV///vf429/+1t07949HnzwwQj77waruczUggULYunSpVFdXR39+vWLmTNnxoABA+K0006Lq666Kn70ox/F4sWLiz3c3Jk5c2bccccdcd5558WoUaMipRRlZWVxzDHHxMSJE2PBggXFHmKuzZ07N6qqqmL69Omx4447Fpb//ve/j/Hjx8eAAQNi3333jWHDhsW7775b1LHm0bx582Lp0qXx+uuvx1577RWx+r5922yzTRxwwAHRv3//+MEPfhAPPPBAhONvg5g9e3ZMmzatsD9XV1fHdtttF7/73e/iwAMPjJNOOinf37osoubNm0f37t3jmGOOiYqKihg8eHBMmTIlRo4cuc4ZG/bnups5c2a0bds2jj/++IiIWLFiRWyzzTZx3HHHFc4dZFt/K1euLMymrXHLLbfEM888E1/72tfi9NNPj06dOsUHH3xgVkE9VVdXR0TEPvvsEy1atIjzzjsvLrvssqioqIjOnTvHyJEjY9WqVbKth6ZNm0avXr3ihRdeiBdffDEOP/zwePbZZ+Oggw6Kz372s/Hkk09Gz549XeFgIyxYsCCqqqqiXbt2cdFFF0V1dXXceuut8Ze//CUOPPDAGDx4cEyePDkib8ffYrcqbBoLFy5MO+64Y3rggQfS3XffnZo1a5befPPNlFZ/u6pmBsEPfvCD1LRpU98W3kALFixIO+64Y3rwwQcL3+RJKaVnnnkmnXLKKWvN2PDNqQ1Tk+/555+fHnzwwcLy6urqtGLFitSlS5f085//fK3X5bFpLoaafH/zm9/U+iblDTfckBo3bpxuueWW9Ic//CFddtllqbS0NP3rX/8q6njzpub4e8cdd6S0+ltpBx10UOHxf/7zn9SiRYtUUlKSJk6cmJJ9d4PU7L9//etf029/+9tUUlKSOnTokAYNGpT++9//FmYZDRs2LG2//fbpjTfeKPaQc6Vm/73rrrvSG2+8kbbddtt07733plNOOSW9++67hfV+//vfp9LS0lq/61i/mnzHjBlTWFZzTvbcc8+l8vLydPvttxdxhPlWk+/vf//7dNNNN6XGjRunyy+/PH3jG99ITZo0Sffdd196++2307hx49J2220n6w205vFh+PDhqV+/foXziBkzZqQddtghPfXUU+nCCy9Me+65p2+wNpC5c+emT37yk+mCCy4oLKt5b/Hf//43de7cOY0ePbqII8y/muPwqlWr0ujRo1OvXr3SkCFDCrNjli1b5v3cBlq8eHG6//77C49rMv7ud7+bTjzxxFrLXN2gfubPn1/479tuuy21bNkyjR07Nk2fPj29+uqrqXPnzmnIkCFFHeOW4LbbbivM/Fy+fHnaf//9U3l5efr2t79dWMd7uQ23ZMmSdNhhh6WSkpJ03HHH1ZqN+MQTT6RPfvKT6YYbbijqGPPu4IMPTn379k3HHXdcevjhh2s9179//3T00UcXbWz1peLaSjRr1iwOOeSQuOOOO6Jz585x4IEHxrBhw+K9996LsrKyQut80kknRevWrePVV18t9pBzpXnz5nHIIYfE7bffHmVlZYXlPXr0iGHDhkXXrl1jxIgR8eijj0ZExIgRI+JPf/pTEUecL82bN4++ffsW7kEQq7/JU1paGmVlZdG6deta6990000xdepULX4dNW/ePA499NC47777Ct+QqqqqisrKynjkkUfirLPOiuOOOy5GjhwZu+22W/zjH/8o9pBzpVmzZtGnT5944IEHYv78+bHNNtvEwQcfXMj6iiuuiPLy8th1113joosu8g2UDdS8efP43Oc+FzfccEOceOKJceONN0aTJk1i5MiRsfvuu0dZWVm0aNEihgwZEuXl5X6/baBmzZrFoYceGvfff3984hOfiBNOOCG+/vWvx1//+tfCPpxSiuOPPz4OOuggx4cNtN1228Vhhx0Wf//73wv3digpKYlVq1ZFt27d4vTTT48777wzKisriz3UXKo5/73zzjvj2GOPjREjRsTcuXPj1VdfjQsuuCAGDx4cHTp0iKOOOioOPvjgePTRR/P17bQiW/P4sPfee8eyZcvigAMOiHPOOSc+/elPx4knnhg9e/aMz3/+87F8+XIzETdSzb7ZrFmzOOmkk2LChAlx6623RkQUZmXsvPPO0aJFi1rX0Kdu1vx/v6SkJKqrq6OkpCTOO++8GDRoULz66qtx8cUXx7vvvhtDhw6Nww8/fK3XsW4rV66M5s2bxwknnBCxOrOaeyFWV1fH//73v4jVuV977bVxwQUXFHW8eVOzD7Zs2bKwbP/9949x48bFF7/4xdhjjz1i7733jk996lNRVVVVxJHm04f/H99hhx1i1qxZERFx1llnxbvvvhvf+ta34t57740rr7wyYvU9NqibNX+3PfDAA3HeeefFV77ylWjTpk3huR49ekRExHvvvVfUseZRSqmQ4/Dhw2P+/Pkxbty42H777SMiCvcs+exnP5vL32f+T9tKlJaWRr9+/WLChAnRrFmzGDZsWFRWVsb5558fM2bMiPLy8ojVB+imTZu6gdQGKi0tjSOOOCL+8Y9/xNy5cyPWmJ544IEHxne+853o2rVrXHjhhfGFL3whrr/++th1112LPOr8KC0tjUMOOSSeeOKJwmVQPqzmplGXXnppDBs2LJcH5GKpOT6suf82btw4rrzyyujTp08hy4ULF0b79u2jU6dORR5xvpSWlkbfvn3jH//4R8yfPz8iIjp06BBPP/10nHzyyfGXv/wlHn300XjooYfihRdeiIEDBxZ7yLlSWloa/fv3j7/97W8xbdq0OOecc2Ls2LGxzz77RKxxotyoUaNo165dtG3btsgjzpea48MTTzwR06dPj4suuih69uwZ8+fPj6lTp0as/hAiImLbbbeNXXbZpcgjzpfy8vI4/PDDax1/V61aVXgzfOihh8Zzzz1XuNGy87MNU7P//vOf/4xly5bF1VdfHddff31st912hX11xYoVkVKK0tLS6NixY2F/Zv1KS0vj8MMPj4kTJ0a/fv3iJz/5SfTv3z+WL18eP/rRjwofuNd8oLnmF3+omzfffDOeeuqpiDVuktq4ceMYOnRo7LLLLnHXXXfF9ddfX1i/vLw82rdvH02bNo3wgft6fTjfNfMqKysrHHNrio3XXnstevXqFffee2/hBsGOGeu2ZralpaVrlUY1WrVqVfhw7bvf/W5UVFTUuoQS6/Zx+25KKbp27RoHHXRQ4XGsPk/r2LFjrWWs28fle9hhh0WbNm2iX79+8fDDD8f48ePjqquuim9961tx++23x7x58+S7Hh+V7/bbbx9XX311HHPMMYXnVq1aFStWrIg999zT/ltHH863xmGHHRZHHXVUNGrUKC6//PKIiMKtCebPnx8tW7YsnBfnRrGnipC9Nadvdu3atXDznZ/97Gfp4IMPTt26dUtPPPFEevrpp9Mll1yS2rdvn95+++0ijjhf1sx3//33T4MHD17ncxMmTEg777xzatmypRvWboAP51szPTmtnta5cuXK1LVr13T77ben0aNHpyZNmqR///vfRRpt/tR1/02rp4d37tw5vfPOO5t0jHn24Xy/+MUvppRSGjduXGratGnae++9a+2vzz//vMv/bYCPynddLrroorT//vvXmsrMx1sz327duqWTTz45pZTS448/nnr37p2aNGmS/u///i/99re/LZw/2H/rrq7H3759+6Z+/fqlFStWbPIx5tmH999BgwYVHg8dOjTts88+6Y033khvvfVWuvLKK1P79u3Ta6+9VqTR5s+a+X7mM59Jp5xySuHxh/fVoUOHpoMPPjgtXrx4k44x71599dXUqlWr1K5du/Too48Wltfk++6776bTTz897bfffql///7phhtuSF/72tdSs2bN0tSpU4s48nz4qHw/fP5bcxmZqqqq1L1799SyZcv08ssvb/Lx5klds00ppWuuuSZ97WtfSxUVFalJkybp2Wef3cSjzZ8Nybdm+WWXXZZ23nln52l18FH51pg7d27afffdU4cOHdJzzz1XWD5nzpw0d+7cTTza/Knv/rvbbrsVLqHPR/uofGsumbhgwYJ0zTXXpNatW6fOnTunYcOGpS9/+cupRYsWafLkyUUcef0oNbZQy5Ytq/W45uT3Rz/6Udpvv/3SW2+9lVJK6c9//nM68cQT0zbbbJM+/elPp44dO/pAuA4+Lt9u3bqlV199NaXVB+BVq1allStXpuHDh6dtt93WSXAd1DXfmjcZ/fv3TzvttFNq2rRpmjRpUhFGnC91zbfG888/n77zne+kHXbYIT3//PObdKx59HH5du3atXAy9r3vfU/BWQ8buv/++9//TkOHDk077LBDeuGFFzbpWPNofecP06dPT2n1CfPw4cNThw4dUpcuXVKPHj0cH+pgQ88fUkrpqquuSj169HA/gjpYX76vvPJKSiml8ePHpyOOOCKVlJSkrl27pk9+8pP23zpY3++3mg/Sa5Y/+uij6ayzznL8rYfZs2en/v37p379+qUvfelLa92frybjefPmpXvvvTf169cv9erVKw0YMMC5RR2sL98Pf7i2fPnydN5556Vtt91WvuuxodlefvnlqaSkJDVt2lShUQcbmu+TTz6ZzjzzzNS6detaH8CzbuvLt+aD4XfeeUd5XA8buv8+8cQT6Wtf+1pq1aqV87Q6qOv++8EHH6RJkyal008/PR177LHp9NNPz2WhkZQaW6bp06eno48+Ov385z9PS5YsqfXcjBkzUqtWrdIll1xSa/nkyZPTW2+95RusdVCXfC+77LJay6dOnZp69OihMKqD+uR77LHHpu233z699NJLm3i0+bOh+f7nP/9Jo0aNSr169fImrg7qku/FF19ctPHl3Ybuv9OmTUuXX3556tatm/23DuqS73e/+921li9atCgtXLhwE482f+rz+y2llBYtWlT4MgofrS75XnrppYVls2fPTmPHjk3jx483A7EO6rP/jh8/Ph166KHOzzbQvHnz0rPPPpsGDBiQxo8fnyZOnJi+8pWv1PpwYtWqVWvdqHr58uWpqqqqSKPOj7rm+2GXX36593LrUZ9sf/nLX6aOHTumKVOmFGnU+bGh+S5dujTdeeed6YwzzpBvHdQ1XzNn66c+++8vfvGLdOqpp/picB3U99wh5fzG9kqNLdArr7ySjj766FRWVpY+97nPpQsvvDAtXrw4LV26NKWU0ve///3UqVOn3DZxxVbXfGu+DVjjw28AWbf65PvQQw+ZilhH9cn37bffNpW2juqarzcW9VOf/feNN95Q2NdRffbfj5oqztrqs//m+U3Gpub8N1v1Pf/93//+V6QR59OCBQvSjjvumB588ME0Z86cwvJnnnkmnXLKKR/5rUvqRr7ZqW+2c+fOTe+++25RxpwnG5pvzQfvy5YtcxyuA8eGbNV3/126dGl6//33izLmPNnY/TfP7+eUGluwl156KZ1xxhlpjz32SLvttls6//zz00svvZSeffbZtOuuu6YHHnggJW+Y662u+dYckPN8oCiGuuZL/cg3W46/2bL/Zsv+my35Zquu+fpAon429PyXDVNdXZ2OP/74dNxxx611ybl//etfhQ8nHnnkkZRSSueee2566KGHijTa/JFvduqT7R//+McijTZ/6pPvgw8+WKTR5o9jQ7bsv9namvdfpcYWbtmyZWnBggVpxIgRqXfv3qmsrCxdfvnlqXXr1ukzn/mM2QMbSb7Zkm+25Jst+WZLvtmSb7bkmy35Zku+2brllltSq1atCjf0XbMgmjhxYjrllFPSfvvtlwYMGJBKSkpcZ3wDyTc7ss2WfLMl32zJN1tba74lKaUUbBXmzp0bf/rTn+KOO+6ISZMmRXl5eUybNi3atGlT7KFtEeSbLflmS77Zkm+25Jst+WZLvtmSb7bk23BSSlFSUhIREd27d4+99tor7rvvvrWee+qpp2Lw4MGxdOnS+Pvf/x777bdfUcedF/LNjmyzJd9syTdb8s3W1p6vUmMrsOaOHBFRWVkZ//3vf6N169ax5557FnVsWwL5Zku+2ZJvtuSbLflmS77Zkm+25Jst+TaMqqqqaNy4ceFxdXV1lJWVxY9//OO4++6745577omOHTtGzVv2lFJccMEF8dOf/jQmTpwYXbp0KeLoN3/yzY5ssyXfbMk3W/LNlnz/f42KPQCyt+YbjoiItm3bxoEHHugNRwORb7bkmy35Zku+2ZJvtuSbLflmS77Zku/Ge/PNN2PQoEHxi1/8It5///2IiCgrK4uIiJNOOineeuutuPvuuyNW511SUhKvv/56TJgwIZ588skt6kOJLMg3O7LNlnyzJd9syTdb8q1NqQEAAACb0LJlyyIi4qyzzoqjjjoqLrrooliyZEksW7Ysdt1117jwwgtj7NixMXXq1MJrPv3pT8f48eNj//33L+LI80G+2ZFttuSbLflmS77Zkm9tSg0AAADYhDp16hQPPfRQPPfcc7HPPvvE7373u+jSpUtceuml8fLLL0e/fv1iyZIl8frrr0esvrxERETTpk2LPPJ8kG92ZJst+WZLvtmSb7bkW5t7agAAAECRVFVVxdKlS2PUqFHx9NNPx7/+9a+45JJL4pZbbolddtklnnzyyWjWrFmxh5lb8s2ObLMl32zJN1vyzZZ8lRoAAACwWZg7d2786U9/ijvuuCMmTZoU5eXlMW3atGjTpk2xh7ZFkG92ZJst+WZLvtmSb7a21nyVGgAAAFBEKaVaN2CvrKyM//73v9G6dWs3YG8A8s2ObLMl32zJN1vyzdbWnq9SAwAAAAAAyAU3CgcAAAAAAHJBqQEAAAAAAOSCUgMAAAAAAMgFpQYAAAAAAJALSg0AAAAAACAXlBoAAAAAAEAuKDUAAAAAAIBcUGoAAAAAAAC5oNQAAAAAAAByQakBAABs1v785z/HgAEDok2bNlFeXh7t2rWLY445Jh5++OFIKWX29/7oRz+KkpKSzLYPAABsOKUGAACw2brkkktiwIAB0aRJk7jpppviL3/5S9x0002x/fbbx8CBA2PcuHHFHiIAALAJlRV7AAAAAOvy8MMPx7XXXhtXXHFFVFRU1Hpu8ODBce6550ajRuv+ntbKlStj1apVUV5evolGCwAAbApmagAAAJul0aNHx0477RSXXnrpOp//7Gc/G927d4+IiD59+sTRRx8dv/rVr6Jjx47RuHHjeOGFF2LmzJkxZMiQ2HPPPWPbbbeNT33qU3HJJZdEVVVVrW0tXrw4vvrVr0bz5s2jTZs2ceGFF0Z1dfVaf+fChQvj7LPPjp122ikaN24c3bt3j8ceeyyjBAAAgA8zUwMAANjsVFdXx4QJE2LQoEFRVla3ty3PPvtsvP322/G9730vdthhh+jQoUPMmTMnWrVqFaNHj46WLVvG66+/HhUVFTFr1qy4/fbbC68dMmRIPProo/H9738/9thjj7j55pvjxRdfrLX95cuXxxFHHBGzZ8+OUaNGxS677BK//vWv4wtf+EI899xzse+++zZ4DgAAQG0lKcs76wEAANTD7Nmzo3379jFy5Mi49tprC8tTSrFy5crC40aNGkWjRo2iT58+8fTTT8cbb7wRu+6660dut7q6Ou6777447bTTYtGiRbHddtvF1KlTo3PnzvGLX/wihgwZUlhvr732irfffrtwM/Jf/vKXccYZZ8SLL74Y++yzT2GbPXr0iN133z3uu+++jNIAAABquPwUAACw2akpEkpKSmotv//++6O8vLzwZ9iwYYXn9ttvv7UKjZRSjBkzJvbZZ5/Ydttto7y8PE455ZSorq6O6dOnR0TExIkTI6UUxx9/fOF1ZWVlceyxx9ba1mOPPRb77rtv7L333lFdXV3407dv35g0aVImOQAAALW5/BQAALDZad26dTRu3DjeeeedWsvXLBAGDhxY67m2bduutZ0xY8bEiBEj4sILL4zDDjssWrZsGZMmTYpzzjknli1bFhERM2fOjPLy8mjZsmWt17Zr167W47lz58bzzz+/zpuPl5aWbsRPCwAA1JVSAwAA2OyUlZVF79694y9/+UusXLmyUBq0bNkyDjjggIiI2GabbWq95sOzOiIifve738XAgQNrXcLqlVdeqbXOTjvtFCtWrIgFCxbUKjZmz55da71WrVrFfvvtF7fddlsD/ZQAAMCGcvkpAABgszR8+PB477334pprrqn3NpYuXbpW+XH33XfXevzZz342SkpK4g9/+ENhWXV1dfzxj3+stV6/fv1i+vTpsfPOO8cBBxyw1h8AACB7ZmoAAACbpS984QsxcuTIuPzyy+OFF16Ik046KXbaaadYtGhR/POf/4xZs2ZF8+bNP3YbRxxxRFx//fVx0003xd577x133313/Oc//6m1zj777BPHHXdcnHvuubFs2bL4xCc+ETfffHOtG5JHRHz1q1+NW2+9Nfr06RMjRoyIvffeOxYuXBjPP/98LF++vNZsEAAAIBslqeYOfAAAAJuhhx9+OG6++eaYOHFiLFq0KFq1ahXdu3ePr3zlK/GlL30pGjVqFH369IlmzZrFn/70p1qvff/992Po0KGFWReDBg2KgQMHxjHHHBOTJk0qzLBYuHBhfPvb344HHnggmjRpEqeddlq0adMmLr744ljzLdPixYujoqIi7r///pg5c2a0bt06unXrFmeffXZ84Qtf2MTJAADA1kepAQAAAAAA5IJ7agAAAAAAALmg1AAAAAAAAHJBqQEAAAAAAOSCUgMAAAAAAMgFpQYAAAAAAJALSg0AAAAAACAXlBoAAAAAAEAuKDUAAAAAAIBcUGoAAAAAAAC5oNQAAAAAAAByQakBAAAAAADkglIDAAAAAADIhf8H4PNMZkr54VQAAAAASUVORK5CYII="/>
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<p>So the grade distribution actually varies quite a bit from board to board. Some key differences in grades are the angle at which the climb is. Note that climbs can be done at different angles.</p>
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<h2 id="Angle-Distribution">Angle Distribution<a class="anchor-link" href="#Angle-Distribution">¶</a></h2><p>What about the angle distribution? Since the TB1 goes from 0 to 50 and the TB2 goes from 0 to 65 (although my local board only goes to 60?), let's do an analysis on each.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Angle distribution</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># TB1 goes up to 50 degrees, TB2 up to 65. (Although my local TB2 only goes up to 60 -- brutal climbing)</span>
<span class="c1"># Prepare data in long format</span>
<span class="n">df_long</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'angle'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="c1"># Calculate total for background</span>
<span class="n">df_total</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'angle'</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'count'</span><span class="p">)</span>
<span class="n">df_total</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'All Layouts'</span>
<span class="c1"># Reindex to correct order</span>
<span class="n">angle_order</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span>
<span class="c1"># Plot</span>
<span class="n">fix</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="c1"># Plot All Layouts</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_total</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'angle'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">angle_order</span>
<span class="p">)</span>
<span class="c1"># Plt indivudual layouts</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_long</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'angle'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'count'</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="s1">'layout_name'</span><span class="p">,</span>
<span class="n">palette</span><span class="o">=</span><span class="n">palette</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">angle_order</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="n">handles</span><span class="p">,</span><span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
<span class="n">all_layouts_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'All Layouts'</span><span class="p">)</span>
<span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">all_layouts_patch</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">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Layout'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Angle'</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">'Number of Climbs'</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="s1">'Angle Distribution by Board Layout'</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="n">axis</span><span class="o">=</span><span class="s1">'y'</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">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Angle Distribution by Board Layout'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/angle_distribution_by_layout.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>We see that for all the boards, 40 degrees is the most common angle.</p>
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<h2 id="Angle-vs-grade">Angle vs grade<a class="anchor-link" href="#Angle-vs-grade">¶</a></h2><p>How is the distribution between angles and grades? Let's do this with a heatmap.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Angle vs grade</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="c1"># Create mapping from difficulty to V-grade</span>
<span class="n">grade_mapping</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'display_difficulty'</span><span class="p">)[</span><span class="s1">'boulder_grade'</span><span class="p">]</span><span class="o">.</span><span class="n">first</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
<span class="c1"># Plot "All Layouts" as faint background boxes</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</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="n">x</span><span class="o">=</span><span class="s1">'angle'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'display_difficulty'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">angle_order</span><span class="p">,</span>
<span class="n">showfliers</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">width</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">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
<span class="c1"># Plot individual layouts in front</span>
<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</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="n">x</span><span class="o">=</span><span class="s1">'angle'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'display_difficulty'</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="s1">'layout_name'</span><span class="p">,</span>
<span class="n">hue_order</span><span class="o">=</span><span class="p">[</span><span class="s1">'Original Layout'</span><span class="p">,</span> <span class="s1">'Tension Board 2 Mirror'</span><span class="p">,</span> <span class="s1">'Tension Board 2 Spray'</span><span class="p">],</span> <span class="c1"># For some reason this plot goes TB2 Spray / TB1 Orig / TB2 Mirror. Simple fix.</span>
<span class="n">palette</span><span class="o">=</span><span class="p">[</span><span class="s1">'steelblue'</span><span class="p">,</span> <span class="s1">'coral'</span><span class="p">,</span> <span class="s1">'seagreen'</span><span class="p">],</span>
<span class="n">order</span><span class="o">=</span><span class="n">angle_order</span><span class="p">,</span>
<span class="n">showfliers</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="c1"># Relabel y-axis with boulder_grades</span>
<span class="n">yticks_rounded</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">t</span><span class="p">))</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s1">'display_difficulty'</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="ow">not</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">t</span><span class="p">)))</span>
<span class="n">ylabels</span> <span class="o">=</span> <span class="p">[</span><span class="n">grade_mapping</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">yticks_rounded</span><span class="p">]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">yticks_rounded</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">ylabels</span><span class="p">)</span>
<span class="c1"># Custom legend with "All Layouts"</span>
<span class="n">handles</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
<span class="n">all_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'All Layouts'</span><span class="p">)</span>
<span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">all_patch</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">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Layout'</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">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Angle (degrees)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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">'V-Grade'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</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="s1">'Difficulty Distribution by Angle and Layout'</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">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">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/difficulty_by_angle_boxplot_by_layout.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<h2 id="The-Quality-of-a-climb">The Quality of a climb<a class="anchor-link" href="#The-Quality-of-a-climb">¶</a></h2><p>Next we examine the quality of a climb. First we look at how quality relates to the number of ascents.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Climb quality vs popularity</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Filter to climbs with quality ratings</span>
<span class="n">df_quality</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">'quality_average'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">())</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'quality_average'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)]</span>
<span class="c1"># Sample for performance</span>
<span class="n">df_sample</span> <span class="o">=</span> <span class="n">df_quality</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_quality</span><span class="p">)))</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_sample</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'quality_average'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="s1">'ascensionist_count'</span><span class="p">,</span>
<span class="n">kind</span><span class="o">=</span><span class="s1">'scatter'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">5</span>
<span class="p">)</span>
<span class="n">g</span><span class="o">.</span><span class="n">ax_joint</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Quality Rating'</span><span class="p">)</span>
<span class="n">g</span><span class="o">.</span><span class="n">ax_joint</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Ascensionist Count'</span><span class="p">)</span>
<span class="n">g</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Quality vs Popularity'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/quality_popularity.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Next we visualize the average quality vs the angle and grade, by means of a heatmap. Keep in mind that the harder the climb and steeper the angle, the less people will be doing it. So harder climbs are skewed towards people who can actually do it. The point is that, on boards, the climb quality isn't always the best metric. As such, we won't spend too much time on the quality and will only do a heatmap which takes into account all layouts.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">### Average quality by angle and grade</span>
<span class="c1"># Filter to climbs with quality ratings</span>
<span class="n">df_quality</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">'quality_average'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">())</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'quality_average'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)]</span>
<span class="c1"># Create pivot table</span>
<span class="n">quality_pivot</span> <span class="o">=</span> <span class="n">df_quality</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">'boulder_grade'</span><span class="p">,</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'angle'</span><span class="p">,</span>
<span class="n">values</span><span class="o">=</span><span class="s1">'quality_average'</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="n">quality_pivot</span> <span class="o">=</span> <span class="n">quality_pivot</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">grade_order</span><span class="p">)</span>
<span class="n">quality_pivot</span> <span class="o">=</span> <span class="n">quality_pivot</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">a</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">angle_order</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">quality_pivot</span><span class="o">.</span><span class="n">columns</span><span class="p">])</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span>
<span class="n">quality_pivot</span><span class="p">,</span>
<span class="n">cmap</span><span class="o">=</span><span class="s1">'RdYlGn'</span><span class="p">,</span>
<span class="n">cbar_kws</span><span class="o">=</span><span class="p">{</span><span class="s1">'label'</span><span class="p">:</span> <span class="s1">'Avg Quality Rating'</span><span class="p">},</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span>
<span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Angle (°)'</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">'Grade'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</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="s1">'Average Quality Rating by Grade and Angle (All Layouts)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/quality_heatmap_all_layouts.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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Fxc5OLioqpVq2ratGkKDw/XsGHDrPqlpaVp/Pjxqlu3rsqUKaMyZcrIzc1N6enpNz2nzp07y93d3fK6SpUqqly5sqXkSXp6unbt2qU+ffqobNmyln5ubm7q1atXgc/j7u5us6p64MCBysnJ0VdffWXpM2TIEMXExFjK7mzevFn79+8v8kM24+LiLNfYxcXFsjr4ar1795aLi4tNe2xsrNq2bSs3NzfL+2jx4sVW1zwxMVGS9NRTT9nM7WqXLl3Spk2b9I9//EPly5e3+tz07NlTly5dyrM8x9X27Nmj3r17y8vLy/I5Hjx4sLKzs23KoHh7e6tly5ZWbY0bN7YqZZOYmKhGjRrp/vvvv27s11OQ+93VAgMD5ezsbBWTJEtcP//8s1JSUmzuQ7Vq1VLbtm1vGM/N3pv++OMPy1+83Oxcb6WC3NN1ZfX23r179fXXX0tXSuj85z//UXBwsNzc3Cxj6Urpoqv169dPd911lzZt2lRscdetW1d33323xo8fr8jISO3fvz/fvrnfh+PHjxfb+QEAAETNbQAA8nf48GF99dVXCgwMlGEYOnv2rM6ePau+fftKklXpgNDQUG3fvl0HDx6UJEVHR8tsNmvAgAGWPn/++ad++OEHq+Sci4uL3N3dZRiG/v77b6vzV61a1Sam7777Tt26dZOu1LH9+uuvtXPnTr3yyivSlT+1l6RTp05JV5KbVytTpoy8vLys2v788099+umnNnHl1nC9Nq6r5f4Z/ZEjR/Ltk7uvZs2a+fbJz9y5czV8+HA9+OCDWrVqlXbs2KGdO3fqkUceKfYHbIaGhsrV1VWRkZHSlZILrq6uVr+guJ46depo586d+u677xQbG6v7779fERERWr58uVW/gQMH6p133tEzzzyjL774Qt9995127typSpUq3fScrv3eSpLZbLaMe+bMGRmGYfO+UB7vlevJq6+3t7d01XtPkkaNGqXU1FR99NFHkqR33nlHNWrUUFBQ0HXHz31fXZ281ZXSBjt37tTOnTv16KOP5nlsXp+b1atXq3///qpevbqWLl2q7du3a+fOnQoNDbV62OepU6fy/Izkzu3qfllZWVqwYIHN56Znz57SDT43ycnJat++vY4fP663335bW7du1c6dOy2lJa59H9zo+5ob07Vx5hV7fgpzv8svrtxyFze6D+XXdq2buTflxlGuXLlimeutUtB7uiQFBQXJx8fH8j7J/cXR1SV+ct/DV5dC0pVfLnp7e1t9Pm+Wh4eHkpKS1KRJE02cOFGNGjVStWrVNGXKFJva8bnfh5L6YGQAAFB6UXMbAIB8REVFyTAMffLJJ/rkk09s9i9ZskQzZsyQs7OzBgwYoLFjxyomJkYzZ87Uf/7zH/Xp08dq5fU999wjV1fXfBMn99xzj9XrvFYLL1++XC4uLlq3bp1V0mbt2rVW/XKTTn/++aeqV69uac/KyrJJbtxzzz1q3LixZs6cmWdc1apVy7NdV1YKlylTRmvXrtVzzz2XZ5/c2Lp06WJpM5vNedbcvTa2pUuXWmq1Xq2wD8krCA8PDwUHB+uDDz7Qiy++qOjoaA0cOFCenp4FOr5cuXJq3ry5JKlFixbq3LmzGjVqpH/+85969NFH5ebmpnPnzmndunWaMmWKJkyYYDk2tyb0rXb33XfLZDLlWV87JSWlwONc7/irE55169ZVjx49tHDhQvXo0UPx8fGaOnWq1WrfvHTt2lUTJ05UfHy8JfEnSZ6enpZrnFfCV/l8bpYuXSpfX1+tWLHCav+170EvLy/LZ+Tq8a+9NnfffbecnZ01aNCgfGuH+/r65ju/tWvXKj09XatXr1bt2rUt7Xv37s33mBvx8vLK83tY0O9rYe53hYlJN3i/XM/N3Jtyj8/rc3Ur5lpUBb2n68pfmIwcOVITJ07Um2++qXfffVcPPfSQ6tevb+mT+x7+66+/rBLchmEoJSVFLVq0sLQV9D58PQEBAVq+fLkMw9APP/ygmJgYTZs2Ta6urlb3uNzvw7U/5wAAAG4WK7cBAMhDdna2lixZojp16igxMdFmGzdunE6cOKHPP/9cupLs6tOnjz788EOtW7dOKSkpNit+H330Uf3666/y8vJS8+bNbTYfH58bxmUymVSmTBmrpMvFixf1n//8x6pfhw4dJEkrVqywav/kk0+UlZVlE9ePP/6oOnXq5BnX9RJI3t7eCgsL0xdffGFzLkk6dOiQXn/9dfn6+lqt1vXx8dEPP/xg1Xfz5s1KS0uzme+1Dz/74YcfrB4IWRjXriy9Vu7DAPv27auzZ88WuXyGriSZZs2apT///FMLFiyQrszHMAybOX3wwQfKzs4uVKxFcdddd6l58+Zau3atMjMzLe1paWlat25dgcdJTU1VfHy8VdvHH38sJycny3sv1+jRo/XDDz8oODhYzs7OevbZZ284fvPmzdWtWzf9+9//1tatWwscV35MJpPKli1rldhOSUlRXFycVb/OnTtLkmWlea6PP/7Y6nX58uXVuXNn7dmzR40bN87zc5Nf8l1XJeCvfh8YhqF///vfRZ5j586d9dNPP2nfvn3XjT0vhb3fFVT9+vXl7e2tlStXWrUnJyfrm2++ueHxN3NvkqQGDRro119/vS1zLaqC3tNzPfPMMypbtqyeeuop/fzzzzb3qIceeki68gudq61atUrp6emW/SrEfbgg9yKTyaT7779fb731ljw9PbV7926r/b/99psk5flwTwAAgJvBym0AAPLw+eef648//tDrr7+uTp062ey/77779M4772jx4sWW8gihoaFasWKFnn/+edWoUUMPP/yw1TH//Oc/tWrVKnXo0EFjxoxR48aNlZOTo+TkZH355ZcaN26cHnzwwevGFRgYqLlz52rgwIEaOnSoTp06pTlz5tgkSxs1aqQBAwbozTfflLOzs7p06aKffvpJb775pjw8POTk9H+/3542bZoSEhLUpk0bvfDCC6pfv74uXbqko0ePav369YqMjFSNGjXyjWnu3Lk6ePCgnn76aX311Vfq1auXzGazduzYoTlz5khXViFeXQd50KBBevXVVzV58mR17NhR+/fv1zvvvGNTQ/nRRx/V9OnTNWXKFHXs2FE///yzpk2bJl9fX5skfUEEBARIkl5//XX16NFDzs7Oaty4saX+dL169fTII4/o888/V7t27WzqFxfW4MGDNXfuXM2ZM0cjR45UhQoV1KFDB82ePVv33HOPfHx8lJSUpMWLF9usEL/vvvskSe+//77c3d1Vrlw5+fr6XjdpWhDTpk1TYGCgunfvrtGjRys7O1uzZ8+Wm5tbgVePe3l5afjw4UpOTla9evW0fv16/fvf/9bw4cMtJUVyde3aVQ0bNlRiYqKefvrpfGsgX2vp0qXq3r27Hn74YYWEhKh79+6qXLmyzp8/rx9++EEbN25UhQoVCjTWo48+qtWrV2vEiBHq27ev/ve//2n69OmqWrWqVX3lbt26qUOHDnr55ZeVnp6u5s2b6+uvv84z0fj222+rXbt2at++vYYPHy4fHx+lpqbq8OHD+vTTTy21j/PStWtXlS1bVgMGDNDLL7+sS5cuadGiRTpz5kyB5pOXf/7zn4qKilJgYKBmzJihKlWq6KOPPrKUSrqeotzvCsLJyUlTp07VsGHD1LdvX4WGhurs2bOaOnWqqlatanUfysvN3ps6deqkadOm6cKFCypfvvwtnev15Fd/vWPHjgW+p+fy9PTU4MGDtWjRItWuXdumVn7Xrl3VvXt3jR8/XufPn1fbtm31ww8/aMqUKWratKkGDRpk6VvQ+3B+96Lt27fr3XffVZ8+feTn5yfDMLR69WqdPXtWXbt2tbkGzs7ONr/8AgAAuGn2fqIlAAAlUZ8+fYyyZcsaJ0+ezLfPk08+aZQpU8ZISUkxDMMwsrOzjZo1axqSjFdeeSXPY9LS0oxJkyYZ9evXN8qWLWt4eHgYAQEBxpgxYyzjGIZhSDJGjhyZ5xhRUVFG/fr1DbPZbPj5+RkRERHG4sWLDUnGkSNHLP0uXbpkjB071qhcubJRrlw5o1WrVsb27dsNDw8PY8yYMVZj/vXXX8YLL7xg+Pr6Gi4uLkbFihWNBx54wHjllVeMtLS0G16vzMxMY8GCBcaDDz5ouLm5GZIMSUabNm2M33//3aZ/RkaG8fLLLxs1a9Y0XF1djY4dOxp79+41ateubQQHB1v1e/HFF43q1asb5cqVM5o1a2asXbvWCA4ONmrXrm01piRjypQplteJiYmGJCMxMdFqvGeeecaoVKmSYTKZbK6ZYRhGTEyMIclYvnz5Deedq2PHjkajRo3y3PfZZ58ZkoypU6cahmEYv//+u/H4448bd999t+Hu7m488sgjxo8//mgzd8MwjHnz5hm+vr6Gs7OzIcmIjo42DMPId/55vWfyGnfNmjVGQECAUbZsWaNWrVrGrFmzjBdeeMG4++67CzzXLVu2GM2bNzfMZrNRtWpVY+LEicbly5fzPOa1114zJBk7duy44fhXu3TpkrFgwQKjXbt2hqenp1GmTBmjYsWKRvv27Y3XX3/dOHXqlKXvkSNHDEnG7Nmz8xxr1qxZho+Pj2E2m417773X+Pe//21MmTLFuPafw2fPnjVCQ0MNT09Po3z58kbXrl2NgwcP2ry/cs8ZGhpqVK9e3XBxcTEqVapktGnTxpgxY8YN5/bpp58a999/v1GuXDmjevXqxksvvWR8/vnnNu/Z/N5beb0H9u/fb3Tt2tUoV66cUbFiRSMsLMyIi4uzGfNahb3fXe9a53Wd3n//faNu3bpG2bJljXr16hlRUVFGUFCQ0bRp0xseezP3psOHDxsmk8lYuXJlkeeaV1x53Vvyktsvvy33+ILe03Nt2bLFkGTMmjUrz/NevHjRGD9+vFG7dm3DxcXFqFq1qjF8+HDjzJkzVv0Keh828rkXHTx40BgwYIBRp04dw9XV1fDw8DBatmxpxMTE2MTUvn17o1evXte9XgAAAEVhMv7/P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<h2 id="%22Match%22-vs.-%22No-Match%22">"Match" vs. "No Match"<a class="anchor-link" href="#%22Match%22-vs.-%22No-Match%22">¶</a></h2><p>Some setters opt to put the "no match" tag onto their climbs. This means that the climber is not allowed to match their hands on any hold. Let's do an analysis of the differences with regular climbs.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Match vs No Match analysis</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Create status column (Match vs No Match)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'status'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</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">'No Match'</span> <span class="k">if</span> <span class="p">(</span>
<span class="n">pd</span><span class="o">.</span><span class="n">notna</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s1">'description'</span><span class="p">])</span> <span class="ow">and</span> <span class="s1">'No matching'</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s1">'description'</span><span class="p">])</span>
<span class="p">)</span> <span class="ow">or</span> <span class="n">x</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'is_nomatch'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">'Matched'</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="c1"># Aggregate by layout and status</span>
<span class="n">df_agg</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'layout_name'</span><span class="p">,</span> <span class="s1">'status'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">count</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="n">avg_ascensionists</span><span class="o">=</span><span class="p">(</span><span class="s1">'ascensionist_count'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">),</span>
<span class="n">avg_difficulty</span><span class="o">=</span><span class="p">(</span><span class="s1">'display_difficulty'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># Calculate All Layouts totals</span>
<span class="n">df_all</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'status'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">count</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">),</span>
<span class="n">avg_ascensionists</span><span class="o">=</span><span class="p">(</span><span class="s1">'ascensionist_count'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">),</span>
<span class="n">avg_difficulty</span><span class="o">=</span><span class="p">(</span><span class="s1">'display_difficulty'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">df_all</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'All Layouts'</span>
<span class="c1"># Combine</span>
<span class="n">df_combined</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_agg</span><span class="p">,</span> <span class="n">df_all</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Order</span>
<span class="n">status_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Matched'</span><span class="p">,</span> <span class="s1">'No Match'</span><span class="p">]</span>
<span class="c1"># Plot</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="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">metric</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'avg_difficulty'</span><span class="p">,</span> <span class="s1">'avg_ascensionists'</span><span class="p">],</span>
<span class="p">[</span><span class="s1">'Total Climbs'</span><span class="p">,</span> <span class="s1">'Average Difficulty'</span><span class="p">,</span> <span class="s1">'Avg Ascensionists'</span><span class="p">]):</span>
<span class="c1"># All Layouts as background (separate for each status)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">status</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">status_order</span><span class="p">):</span>
<span class="n">df_bg</span> <span class="o">=</span> <span class="n">df_combined</span><span class="p">[(</span><span class="n">df_combined</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'All Layouts'</span><span class="p">)</span> <span class="o">&amp;</span>
<span class="p">(</span><span class="n">df_combined</span><span class="p">[</span><span class="s1">'status'</span><span class="p">]</span> <span class="o">==</span> <span class="n">status</span><span class="p">)]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">df_bg</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
<span class="c1"># Position for status</span>
<span class="n">x_pos</span> <span class="o">=</span> <span class="n">i</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
<span class="n">x_pos</span><span class="p">,</span>
<span class="n">df_bg</span><span class="p">[</span><span class="n">metric</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">width</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
<span class="c1"># Individual layouts in front</span>
<span class="n">df_plot</span> <span class="o">=</span> <span class="n">df_combined</span><span class="p">[</span><span class="n">df_combined</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">'All Layouts'</span><span class="p">]</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
<span class="n">data</span><span class="o">=</span><span class="n">df_plot</span><span class="p">,</span>
<span class="n">x</span><span class="o">=</span><span class="s1">'status'</span><span class="p">,</span>
<span class="n">y</span><span class="o">=</span><span class="n">metric</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="s1">'layout_name'</span><span class="p">,</span>
<span class="n">palette</span><span class="o">=</span><span class="n">palette</span><span class="p">,</span>
<span class="n">order</span><span class="o">=</span><span class="n">status_order</span><span class="p">,</span>
<span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
<span class="n">zorder</span><span class="o">=</span><span class="mi">2</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="n">title</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">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">''</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="n">title</span> <span class="k">if</span> <span class="n">metric</span> <span class="o">==</span> <span class="s1">'count'</span> <span class="k">else</span> <span class="p">(</span><span class="s1">'Grade'</span> <span class="k">if</span> <span class="n">metric</span> <span class="o">==</span> <span class="s1">'avg_difficulty'</span> <span class="k">else</span> <span class="s1">'Count'</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend_</span><span class="o">.</span><span class="n">remove</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="n">axis</span><span class="o">=</span><span class="s1">'y'</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"># Y-axis labels for difficulty plot</span>
<span class="n">yticks</span> <span class="o">=</span> <span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">23</span><span class="p">]</span>
<span class="n">ylabels</span> <span class="o">=</span> <span class="p">[</span><span class="n">grade_mapping</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"V</span><span class="si">{</span><span class="n">t</span><span class="o">-</span><span class="mi">10</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">yticks</span><span class="p">]</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">yticks</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">ylabels</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c1"># Add V-grade annotations on difficulty bars</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">status</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">status_order</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">layout</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()):</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">df_combined</span><span class="p">[(</span><span class="n">df_combined</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">layout</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_combined</span><span class="p">[</span><span class="s1">'status'</span><span class="p">]</span> <span class="o">==</span> <span class="n">status</span><span class="p">)]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">row</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
<span class="n">diff</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'avg_difficulty'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">v_grade</span> <span class="o">=</span> <span class="n">grade_mapping</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">diff</span><span class="p">),</span> <span class="s1">''</span><span class="p">)</span>
<span class="c1"># Position: x = status index + offset for layout</span>
<span class="n">x_pos</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="p">(</span><span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.27</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">diff</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">v_grade</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">fontsize</span><span class="o">=</span><span class="mi">8</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"># Custom Legend</span>
<span class="n">handles</span><span class="p">,</span><span class="n">labels</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
<span class="n">all_layouts_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'All Layouts'</span><span class="p">)</span>
<span class="n">handles</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">all_layouts_patch</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Layout'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.08</span><span class="p">,</span> <span class="mf">0.9</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">'Match vs No Match Climbs by Layout'</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">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">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/match_vs_nomatch_by_layout.png'</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>So we gather the following about "no match" climbs:</p>
<ul>
<li>they are far fewer than "match" climbs,</li>
<li>they are on average harder than "match" climbs,</li>
<li>and that they tend to have a bit more ascensionists on the TB2, and less on the TB1 (although, much more overall).</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Match vs No Match Summary</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">summary</span> <span class="o">=</span> <span class="n">df_combined</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">'layout_name'</span><span class="p">,</span>
<span class="n">columns</span><span class="o">=</span><span class="s1">'status'</span><span class="p">,</span>
<span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'avg_difficulty'</span><span class="p">,</span> <span class="s1">'avg_ascensionists'</span><span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">summary</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
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<style scoped="">
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<th></th>
<th colspan="2" halign="left">avg_ascensionists</th>
<th colspan="2" halign="left">avg_difficulty</th>
<th colspan="2" halign="left">count</th>
</tr>
<tr>
<th>status</th>
<th>Matched</th>
<th>No Match</th>
<th>Matched</th>
<th>No Match</th>
<th>Matched</th>
<th>No Match</th>
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<th>layout_name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
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<th>All Layouts</th>
<td>22.11</td>
<td>31.22</td>
<td>18.18</td>
<td>20.16</td>
<td>85202.0</td>
<td>61844.0</td>
</tr>
<tr>
<th>Original Layout</th>
<td>22.27</td>
<td>21.46</td>
<td>17.36</td>
<td>19.62</td>
<td>49093.0</td>
<td>27163.0</td>
</tr>
<tr>
<th>Tension Board 2 Mirror</th>
<td>19.97</td>
<td>44.92</td>
<td>19.21</td>
<td>20.52</td>
<td>21657.0</td>
<td>23329.0</td>
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<th>Tension Board 2 Spray</th>
<td>24.81</td>
<td>26.42</td>
<td>19.44</td>
<td>20.72</td>
<td>14452.0</td>
<td>11352.0</td>
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<h1 id="Prolific-statistics">Prolific statistics<a class="anchor-link" href="#Prolific-statistics">¶</a></h1><p>Here we will take note of some prolific statistics: what are the most popular climbs and who are the most popular setters?</p>
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<h2 id="Most-popular-climbs">Most popular climbs<a class="anchor-link" href="#Most-popular-climbs">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Most popular climbs</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># The ascensionist_count column will allow us to easily deduce the top 15 climbs. </span>
<span class="c1"># Create a DataFrame with the top 15 climbs</span>
<span class="n">df_popular_climbs</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'ascensionist_count'</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">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="c1"># We want the y-axis to say "Climb @ angle". Let's just create a new column for this. </span>
<span class="n">df_popular_climbs</span><span class="p">[</span><span class="s1">'y_label'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_popular_climbs</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">row</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">'climb_name'</span><span class="p">]</span><span class="si">}</span><span class="s2"> @ </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'angle'</span><span class="p">]</span><span class="si">}</span><span class="s2">°"</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="c1"># Plot it</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
<span class="n">bars</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">df_popular_climbs</span><span class="p">[</span><span class="s1">'y_label'</span><span class="p">],</span> <span class="n">df_popular_climbs</span><span class="p">[</span><span class="s1">'ascensionist_count'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Ascensionist Count'</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="s1">'Top 15 Most Popular Climbs (at a specific angle)'</span><span class="p">)</span>
<span class="c1"># Add grade and angle labels</span>
<span class="k">for</span> <span class="n">bar</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">bars</span><span class="p">,</span> <span class="n">df_popular_climbs</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()):</span>
<span class="n">label</span> <span class="o">=</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">'boulder_grade'</span><span class="p">]</span><span class="si">}</span><span class="s2"> on </span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span>
<span class="n">bar</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</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="o">/</span><span class="mi">2</span><span class="p">,</span>
<span class="n">label</span><span class="p">,</span>
<span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/top_15_climbs.png'</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>It's unsuprising that every one of these climbs is at 40° given that 40° is the most popular angle, by a long shot.</p>
<p>What about an angle-agnostic analysis? What are the top climbs amonst all angles?</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Top 15 most popular climbs (angle agnostic)</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Aggregate by climb_name (sum counts across all angles)</span>
<span class="n">df_agg</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'climb_name'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">total_ascensionists</span><span class="o">=</span><span class="p">(</span><span class="s1">'ascensionist_count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">),</span>
<span class="n">avg_difficulty</span><span class="o">=</span><span class="p">(</span><span class="s1">'display_difficulty'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="c1"># Sort and select top 15</span>
<span class="n">df_popular_climbs_aa</span> <span class="o">=</span> <span class="n">df_agg</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'total_ascensionists'</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">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
<span class="n">df_popular_climbs_aa</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">bars</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">df_popular_climbs_aa</span><span class="p">[</span><span class="s1">'climb_name'</span><span class="p">],</span> <span class="n">df_popular_climbs_aa</span><span class="p">[</span><span class="s1">'total_ascensionists'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'teal'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Total Ascensionist Count (All Angles)'</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="s1">'Top 15 Most Popular Climbs (Angle Agnostic)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
<span class="c1"># Add grade labels inside bars</span>
<span class="k">for</span> <span class="n">bar</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">bars</span><span class="p">,</span> <span class="n">df_popular_climbs</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()):</span>
<span class="c1"># Create a label like "(Tension Board 2 Mirror)"</span>
<span class="n">label</span> <span class="o">=</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">'layout_name'</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
<span class="mi">100</span><span class="p">,</span> <span class="c1"># Position inside bar</span>
<span class="n">bar</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</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="o">/</span> <span class="mi">2</span><span class="p">,</span>
<span class="n">label</span><span class="p">,</span>
<span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span>
<span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="s1">'white'</span>
<span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/01_climb_stats/top_15_climbs_angle_agnostic.png'</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<p>What about the top climbs (with/without angles) for each of the board layouts? Let's do with angles first.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Top 15 most popular climbs by layout</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">layouts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'layout_name'</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">layout</span> <span class="ow">in</span> <span class="n">layouts</span><span class="p">:</span>
<span class="c1"># Filter data for this layout</span>
<span class="n">df_layout</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">'layout_name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">layout</span><span class="p">]</span>
<span class="c1"># Sort by popularity and take top 15</span>
<span class="n">df_top</span> <span class="o">=</span> <span class="n">df_layout</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'ascensionist_count'</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">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Select desired columns</span>
<span class="n">df_display</span> <span class="o">=</span> <span class="n">df_top</span><span class="p">[[</span><span class="s1">'climb_name'</span><span class="p">,</span> <span class="s1">'angle'</span><span class="p">,</span> <span class="s1">'boulder_grade'</span><span class="p">,</span> <span class="s1">'ascensionist_count'</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># Rename columns for display</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Name'</span><span class="p">,</span> <span class="s1">'Angle'</span><span class="p">,</span> <span class="s1">'Grade'</span><span class="p">,</span> <span class="s1">'Ascensionists'</span><span class="p">]</span>
<span class="c1"># Format angle as string with degree symbol</span>
<span class="n">df_display</span><span class="p">[</span><span class="s1">'Angle'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_display</span><span class="p">[</span><span class="s1">'Angle'</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="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'°'</span>
<span class="c1"># Reset index to show rank 1-15</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</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="si">{</span><span class="n">layout</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_display</span><span class="p">)</span>
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<pre>
Original Layout
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Name</th>
<th>Angle</th>
<th>Grade</th>
<th>Ascensionists</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>Pre-game</td>
<td>40°</td>
<td>4a/V0</td>
<td>10378</td>
</tr>
<tr>
<th>2</th>
<td>Laser Camera</td>
<td>40°</td>
<td>6a/V3</td>
<td>8457</td>
</tr>
<tr>
<th>3</th>
<td>lefty loosy</td>
<td>40°</td>
<td>5a/V1</td>
<td>7248</td>
</tr>
<tr>
<th>4</th>
<td>Riopio</td>
<td>40°</td>
<td>4a/V0</td>
<td>6961</td>
</tr>
<tr>
<th>5</th>
<td>Sea Freight</td>
<td>40°</td>
<td>5c/V2</td>
<td>6953</td>
</tr>
<tr>
<th>6</th>
<td>Pre-game</td>
<td>30°</td>
<td>4a/V0</td>
<td>6863</td>
</tr>
<tr>
<th>7</th>
<td>Bubbles</td>
<td>40°</td>
<td>6b+/V4</td>
<td>6653</td>
</tr>
<tr>
<th>8</th>
<td>Laser Camera</td>
<td>30°</td>
<td>5a/V1</td>
<td>6409</td>
</tr>
<tr>
<th>9</th>
<td>Holler</td>
<td>40°</td>
<td>6a/V3</td>
<td>6288</td>
</tr>
<tr>
<th>10</th>
<td>Jiggles</td>
<td>40°</td>
<td>6b/V4</td>
<td>5568</td>
</tr>
<tr>
<th>11</th>
<td>Shimmy</td>
<td>40°</td>
<td>6c/V5</td>
<td>5489</td>
</tr>
<tr>
<th>12</th>
<td>Bubbles</td>
<td>30°</td>
<td>6a/V3</td>
<td>5473</td>
</tr>
<tr>
<th>13</th>
<td>Gunter</td>
<td>40°</td>
<td>4a/V0</td>
<td>5461</td>
</tr>
<tr>
<th>14</th>
<td>It's Alive!</td>
<td>30°</td>
<td>5c/V2</td>
<td>5357</td>
</tr>
<tr>
<th>15</th>
<td>It's Alive!</td>
<td>40°</td>
<td>5b/V1</td>
<td>5301</td>
</tr>
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<pre>
Tension Board 2 Mirror
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Name</th>
<th>Angle</th>
<th>Grade</th>
<th>Ascensionists</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>Masquerade</td>
<td>40°</td>
<td>6a/V3</td>
<td>12123</td>
</tr>
<tr>
<th>2</th>
<td>Drain from the Brain</td>
<td>40°</td>
<td>6c/V5</td>
<td>10242</td>
</tr>
<tr>
<th>3</th>
<td>Compliments To The Climber</td>
<td>40°</td>
<td>6b/V4</td>
<td>10204</td>
</tr>
<tr>
<th>4</th>
<td>Putty</td>
<td>40°</td>
<td>5c/V2</td>
<td>10162</td>
</tr>
<tr>
<th>5</th>
<td>Propagation</td>
<td>40°</td>
<td>6b/V4</td>
<td>9640</td>
</tr>
<tr>
<th>6</th>
<td>Thunderstruck</td>
<td>40°</td>
<td>6b/V4</td>
<td>9352</td>
</tr>
<tr>
<th>7</th>
<td>All Plastic</td>
<td>40°</td>
<td>5a/V1</td>
<td>9052</td>
</tr>
<tr>
<th>8</th>
<td>You Look Great Today</td>
<td>40°</td>
<td>6a/V3</td>
<td>8881</td>
</tr>
<tr>
<th>9</th>
<td>love is an open door.</td>
<td>40°</td>
<td>6c/V5</td>
<td>8693</td>
</tr>
<tr>
<th>10</th>
<td>Sherman</td>
<td>40°</td>
<td>6a/V3</td>
<td>8212</td>
</tr>
<tr>
<th>11</th>
<td>hop scotch</td>
<td>40°</td>
<td>6b/V4</td>
<td>7936</td>
</tr>
<tr>
<th>12</th>
<td>Dear John</td>
<td>40°</td>
<td>5c/V2</td>
<td>7821</td>
</tr>
<tr>
<th>13</th>
<td>Dancing in the Moonlight</td>
<td>40°</td>
<td>6b/V4</td>
<td>7109</td>
</tr>
<tr>
<th>14</th>
<td>Doomscroll</td>
<td>40°</td>
<td>4c/V0</td>
<td>7107</td>
</tr>
<tr>
<th>15</th>
<td>Poseidon</td>
<td>40°</td>
<td>6c/V5</td>
<td>7078</td>
</tr>
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<pre>
Tension Board 2 Spray
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Name</th>
<th>Angle</th>
<th>Grade</th>
<th>Ascensionists</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>Pizza Box</td>
<td>40°</td>
<td>6a/V3</td>
<td>6649</td>
</tr>
<tr>
<th>2</th>
<td>Aw, shoot</td>
<td>40°</td>
<td>6b/V4</td>
<td>5453</td>
</tr>
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<th>3</th>
<td>Write This Way</td>
<td>40°</td>
<td>6a/V3</td>
<td>5145</td>
</tr>
<tr>
<th>4</th>
<td>Shoulder Rust</td>
<td>40°</td>
<td>5a/V1</td>
<td>5018</td>
</tr>
<tr>
<th>5</th>
<td>Nacho Mango</td>
<td>40°</td>
<td>5c/V2</td>
<td>4841</td>
</tr>
<tr>
<th>6</th>
<td>Frictionless</td>
<td>40°</td>
<td>6c/V5</td>
<td>4536</td>
</tr>
<tr>
<th>7</th>
<td>Put It Up</td>
<td>40°</td>
<td>6b/V4</td>
<td>4499</td>
</tr>
<tr>
<th>8</th>
<td>Pour Cece</td>
<td>40°</td>
<td>6a/V3</td>
<td>4225</td>
</tr>
<tr>
<th>9</th>
<td>Authorized</td>
<td>40°</td>
<td>6c/V5</td>
<td>4207</td>
</tr>
<tr>
<th>10</th>
<td>It’s A Mid-West Alcohol</td>
<td>40°</td>
<td>6b/V4</td>
<td>4081</td>
</tr>
<tr>
<th>11</th>
<td>Center Left</td>
<td>40°</td>
<td>6c/V5</td>
<td>4008</td>
</tr>
<tr>
<th>12</th>
<td>Lost Glove</td>
<td>40°</td>
<td>6b/V4</td>
<td>3563</td>
</tr>
<tr>
<th>13</th>
<td>Perfect Pizza Party</td>
<td>40°</td>
<td>6a/V3</td>
<td>3420</td>
</tr>
<tr>
<th>14</th>
<td>Lightyears</td>
<td>40°</td>
<td>5c/V2</td>
<td>3379</td>
</tr>
<tr>
<th>15</th>
<td>The Where</td>
<td>40°</td>
<td>6b/V4</td>
<td>3310</td>
</tr>
</tbody>
</table>
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<p>On the TB2, it looks like 40 degrees constantly wins out. It is cool to see that, on the TB1, at least one climb made it to the top 15 twice, at two different angles! Congrats "It's Alive."</p>
<p>Now let us do a per-board angle agnostic analysis.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Top 15 climbs by layout (angle agnostic)</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="n">layouts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="c1"># Aggregate counts and collect angles</span>
<span class="n">df_agg</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'climb_name'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">total_ascensionists</span><span class="o">=</span><span class="p">(</span><span class="s1">'ascensionist_count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="k">for</span> <span class="n">layout</span> <span class="ow">in</span> <span class="n">layouts</span><span class="p">:</span>
<span class="n">df_layout</span> <span class="o">=</span> <span class="n">df_agg</span><span class="p">[</span><span class="n">df_agg</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">layout</span><span class="p">]</span>
<span class="n">df_top</span> <span class="o">=</span> <span class="n">df_layout</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'total_ascensionists'</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">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df_display</span> <span class="o">=</span> <span class="n">df_top</span><span class="p">[[</span><span class="s1">'climb_name'</span><span class="p">,</span> <span class="s1">'total_ascensionists'</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Name'</span><span class="p">,</span> <span class="s1">'Total Ascensionists'</span><span class="p">]</span>
<span class="c1"># Appropriate index</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</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">layout</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_display</span><span class="p">)</span>
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### Original Layout
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<thead>
<tr style="text-align: right;">
<th></th>
<th>Name</th>
<th>Total Ascensionists</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>Pre-game</td>
<td>27468</td>
</tr>
<tr>
<th>2</th>
<td>Laser Camera</td>
<td>20968</td>
</tr>
<tr>
<th>3</th>
<td>Bubbles</td>
<td>16161</td>
</tr>
<tr>
<th>4</th>
<td>Sea Freight</td>
<td>15264</td>
</tr>
<tr>
<th>5</th>
<td>Holler</td>
<td>14275</td>
</tr>
<tr>
<th>6</th>
<td>It's Alive!</td>
<td>13545</td>
</tr>
<tr>
<th>7</th>
<td>Dull Scissors</td>
<td>11488</td>
</tr>
<tr>
<th>8</th>
<td>lefty loosy</td>
<td>11275</td>
</tr>
<tr>
<th>9</th>
<td>Getting By</td>
<td>11116</td>
</tr>
<tr>
<th>10</th>
<td>Go Figure</td>
<td>10483</td>
</tr>
<tr>
<th>11</th>
<td>Intro To Power</td>
<td>10377</td>
</tr>
<tr>
<th>12</th>
<td>Switching Places</td>
<td>9748</td>
</tr>
<tr>
<th>13</th>
<td>Shimmy</td>
<td>9427</td>
</tr>
<tr>
<th>14</th>
<td>Foles Gold</td>
<td>9339</td>
</tr>
<tr>
<th>15</th>
<td>Big Pinch Pinchin'</td>
<td>8823</td>
</tr>
</tbody>
</table>
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### Tension Board 2 Mirror
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<th>Total Ascensionists</th>
</tr>
</thead>
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<th>1</th>
<td>Masquerade</td>
<td>19655</td>
</tr>
<tr>
<th>2</th>
<td>Putty</td>
<td>19445</td>
</tr>
<tr>
<th>3</th>
<td>Compliments To The Climber</td>
<td>16844</td>
</tr>
<tr>
<th>4</th>
<td>Thunderstruck</td>
<td>16121</td>
</tr>
<tr>
<th>5</th>
<td>Further</td>
<td>15981</td>
</tr>
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<th>6</th>
<td>Propagation</td>
<td>15606</td>
</tr>
<tr>
<th>7</th>
<td>Poseidon</td>
<td>15549</td>
</tr>
<tr>
<th>8</th>
<td>All Plastic</td>
<td>15135</td>
</tr>
<tr>
<th>9</th>
<td>You Look Great Today</td>
<td>14607</td>
</tr>
<tr>
<th>10</th>
<td>Doomscroll</td>
<td>14150</td>
</tr>
<tr>
<th>11</th>
<td>Drain from the Brain</td>
<td>12910</td>
</tr>
<tr>
<th>12</th>
<td>Prime</td>
<td>11384</td>
</tr>
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<th>13</th>
<td>Endearing</td>
<td>10946</td>
</tr>
<tr>
<th>14</th>
<td>Dancing in the Moonlight</td>
<td>10855</td>
</tr>
<tr>
<th>15</th>
<td>Guided By Angels</td>
<td>10757</td>
</tr>
</tbody>
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<pre>
### Tension Board 2 Spray
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<th>1</th>
<td>Pizza Box</td>
<td>11884</td>
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<th>2</th>
<td>Nacho Mango</td>
<td>10325</td>
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<th>3</th>
<td>Write This Way</td>
<td>9299</td>
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<th>4</th>
<td>Shoulder Rust</td>
<td>8749</td>
</tr>
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<th>5</th>
<td>Marshmallow Dragon</td>
<td>8001</td>
</tr>
<tr>
<th>6</th>
<td>Authorized</td>
<td>7387</td>
</tr>
<tr>
<th>7</th>
<td>The Where</td>
<td>7325</td>
</tr>
<tr>
<th>8</th>
<td>Aw, shoot</td>
<td>7006</td>
</tr>
<tr>
<th>9</th>
<td>Perfect Pizza Party</td>
<td>6575</td>
</tr>
<tr>
<th>10</th>
<td>Frictionless</td>
<td>6536</td>
</tr>
<tr>
<th>11</th>
<td>New Gold</td>
<td>6529</td>
</tr>
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<th>12</th>
<td>Pour Cece</td>
<td>6022</td>
</tr>
<tr>
<th>13</th>
<td>Put It Up</td>
<td>6012</td>
</tr>
<tr>
<th>14</th>
<td>It’s A Mid-West Alcohol</td>
<td>5863</td>
</tr>
<tr>
<th>15</th>
<td>Bring The Ruckus</td>
<td>5689</td>
</tr>
</tbody>
</table>
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<h2 id="Prolific-setters">Prolific setters<a class="anchor-link" href="#Prolific-setters">¶</a></h2><p>Next, we will make a simple table of the most prolific setters by board.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
<span class="sd">==================================</span>
<span class="sd">Top 10 setters by layout</span>
<span class="sd">==================================</span>
<span class="sd">"""</span>
<span class="c1"># Make a DataFrame for the setters</span>
<span class="n">df_setters</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'setter_username'</span><span class="p">,</span> <span class="s1">'layout_name'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span>
<span class="n">climb_count</span><span class="o">=</span><span class="p">(</span><span class="s1">'uuid'</span><span class="p">,</span> <span class="s1">'nunique'</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">layouts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'layout_name'</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">layout</span> <span class="ow">in</span> <span class="n">layouts</span><span class="p">:</span>
<span class="n">df_layout</span> <span class="o">=</span> <span class="n">df_setters</span><span class="p">[</span><span class="n">df_setters</span><span class="p">[</span><span class="s1">'layout_name'</span><span class="p">]</span> <span class="o">==</span> <span class="n">layout</span><span class="p">]</span>
<span class="n">df_top</span> <span class="o">=</span> <span class="n">df_layout</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'climb_count'</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">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df_display</span> <span class="o">=</span> <span class="n">df_top</span><span class="p">[[</span><span class="s1">'setter_username'</span><span class="p">,</span> <span class="s1">'climb_count'</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Username'</span><span class="p">,</span> <span class="s1">'Climbs'</span><span class="p">]</span>
<span class="c1"># Reset index to show rank</span>
<span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">df_display</span><span class="o">.</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span>
<span class="c1"># Display</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="si">{</span><span class="n">layout</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_display</span><span class="p">)</span>
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Original Layout
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<th>1</th>
<td>adamf1234</td>
<td>466</td>
</tr>
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<th>2</th>
<td>jonlackman</td>
<td>383</td>
</tr>
<tr>
<th>3</th>
<td>willanglin</td>
<td>332</td>
</tr>
<tr>
<th>4</th>
<td>kylejosephharding</td>
<td>328</td>
</tr>
<tr>
<th>5</th>
<td>str8_crimpin</td>
<td>247</td>
</tr>
<tr>
<th>6</th>
<td>tmon</td>
<td>246</td>
</tr>
<tr>
<th>7</th>
<td>gibbs</td>
<td>240</td>
</tr>
<tr>
<th>8</th>
<td>topoutclimbinggym</td>
<td>229</td>
</tr>
<tr>
<th>9</th>
<td>senderone</td>
<td>227</td>
</tr>
<tr>
<th>10</th>
<td>memphisben</td>
<td>198</td>
</tr>
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Tension Board 2 Mirror
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<th>1</th>
<td>tensionclimbing</td>
<td>513</td>
</tr>
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<th>2</th>
<td>SocksonBlocs</td>
<td>372</td>
</tr>
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<th>3</th>
<td>limberlimb</td>
<td>353</td>
</tr>
<tr>
<th>4</th>
<td>jaketiger111</td>
<td>320</td>
</tr>
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<th>5</th>
<td>mo3_3az</td>
<td>307</td>
</tr>
<tr>
<th>6</th>
<td>lijahl</td>
<td>306</td>
</tr>
<tr>
<th>7</th>
<td>iansutherland</td>
<td>218</td>
</tr>
<tr>
<th>8</th>
<td>theruz</td>
<td>203</td>
</tr>
<tr>
<th>9</th>
<td>AlexK</td>
<td>203</td>
</tr>
<tr>
<th>10</th>
<td>nicholson.brendan</td>
<td>174</td>
</tr>
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Tension Board 2 Spray
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<th>1</th>
<td>MaxClark</td>
<td>246</td>
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<th>2</th>
<td>ianmek</td>
<td>217</td>
</tr>
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<th>3</th>
<td>tensionclimbing</td>
<td>175</td>
</tr>
<tr>
<th>4</th>
<td>Jeremy_Fullerton</td>
<td>171</td>
</tr>
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<th>5</th>
<td>danielwoodseyetattoo</td>
<td>148</td>
</tr>
<tr>
<th>6</th>
<td>Fatbeninco</td>
<td>136</td>
</tr>
<tr>
<th>7</th>
<td>rprops</td>
<td>130</td>
</tr>
<tr>
<th>8</th>
<td>SPIGGOTT</td>
<td>125</td>
</tr>
<tr>
<th>9</th>
<td>milo_forbes</td>
<td>118</td>
</tr>
<tr>
<th>10</th>
<td>cotton125</td>
<td>116</td>
</tr>
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<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h1><p>At this point we have a board-level and climb-level picture of the dataset. In particular, we now know:</p>
<ul>
<li>how large the dataset is,</li>
<li>how the grade and angle distributions vary across layouts,</li>
<li>which climbs and setters appear most often,</li>
<li>and where simple descriptive trends begin to show up.</li>
</ul>
<p>That gives us enough context to move from <em>global statistics</em> to <em>hold-level structure</em>. The next notebook focuses on hold usage patterns and board heatmaps, where we stop asking only <strong>how many climbs there are</strong> and start asking <strong>which physical parts of the board are driving those climbs</strong>.</p>
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