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/* Icons urls */
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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,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5OCkiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik05LjI1IDEwLjA2OTNMNy4zNzUgMTAuMDY5M0w3LjM3NSA4LjE5NDM0QzcuMzc1IDcuOTg4MDkgNy4yMDYyNSA3LjgxOTM0IDcgNy44MTkzNEM2Ljc5Mzc1IDcuODE5MzQgNi42MjUgNy45ODgwOSA2LjYyNSA4LjE5NDM0TDYuNjI1IDEwLjA2OTNMNC43NSAxMC4wNjkzQzQuNTQzNzUgMTAuMDY5MyA0LjM3NSAxMC4yMzgxIDQuMzc1IDEwLjQ0NDNDNC4zNzUgMTAuNjUwNiA0LjU0Mzc1IDEwLjgxOTMgNC43NSAxMC44MTkzTDYuNjI1IDEwLjgxOTNMNi42MjUgMTIuNjk0M0M2LjYyNSAxMi45MDA2IDYuNzkzNzUgMTMuMDY5MyA3IDEzLjA2OTNDNy4yMDYyNSAxMy4wNjkzIDcuMzc1IDEyLjkwMDYgNy4zNzUgMTIuNjk0M0w3LjM3NSAxMC44MTkzTDkuMjUgMTAuODE5M0M5LjQ1NjI1IDEwLjgxOTMgOS42MjUgMTAuNjUwNiA5LjYyNSAxMC40NDQzQzkuNjI1IDEwLjIzODEgOS40NTYyNSAxMC4wNjkzIDkuMjUgMTAuMDY5M1oiIGZpbGw9IiM2MTYxNjEiIHN0cm9rZT0iIzYxNjE2MSIgc3Ryb2tlLXdpZHRoPSIwLjciLz4KPC9nPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJNMi41IDUuNUwyLjUgMy41TDExLjUgMy41TDExLjUgNS41TDIuNSA1LjVaTTIgN0MxLjQ0NzcyIDcgMSA2LjU1MjI4IDEgNkwxIDNDMSAyLjQ0NzcyIDEuNDQ3NzIgMiAyIDJMMTIgMkMxMi41NTIzIDIgMTMgMi40NDc3MiAxMyAzTDEzIDZDMTMgNi41NTIyOSAxMi41NTIzIDcgMTIgN0wyIDdaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5OCI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KDEgMS43NDg0NmUtMDcgMS43NDg0NmUtMDcgLTEgNCAxMy40NDQzKSIvPgo8L2NsaXBQYXRoPgo8L2RlZnM+Cjwvc3ZnPgo=);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE2IDE2IiB2ZXJzaW9uPSIxLjEiPgogICA8cGF0aCBjbGFzcz0ianAtaWNvbjIganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMzMzMzMzIgogICAgICBkPSJtOCAwLjI5Yy0xLjQgMC0yLjcgMC43My0zLjYgMS44LTEuMiAxLjUtMS40IDMuNC0xLjUgNS4yLTAuMTggMi4yLTAuNDQgNC0yLjMgNS4zbDAuMjggMS4zaDVjMC4wMjYgMC42NiAwLjMyIDEuMSAwLjcxIDEuNSAwLjg0IDAuNjEgMiAwLjYxIDIuOCAwIDAuNTItMC40IDAuNi0xIDAuNzEtMS41aDVsMC4yOC0xLjNjLTEuOS0wLjk3LTIuMi0zLjMtMi4zLTUuMy0wLjEzLTEuOC0wLjI2LTMuNy0xLjUtNS4yLTAuODUtMS0yLjItMS44LTMuNi0xLjh6bTAgMS40YzAuODggMCAxLjkgMC41NSAyLjUgMS4zIDAuODggMS4xIDEuMSAyLjcgMS4yIDQuNCAwLjEzIDEuNyAwLjIzIDMuNiAxLjMgNS4yaC0xMGMxLjEtMS42IDEuMi0zLjQgMS4zLTUuMiAwLjEzLTEuNyAwLjMtMy4zIDEuMi00LjQgMC41OS0wLjcyIDEuNi0xLjMgMi41LTEuM3ptLTAuNzQgMTJoMS41Yy0wLjAwMTUgMC4yOCAwLjAxNSAwLjc5LTAuNzQgMC43OS0wLjczIDAuMDAxNi0wLjcyLTAuNTMtMC43NC0wLjc5eiIgLz4KPC9zdmc+Cg==);
--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="Tutorial:-Unstructured-convolutional-autoencoder-via-continuous-convolution">Tutorial: Unstructured convolutional autoencoder via continuous convolution<a class="anchor-link" href="#Tutorial:-Unstructured-convolutional-autoencoder-via-continuous-convolution">¶</a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial4/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
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<p>In this tutorial, we will show how to use the Continuous Convolutional Filter, and how to build common Deep Learning architectures with it. The implementation of the filter follows the original work <a href="https://arxiv.org/abs/2210.13416"><em>A Continuous Convolutional Trainable Filter for Modelling Unstructured Data</em></a>.</p>
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<p>First of all we import the modules needed for the tutorial:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1">## routine needed to run the notebook on Google Colab</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">google.colab</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">except</span><span class="p">:</span>
<span class="n">IN_COLAB</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">IN_COLAB</span><span class="p">:</span>
<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"pina-mathlab"</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</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">torchvision</span> <span class="c1"># for MNIST dataset</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina</span><span class="w"> </span><span class="kn">import</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.problem.zoo</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedProblem</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.solver</span><span class="w"> </span><span class="kn">import</span> <span class="n">SupervisedSolver</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.trainer</span><span class="w"> </span><span class="kn">import</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model.block</span><span class="w"> </span><span class="kn">import</span> <span class="n">ContinuousConvBlock</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.model</span><span class="w"> </span><span class="kn">import</span> <span class="n">FeedForward</span> <span class="c1"># for building AE and MNIST classification</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span>
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<p>The tutorial is structured as follow:</p>
<ul>
<li><a href="#continuous-filter-background">Continuous filter background</a>: understand how the convolutional filter works and how to use it.</li>
<li><a href="#building-a-mnist-classifier">Building a MNIST Classifier</a>: show how to build a simple classifier using the MNIST dataset and how to combine a continuous convolutional layer with a feedforward neural network.</li>
<li><a href="#building-a-continuous-convolutional-autoencoder">Building a Continuous Convolutional Autoencoder</a>: show how to use the continuous filter to work with unstructured data for autoencoding and up-sampling.</li>
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<h2 id="Continuous-filter-background">Continuous filter background<a class="anchor-link" href="#Continuous-filter-background">¶</a></h2>
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<p>As reported by the authors in the original paper: in contrast to discrete convolution, continuous convolution is mathematically defined as:</p>
<p>$$
\mathcal{I}_{\rm{out}}(\mathbf{x}) = \int_{\mathcal{X}} \mathcal{I}(\mathbf{x} + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{\tau}) d\mathbf{\tau},
$$
where $\mathcal{K} : \mathcal{X} \rightarrow \mathbb{R}$ is the <em>continuous filter</em> function, and $\mathcal{I} : \Omega \subset \mathbb{R}^N \rightarrow \mathbb{R}$ is the input function. The continuous filter function is approximated using a FeedForward Neural Network, thus trainable during the training phase. The way in which the integral is approximated can be different, currently on <strong>PINA</strong> we approximate it using a simple sum, as suggested by the authors. Thus, given $\{\mathbf{x}_i\}_{i=1}^{n}$ points in $\mathbb{R}^N$ of the input function mapped on the $\mathcal{X}$ filter domain, we approximate the above equation as:
$$
\mathcal{I}_{\rm{out}}(\mathbf{\tilde{x}}_i) = \sum_{{\mathbf{x}_i}\in\mathcal{X}} \mathcal{I}(\mathbf{x}_i + \mathbf{\tau}) \cdot \mathcal{K}(\mathbf{x}_i),
$$
where $\mathbf{\tau} \in \mathcal{S}$, with $\mathcal{S}$ the set of available strides, corresponds to the current stride position of the filter, and $\mathbf{\tilde{x}}_i$ points are obtained by taking the centroid of the filter position mapped on the $\Omega$ domain.</p>
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<p>We will now try to pratically see how to work with the filter. From the above definition we see that what is needed is:</p>
<ol>
<li>A domain and a function defined on that domain (the input)</li>
<li>A stride, corresponding to the positions where the filter needs to be $\rightarrow$ <code>stride</code> variable in <code>ContinuousConv</code></li>
<li>The filter rectangular domain $\rightarrow$ <code>filter_dim</code> variable in <code>ContinuousConv</code></li>
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<h3 id="Input-function">Input function<a class="anchor-link" href="#Input-function">¶</a></h3><p>The input function for the continuous filter is defined as a tensor of shape: $$[B \times N_{in} \times N \times D]$$ where $B$ is the batch_size, $N_{in}$ is the number of input fields, $N$ the number of points in the mesh, $D$ the dimension of the problem. In particular:</p>
<ul>
<li>$D$ is the number of spatial variables + 1. The last column must contain the field value. For example for 2D problems $D=3$ and the tensor will be something like <code>[first coordinate, second coordinate, field value]</code></li>
<li>$N_{in}$ represents the number of vectorial function presented. For example a vectorial function $f = [f_1, f_2]$ will have $N_{in}=2$</li>
</ul>
<p>Let's see an example to clear the ideas. We will be verbose to explain in details the input form. We wish to create the function:
$$
f(x, y) = [\sin(\pi x) \sin(\pi y), -\sin(\pi x) \sin(\pi y)] \quad (x,y)\in[0,1]\times[0,1]
$$</p>
<p>using a batch size equal to 1.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># batch size fixed to 1</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># points in the mesh fixed to 200</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">200</span>
<span class="c1"># vectorial 2 dimensional function, number_input_fields=2</span>
<span class="n">number_input_fields</span> <span class="o">=</span> <span class="mi">2</span>
<span class="c1"># 2 dimensional spatial variables, D = 2 + 1 = 3</span>
<span class="n">D</span> <span class="o">=</span> <span class="mi">3</span>
<span class="c1"># create the function f domain as random 2d points in [0, 1]</span>
<span class="n">domain</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">number_input_fields</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">D</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Domain has shape: </span><span class="si">{</span><span class="n">domain</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># create the functions</span>
<span class="n">pi</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mf">1.0</span><span class="p">]))</span> <span class="c1"># pi value</span>
<span class="n">f1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">f2</span> <span class="o">=</span> <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">domain</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># stacking the input domain and field values</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">number_input_fields</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">D</span><span class="p">))</span>
<span class="n">data</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">domain</span> <span class="c1"># copy the domain</span>
<span class="n">data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1</span> <span class="c1"># copy first field value</span>
<span class="n">data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1</span> <span class="c1"># copy second field value</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter input data has shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Domain has shape: torch.Size([1, 2, 200, 2])
Filter input data has shape: torch.Size([1, 2, 200, 3])
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<h3 id="Stride">Stride<a class="anchor-link" href="#Stride">¶</a></h3><p>The stride is passed as a dictionary <code>stride</code> which tells the filter where to go. Here is an example for the $[0,1]\times[0,5]$ domain:</p>
<div class="highlight"><pre><span></span><span class="c1"># stride definition</span>
<span class="n">stride</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jump"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">}</span>
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<p>This tells the filter:</p>
<ol>
<li><code>domain</code>: square domain (the only implemented) $[0,1]\times[0,5]$. The minimum value is always zero, while the maximum is specified by the user</li>
<li><code>start</code>: start position of the filter, coordinate $(0, 0)$</li>
<li><code>jump</code>: the jumps of the centroid of the filter to the next position $(0.1, 0.3)$</li>
<li><code>direction</code>: the directions of the jump, with <code>1 = right</code>, <code>0 = no jump</code>, <code>-1 = left</code> with respect to the current position</li>
</ol>
<p><strong>Note</strong></p>
<p>We are planning to release the possibility to directly pass a list of possible strides!</p>
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<h3 id="Filter-definition">Filter definition<a class="anchor-link" href="#Filter-definition">¶</a></h3><p>Having defined all the previous blocks, we are now able to construct the continuous filter.</p>
<p>Suppose we would like to get an output with only one field, and let us fix the filter dimension to be $[0.1, 0.1]$.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># filter dim</span>
<span class="n">filter_dim</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span>
<span class="c1"># stride</span>
<span class="n">stride</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jump"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.08</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">}</span>
<span class="c1"># creating the filter</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="p">)</span>
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<p>That's it! In just one line of code we have created the continuous convolutional filter. By default the <code>pina.model.FeedForward</code> neural network is intitialised, more on the <a href="https://mathlab.github.io/PINA/_rst/fnn.html">documentation</a>. In case the mesh doesn't change during training we can set the <code>optimize</code> flag equals to <code>True</code>, to exploit optimizations for finding the points to convolve.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># creating the filter + optimization</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
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<p>Let's try to do a forward pass:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter input data has shape: </span><span class="si">{</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># input to the filter</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">cConv</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Filter output data has shape: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Filter input data has shape: torch.Size([1, 2, 200, 3])
Filter output data has shape: torch.Size([1, 1, 169, 3])
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<p>If we don't want to use the default <code>FeedForward</code> neural network, we can pass a specified torch model in the <code>model</code> keyword as follow:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">SimpleKernel</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cConv</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="n">number_input_fields</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="n">filter_dim</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">SimpleKernel</span><span class="p">,</span>
<span class="p">)</span>
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<p>Notice that we pass the class and not an already built object!</p>
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<h2 id="Building-a-MNIST-Classifier">Building a MNIST Classifier<a class="anchor-link" href="#Building-a-MNIST-Classifier">¶</a></h2><p>Let's see how we can build a MNIST classifier using a continuous convolutional filter. We will use the MNIST dataset from PyTorch. In order to keep small training times we use only 6000 samples for training and 1000 samples for testing.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">torch.utils.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">SubsetRandomSampler</span>
<span class="n">numb_training</span> <span class="o">=</span> <span class="mi">6000</span> <span class="c1"># get just 6000 images for training</span>
<span class="n">numb_testing</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># get just 1000 images for training</span>
<span class="n">seed</span> <span class="o">=</span> <span class="mi">111</span> <span class="c1"># for reproducibility</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">8</span> <span class="c1"># setting batch size</span>
<span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="c1"># downloading the dataset</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
<span class="s2">"./data/"</span><span class="p">,</span>
<span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
<span class="p">[</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">torchvision</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,)),</span>
<span class="p">]</span>
<span class="p">),</span>
<span class="p">)</span>
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<pre>Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
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<pre>Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz
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<pre>Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw
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Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz
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<pre>Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz
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<pre>Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 404: Not Found
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
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<pre>Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz
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<p>Let's now build a simple classifier. The MNIST dataset is composed by vectors of shape <code>[batch, 1, 28, 28]</code>, but we can image them as one field functions where the pixels $ij$ are the coordinate $x=i, y=j$ in a $[0, 27]\times[0,27]$ domain, and the pixels values are the field values. We just need a function to transform the regular tensor in a tensor compatible for the continuous filter:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">transform_input</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">dim_grid</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># creating the n dimensional mesh grid for a single channel image</span>
<span class="n">values_mesh</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">dim_grid</span><span class="p">]</span>
<span class="n">mesh</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">values_mesh</span><span class="p">)</span>
<span class="n">coordinates_mesh</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mesh</span><span class="p">]</span>
<span class="n">coordinates</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">coordinates_mesh</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="o">.</span><span class="n">repeat</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">coordinates</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
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<p>We can now build a simple classifier! We will use just one convolutional filter followed by a feedforward neural network</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ContinuousClassifier</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># number of classes for classification</span>
<span class="n">numb_class</span> <span class="o">=</span> <span class="mi">10</span>
<span class="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">27</span><span class="p">,</span> <span class="mi">27</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="mi">196</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="n">numb_class</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">120</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
<span class="n">func</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># transform input + convolution</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">transform_input</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># feed forward classification</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
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<p>We now aim to solve the classification problem. For this we will use the <code>SupervisedSolver</code> and the <code>SupervisedProblem</code>. The input of the supervised problems are the images, while the output the corresponding class.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">SupervisedProblem</span><span class="p">(</span>
<span class="n">input_</span><span class="o">=</span><span class="n">train_data</span><span class="o">.</span><span class="n">train_data</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="c1"># adding channel dimension</span>
<span class="n">output_</span><span class="o">=</span><span class="n">train_data</span><span class="o">.</span><span class="n">train_labels</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># setting the solver</span>
<span class="n">solver</span> <span class="o">=</span> <span class="n">SupervisedSolver</span><span class="p">(</span>
<span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">ContinuousClassifier</span><span class="p">(),</span>
<span class="n">loss</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">(),</span>
<span class="n">use_lt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># setting the trainer</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">solver</span><span class="o">=</span><span class="n">solver</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span>
<span class="n">enable_model_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">train_size</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<pre>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>Missing logger folder: /home/runner/work/PINA/PINA/tutorials/tutorial4/lightning_logs
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<pre>`Trainer.fit` stopped: `max_epochs=1` reached.
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<p>Let's see the performance on the test set!</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">setup</span><span class="p">(</span><span class="s2">"test"</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">trainer</span><span class="o">.</span><span class="n">data_module</span><span class="o">.</span><span class="n">test_dataloader</span><span class="p">():</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"data"</span><span class="p">]</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">test_data</span><span class="p">[</span><span class="s2">"input"</span><span class="p">],</span> <span class="n">test_data</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="c1"># calculate outputs by running images through the network</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="n">solver</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="c1"># the class with the highest energy is what we choose as prediction</span>
<span class="n">_</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">total</span> <span class="o">+=</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy of the network on the test images: </span><span class="si">{</span><span class="p">(</span><span class="n">correct</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">total</span><span class="p">)</span><span class="si">:</span><span class="s2">.3%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Accuracy of the network on the test images: 81.550%
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<p>As we can see we have very good performance for having trained only for 1 epoch! Nevertheless, we are still using structured data... Let's see how we can build an autoencoder for unstructured data now.</p>
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<h2 id="Building-a-Continuous-Convolutional-Autoencoder">Building a Continuous Convolutional Autoencoder<a class="anchor-link" href="#Building-a-Continuous-Convolutional-Autoencoder">¶</a></h2><p>Just as toy problem, we will now build an autoencoder for the following function $f(x,y)=\sin(\pi x)\sin(\pi y)$ on the unit circle domain centered in $(0.5, 0.5)$. We will also see the ability to up-sample (once trained) the results without retraining. Let's first create the input and visualize it, we will use firstly a mesh of $100$ points.</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="c1"># create inputs</span>
<span class="k">def</span><span class="w"> </span><span class="nf">circle_grid</span><span class="p">(</span><span class="n">N</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Generate points withing a unit 2D circle centered in (0.5, 0.5)</span>
<span class="sd"> :param N: number of points</span>
<span class="sd"> :type N: float</span>
<span class="sd"> :return: [x, y] array of points</span>
<span class="sd"> :rtype: torch.tensor</span>
<span class="sd"> """</span>
<span class="n">PI</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">R</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">centerX</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">centerY</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">R</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">))</span>
<span class="n">theta</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">PI</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">centerX</span> <span class="o">+</span> <span class="n">r</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">centerY</span> <span class="o">+</span> <span class="n">r</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
<span class="c1"># create the grid</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">500</span><span class="p">)</span>
<span class="c1"># create input</span>
<span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid</span>
<span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span>
<span class="n">pi</span> <span class="o">*</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># visualize data</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Training sample with 500 points"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>Let's now build a simple autoencoder using the continuous convolutional filter. The data is clearly unstructured and a simple convolutional filter might not work without projecting or interpolating first. Let's first build and <code>Encoder</code> and <code>Decoder</code> class, and then a <code>Autoencoder</code> class that contains both.</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="k">class</span><span class="w"> </span><span class="nc">Encoder</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_dimension</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="n">hidden_dimension</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">240</span><span class="p">,</span> <span class="mi">120</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># convolution</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># feed forward pass</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Decoder</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_dimension</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># convolutional block</span>
<span class="bp">self</span><span class="o">.</span><span class="n">convolution</span> <span class="o">=</span> <span class="n">ContinuousConvBlock</span><span class="p">(</span>
<span class="n">input_numb_field</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">output_numb_field</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"domain"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="s2">"start"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="s2">"jumps"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">],</span>
<span class="s2">"direction"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span>
<span class="p">},</span>
<span class="n">filter_dim</span><span class="o">=</span><span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">],</span>
<span class="n">optimize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># feedforward net</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nn</span> <span class="o">=</span> <span class="n">FeedForward</span><span class="p">(</span>
<span class="n">input_dimensions</span><span class="o">=</span><span class="n">hidden_dimension</span><span class="p">,</span>
<span class="n">output_dimensions</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="mi">120</span><span class="p">,</span> <span class="mi">240</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">grid</span><span class="p">):</span>
<span class="c1"># feed forward pass</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nn</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="c1"># transpose convolution</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">convolution</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">grid</span><span class="p">))</span>
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<p>Very good! Notice that in the <code>Decoder</code> class in the <code>forward</code> pass we have used the <code>.transpose()</code> method of the <code>ContinuousConvolution</code> class. This method accepts the <code>weights</code> for upsampling and the <code>grid</code> on where to upsample. Let's now build the autoencoder! We set the hidden dimension in the <code>hidden_dimension</code> variable. We apply the sigmoid on the output since the field value is between $[0, 1]$.</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="k">class</span><span class="w"> </span><span class="nc">Autoencoder</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_dimension</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">Encoder</span><span class="p">(</span><span class="n">hidden_dimension</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">Decoder</span><span class="p">(</span><span class="n">hidden_dimension</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># saving grid for later upsampling</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># encoder</span>
<span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># decoder</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">grid</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span>
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<p>Let's now train the autoencoder, minimizing the mean square error loss and optimizing using Adam. We use the <code>SupervisedSolver</code> as solver, and the problem is a simple problem created by inheriting from <code>AbstractProblem</code>. It takes approximately two minutes to train on CPU.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># define the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">SupervisedProblem</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
<span class="c1"># define the solver</span>
<span class="n">solver</span> <span class="o">=</span> <span class="n">SupervisedSolver</span><span class="p">(</span>
<span class="n">problem</span><span class="o">=</span><span class="n">problem</span><span class="p">,</span>
<span class="n">model</span><span class="o">=</span><span class="n">Autoencoder</span><span class="p">(),</span>
<span class="n">loss</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">(),</span>
<span class="n">use_lt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># train</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">solver</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span>
<span class="n">enable_model_summary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="c1"># we train on CPU and avoid model summary at beginning of training (optional)</span>
<span class="n">train_size</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">val_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">test_size</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<pre>GPU available: False, used: False
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<pre>TPU available: False, using: 0 TPU cores
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<pre>HPU available: False, using: 0 HPUs
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<pre>`Trainer.fit` stopped: `max_epochs=100` reached.
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<p>Let's visualize the two solutions side by side!</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">solver</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># get output and detach from computational graph for plotting</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># visualize data</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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_title</span><span class="p">(</span><span class="s2">"Autoencoder"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>As we can see, the two solutions are really similar! We can compute the $l_2$ error quite easily as well:</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="k">def</span><span class="w"> </span><span class="nf">l2_error</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">input_</span> <span class="o">-</span> <span class="n">target</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
<span class="n">input_</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">2</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>l2 error: 4.78%
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<p>More or less $4\%$ in $l_2$ error, which is really low considering the fact that we use just <strong>one</strong> convolutional layer and a simple feedforward to decrease the dimension. Let's see now some peculiarity of the filter.</p>
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<h3 id="Filter-for-upsampling">Filter for upsampling<a class="anchor-link" href="#Filter-for-upsampling">¶</a></h3><p>Suppose we have already the hidden representation and we want to upsample on a differen grid with more points. Let's see how to do it:</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="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">grid2</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">1500</span><span class="p">)</span> <span class="c1"># triple number of points</span>
<span class="n">input_data2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid2</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span>
<span class="n">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># get the hidden representation from original input</span>
<span class="n">latent</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
<span class="c1"># upsample on the second input_data2</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">latent</span><span class="p">,</span> <span class="n">input_data2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># show the picture</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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_title</span><span class="p">(</span><span class="s2">"Up-sampling"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<p>As we can see we have a very good approximation of the original function, even thought some noise is present. Let's calculate the error now:</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="nb">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
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<pre>l2 error: 9.72%
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<h3 id="Autoencoding-at-different-resolutions">Autoencoding at different resolutions<a class="anchor-link" href="#Autoencoding-at-different-resolutions">¶</a></h3><p>In the previous example we already had the hidden representation (of the original input) and we used it to upsample. Sometimes however we could have a finer mesh solution and we would simply want to encode it. This can be done without retraining! This procedure can be useful in case we have many points in the mesh and just a smaller part of them are needed for training. Let's see the results of this:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># setting the seed</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">grid2</span> <span class="o">=</span> <span class="n">circle_grid</span><span class="p">(</span><span class="mi">3500</span><span class="p">)</span> <span class="c1"># very fine mesh</span>
<span class="n">input_data2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">grid2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">grid2</span>
<span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span>
<span class="n">pi</span> <span class="o">*</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># get the hidden representation from finer mesh input</span>
<span class="n">latent</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">input_data2</span><span class="p">)</span>
<span class="c1"># upsample on the second input_data2</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">latent</span><span class="p">,</span> <span class="n">input_data2</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="c1"># show the picture</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">pic1</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Real"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">pic2</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">grid2</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">grid2</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</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_title</span><span class="p">(</span><span class="s2">"Autoencoder not re-trained"</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pic2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># calculate l2 error</span>
<span class="nb">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"l2 error: </span><span class="si">{</span><span class="n">l2_error</span><span class="p">(</span><span class="n">input_data2</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="w"> </span><span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="p">:,</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
</pre></div>
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<pre>l2 error: 9.62%
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<h2 id="What's-next?">What's next?<a class="anchor-link" href="#What's-next?">¶</a></h2><p>We have shown the basic usage of a convolutional filter. There are additional extensions possible:</p>
<ol>
<li><p>Train using Physics Informed strategies</p>
</li>
<li><p>Use the filter to build an unstructured convolutional autoencoder for reduced order modelling</p>
</li>
<li><p>Many more...</p>
</li>
</ol>
</div>
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