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PINA/docs/source/tutorials/tutorial3/tutorial.html
github-actions[bot] e917753702 export tutorials changed in c161138 (#498) 
Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>
2025-03-19 17:48:29 +01:00

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<title>tutorial</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script><script>
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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);
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bell: url(data:image/svg+xml;base64,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);
--jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,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);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,PHN2ZyBmaWxsPSJub25lIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI1Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgc3Ryb2tlPSIjMzMzMzMzIiBzdHJva2Utd2lkdGg9IjIiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDIgMykiIGQ9Ik0xLjg2MDk0IDExLjQ0MDlDMC44MjY0NDggOC43NzAyNyAwLjg2Mzc3OSA2LjA1NzY0IDEuMjQ5MDcgNC4xOTkzMkMyLjQ4MjA2IDMuOTMzNDcgNC4wODA2OCAzLjQwMzQ3IDUuNjAxMDIgMi44NDQ5QzcuMjM1NDkgMi4yNDQ0IDguODU2NjYgMS41ODE1IDkuOTg3NiAxLjA5NTM5QzExLjA1OTcgMS41ODM0MSAxMi42MDk0IDIuMjQ0NCAxNC4yMTggMi44NDMzOUMxNS43NTAzIDMuNDEzOTQgMTcuMzk5NSAzLjk1MjU4IDE4Ljc1MzkgNC4yMTM4NUMxOS4xMzY0IDYuMDcxNzcgMTkuMTcwOSA4Ljc3NzIyIDE4LjEzOSAxMS40NDA5QzE3LjAzMDMgMTQuMzAzMiAxNC42NjY4IDE3LjE4NDQgOS45OTk5OSAxOC45MzU0QzUuMzMzMiAxNy4xODQ0IDIuOTY5NjggMTQuMzAzMiAxLjg2MDk0IDExLjQ0MDlaIi8+CiAgICA8cGF0aCBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiMzMzMzMzMiIHN0cm9rZT0iIzMzMzMzMyIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoOCA5Ljg2NzE5KSIgZD0iTTIuODYwMTUgNC44NjUzNUwwLjcyNjU0OSAyLjk5OTU5TDAgMy42MzA0NUwyLjg2MDE1IDYuMTMxNTdMOCAwLjYzMDg3Mkw3LjI3ODU3IDBMMi44NjAxNSA0Ljg2NTM1WiIvPgo8L3N2Zz4K);
--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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KIDxnIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzQxNDE0MSI+CiAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiA8L2c+CiA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSguNDMgLjA0MDEpIiBmaWxsPSIjZmZmIj4KICA8cGF0aCBkPSJtNC4xNCA4Ljc2cTAuMDY4Mi0xLjg5IDIuNDItMS44OSAxLjE2IDAgMS42OCAwLjQyIDAuNTY3IDAuNDEgMC41NjcgMS4xNnYzLjQ3cTAgMC40NjIgMC41MTQgMC40NjIgMC4xMDMgMCAwLjItMC4wMjMxdjAuNzE0cS0wLjM5OSAwLjEwMy0wLjY1MSAwLjEwMy0wLjQ1MiAwLTAuNjkzLTAuMjItMC4yMzEtMC4yLTAuMjg0LTAuNjYyLTAuOTU2IDAuODcyLTIgMC44NzItMC45MDMgMC0xLjQ3LTAuNDcyLTAuNTI1LTAuNDcyLTAuNTI1LTEuMjYgMC0wLjI2MiAwLjA0NTItMC40NzIgMC4wNTY3LTAuMjIgMC4xMTYtMC4zNzggMC4wNjgyLTAuMTY4IDAuMjMxLTAuMzA0IDAuMTU4LTAuMTQ3IDAuMjYyLTAuMjQyIDAuMTE2LTAuMDkxNCAwLjM2OC0wLjE2OCAwLjI2Mi0wLjA5MTQgMC4zOTktMC4xMjYgMC4xMzYtMC4wNDUyIDAuNDcyLTAuMTAzIDAuMzM2LTAuMDU3OCAwLjUwNC0wLjA3OTggMC4xNTgtMC4wMjMxIDAuNTY3LTAuMDc5OCAwLjU1Ni0wLjA2ODIgMC43NzctMC4yMjEgMC4yMi0wLjE1MiAwLjIyLTAuNDQxdi0wLjI1MnEwLTAuNDMtMC4zNTctMC42NjItMC4zMzYtMC4yMzEtMC45NzYtMC4yMzEtMC42NjIgMC0wLjk5OCAwLjI2Mi0wLjMzNiAwLjI1Mi0wLjM5OSAwLjc5OHptMS44OSAzLjY4cTAuNzg4IDAgMS4yNi0wLjQxIDAuNTA0LTAuNDIgMC41MDQtMC45MDN2LTEuMDVxLTAuMjg0IDAuMTM2LTAuODYxIDAuMjMxLTAuNTY3IDAuMDkxNC0wLjk4NyAwLjE1OC0wLjQyIDAuMDY4Mi0wLjc2NiAwLjMyNi0wLjMzNiAwLjI1Mi0wLjMzNiAwLjcwNHQwLjMwNCAwLjcwNCAwLjg2MSAwLjI1MnoiIHN0cm9rZS13aWR0aD0iMS4wNSIvPgogIDxwYXRoIGQ9Im0xMCA0LjU2aDAuOTQ1djMuMTVxMC42NTEtMC45NzYgMS44OS0wLjk3NiAxLjE2IDAgMS44OSAwLjg0IDAuNjgyIDAuODQgMC42ODIgMi4zMSAwIDEuNDctMC43MDQgMi40Mi0wLjcwNCAwLjg4Mi0xLjg5IDAuODgyLTEuMjYgMC0xLjg5LTEuMDJ2MC43NjZoLTAuODV6bTIuNjIgMy4wNHEtMC43NDYgMC0xLjE2IDAuNjQtMC40NTIgMC42My0wLjQ1MiAxLjY4IDAgMS4wNSAwLjQ1MiAxLjY4dDEuMTYgMC42M3EwLjc3NyAwIDEuMjYtMC42MyAwLjQ5NC0wLjY0IDAuNDk0LTEuNjggMC0xLjA1LTAuNDcyLTEuNjgtMC40NjItMC42NC0xLjI2LTAuNjR6IiBzdHJva2Utd2lkdGg9IjEuMDUiLz4KICA8cGF0aCBkPSJtMi43MyAxNS44IDEzLjYgMC4wMDgxYzAuMDA2OSAwIDAtMi42IDAtMi42IDAtMC4wMDc4LTEuMTUgMC0xLjE1IDAtMC4wMDY5IDAtMC4wMDgzIDEuNS0wLjAwODMgMS41LTJlLTMgLTAuMDAxNC0xMS4zLTAuMDAxNC0xMS4zLTAuMDAxNGwtMC4wMDU5Mi0xLjVjMC0wLjAwNzgtMS4xNyAwLjAwMTMtMS4xNyAwLjAwMTN6IiBzdHJva2Utd2lkdGg9Ii45NzUiLz4KIDwvZz4KPC9zdmc+Cg==);
--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:-Two-dimensional-Wave-problem-with-hard-constraint">Tutorial: Two dimensional Wave problem with hard constraint<a class="anchor-link" href="#Tutorial:-Two-dimensional-Wave-problem-with-hard-constraint"></a></h1><p><a href="https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial3/tutorial.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"/></a></p>
<p>In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum <code>torch</code> model and pass it to the <code>PINN</code> solver.</p>
<p>First of all, some useful imports.</p>
</div>
</div>
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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">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">Condition</span><span class="p">,</span> <span class="n">LabelTensor</span><span class="p">,</span> <span class="n">Trainer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.problem</span><span class="w"> </span><span class="kn">import</span> <span class="n">SpatialProblem</span><span class="p">,</span> <span class="n">TimeDependentProblem</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.operator</span><span class="w"> </span><span class="kn">import</span> <span class="n">laplacian</span><span class="p">,</span> <span class="n">grad</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.domain</span><span class="w"> </span><span class="kn">import</span> <span class="n">CartesianDomain</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">PINN</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.equation</span><span class="w"> </span><span class="kn">import</span> <span class="n">Equation</span><span class="p">,</span> <span class="n">FixedValue</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pina.callback</span><span class="w"> </span><span class="kn">import</span> <span class="n">MetricTracker</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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<h2 id="The-problem-definition">The problem definition<a class="anchor-link" href="#The-problem-definition"></a></h2>
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<p>The problem is written in the following form:</p>
<p>\begin{equation}
\begin{cases}
\Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\
u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\
u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4,
\end{cases}
\end{equation}</p>
<p>where $D$ is a squared domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one.</p>
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<p>Now, the wave problem is written in PINA code as a class, inheriting from <code>SpatialProblem</code> and <code>TimeDependentProblem</code> since we deal with spatial, and time dependent variables. The equations are written as <code>conditions</code> that should be satisfied in the corresponding domains. <code>solution</code> is the exact solution which will be compared with the predicted one.</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">wave_equation</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">output_</span><span class="p">):</span>
<span class="n">u_t</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">output_</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"u"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"t"</span><span class="p">])</span>
<span class="n">u_tt</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">u_t</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"dudt"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"t"</span><span class="p">])</span>
<span class="n">nabla_u</span> <span class="o">=</span> <span class="n">laplacian</span><span class="p">(</span><span class="n">output_</span><span class="p">,</span> <span class="n">input_</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s2">"u"</span><span class="p">],</span> <span class="n">d</span><span class="o">=</span><span class="p">[</span><span class="s2">"x"</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">])</span>
<span class="k">return</span> <span class="n">nabla_u</span> <span class="o">-</span> <span class="n">u_tt</span>
<span class="k">def</span><span class="w"> </span><span class="nf">initial_condition</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">output_</span><span class="p">):</span>
<span class="n">u_expected</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">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">input_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</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">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">input_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">output_</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"u"</span><span class="p">])</span> <span class="o">-</span> <span class="n">u_expected</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Wave</span><span class="p">(</span><span class="n">TimeDependentProblem</span><span class="p">,</span> <span class="n">SpatialProblem</span><span class="p">):</span>
<span class="n">output_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"u"</span><span class="p">]</span>
<span class="n">spatial_domain</span> <span class="o">=</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
<span class="n">temporal_domain</span> <span class="o">=</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
<span class="n">domains</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"g1"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g2"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g3"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"g4"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="s2">"initial"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="mi">0</span><span class="p">}),</span>
<span class="s2">"D"</span><span class="p">:</span> <span class="n">CartesianDomain</span><span class="p">({</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"y"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"t"</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}),</span>
<span class="p">}</span>
<span class="n">conditions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"g1"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g1"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g2"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g2"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g3"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g3"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"g4"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"g4"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">FixedValue</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)),</span>
<span class="s2">"initial"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span>
<span class="n">domain</span><span class="o">=</span><span class="s2">"initial"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">Equation</span><span class="p">(</span><span class="n">initial_condition</span><span class="p">)</span>
<span class="p">),</span>
<span class="s2">"D"</span><span class="p">:</span> <span class="n">Condition</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="s2">"D"</span><span class="p">,</span> <span class="n">equation</span><span class="o">=</span><span class="n">Equation</span><span class="p">(</span><span class="n">wave_equation</span><span class="p">)),</span>
<span class="p">}</span>
<span class="k">def</span><span class="w"> </span><span class="nf">solution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pts</span><span class="p">):</span>
<span class="n">f</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</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">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</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">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">tensor</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">pts</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">LabelTensor</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_variables</span><span class="p">)</span>
<span class="c1"># define problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">Wave</span><span class="p">()</span>
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<h2 id="Hard-Constraint-Model">Hard Constraint Model<a class="anchor-link" href="#Hard-Constraint-Model"></a></h2>
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<p>After the problem, a <strong>torch</strong> model is needed to solve the PINN. Usually, many models are already implemented in <strong>PINA</strong>, but the user has the possibility to build his/her own model in <code>torch</code>. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as:</p>
<p>$$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t), $$</p>
<p>where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unknown field $u$. By construction, it is zero on the boundaries. The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the method <code>discretise_domain</code>) and the loss minimized by the neural network is the sum of the residuals.</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">HardMLP</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">input_dim</span><span class="p">,</span> <span class="n">output_dim</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">layers</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="n">input_dim</span><span class="p">,</span> <span class="mi">40</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">40</span><span class="p">,</span> <span class="mi">40</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">40</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># here in the foward we implement the hard constraints</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="n">hard</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">hard</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<h2 id="Train-and-Inference">Train and Inference<a class="anchor-link" href="#Train-and-Inference"></a></h2>
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<p>In this tutorial, the neural network is trained for 1000 epochs with a learning rate of 0.001 (default in <code>PINN</code>). As always, we will log using <code>Tensorboard</code>.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># generate the data</span>
<span class="n">problem</span><span class="o">.</span><span class="n">discretise_domain</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">"random"</span><span class="p">,</span> <span class="n">domains</span><span class="o">=</span><span class="s2">"all"</span><span class="p">)</span>
<span class="c1"># define model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">HardMLP</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">input_variables</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">output_variables</span><span class="p">))</span>
<span class="c1"># crete the solver</span>
<span class="n">pinn</span> <span class="o">=</span> <span class="n">PINN</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">model</span><span class="p">)</span>
<span class="c1"># create trainer and 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="o">=</span><span class="n">pinn</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1000</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">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="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">MetricTracker</span><span class="p">([</span><span class="s2">"train_loss"</span><span class="p">,</span> <span class="s2">"initial_loss"</span><span class="p">,</span> <span class="s2">"D_loss"</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/tutorial3/lightning_logs
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<pre>`Trainer.fit` stopped: `max_epochs=1000` reached.
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<p>Let's now plot the losses inside <code>MetricTracker</code> to see how they vary during training.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">trainer_metrics</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">callbacks</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">metrics</span>
<span class="k">for</span> <span class="n">metric</span><span class="p">,</span> <span class="n">loss</span> <span class="ow">in</span> <span class="n">trainer_metrics</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">loss</span><span class="p">)),</span> <span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">metric</span><span class="p">)</span>
<span class="c1"># plotting</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"epoch"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"loss"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
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<pre>&lt;matplotlib.legend.Legend at 0x7f66c8f02610&gt;</pre>
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UmR/talikUxYw5+rVgos9TpISrMSuu4cFJjw2q0ZUtxuRuH1ezfO293XilfrdpDSkwYkQ4LXi+0ivN1/RWUuejQIipgtl5+iYs+j31Xq/cTG25j5cPn1OoxoU5BqI4oCImIHEXRXijZ51vNPGcdZK+B7FWQ/ZsvQNnCIW+7L/BUx2KHpC6+2XBF2b4ZckmdoGVf6HkZmOt3M92KrrqK5QgqWptW7MyjsMzN1n1F5BY7KSr3DRavbWiqzGI2kRLtICHKjgkTUQ4raQnhpMX7ppp/91s2q3blYzJBYqSd5OiwKgcuVxbt8LVUdWgRRZ82sfyeV8ony3YBMPSkRFbszKNDciTlLi/n927JGV2SWbhlP8kxDj5euoudB0qYcHZnLurb+rjfVwBnMexZ6RvLlrfDd4tIhE5nQ9rJ9f7zrKAgVEcUhERETlBpHuzfDPs2wO7lvrCzYwHEpfvGKmWtqv6xjhjfsgExrXytShY7tOrna0EKi/W1MiX3OLgxc/0zDINSl4ecgnK255aQV+KkoNQVsDBmfqmLvFIXBaUuHFYzheVudueVntB6TgA9W8dwoNi34GdNBpef1TWZf48bdEKvWWs7F8G0P/jGpFUlpjWk9oK4tr7g6z44GeDcp33j0OqQglAdURASEaln+zfD/k1wYDs4onxLB+zfBGtmgLMG07Ct4b7HtRkMhge6XuDrtotu6QtbpXmQ3M13TZBUdNUVl7vJKigjv8QXaPJKnezMLWVnbgm780tpFRvO+NNOIi7CTk5hGTmF5Xi9Bt1bxdAyNnBj5w3ZhSRHO9i6r5i8Ehc7D5Sw6vd8fj9Qyvb9xTw0pjvn9jzxsVnHdGA7OKJ9P8d3LvH9zCJbQIuuB9e9au2b7bj+K99q7lWZuKlmsyhrQUGojigIiYgESXkhHNh2sEVpo29WXFmer9vFWexbNiBvR/W/XCuzOGDwTb5WpIhEX8tETEvfQG6zxTdIvLnwHmyZOtEuKo8Lpt8Cqz8CTMDBONHuVPjjB2A/bGVpV6mvJfDAVt/PLf93X9dpZDIMzajzcWIKQnVEQUhEJIR5XLB3vW8dpaxffb9ct8zxhZ29G3wtRFZH9V014Otu63KebzmAVv18Yan9aRDf1he4mlJIWvs5fHwjdD0fLn/z6Nd+/wis+xJOvwd6Xe5bimH5/6DtMOhwJsy4DVa+G/iYk0bAVf8NautbBQWhOqIgJCLSSFWsbWQYsOYTWDrVt29bWR4UZvtaJjzOqh9rDYP4drB3nS8kxbT2BaWTb/G1UIXH+4JSY5N5Muxd67t/20JI7hp4vmCPb9zV1rnwzsW+Y2YbjJ8D714FBb4VqGnR1ffZmCzwh3d960kV5fgGRFtsDfVujkpBqI4oCImINFFlBb7umqxVsGU22CJ8s94Kfvd1vx2NyQw9L/d1r7UZ5AtHiR18q3SH2vpCHhfs2+gbdP5sh0Mz+Eb8DbJWwuY5cNkbvpa0hVN8IQfTocB0NBe+Av2vq8fij5+CUB1REBIRaWa8XljxX99yAPYIWPC6r0UoLMa3COXR2CJ8rUlDb/PNjPr6Hl/L0RVTfbPe7JGHglLFr99jBSe307ckQVKXwC4nw4CV03wtM8PvChxj43bClxN8df++yLf2U0IHyN1c88/BZIYr/gMfXHvo2B+m+bZu2Twb+lwN/a+t/vFBpiBURxSERESaObfzUHdPUbZv/NEPT/hCSGyab4xSRIJvnFJ1XW0VwmIhtbdvn7ffPvUFoyv+42tNqniNDd/6bp3Phc7nwEf/5xuQ3LIPnDoR5mf6lhPYNAvKDy5o2aofnPWwr0XKVQLL/uMb41OV1gNg19Kqz3U82xeWcrf4XuusB2H+azDvJeh7NYys5jlDkIJQHVEQEhGRGinY45v2v2MBzH7C16LS94+wff7RW2LMVl93VfowcBX71lqq0Kpf4Pd14cJXYM7TULDLN/7nghd9rUcJHeD6r3xhzVXimxLfiCkI1REFIRERqbXfl/qmhqd0921NsvE7X0tMeQFs/8U3GDkqxbcAYfZhC0qarZA2BHbMPzSexx59aE2ldqf6WqHCYuDKd3xT0b9/2NdFVhMT1vo24p0zCQbd5Gt1Ks3zLV7ZQKs+NwQFoTqiICQiIvXG6/WtkVS8zze1PbY19L7KN3MraxWs/9o3yLnPH32z3Ayvb3Nbr9c3tqjy+CJ3uS8UOWJ8A5+Tu8Gsx3wtVBe+5OtS630VDLk5aG+3ISkI1REFIRERadS8njrfvqIxqOnv76bTBiYiIiJHaoYhqDYUhERERKTZUhASERGRZktBSERERJotBSERERFpthSEREREpNlSEBIREZFmq1kEoUsuuYT4+Hguv/zyYJciIiIiIaRZBKE777yTt99+O9hliIiISIhpFkHojDPOIDq6cW8eJyIiInUv6EFo7ty5jBkzhlatWmEymZgxY8YR12RmZtKuXTvCwsIYMmQIixYtavhCRUREpMmxBruA4uJi+vTpww033MCll156xPn333+fCRMmMGXKFIYMGcLkyZMZNWoU69evJzk5GYC+ffvidruPeOx3331Hq1atalVPeXk55eXl/u8LCgpq+Y5ERESksQh6EBo9ejSjR4+u9vwLL7zATTfdxPXXXw/AlClT+PLLL3nzzTe59957AVixYkWd1TNp0iQeffTROns+ERERCV1B7xo7GqfTydKlSxk5cqT/mNlsZuTIkcyfP79eXvO+++4jPz/ff9u5c2e9vI6IiIgEX9BbhI5m3759eDweUlJSAo6npKSwbt26Gj/PyJEjWblyJcXFxbRp04YPP/yQoUOHVnmtw+HA4XCcUN0iIiLSOIR0EKor33//fbBLEBERkRAU0l1jSUlJWCwWsrOzA45nZ2eTmpoapKpERESkqQjpIGS32xkwYACzZs3yH/N6vcyaNavarq26kpmZSffu3Rk0aFC9vo6IiIgET9C7xoqKiti0aZP/+61bt7JixQoSEhJIT09nwoQJjB07loEDBzJ48GAmT55McXGxfxZZfcnIyCAjI4OCggJiY2Pr9bVEREQkOIIehJYsWcKIESP830+YMAGAsWPHMnXqVK666ir27t3LQw89RFZWFn379uWbb745YgC1iIiISG2ZDMMwgl1EKKtoEcrPzycmJibY5YiIiEgN1PT3d0iPERIRERGpTwpCIiIi0mwpCFVDs8ZERESaPo0ROgaNERIREWl8NEZIRERE5BgUhERERKTZUhASERGRZktBSERERJotBaFqaNaYiIhI06dZY8egWWMiIiKNj2aNiYiIiByDgpCIiIg0WwpCIiIi0mwpCImIiEizpSAkIiIizZaCUDU0fV5ERKTp0/T5Y9D0eRERkcZH0+dFRETkhBmGwSO/PMJbq98Kdin1whrsAkRERCR0rT+wno83fgzANd2uwW6xB7miuqUWIREREamW1/D672/O2xzESuqHgpCIiIhUq9xT7r+/4cCGIFZSPxSEREREpFpl7jL//e0F24NYSf1QEBIREZFqVQ5COwt3BrGS+qEgVA2tIyQiIs3FL7t/YU/RnirPlXmadouQ1hE6Bq0jJCIiTdnsHbO5Y/YddI7vzMcXfnzE+ekbp/PQLw8BYDfbOTP9TFbvW8213a+l1F3Ktd2vDcmZZDX9/a3p8yIiIs3Y++vfB3wDobOLs0mJTOFA2QGibFHYLLaAFiGn18k3274BYNKiSQAUOgvZmr+VvPI8/nnOP3FYHA3/Jk6AusZERESaMbfX7b+/ev9qckpyOO3907ju6+uAwDFCVXl//fv8sPMHluUs49NNn9ZrrfVBQUhERKQZc3ld/vu7CncxZ+ccwBeKDMPwtwi1jmqNCRPJ4clYTYc6lIpcRf77y3OWN0jNdUlBSEREpBlzG4dahHYV7cJmtvm/31+2398idGb6mXxxyRd8dslnnNrm1Cqf67f9v9VvsfVAQUhERKQZq9w19nvR7xQ6C/3f7y7aTam7FIAwSxjpMelE2iJpF9Ouyufamr+VEldJvdZb1xSEREREmrGAIFT4O7lluf7vdxXtYv7u+QC0imrlP946qnWVz2VgsP7A+nqqtH4oCImIiDRR+0r38cvuXzjaSjmVg9DOwp18uvnQgOel2UvZVrANq8nK6Paj/cfPP+l82kS1oWVkS0yYAp5vQ27j2oZD0+dFRESaqCs/v5K9pXuZPGIyZ6WfVeU1lYOQy+tiX+k+//dzf58LQLvYdkTaIv3Ho+xRfHXpV5hMJpbnLPe3HH22+TMKXYe61hoDtQhVQytLi4hIY7e3dC8A32z9hvfWvRfQ7VWhchA63J5i32rTHeM6HnHOZPK1BPVL7scFJ11AtD0agGJX8QnX3ZAUhKqRkZHBb7/9xuLFi4NdioiISK0YhhEQcL7Z9g1PLnySm2fefMS1Fdf1TurtP3Zt92sDrukQ1+GYrxlhjQDQYGkREREJHsMwuPX7Wzn/k/OPOLcud90Rxyqmz1/e+XKSwpPomdiTyzpdFnBNp7hOx3zdCNvBIORuXEFIY4RERESakDJPGfN2z6v2fH55PgXlBdz3833c3Ptm/4KKfZL7MPvK2QA4Pc6Ax3SMP7Jr7HAVY4jUNSYiIiJBU+4uP+r57QXb+cucv7By70pum3Wbv2vMZjq0kOLhm6imR6cf83X9XWONrEVIQUhERKQJqbxJalW2F2wPWOunIghZzYGdRLf3vR2Ayzpd5h8YfTQVLUKNbYyQusZERESakHLP0VuEdhftDvi+omvs8CA0vvd4zkg7g7YxbWv0uo11sLSCkIiISBNyrN3iS9wlWM3WI6bNHx6ETCYTXRK61Ph1KwZLa4yQiIiIBM2xWoQKnYVVrh10eBCqrcSwRMC3mvXRVrIONQpCIiIiIa7EVcIDPz/AZ5s/O+a1xwpC+0v3V3n8RINQamQqJkyUecqqXLgxVKlrTEREJER5vB4sZgvfbf+OzzZ/xmebP6NrQlfyyvJICEuoclr7sYJQ5S00KjvRIGSz2EiOSCa7JJtdRbtIDE88oedrKApCIiIiIeBA2QFeX/k6sY5Ybux1I19s/oInFjxBz6SetIlu47/uvXXv8eGGDwH49bpfj5jRdazp89UGIdOJR4LWUa3JLslmd9FuerfofewHhAAFIRERkRAwaeEkvt72NQCLsxazLHsZBgYr9q5gxd4V/usqQhDA/rL9JIUnBTxP5enzZ6SdwZydcwLOVxWErCZrjabIH0vrqNYsy1nGrqJdJ/xcDUVjhKqhTVdFROR4zf19Louzjr1XZZGziBU5K5j7+1x/CAJYmr0Ug2MPON6ct5ms4iwmL53MlvwtwKGusVNbn8rLI14+4jFOr/OIYyfaLVahVVQrgEYVhNQiVI2MjAwyMjIoKCggNjY22OWIiEgjsTV/KxmzMgBY9qdl2Cw2soqzSI5IxmwyU+Qs4uttX3Nyy5N5adlLfLvt22qfa8xJY/h8y+f+74e2HMr8PfP933+2+TPiHfH857f/8O/V/6ZtTFsGpgwEIMwaVuNWHovZcjxv9Qito1oDR65VFMoUhEREROrQvF2H9vnalLeJDQc28Ld5f+PO/nfyfz3/j4xZGSzLWYbVZPVveFrhwZMf5NPNn/Lr3l+Jc8RxeefLA4LQuB7jWJi1EK/hBXxBqPLYnu0F29lesB0Ah8VR45qbc4uQusZERETq0PKc5f77q/ev5m/z/gbAS8te4tbvb2VZzjKAI0LQ0JZDubLLlTx72rNc0fkKnhj+BN0Su5EckQzA4NTBDGs9jPfOf4/R7Ub7H3f481SoCCW39b0NgOGth1dbc21C09FUbhGqCGuhTi1CIiIitbTpwCaWZC/h4o4XE2YNA3zjgubtmsfc3+f6r3t37bsBj6tqV/hLO13KgJQB/i6tVlGteGjoQ/7zMy+fyZ7iPSSEJQDQLbEbXRK6BIwpAvhzvz8Ta4/liYVPkByRzNVdrwbg1j63MjJ9JHaLnQumX+C/PjUylaziLABOij3puD+LylIiUzCbzDi9TvaX7qdFRIs6ed76pCAkIiJSS4/Of5QVe1fw1dav6Jfcjx6JPfjbvL9R6i4NuG5T3ib/fbPJzFnpZ3Fllytxe93c+v2tAJyZdianp51e7WuZTWZ/S0uFw9foibRFclOvmzCZTAxKHURKZIp/E1SATvGdyC/PD3hMasShINQ5vnMt3n31bGYbKREp7Cnew87CnVUGof2l+3ls/mMszFrI0JZDefKUJ/3bcwSDgpCIiEglhmHwlzl/YXPeZqadP43vtn9H+9j29EvuB4DX8Pqnsy/PWR7QFVZhWKthOD1OlmQvAeCPXf/IXQPuItwaDvj2A2sT1QabxcawVsNqXeP57c/nvXXvsWb/GgDaxrT1D4w+Ka7q1p2KTVErpEamwl7f/b7JfWtdQ3W6xHdhT/EeVu1bRf+U/v7jLq+Lt1a/xfvr3ienNAeA73d8j3uum8kjJtfZgO3a0hghERGRSpblLGPWjllsK9jGM4uf4eFfHua6r69jRc4KLv3sUt5b9161j+2X3I8rOl/BPYPuCRiTMyBlgD8EgW9G14yLZ/DhmA+xWWy1rtFmsfHmqDf931d0mx3rMZUHVqdFp/nvD29V/fih2qpYSHHy0snM3D7Tf/yphU/xyvJXyCnNIT06nQdPfhCHxcGPv//I0uyldfb6taUWIRERkUqWZS/z35++abr//nVfX4eBwaRFk6p97BPDnyA9Jh3w7d4+ZeUUuid2r7Lr60QHKEfYIuie2J3f9v/Gjb1urNFjwm3hFDoLAWgR0YIPLvgAm9lWp11Tl3S6hBmbZrCjcAd/nfNX3jnvHawmKx9t+AgTJiYOnMjlnS8nwhZBQlgCJpOJwS0H19nr15aCkIiISCUbD2ys8vjhCxxe3vlyTm9zOolhidwx+w66xHfxhyCA9rHt+ekPPxFmqfl6PrX12lmvke/Mr/Fg53DroSAUaYukW2K3Oq8pKTyJ6RdN596f7mXm9pm8vuJ17BY7ABecdAHX9bjOf+3ItiPr/PVrS0FIRESaNbfXzdr9a+mc0Jm9JXvZmFd1EDrcmJPG+MfAzLx8JhbTkWNcKneH1YfE8MRabW5aeZxQpDXyKFeeGLvFzl8G/IVZO2b5Z8qZMHFT75vq7TWPl4KQiIg0eav2ruLVFa9yautTubDjhUTboskuySbSFskDPz/A7J2za/Q89w+5n+0F20mNSA0YCFxXCxLWt8rBLMYRU6+vlRadxqh2o/h6q2+a/3knnUf72Pb1+prHo3H85ERERGpgZ8FO/rnqn9zY60baxrT1H39uyXMsy1nGL7t/4e+L/06/5H6sy113xHT3CmGWMNJj0tlwYAMd4zpya59b2Vawjcs7X47NXPvBzaGichCqTUvS8frbyX/zjUGyRnBn/zvr/fWOh4KQiIg0emv2r2HhnoX8b+3/yCnJYUXOCjrEdWBYq2Fc2ulS1uWuC7i+qinvlY1IH8FtfW5jyq9TyOibETDDqjGr3HJ1+K719SHGHsOTpzxZ769zIhSERESk0XJ6nCzOWsyD8x5kb+le//FtBdvYVrCNWTtmEeeIo8RdctTnuXfwvZyZdiapkanklecR64jFbDLz9KlP1/dbaFAVq2ADRNuig1hJ6FAQCgKv4WX2ztnsLtrNFZ2vCPgPU0REAhmGwbzd82gf256Z22byr9X/4pKOlzBhwATumH1HwCanVXl8weOAr1uo1F1Ki/AWdIrvhN1ip8RVgtvr5rz25xEfFg/g/9oUdYrr5N8CpL5msjU2CkJBYMLE337+G0WuIoa3Gl7tKqAiIs3Zr3t/ZVvBNqZvnO5fobnC1DVT+e9v/612w9HK8srzAMjom0G/5H60jWlLrCO2PkoOedf3vJ6fd/3MGWlnBLuUkKEgFAQmk4lWUa3YcGADu4p2KQiJSLNX4iph/u75nJ52OlazlezibMZ9Mw6X11XtYyqHoAkDJtA/pT+7CndxdtuzmbVjFonhidzw7Q3+a84/6fwGGRcTymIdsXx04UfBLiOkKAgFSUUQ2l20O9iliIgE3cO/PMw3275hbPex7Cnew/Kc5QEh6JKOl9C7RW/cXjcXdriQ6Zum8/nmz+ma0JWbet/k35S0T4s+AJzb/lwA7uh3B6+teI2MfhnNPgRJ1RSEqpGZmUlmZiYej6denr/if9pdxbvq5flFREKZ2+v2z2Bas28N32z7BoD//PafgOvu7H9nldtHXNPtGq7pds0xX+em3jdxXY/rTng7C2m6FISqkZGRQUZGBgUFBcTG1n1fcqvIVgBqERKRZuOzzZ+xOGsxHWI78MLSF7iyy5WcmXYmf/3xr9U+5ow2Z5zw6yoEydEoCAVJRYuQgpCINHUFzgLm7JzDAz8/EHD8/fXv8/769wHon9wfh8XB/D3zARjfezypkal0jO/Y0OVKM6MgFCStonwtQruK1DUmIk3H7qLdPLXwKbYXbKddbDueGP4Et826jV/3/lrtY9rHtueNc96g1F3KEwueYEjLIVze+fIGrFqaMwWhIKkIQrlluZS6S+t9Yz4RkfqWVZzFxB8nsmrfKsC3qOGj8x89IgS1iWrDue3P5V+r/gX4Ni+1W+zYLXaePf3ZBq9bmjcFoSCJsccQZYuiyFXEnqI9mkIvIkGTV+ZbSfnwBfZW71vN80ue54y0MxjbY6z/+JMLnmRJ9hJeO+s1NuVtYvX+1RiGwesrXz/iuWdun+m/PzJ9JA8PfZi4sDgMw6DEVcKirEWM6TCm/t6cyDEoCAWJ1hISkVDwzdZvuHvu3UwcONEfdrKKs3hz9Zt8uP5D3IabJdlLWLBnARaThbPbns17698D4IovriC/PP+I5zy77dk8dcpT3DzzZpblLCPGHsPbo9+mQ1wH/zUmk4n7htzXMG9S5CgUhIJIawmJSLA4PU6yS7J56JeHAN/u7O+te4+BqQMpcZXw3fbvAq7/edfPAPz4+4/+Y1WFoCs6X8G13a8lzBrG6yNfZ+b2mQxpOYTUyNR6fDcix09BKIi0lpCINAS3182khZNYd2AdV3a+kghbBDO3z+TrrV8HXPd70e/8vun3gGM9EnuwZv+aI54zzBJGmacMgPTodHYV7eLpU5/2L2QIEGGL4KKOF9XDOxKpOwpCQaS1hESkvpW4Snhz9Zt8sOEDgKPO3qos3BrOv8/5Nz2TejJ752z2FO/B7XUzZeUUbu93O6e0PoXM5Zlc3OliTm55MrlluVq5WRolBaEg0lpCIlKfdhft5tqvriWnNKfWjx2QMoBeLXoBcGb6mf7j13W/zj+o+pnTn/EfVwiSxkpBKIi0lpCI1JVSdylmk5kNuRtwWB28u/Zdvtn2DcWuYqwmK1d3u5pt+dv4addPAY8b3mo4N/e5mVh7LHaLndGfjAZ8Qagqh88sE2nsFISCqEVEC8C3lpBhGPoHRkRqzTAMvtr6FY/Of5RSd+kR5xPDEnn3/Hf9f3gZhsGLS1/k440fk3lWJn2T+wZc/4cuf2BZzjIu76QFDaV5MBmGYQS7iFBWsddYfn4+MTExdfrchc5Chk0bBsCSPy3RfjgiUqU1+3yDlU+KO4mH5j3Egj0LGNVuFIlhiby28rVqH3dDzxu4oecNxDqO3C9Rf3xJU1fT399qEQqiMEuY/36Zu0xBSEQCbMnfwvfbv+eV5a8cca5ij64Kw1sN54aeN2C32Nlfuh/wje2pLuwoBIn4KAgFkdVsxWwy4zW8lHvKg12OiASRYRjM2jGLpPAk+ib35YP1H/D4gsePuC7OEUeRswi34fYfi3XEMnHgRG1QKnIcFISCyGQy4bA4KHWXUu5WEBJpjgzD4L9r/8tLy17y/0EUbY+m0Fnov+bU1qdyWpvT6Jfcz7cKvQFPLHyClTkreWTYI0eM8xGRmlMQCrIwSxil7lL/wmQi0rxM3zSdZxY/E3CsIgQNSBnAy2e+TJQtCrPJHHDNo8MebbAaRZoyBaEgc1gdUI66xkSaEafHyX/X/pdvtn7D2ty1Aefu6HcHbsPNvpJ9/F+v/yPGXreTNEQkkIJQkFUMmC5zq0VIpKlxe918t+07UiJTGJAygBJXCVazlWcWPxMw2Pmk2JN49axX2V6wneGthmsgs0gDUhAKsoqZYmoREmk6PF4PXsPL3xf/3R94Im2RlLhKMDi0Ysn/9fw/ru1+LQlhCZhMJtKi04JVskizpSAUZA6rLwhpjJBI0+Dyurh15q0szFoYcLzYVRzw/c29b+b2frc3ZGkiUgUFoSCr6BrTrDGRxm1L/hbe+e0dyt3lASHorPSzePDkB8kuySY5IplNeZvwer0MbTU0iNWKSAUFoSBT15hI47WnaA93zL6DlIgUNhzYwJ7iPf5zneI7cXbbs7mu+3VE2iJJDE8EtDmpSKhREAqyMOvBwdLqGhNpdF5f+TrrctexLnddwPGk8CTeGvVWlVtbiEhoURAKMn+LkLrGRBqNNfvWMH3TdKZvmh5wfGz3sQxKHUSHuA4KQSKNRJMPQjt37uTaa68lJycHq9XKgw8+yBVXXBHssvwqgpBahEQah6ziLK7/9nr/Tu/dErpxWafL2FW0i9v73Y7dYg9yhSJSG00+CFmtViZPnkzfvn3JyspiwIABnHfeeURGRga7NOBQ15jGCImEpnJPuW9VZwPchpvJyyZT6i7FhIlz2p3Dnf3uJC1G095FGqsmH4RatmxJy5YtAUhNTSUpKYnc3NyQCUL+FiEtqCgSMrbmbyWnJIcZm2bw7bZv8Ri+dYEqmDAx7YJp9EjsEcQqRaQumI99Sf2aO3cuY8aMoVWrVphMJmbMmHHENZmZmbRr146wsDCGDBnCokWLjuu1li5disfjIS0tdP5680+fV4uQSFDllOSQXZzNs4uf5eJPL+bG727kiy1f4PK6AkIQwLie4xSCRJqIoLcIFRcX06dPH2644QYuvfTSI86///77TJgwgSlTpjBkyBAmT57MqFGjWL9+PcnJyQD07dsXt9t9xGO/++47WrVqBUBubi7XXXcdb7zxRv2+oVqqWFBRQUgkOLYXbOf+n+/n172/HnEu2h7Ngyc/SM+knpS5y3BYHNjMNlpGtQxCpSJSH4IehEaPHs3o0aOrPf/CCy9w0003cf311wMwZcoUvvzyS958803uvfdeAFasWHHU1ygvL+fiiy/m3nvvZdiwYce8trz8UCgpKCio4Ts5PuoaE2lY+0v3M3XNVJbnLGd7wXbyyvMCzqdHp3Nr31sZ3W40ZpNZ+36JNHFBD0JH43Q6Wbp0Kffdd5//mNlsZuTIkcyfP79Gz2EYBuPGjePMM8/k2muvPeb1kyZN4tFHHz3ummtLXWMiDSerOIs///DnI9b9aR/bnokDJxJjj6FzfGcibBFBqlBEGlpIB6F9+/bh8XhISUkJOJ6SksK6deuqeVSgefPm8f7779O7d2//+KN33nmHXr16VXn9fffdx4QJE/zfFxQU1OuYIu01JlL/tuZv5ckFT7I4ezFew0uULYqbe99Mekw63RO7kxqZGuwSRSRIQjoI1YVTTjkFr9d77AsPcjgcOByOeqwokPYaE6kfhmGwNnctpe5SHvnlEbYVbANgQMoAJg6cSM+knsEtUERCQkgHoaSkJCwWC9nZ2QHHs7OzSU1tGn/Baa8xkbqxZt8avt3+LRsPbOSu/nfx9m9v89nmz/znE8ISyDwrUwFIRAKEdBCy2+0MGDCAWbNmcfHFFwPg9XqZNWsWt99+e72+dmZmJpmZmXg8nnp9He01JnLisouz+dPXf8Lt9c0e/XnXz/5zYZYwUiNTefb0Z+ma0DVYJYpIiAp6ECoqKmLTpk3+77du3cqKFStISEggPT2dCRMmMHbsWAYOHMjgwYOZPHkyxcXF/llk9SUjI4OMjAwKCgqIja2/PYO015jI8TMMg3m755G5PNMfgkyYMDAAuHfwvVzT7ZpgligiIS7oQWjJkiWMGDHC/33FQOWxY8cydepUrrrqKvbu3ctDDz1EVlYWffv25ZtvvjliAHVjVRGEnF5nkCsRaTxcXhdfbP6Ct397m015h/6QeuqUpxjdfjTfbvsWm9nG2W3PDmKVItIYmAzDMIJdRCiraBHKz88nJiamzp9/S/4WLppxEdH2aH65+pc6f36RpiK/PJ/V+1bTKqoVj81/jCXZSwCIsEZwUceLGNVuFANSBgS5ShEJFTX9/X1cLUL/+c9/SEpK4vzzzwfg//2//8c///lPunfvzrRp02jbtu3xVd0MVbQIuTyuIFciEnoKnYXsLd3LDzt+4M1Vb1LoKvSfi7JFMb73eC7rfBkx9rr/I0VEmofj2mvsqaeeIjw8HID58+eTmZnJM888Q1JSEn/5y1/qtMCmzm62A76uMTXOiRwya8cszvzgTC6acREvLXuJQlehP/C0jGzJ1HOncn3P6xWCROSEHFeL0M6dO+nYsSMAM2bM4LLLLmP8+PEMHz6cM844oy7rC5qGmjVmt/iCkNfw4jbc2Ey2en09kVC2cM9CvtzyJWnRaUxdM9U/m7J1VGtu63sb57c/n0JnIVH2KKzmoA9xFJEm4Lj+JYmKimL//v2kp6fz3Xff+Qc4h4WFUVpaWqcFBktDzRqrCELg6x6zmRWEpHn6csuXPPDzA3iMQ398dE/szn9H/xeb5dD/F3FhcUGoTkSaquMKQmeffTY33ngj/fr1Y8OGDZx33nkArFmzhnbt2tVlfU1eRdcYgNPj1B5H0qwUu4rJmJXButx1FLuKAegY15FtBduId8Qz6dRJASFIRKSuHVcQyszM5G9/+xs7d+7k448/JjExEYClS5dy9dVX12mBTZ3FbMFisuAxPFpdWpo8l8fF51s+p6C8gL7JfZm2bhpLs5cCvvV//tD1D9w7+F4KygsIt4X7JxOIiNSX4wpCcXFxvPrqq0ccb8hd25sSu8VOqbtUawlJk7Rm/xr2luwl2h7N6yteZ2HWwiOuuXfwvVzS8RJ/i6i6v0SkoRxXEPrmm2+IiorilFNOAXwtRG+88Qbdu3cnMzOT+Pj4Oi2yqasIQppCL03J+tz1TFk5he93fB9wPNwaTv+U/qzZt4YoWxR39r+Tc9ufG6QqRaS5O64gdPfdd/P3v/8dgFWrVvHXv/6VCRMmMHv2bCZMmMBbb71Vp0UGQ0PNGoND44TUNSZNxbrcdVz39XWUun2TJ2LsMdgtdqJsUTwy7BEtfCgiIeO4gtDWrVvp3r07AB9//DEXXHABTz31FMuWLfMPnG7sGmrWGByaOaauMWkK8sryuGv2XZS6S+mf3J/b+93OoNRBwS5LRKRKx7Wgot1up6SkBIDvv/+ec845B4CEhAQKCgrqrrpmwh+EPApC0rjN3z2fiz69iF1Fu0iLTuPlM19WCBKRkHZcLUKnnHIKEyZMYPjw4SxatIj3338fgA0bNtCmTZs6LbA58K8urSAkIcpreClzlx2xvMPsHbP5fMvnuLwutuZvZXvBdgBiHbG8NOIlYh3125oqInKijqtF6NVXX8VqtfLRRx/x+uuv07p1awC+/vprzj1Xgx5ry78DvYKQhKgH5z3IkHeHcOv3t7KvdB8A83bN4645dzFz+0zm7JzjD0GXd76cLy/5kk7xnYJYsYhIzRxXi1B6ejpffPHFEcdffPHFEy6oOapYME5jhCTUeLweMldk8tnmzwD4edfPXPbZZUwcOJFJCyfhNbwMaTmEgSkDMZvMdE3oymltTgty1SIiNXfcm/V4PB5mzJjB2rVrAejRowcXXnghFoulzoprLtQ1JqHI4/Vwy/e3sGDPAgDOaHMG2wq2sa1gG/f/fD8AfVv05bWzXgvYKkZEpDE5riC0adMmzjvvPHbt2kWXLl0AmDRpEmlpaXz55Zd06NChTosMhoacPq+uMQkleWV5rN6/mrfXvM2CPQuwmq08MvQRLup4EXtL9vLEgif48fcf6Zvcl+dPf14hSEQaNZNhGEZtH3TeeedhGAb/+9//SEhIAGD//v386U9/wmw28+WXX9Z5ocFSMX0+Pz+fmJiYenmNCXMmMHP7TO4bfB9/7PbHenkNkZpYl7uO8d+N50D5Af+xB09+kCu7XBlwndfwYjYd1xBDEZEGUdPf38fVIvTjjz+yYMECfwgCSExM5Omnn2b48OHH85TNWsVf1C6vVpaW4Nhfup/XV77ORxs+wmN4sJqsuA0313S75ogQBCgEiUiTcVxByOFwUFhYeMTxoqIi7HY1k9eWxghJMOSX57MoaxFfbP6C+Xvm+1eBPq3NaTw27DGi7dHYzNr5XUSatuMKQhdccAHjx4/n3//+N4MHDwZg4cKF3HLLLVx44YV1WmBzUNEipC02pL5tOrCJmTtm8s3Wb9iSvyXgXPfE7kwcOFELIIpIs3JcQejll19m7NixDB06FJvN9xejy+XioosuYvLkyXVZX7OgLTakvi3NXsprK15jUdaigONRtij6Jffj5j430zupNyaTKUgViogEx3EFobi4OD799FM2bdrknz7frVs3OnbsWKfFNRcVXWPafV7qUpm7jE82fsJ3279jafZSACwmC6e0PoWz0s+if0p/0qPTFX5EpFmrcRCaMGHCUc/Pnj3bf/+FF144/oqaoYrp8+oak7rg8rj4eOPHvPHrG+SU5gC+wc2XdbqM8b3HkxqZGuQKRURCR42D0PLly2t0XVP567Ih1xHyryytwdJyAgzDYMamGUxZOYXdxbsBaBnZkss6XcaodqNoF9suuAWKiISgGgehyi0+zUFGRgYZGRn+dQjqk3/WmMYIyXEqcZXwzOJn+HjjxwC0CG/B+N7jubTTpVrwUETkKI57iw2pO1pZWo6Xx+th3u55/H3R39lRuAOA2/rcxrie4wi3hge5OhGR0KcgFAL8s8YUhKSGnB4n/171bz7Y8IF/N/iUiBQeHvowp7Y5NcjViYg0HgpCIUC7z0tN5ZXl8eXWL3lv3XtsK9gGQIw9hos6XsTNvW8m1lG/3bgiIk2NglAIUNeYHIvL4+K5Jc/x4YYP/VuxJIQlcM+gezi77dn+MC0iIrWjIBQCtMWGVLYiZwX7y/bTMrIle4r3sGD3Ar7a+hUFzgIAuiV04+KOF3NBhwuIsdfPRsAiIs2FglAIsJp9Pwa31x3kSiSYvIaXzBWZ/PPXf1Z5PiEsgb8O/CsXdtA2NiIidUVBKARUbGyp3eebJ5fXxeKsxfx90d/9+38lhyeDyTf+p1VUKy7teClnpJ2BxWwJcrUiIk2LglAIqBjfoRah5sMwDGZun8l/1vyHbQXb/N1eUbYoJg6cyGWdLwtyhSIizYOCUDUacmVpq8n3Y1CLUPPg9Di5bdZtLNyz0H8s3hHPyLYjua3vbSSFJwWxOhGR5kVBqBoNubK0WoSaj40HNnLX7LvYUbgDq9nKdd2vY3ir4fRJ7uOfPSgiIg1HQSgEqEWo6XN6nDyx4Ammb5ruP/biGS9yRtoZwStKREQUhEKBWoSati35W7jjhzvYXrAdgI5xHbmr/12cnnZ6kCsTEREFoRBQMX1eLUJNy/rc9Xy44UM+2/wZpe5SAO7sfyc39roxyJWJiEgFBaEQUDF9Xi1CTYPb6+a5Jc8xbd00vIYXgMGpg5kwcAI9EnsEuToREalMQSgEVLQIeQwPXsOL2WT2n1uWvYzXVr7GDT1uYFjrYcEqUY7B7XVT4i6h3F3OI/MfYe7vcwE4u+3ZXNbpMoa2GhrwcxURkdCgIBQCKlqEwPcLtWI3eq/h5c7Zd5JXnsfirMUsvmax/5yEjvW567lt1m3klOT4j9nMNiadOolR7UYFsTIRETkW/YkaAioHocrjhLYVbCOvPA/whaJV+1Y1dGlyDF9u+ZJrv742IAT1SurFtPOnKQSJiDQCahEKARVdY+DbZZyDuejXvb8GXLc8ZzkDUgY0ZGlymBJXCav2rWJR1iJmbZ/F5vzNAHRN6MpLI17C7XWTFp2GyWQKcqUiIlITCkIhwGKyYMKEgYHbODRgekfBjoDrtuRtaejSpJLvt3/Pw7887N8Oo8J57c/jvsH3ERcWF5zCRETkuCkIhQCTyYTVbMXldflahA7aXbwbgN4tevPr3l/9rQ/S8L7Z9g33zL0Hr+ElOSKZ/sn9ObnlyfRL7sdJcScFuzwRETlOCkIhwma24fK6AqbQ7ynaA8AprU7h172/HtFCJPXHa3jJLcul0FnIV1u/YsrKKQBc2OFCHhn2SMC4LhERabwUhEJEVYsqZhVnAdAvpR8ARa4iipxFRNmjGr7AZsBreFmctZjPN3/ODzt+oNBVGHD+T93+xMSBE7GYLUGqUERE6pqCUDUacvd5ODRzrHIQOlB+AIA2UW2ItkdT6CwkuyRbQagebMnfwnOLn+OnXT8FHHdYHLSLaccfu/2RSztdGqTqRESkvigIVaMhd5+HQy1CFV1jpe5S/7YM8WHxpEam+oJQcTYd4jrUez3NyZur3+TFpS8Cvp/DBSddwIUdLqRnUk8sJovWbhIRacIUhELE4S1CeWV5/uMR1ghSIlLYeGAj2SXZwSqxSZm/ez5TVk7ht/2/UeYpA+CMNmcwvvd4erXoFeTqRESkoSgIhYiKHegrglBueS7gaw0ymUykRKQAh8YNyfExDIN//vpPXl3xqv+Y1WTl1r63Mr73+CBWJiIiwaAgFCIO7xqraBGKd8QDkBLpC0JqETp+B8oO8PAvDzN752wArux8Jdd0u4bUyFQibBFBrk5ERIJBQShEHN41ll+eD0Cswzc+KTUiFYCsErUIHY/V+1Yz8ceJ7CrahdVs5b7B93FllyuDXZaIiASZglCIOLxFqMhVBECUzTdDrKJrLLtYLUK19b+1/+P5Jc/j8rpoHdWaySMm0zWha7DLEhGREKAgFCIObxEqdPrWsKmYKp8QngD4unekZlweF08sfIJPNn4CwKmtT+WJU54gISwhyJWJiEioUBAKEYe3CBW7ioFDLUIVY4XyyvMwDEObeh6Dy+vizz/8mXm752E2mbmr/12M6zFOn5uIiARQEAoRx2oRig/zBSGP4aHAWeAfOyRHMgyDSQsnMW/3PMKt4bx4xosMbz082GWJiEgIMge7APE5VouQ3WIn0hYJqHvsaMrcZdw5+04+3PAhJkw8c9ozCkEiIlItBaEQcUSLkCuwRQgCu8fkSC6vi/t/vp/ZO2djM9t4eOjDnJF2RrDLEhGREKausRDhD0IeXxAqcvpmjUXbov3XxIfF83vR7+SW5TZ8gSGu2FXMxB8n8vOun7GarUwZOYXBLQcHuywREQlxCkIhwt81ZgR2jVVe6K9inJBahAJlFWdx+6zbWX9gPWGWMJ47/TmFIBERqREFoRBxeItQxf5X4dZw/zVxjjgAtQgd5Pa6+WzzZ7yy/BX2le4jISyBV898VXuFiYhIjSkIhYjDW4Qqdp6vHIQq1r/RYGnYnLeZiT9OZFPeJgA6xHYgc2QmraNaB7kyERFpTBSEQsThLUJVBSF1jfl8teUrHpn/CKXuUuId8dzY60au7HIlYdawYJcmIiKNjIJQiKgIQhXT58vcR3aNVcwaa65dY17Dy/NLnuft394GYEjLIfz91L+TGJ4Y5MpERKSxUhCqRmZmJpmZmXg8ngZ5vYquMZfXhcfrodxTDgQGoRhHDAAFzoIGqSmUGIbBkwue5IMNHwBwU6+byOibgcVsCXJlIiLSmGkdoWpkZGTw22+/sXjx4gZ5vcotQhUDpeGwIGT3BaGKVaebC8MweGbxM3yw4QNMmHjqlKe4o/8dCkEiInLCFIRChM1yaEHFivFBJkw4LA7/NdF235pCzS0IvbL8Ff679r8APDrsUcZ0GBPkikREpKlQEAoRlVuEKg+UrrxJaHMMQv9a9S/eWPUGAA8MeYBLOl0S5IpERKQpURAKEZXHCFUEocNnQVV0jZV7yv1jiJqyH3f+yEvLXgJg4sCJ/KHrH4JckYiINDUKQiGiuhahyiJtkZjwtRA19VahHQU7uO+n+wC4qstVjO0xNsgViYhIU6QgFCIqtwg5PU7gyCBkNpn9m7A25SCUXZzNzTNvptBVSJ8Wfbhn0D3BLklERJooBaEQ4V9Z2uv2ryFkt9iPuK6ie6ypTqHfV7qPG7+7kd+LfqdNVBteOOMF/0ByERGRuqYgFCIsJt9UcLfX7W8RqjxjrEJTHjCdX57P+Jnj2VawjdTIVP416l8kRyQHuywREWnCFIRCROW9xioGQlfVItRUg1CJq4SbZ97MxgMbaRHegn+d8y/tGyYiIvVOQShEVO4aqwhCVbYI2ZpeEDIMg0d+eYQ1+9cQ74jnjXPeoG1M22CXJSIizYCCUIioPGusJl1jTWmM0Kwds/h629dYTVZeOvMlOsR1CHZJIiLSTCgIhQir6cgWoebQNVbqLuXZxc8CcEOvG+iX3C/IFYmISHOiIBQiKneNOb3VtwhVbLzaVILQ1NVT2V28m9TIVG7sdWOwyxERkWZGQShEVB4s7Z8+b27a0+d3F+3m36v/DcBfB/71iHWTRERE6puCUIio2Em9OU2ff27Jc5R7yhmYMpBRbUcFuxwREWmGFIRCRFWzxqocI9REZo3N2jGLmdtnYjFZuHfwvQGby4qIiDQUBaEQUdVg6cM3XYWm0SK0NX8rD857EICxPcbSJaFLkCsSEZHmSkEoRPinzxtNe/r8gbIDZMzKoNDp20fstr63BbskERFpxhSEQkRNu8YqBksXOgsxDKPhCqwDTo+Tu2bfxc7CnbSOas1LI16qMuyJiIg0FAWhEBEwff5gi1BFK1FlFS1CLq/LH5gaA8MweHT+oyzLWUaULYpXz3yVxPDEYJclIiLNnIJQiKgchFxeF1B1i1CkLRKzyfdja0zdY/9e/W8+2/wZFpOF509/no7xHYNdkoiIiIJQqKgIQh7Dc6hrrIp1hEwmE5HWSACKXEUNV+AJ2HBgA5nLMwG4b/B9DGs9LMgViYiI+CgIhQiLyeK/X+ouBcBmObJrDCDKHgVAsbO4/gs7QR6vh0d+eQS34eas9LO4qutVwS5JRETET0EoRFQeD+QPQlWMEQJf9xhAoSv0p9C/t/49Vu1bRbQtmvuH3B/sckRERAIoCIWIiq4xOBSEqhojBIcGTBe7QrtFqNhVzD9W/gOAuwbcRXJEcpArEhERCaQgFCIqB6ESdwlw7BahImdojxGatm4aB8oP0C6mHZd2ujTY5YiIiByhyQehvLw8Bg4cSN++fenZsydvvPFGsEuqktlk9s8GK3UdbBGqYrA0HNpmI5QHS3u8Hj5c/yEAN/a6MSDoiYiIhIom/9spOjqauXPnEhERQXFxMT179uTSSy8lMTH01rCxmqw4DSdO78F1hKoZLB1pD/0WoXm757G7eDexjlhGtdOGqiIiEpqafIuQxWIhIiICgPLycgzDCNkVmQ9vNWnMLUIVrUEXdbioyj3TREREQkHQg9DcuXMZM2YMrVq1wmQyMWPGjCOuyczMpF27doSFhTFkyBAWLVpUq9fIy8ujT58+tGnThrvvvpukpKQ6qr5uWcyWgO+rbRGyhfY6QnuK9jB311wALu98eZCrERERqV7Qg1BxcTF9+vQhMzOzyvPvv/8+EyZM4OGHH2bZsmX06dOHUaNGkZOT47+mYvzP4bfdu3cDEBcXx8qVK9m6dSvvvvsu2dnZ1dZTXl5OQUFBwK2hHD44urrB0hXrCIVq19jHGz/Ga3gZkjqE9rHtg12OiIhItYI+Rmj06NGMHj262vMvvPACN910E9dffz0AU6ZM4csvv+TNN9/k3nvvBWDFihU1eq2UlBT69OnDTz/9xOWXV91SMWnSJB599NHavYk6YjUd1jVWzfT5KNvBIBSCLUIur4tPNn4CwBVdrghyNSIiIkcX9Baho3E6nSxdupSRI0f6j5nNZkaOHMn8+fNr9BzZ2dkUFvoWHszPz2fu3Ll06dKl2uvvu+8+8vPz/bedO3ee2JuohcPHCB2zRSgEg9CcnXPYW7qXxLBEzkw7M9jliIiIHFXQW4SOZt++fXg8HlJSUgKOp6SksG7duho9x/bt2xk/frx/kPSf//xnevXqVe31DocDh8NxQnUfryMGSx+rRSgEu8Y+WP8BAJd2urTaMU4iIiKhIqSDUF0YPHhwjbvOgq2xtwhtL9jOgj0LMGHiss6XBbscERGRYwrprrGkpCQsFssRg5uzs7NJTU0NUlX1p3IQspqs/gUWDxeqLUIfbfgIgFNan0LrqNZBrkZEROTYQjoI2e12BgwYwKxZs/zHvF4vs2bNYujQofX62pmZmXTv3p1BgwbV6+tUVnkH+qN1K1UEoRJ3CR6vp97rqolyTzkzNs0A4MouVwa3GBERkRoKetdYUVERmzZt8n+/detWVqxYQUJCAunp6UyYMIGxY8cycOBABg8ezOTJkykuLvbPIqsvGRkZZGRkUFBQQGxsbL2+VoXKXWHVdYvBoa4xgGJ3MTH2mHqtqya+3fYteeV5pEamcmrrU4NdjoiISI0EPQgtWbKEESNG+L+fMGECAGPHjmXq1KlcddVV7N27l4ceeoisrCz69u3LN998c8QA6qagctdYdQOlARwWBzazDZfXRbEz+EHI5XExZeUUAK7qctURC0OKiIiEqqAHoTPOOOOYW17cfvvt3H777Q1UUfBUDkJHaxECX/fYgfIDFLoKaUnL+i7tqD7a+BE7C3eSGJbIH7v+Mai1iIiI1EZIjxFqbmraIgSHuseKXcX1WtOxFLuK/a1Bt/a5lQhbRFDrERERqQ0FoWoEY7B0bVuEAAqdhfVa07E8vehpcstySY9O59LOlwa1FhERkdpSEKpGRkYGv/32G4sXL26w16y8xcYxg1AItAgt3LOQGZtmYMLEo8MePWbNIiIioUZBKIRUHmR8rK6xih3og9Ui5Pa6eXrR04BvuvzA1IFBqUNEROREKAiFkNp0jUXbooHgtQh9uulTNuVtIsYew5/7/TkoNYiIiJwoBaEQUjn81LRFKBjbbJS4SshckQnAzb1vJtbRMOssiYiI1DUFoRBSqxYhu69FKBjbbHyw/gP2lu6lTVQb/tD1Dw3++iIiInVFQagaQZk1Zqr59PlgtQh5DS8fbPDtMH9jrxuPWaeIiEgoUxCqRlBmjTWCFqHFWYvZWbiTSFsko9uPbtDXFhERqWsKQiGkNkGookWooQdLf7PtGwDObXeuFk8UEZFGT0EohNRm+nxFi1Chq+Gmz3u8Hn7Y8QMA57Q7p8FeV0REpL4oCIWQ2iyoGIwWoWU5y8gtyyXWEcug1IYbOyUiIlJfgr7pqhxis9R8+nwwttj4btt3AIxIG6FVpEWkUfB6vTidzmCXIfXAZrNhsViOfeExKAiFkDBLmP9+qG2x4TW8zNoxC4Cz257dIK8pInIinE4nW7duxev1BrsUqSdxcXGkpqZiMpmO+zkUhKqRmZlJZmYmHo+nwV4zzHooCNW0RajcU47L4wpoTaoPa/atYW/pXqJsUZzc8uR6fS0RkRNlGAZ79uzBYrGQlpaG2ayRIE2JYRiUlJSQk5MDQMuWLY/7uRSEqpGRkUFGRgYFBQXExjbMysnh1nD//Qjr0WdkVYwRAt9aQvGW+HqrC2Bh1kIAhrQcorWDRCTkud1uSkpKaNWqFRERmuHaFIWH+35n5uTkkJycfNzdZIrIIaRyi9CxpqZbzVZ/cKrtWkLfbvuWsz48i/M/OZ/lOctr9JgFexYAMDh1cK1eS0QkGCpa8+12/eHWlFWEXJfLddzPoSAUQiq3AlVu8alORfdYbVaX3lW0iwfnPUhOSQ47Cncwcc5ESlwlR31MuaecFTkrANQtJiKNyomMHZHQVxc/XwWhEFJ5sPSxusbg0IDp2gSh11a8Rqm7lE7xnWgZ2ZKc0hw+3vjxUR+zMmcl5Z5yWoS3oH1s+xq/loiISKhTEAohAWOEarBqs79FqIZdY6XuUmZunwnAQyc/xI29bgTg4w0fYxhGtY/zd4u1HKy/rkREpElREAohtRkjBLXvGluevZxSdympkan0adGH0e1H47A42Jy/md/2/1bt4/wDpVOH1Oh1REQk+Nq1a8fkyZPr5LnmzJmDyWQiLy+vTp4vlCgIhZDazBqD2neNLc72bSA7ONXXshNtj+bMtDMB+HTzp1U+ptBZyJp9awCNDxIRqW9nnHEGd911V5081+LFixk/fnydPFdTpiAUQiq3CFUORdWpbdfYqr2rAOif3N9/7MKOFwLw9davcXmOHHW/cM9CPIaHdjHtaBl1/Os0iIjIiTMMA7fbXaNrW7RooaUDakBBqBqZmZl0796dQYMabk+tyoOlaxKEKmaW1aRFyGt4Wb1/NQA9k3r6j5/c8mSSwpPIK89j7u9zj3jcL7t/AWBYq2HHfA0RkVBlGAYlTndQbkcbg1nZuHHj+PHHH3nppZcwmUyYTCamTp2KyWTi66+/ZsCAATgcDn7++Wc2b97MRRddREpKClFRUQwaNIjvv/8+4PkO7xozmUz861//4pJLLiEiIoJOnTrx2WefHfdn+vHHH9OjRw8cDgft2rXj+eefDzj/2muv0alTJ8LCwkhJSeHyyy/3n/voo4/o1asX4eHhJCYmMnLkSIqLG27vzMq0oGI1grGgYpQ9igkDJuA1vMQ6jv2aFTvQ16RFaFv+NopdxYRbw+kQ18F/3Gq2csFJFzB1zVQ+3fwpZ7U9y3/OMAx/EBreenht346ISMgodXno/tC3QXnt3x4bRYT92L9uX3rpJTZs2EDPnj157LHHAFizxjc04d577+W5557jpJNOIj4+np07d3Leeefx5JNP4nA4ePvttxkzZgzr168nPT292td49NFHeeaZZ3j22Wd55ZVXuOaaa9i+fTsJCQm1ek9Lly7lyiuv5JFHHuGqq67il19+4bbbbiMxMZFx48axZMkS7rjjDt555x2GDRtGbm4uP/30EwB79uzh6quv5plnnuGSSy6hsLCQn376qcaBsa4pCIWY63teX+Nra9MitGqfr1usW0I3rObAH/tFHS5i6pqpzP19LntL9tIiogUA2wu2s6toFzazjYEpA2tcl4iI1F5sbCx2u52IiAhSU1MBWLduHQCPPfYYZ599aJ/HhIQE+vTp4//+8ccfZ/r06Xz22Wfcfvvt1b7GuHHjuPrqqwF46qmnePnll1m0aBHnnnturWp94YUXOOuss3jwwQcB6Ny5M7/99hvPPvss48aNY8eOHURGRnLBBRcQHR1N27Zt6devH+ALQm63m0svvZS2bdsC0KtXr1q9fl1SEGrE/C1CNQhCm/M2A9A1oesR5zrGd6Rvi76s2LuC6ZumM763b3BdxSar/VP612gWm4hIqAq3WfjtsVFBe+0TNXBg4B+jRUVFPPLII3z55Zf+YFFaWsqOHTuO+jy9e/f234+MjCQmJsa/X1dtrF27losuuijg2PDhw5k8eTIej4ezzz6btm3bctJJJ3Huuedy7rnn+rvk+vTpw1lnnUWvXr0YNWoU55xzDpdffjnx8fW7VVR1NEaoEfO3CNWga2x7wXYA2sa0rfL8FV2uAHxrCnkNL4Zh8PnmzwE4r/15dVGuiEjQmEwmIuzWoNzqYv21yMjA3QYmTpzI9OnTeeqpp/jpp59YsWIFvXr1wul0HvV5bLbADbpNJhNer/eE6ztcdHQ0y5YtY9q0abRs2ZKHHnqIPn36kJeXh8ViYebMmXz99dd0796dV155hS5durB169Y6r6MmFIQasWhbzVuEdhT6/kqoLgid0/YcYuwx7C7ezVdbv2Lm9plszt+Mw+Lg7LZnV/kYERGpW3a73b9P2tHMmzePcePGcckll9CrVy9SU1PZtm1b/Rd4ULdu3Zg3b94RNXXu3Nm/+anVamXkyJE888wz/Prrr2zbto0ffvgB8AWw4cOH8+ijj7J8+XLsdjvTp09vsPorU9dYIxZp9/2FUOw6+kh7r+FlR4EvCKXHVD2ILswaxvU9r+elZS9x30/3+Y9f3fVqfxeciIjUr3bt2rFw4UK2bdtGVFRUta01nTp14pNPPmHMmDGYTCYefPDBemnZqc5f//pXBg0axOOPP85VV13F/PnzefXVV3nttdcA+OKLL9iyZQunnXYa8fHxfPXVV3i9Xrp06cLChQuZNWsW55xzDsnJySxcuJC9e/fSrVu3Bqu/MrUINWIVLUKFzsKjXpdVnIXT68RqttIqslW1113b/Vo6xXfyf39K61O4s/+ddVOsiIgc08SJE7FYLHTv3p0WLVpUO+bnhRdeID4+nmHDhjFmzBhGjRpF//79q7y2PvTv358PPviA9957j549e/LQQw/x2GOPMW7cOADi4uL45JNPOPPMM+nWrRtTpkxh2rRp9OjRg5iYGObOnct5551H586d+dvf/sbzzz/P6NGjG6z+ykxGsOarNRIV0+fz8/OJiYkJdjkBsouzGfnRSCwmC8uvXV5tP/T83fMZP3M87WPb89nFR18zotRdypydcyh1l3Je+/MCFnkUEWksysrK2Lp1K+3btycsTP+ONVVH+znX9Pe3usYasYouK4/hodRdWu3MLv9A6eiqxwdVFm4NZ3T74KRyERGRhqausWoEY2Xp2gq3hmM2+X6ERxsndKwZYyIi0rzdcsstREVFVXm75ZZbgl1evVKLUDWCsbJ0bZlMJiJtkRQ6Cyl0FdKCFlVeVzFjrLqB0iIi0rw99thjTJw4scpzoTYspK4pCDVyUbYoCp2FFDurbxH6vfB3ANKi0xqqLBERaUSSk5NJTk4OdhlBoa6xRi7K7tuBvtBV/cyx7JJsAFIjUxukJhERkcZCQaiRi7L5glB1Y4SKnEX+cykRKQ1Wl4iISGOgINTIVQSh6rbZyCnx7SETbY/WfmEiIiKHURBq5PxBqJptNrJKsgC1BomIiFRFQaiRqxgjVF2LUHaxb3yQgpCIiMiRFIQauWO1CFUMlE6JVBASEQl1Z5xxBnfddVeNrt22bRsmk4kVK1bU2XMCzJkzB5PJRF5e3jGvnTp1KnFxcTV+7lCk6fONnL9FqJogVDFGSC1CIiKh75NPPsFms9Xo2rS0NPbs2UNSUhLgCzAjRozgwIEDAeGkNs/ZHCkINXKRNt8O9NV2jZWoa0xEpLFISEio8bUWi4XU1GMvi1Kb52yO1DXWyFXsN1Zt19jBMULJEc1zoSwREQAMA5zFwbnVYm/zyt1Y7dq146mnnuKGG24gOjqa9PR0/vnPf/qvrdw1tm3bNkaMGAFAfHw8JpPJvxP84V1j77zzDgMHDiQ6OprU1FT++Mc/kpOTc8IfcYXXX3+dDh06YLfb6dKlC++8847/nGEYPPLII6Snp+NwOGjVqhV33HGH//xrr71Gp06dCAsLIyUlhcsvv7zO6qqOWoQaOX+LkMYIiYhUz1UCT7UKzmvfvxvskcf10Oeff57HH3+c+++/n48++ohbb72V008/nS5dugRcl5aWxscff8xll13G+vXriYmJITw8vMrndLlcPP7443Tp0oWcnBwmTJjAuHHj+Oqrr46rxsqmT5/OnXfeyeTJkxk5ciRffPEF119/PW3atGHEiBF8/PHHvPjii7z33nv06NGDrKwsVq5cCcCSJUu44447eOeddxg2bBi5ubn89NNPJ1zTsSgIVSMzM5PMzEw8Hk+wSzmqaNvBFqEqusbK3GXklecB6hoTEWmMzjvvPG677TYA7rnnHl588UVmz559RBCyWCz+LrDk5OSjDmC+4YYb/PdPOukkXn75ZQYNGkRRURFRUVEnVO9zzz3HuHHj/DVPmDCBBQsW8NxzzzFixAh27NhBamoqI0eOxGazkZ6ezuDBgwHYsWMHkZGRXHDBBURHR9O2bVv69et3QvXUhIJQNRrDpqsAkfbqW4T2luwFfLvUx9ib9qZ5IiJHZYvwtcwE67WPU+/evf33TSYTqampJ9yNtXTpUh555BFWrlzJgQMH8Hq9gC+IdO/e/YSee+3atYwfPz7g2PDhw3nppZcAuOKKK5g8eTInnXQS5557Lueddx5jxozBarVy9tln07ZtW/+5c889l0suuYSIiPpdDFhjhBq5o7UIVV5M0WQyNWhdIiIhxWTydU8F43YC//4ePtvLZDL5g8vxKC4uZtSoUcTExPC///2PxYsXM336dACcTudxP29NpaWlsX79el577TXCw8O57bbbOO2003C5XERHR7Ns2TKmTZtGy5Yteeihh+jTp0+NpvGfCAWhRq5ijFCJuwSPN7Abr2J8kAZKi4g0fXa7HeCoQzrWrVvH/v37efrppzn11FPp2rVrnQ6U7tatG/PmzQs4Nm/evICWpvDwcMaMGcPLL7/MnDlzmD9/PqtWrQLAarUycuRInnnmGX799Ve2bdvGDz/8UGf1VUVdY41cxawxgGJ3cUAXmFaVFhFpPtq2bYvJZOKLL77gvPPOIzw8/IgxP+np6djtdl555RVuueUWVq9ezeOPP15nNdx9991ceeWV9OvXj5EjR/L555/zySef8P333wO+BRg9Hg9DhgwhIiKC//73v4SHh9O2bVu++OILtmzZwmmnnUZ8fDxfffUVXq/3iPFQdU0tQo2c3WLHZvY1nRY7A3eg14wxEZHmo3Xr1jz66KPce++9pKSkcPvttx9xTYsWLZg6dSoffvgh3bt35+mnn+a5556rsxouvvhiXnrpJZ577jl69OjBP/7xD9566y3OOOMMAOLi4njjjTcYPnw4vXv35vvvv+fzzz8nMTGRuLg4PvnkE84880y6devGlClTmDZtGj169Kiz+qpiMoxaLHDQDFUMls7PzycmJjQHHI/4YAT7SvfxwQUf0C2xm//4n3/4M3N2zuFvQ/7GVV2vCl6BIiINrKysjK1bt9K+fXvCwsKCXY7Uk6P9nGv6+1stQk1AQphvyuT+sv0Bx/cU7QGgZVTLBq9JRESkMVAQagISwxIByC3LDTi+u9g3VbRVZJAWERMRkUZt9OjRREVFVXl76qmngl1endBg6SYgMdwXhPaXHmoRKnIWUegsBNQiJCIix+df//oXpaWlVZ5rKnuYKQg1ARUtQpWD0J5iX7dYjD3GP8VeRESkNlq3bh3sEuqdusaaAH+LUNmRQahVlLrFREREqqMg1ARU1TW2u8g3PqhlpLrFREREqqMg1AT4u8YqtQj5B0qrRUhERKRaCkJNQFUtQv6p82oREhERqZaCUBNQ0SJ0oPyAf78xtQiJiIgcm4JQExAfFo/FZMFreP3dY2oREhFpnkwmEzNmzAh2GY2GglATYDVb/TvM7y7aTYmrhL2lewFoE9UmmKWJiEgtjBs3DpPJhMlkwmazkZKSwtlnn82bb76J1+sNdnlNkoJQNTIzM+nevTuDBg0Kdik1UtHys6d4D5vzNgOQFJ5EXFhcEKsSEZHaOvfcc9mzZw/btm3j66+/ZsSIEdx5551ccMEFuN3uYJfX5CgIVSMjI4PffvuNxYsXB7uUGmkd5Vv0alfRLjblbQKgQ1yHYJYkIhIyDMOgxFUSlFtt9zZ3OBykpqbSunVr+vfvz/3338+nn37K119/zdSpU2v93letWsWZZ55JeHg4iYmJjB8/nqKiIv/5OXPmMHjwYCIjI4mLi2P48OFs374dgJUrVzJixAiio6OJiYlhwIABLFmypNY1hDKtLN1EpMWkAbAtfxsHyg4A0CmuUzBLEhEJGaXuUoa8OyQor73wjwuJsEWc0HOceeaZ9OnTh08++YQbb7yxxo8rLi5m1KhRDB06lMWLF5OTk8ONN97I7bffztSpU3G73Vx88cXcdNNNTJs2DafTyaJFizCZTABcc8019OvXj9dffx2LxcKKFSuw2Wwn9F5CjYJQE9ExriMAm/M2E+OIAdQiJCLSlHTt2pVff/21Vo959913KSsr4+233yYy0rfd0quvvsqYMWP4+9//js1mIz8/nwsuuIAOHXy/M7p16+Z//I4dO7j77rvp2rUrAJ06Nb0/sBWEmoiK0LMxbyNWs+/HWhGORESau3BrOAv/uDBor10XDMPwt9TU1Nq1a+nTp48/BAEMHz4cr9fL+vXrOe200xg3bhyjRo3i7LPPZuTIkVx55ZW0bOkbdzphwgRuvPFG3nnnHUaOHMkVV1zhD0xNhcYINRHtYtqRHJ5MuaecYlcxkbZIeiT1CHZZIiIhwWQyEWGLCMqttuGlOmvXrqV9+/Z18lyVvfXWW8yfP59hw4bx/vvv07lzZxYsWADAI488wpo1azj//PP54Ycf6N69O9OnT6/zGoJJQaiJMJvMjGo/yv/9iLQR2MxNqx9XRKS5+uGHH1i1ahWXXXZZrR7XrVs3Vq5cSXFxsf/YvHnzMJvNdOnSxX+sX79+3Hffffzyyy/07NmTd99913+uc+fO/OUvf+G7777j0ksv5a233jrxNxRCFISakBt63kCH2A6cFHsSt/e7PdjliIjIcSgvLycrK4tdu3axbNkynnrqKS666CIuuOACrrvuulo91zXXXENYWBhjx45l9erVzJ49mz//+c9ce+21pKSksHXrVu677z7mz5/P9u3b+e6779i4cSPdunWjtLSU22+/nTlz5rB9+3bmzZvH4sWLA8YQNQUaI9SEJIUnMePiGcEuQ0RETsA333xDy5YtsVqtxMfH06dPH15++WXGjh2L2Vy79ouIiAi+/fZb7rzzTgYNGkRERASXXXYZL7zwgv/8unXr+M9//sP+/ftp2bIlGRkZ3Hzzzbjdbvbv3891111HdnY2SUlJXHrppTz66KP18baDxmTUdoGDZqagoIDY2Fjy8/OJiYkJdjkiIlIDZWVlbN26lfbt2xMWFhbscqSeHO3nXNPf3+oaExERkWZLQUhERKSR+N///kdUVFSVtx49NFP4eGiMkIiISCNx4YUXMmRI1StkN7UVnxuKgpCIiEgjER0dTXR0dLDLaFLUNSYiIk2W5gM1bXXx81UQEhGRJsdisQDgdDqDXInUp5KSEuDEugXVNSYiIk2O1WolIiKCvXv3YrPZar3+joQ2wzAoKSkhJyeHuLg4f/A9HgpCIiLS5JhMJlq2bMnWrVvZvn17sMuRehIXF0dqauoJPYeCkIiINEl2u51OnTqpe6yJstlsJ9QSVEFBSEREmiyz2ayVpeWo1GkqIiIizZaCkIiIiDRbCkIiIiLSbGmM0DFULNZUUFAQ5EpERESkpip+bx9r0UUFoWMoLCwEIC0tLciViIiISG0VFhYSGxtb7XmTofXHj8rr9bJ7926io6MxmUx19rwFBQWkpaWxc+dOYmJi6ux5JZA+54ajz7ph6HNuOPqsG0Z9fc6GYVBYWEirVq2OuqCmWoSOwWw206ZNm3p7/piYGP0P1gD0OTccfdYNQ59zw9Fn3TDq43M+WktQBQ2WFhERkWZLQUhERESaLQWhIHE4HDz88MM4HI5gl9Kk6XNuOPqsG4Y+54ajz7phBPtz1mBpERERabbUIiQiIiLNloKQiIiINFsKQiIiItJsKQiJiIhIs6UgFCSZmZm0a9eOsLAwhgwZwqJFi4JdUqMxadIkBg0aRHR0NMnJyVx88cWsX78+4JqysjIyMjJITEwkKiqKyy67jOzs7IBrduzYwfnnn09ERATJycncfffduN3uhnwrjcrTTz+NyWTirrvu8h/T51x3du3axZ/+9CcSExMJDw+nV69eLFmyxH/eMAweeughWrZsSXh4OCNHjmTjxo0Bz5Gbm8s111xDTEwMcXFx/N///R9FRUUN/VZClsfj4cEHH6R9+/aEh4fToUMHHn/88YC9qPQ5H5+5c+cyZswYWrVqhclkYsaMGQHn6+pz/fXXXzn11FMJCwsjLS2NZ5555sSLN6TBvffee4bdbjfefPNNY82aNcZNN91kxMXFGdnZ2cEurVEYNWqU8dZbbxmrV682VqxYYZx33nlGenq6UVRU5L/mlltuMdLS0oxZs2YZS5YsMU4++WRj2LBh/vNut9vo2bOnMXLkSGP58uXGV199ZSQlJRn33XdfMN5SyFu0aJHRrl07o3fv3sadd97pP67PuW7k5uYabdu2NcaNG2csXLjQ2LJli/Htt98amzZt8l/z9NNPG7GxscaMGTOMlStXGhdeeKHRvn17o7S01H/Nueeea/Tp08dYsGCB8dNPPxkdO3Y0rr766mC8pZD05JNPGomJicYXX3xhbN261fjwww+NqKgo46WXXvJfo8/5+Hz11VfGAw88YHzyyScGYEyfPj3gfF18rvn5+UZKSopxzTXXGKtXrzamTZtmhIeHG//4xz9OqHYFoSAYPHiwkZGR4f/e4/EYrVq1MiZNmhTEqhqvnJwcAzB+/PFHwzAMIy8vz7DZbMaHH37ov2bt2rUGYMyfP98wDN//tGaz2cjKyvJf8/rrrxsxMTFGeXl5w76BEFdYWGh06tTJmDlzpnH66af7g5A+57pzzz33GKecckq1571er5Gammo8++yz/mN5eXmGw+Ewpk2bZhiGYfz2228GYCxevNh/zddff22YTCZj165d9Vd8I3L++ecbN9xwQ8CxSy+91LjmmmsMw9DnXFcOD0J19bm+9tprRnx8fMC/Hffcc4/RpUuXE6pXXWMNzOl0snTpUkaOHOk/ZjabGTlyJPPnzw9iZY1Xfn4+AAkJCQAsXboUl8sV8Bl37dqV9PR0/2c8f/58evXqRUpKiv+aUaNGUVBQwJo1axqw+tCXkZHB+eefH/B5gj7nuvTZZ58xcOBArrjiCpKTk+nXrx9vvPGG//zWrVvJysoK+KxjY2MZMmRIwGcdFxfHwIED/deMHDkSs9nMwoULG+7NhLBhw4Yxa9YsNmzYAMDKlSv5+eefGT16NKDPub7U1ec6f/58TjvtNOx2u/+aUaNGsX79eg4cOHDc9WnT1Qa2b98+PB5PwC8GgJSUFNatWxekqhovr9fLXXfdxfDhw+nZsycAWVlZ2O124uLiAq5NSUkhKyvLf01VP4OKc+Lz3nvvsWzZMhYvXnzEOX3OdWfLli28/vrrTJgwgfvvv5/Fixdzxx13YLfbGTt2rP+zquqzrPxZJycnB5y3Wq0kJCTosz7o3nvvpaCggK5du2KxWPB4PDz55JNcc801APqc60ldfa5ZWVm0b9/+iOeoOBcfH39c9SkISaOWkZHB6tWr+fnnn4NdSpOzc+dO7rzzTmbOnElYWFiwy2nSvF4vAwcO5KmnngKgX79+rF69milTpjB27NggV9d0fPDBB/zvf//j3XffpUePHqxYsYK77rqLVq1a6XNuxtQ11sCSkpKwWCxHzKzJzs4mNTU1SFU1TrfffjtffPEFs2fPpk2bNv7jqampOJ1O8vLyAq6v/BmnpqZW+TOoOCe+rq+cnBz69++P1WrFarXy448/8vLLL2O1WklJSdHnXEdatmxJ9+7dA45169aNHTt2AIc+q6P9u5GamkpOTk7AebfbTW5urj7rg+6++27uvfde/vCHP9CrVy+uvfZa/vKXvzBp0iRAn3N9qavPtb7+PVEQamB2u50BAwYwa9Ys/zGv18usWbMYOnRoECtrPAzD4Pbbb2f69On88MMPRzSVDhgwAJvNFvAZr1+/nh07dvg/46FDh7Jq1aqA//FmzpxJTEzMEb+QmquzzjqLVatWsWLFCv9t4MCBXHPNNf77+pzrxvDhw49YAmLDhg20bdsWgPbt25OamhrwWRcUFLBw4cKAzzovL4+lS5f6r/nhhx/wer0MGTKkAd5F6CspKcFsDvy1Z7FY8Hq9gD7n+lJXn+vQoUOZO3cuLpfLf83MmTPp0qXLcXeLAZo+Hwzvvfee4XA4jKlTpxq//fabMX78eCMuLi5gZo1U79ZbbzViY2ONOXPmGHv27PHfSkpK/NfccsstRnp6uvHDDz8YS5YsMYYOHWoMHTrUf75iWvc555xjrFixwvjmm2+MFi1aaFr3MVSeNWYY+pzryqJFiwyr1Wo8+eSTxsaNG43//e9/RkREhPHf//7Xf83TTz9txMXFGZ9++qnx66+/GhdddFGV04/79etnLFy40Pj555+NTp06Nftp3ZWNHTvWaN26tX/6/CeffGIkJSUZ/+///T//Nfqcj09hYaGxfPlyY/ny5QZgvPDCC8by5cuN7du3G4ZRN59rXl6ekZKSYlx77bXG6tWrjffee8+IiIjQ9PnG6pVXXjHS09MNu91uDB482FiwYEGwS2o0gCpvb731lv+a0tJS47bbbjPi4+ONiIgI45JLLjH27NkT8Dzbtm0zRo8ebYSHhxtJSUnGX//6V8PlcjXwu2lcDg9C+pzrzueff2707NnTcDgcRteuXY1//vOfAee9Xq/x4IMPGikpKYbD4TDOOussY/369QHX7N+/37j66quNqKgoIyYmxrj++uuNwsLChnwbIa2goMC48847jfT0dCMsLMw46aSTjAceeCBgOrY+5+Mze/bsKv9dHjt2rGEYdfe5rly50jjllFMMh8NhtG7d2nj66adPuHaTYVRaUlNERESkGdEYIREREWm2FIRERESk2VIQEhERkWZLQUhERESaLQUhERERabYUhERERKTZUhASERGRZktBSERERJotBSERkVqYM2cOJpPpiM1mRaRxUhASERGRZktBSERERJotBSERaVS8Xi+TJk2iffv2hIeH06dPHz766CPgULfVl19+Se/evQkLC+Pkk09m9erVAc/x8ccf06NHDxwOB+3ateP5558POF9eXs4999xDWloaDoeDjh078u9//zvgmqVLlzJw4EAiIiIYNmwY69evr983LiL1QkFIRBqVSZMm8fbbbzNlyhTWrFnDX/7yF/70pz/x448/+q+5++67ef7551m8eDEtWrRgzJgxuFwuwBdgrrzySv7whz+watUqHnnkER588EGmTp3qf/x1113HtGnTePnll1m7di3/+Mc/iIqKCqjjgQce4Pnnn2fJkiVYrVZuuOGGBnn/IlK3tPu8iDQa5eXlJCQk8P333zN06FD/8RtvvJGSkhLGjx/PiBEjeO+997jqqqsAyM3NpU2bNkydOpUrr7ySa665hr179/Ldd9/5H////t//48svv2TNmjVs2LCBLl26MHPmTEaOHHlEDXPmzGHEiBF8//33nHXWWQB89dVXnH/++ZSWlhIWFlbPn4KI1CW1CIlIo7Fp0yZKSko4++yziYqK8t/efvttNm/e7L+uckhKSEigS5curF27FoC1a9cyfPjwgOcdPnw4GzduxOPxsGLFCiwWC6effvpRa+ndu7f/fsuWLQHIyck54fcoIg3LGuwCRERqqqioCIAvv/yS1q1bB5xzOBwBYeh4hYeH1+g6m83mv28ymQDf+CURaVzUIiQijUb37t1xOBzs2LGDjh07BtzS0tL81y1YsMB//8CBA2zYsIFu3boB0K1bN+bNmxfwvPPmzaNz585YLBZ69eqF1+sNGHMkIk2XWoREpNGIjo5m4sSJ/OUvf8Hr9XLKKaeQn5/PvHnziImJoW3btgA89thjJCYmkpKSwgMPPEBSUhIXX3wxAH/9618ZNGgQjz/+OFdddRXz58/n1Vdf5bXXXgOgXbt2jB07lhtuuIGXX36ZPn36sH37dnJycrjyyiuD9dZFpJ4oCIlIo/L444/TokULJk2axJYtW4iLi6N///7cf//9/q6pp59+mjvvvJONGzfSt29fPv/8c+x2OwD9+/fngw8+4KGHHuLxxx+nZcuWPPbYY4wbN87/Gq+//jr3338/t912G/v37yc9PZ37778/GG9XROqZZo2JSJNRMaPrwIEDxMXFBbscEWkENEZIREREmi0FIREREWm21DUmIiIizZZahERERKTZUhASERGRZktBSERERJotBSERERFpthSEREREpNlSEBIREZFmS0FIREREmi0FIREREWm2/j+yrbTvfLD2wwAAAABJRU5ErkJggg=="/>
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<p>Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the <code>matplotlib</code>. We plot the predicted output on the left side, the true solution at the center and the difference on the right side using the <code>plot_solution</code> function.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
<span class="c1"># get the problem</span>
<span class="n">problem</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">problem</span>
<span class="c1"># get spatial points</span>
<span class="n">spatial_samples</span> <span class="o">=</span> <span class="n">problem</span><span class="o">.</span><span class="n">spatial_domain</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="s2">"grid"</span><span class="p">)</span>
<span class="c1"># get temporal value</span>
<span class="n">time</span> <span class="o">=</span> <span class="n">LabelTensor</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="n">time</span><span class="p">]]),</span> <span class="s2">"t"</span><span class="p">)</span>
<span class="c1"># cross data</span>
<span class="n">points</span> <span class="o">=</span> <span class="n">spatial_samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"cross"</span><span class="p">)</span>
<span class="c1"># compute pinn solution, true solution and absolute difference</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"PINN solution"</span><span class="p">:</span> <span class="n">solver</span><span class="p">(</span><span class="n">points</span><span class="p">),</span>
<span class="s2">"True solution"</span><span class="p">:</span> <span class="n">problem</span><span class="o">.</span><span class="n">solution</span><span class="p">(</span><span class="n">points</span><span class="p">),</span>
<span class="s2">"Absolute Difference"</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
<span class="n">solver</span><span class="p">(</span><span class="n">points</span><span class="p">)</span> <span class="o">-</span> <span class="n">problem</span><span class="o">.</span><span class="n">solution</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="c1"># plot the solution</span>
<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Solution for time </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">field</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">items</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">3</span><span class="p">,</span> <span class="n">idx</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">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tricontourf</span><span class="p">(</span> <span class="c1"># convert to torch tensor + flatten</span>
<span class="n">points</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="s2">"x"</span><span class="p">)</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
<span class="n">points</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="s2">"y"</span><span class="p">)</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
<span class="n">field</span><span class="o">.</span><span class="n">tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</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">tight_layout</span><span class="p">()</span>
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<p>Let's take a look at the results at different times, for example <code>0.0</code>, <code>0.5</code> and <code>1.0</code>:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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"/>
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<p>The results are not so great, and we can clearly see that as time progresses the solution gets worse.... Can we do better?</p>
<p>A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:</p>
<p>$$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)\cdot t + \cos(\sqrt{2}\pi t)\sin(\pi x)\sin(\pi y), $$</p>
<p>Let us build the network first</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">HardMLPtime</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">input_dim</span><span class="p">,</span> <span class="n">output_dim</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">layers</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="n">input_dim</span><span class="p">,</span> <span class="mi">40</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">40</span><span class="p">,</span> <span class="mi">40</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">40</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># here in the foward we implement the hard constraints</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="n">hard_space</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</span><span class="p">]))</span>
<span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">])</span>
<span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</span>
<span class="p">)</span>
<span class="n">hard_t</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"x"</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">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"y"</span><span class="p">]))</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">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">tensor</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">hard_space</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">extract</span><span class="p">([</span><span class="s2">"t"</span><span class="p">])</span> <span class="o">+</span> <span class="n">hard_t</span>
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<p>Now let's train with the same configuration as the previous test</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># define model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">HardMLPtime</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">input_variables</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">problem</span><span class="o">.</span><span class="n">output_variables</span><span class="p">))</span>
<span class="c1"># crete the solver</span>
<span class="n">pinn</span> <span class="o">=</span> <span class="n">PINN</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">model</span><span class="p">)</span>
<span class="c1"># create trainer and 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="o">=</span><span class="n">pinn</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1000</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">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="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">MetricTracker</span><span class="p">([</span><span class="s2">"train_loss"</span><span class="p">,</span> <span class="s2">"initial_loss"</span><span class="p">,</span> <span class="s2">"D_loss"</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=1000` reached.
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<p>We can clearly see that the loss is way lower now. Let's plot the results</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">plot_solution</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">pinn</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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7//3m9oYE2FL5AOHTro3Xff1b59+/yujqxZs8b37wDgZYGOvaWlpdV61Hbq1En79+/3XQUOxxlnnKEzzjhDDzzwgObOnavBgwdr3rx5uvrqqyM6XldcLd69e7ffhOSBru4HW0Mq1rd27VrfHXMrrF27lvoBIGbmzJmjli1b+u5eV9lrr72m119/XdOnT/cLOSp6wFT2zTffVLtbdadOnXTrrbfq1ltv1bfffqtu3brpkUce0d/+9je/417lc+/S0lJt2LCh1uN/5eNwZaEchzt16qR3331XZ511VsgXU4JRMdl6qKNTahNpbazYz/Xq1YuovlbdJulIEFdTCFexTHp6etTW6yR9+/ZV3759a/z3kpIS3XXXXfqf//kf7d69W126dNHkyZN9d/INR3JysjIzM8N+vt0YyoegVA6aKnfJrc3dd9+tQ4cO6S9/+UtQy//4449+v6ekpOjEE0+UZVk6dOiQkpKSdOGFF+qf//yn321ni4uLNXfuXJ199tlKT0+v9T18/PHHKi0t9T321ltvVRv+17BhQ0nVC18gF110kcrKyvTUU0/5Pf7YY48pISGh1gMSAHhBp06dqs2BMWPGjGpXuS+55BItW7ZMCxcurPYau3fv1uHDh2tcx08//VTtSm7FFdqK4XyRHK8rTrArv48DBw7ohRdeqLZsw4YNg6of3bt3V8uWLTV9+nS/IYf//ve/tXr1avXr16/O1wCAUP3888967bXX9Nvf/lYXX3xxtZ+CggLt27dPb775pt/z3njjDb+5iJYvX65PPvnEd+w8ePCgfvnlF7/ndOrUSY0bN/Yd4/Ly8pSSkqInnnjC75j9/PPPa8+ePbUe9zp06KCkpKRq9eTpp5+utmxN5/KXXHKJysrKdN9991V7zuHDh4M6dtdk7ty5eu6559SjRw9dcMEFYb9OVZHURklq2bKlevXqpb/+9a8Bp1jZsWNHyNt04YUXqnHjxpo0aVK1z7zic83JyVGnTp00ZcoU3xDHSNfrJgUFBVq2bJnmzZun//u//9Of/vQn9enTJ2AAHKxvv/1WrVu3VseOHTV48GBt2rQpilsce/SYQtAqhuetXbtWJ510Up3LV4RZgU7cA7nwwguVmZmps846S61atdLq1av11FNPqV+/fr6r2/fff78WLVqks88+WzfccIOSk5P117/+VSUlJXUGYFdffbVeffVV9enTR5dcconWr1+vv/3tb36TkVdsd9OmTTV9+nQ1btxYDRs2VG5ubrUx25LUv39/nXfeebrrrru0ceNGde3aVe+8847++c9/6pZbbqn22gDgNVdffbWuu+46DRw4UL1799YXX3yhhQsXqkWLFn7L3X777XrzzTf129/+VsOGDVNOTo4OHDigL7/8Uq+++qo2btxY7TkVXnjhBT399NP6wx/+oE6dOmnfvn169tlnlZ6erosuukhSZMfrCy+8UO3bt9eIESN0++23KykpSTNnzlRGRka1E7+cnBw988wzuv/++3XssceqZcuW1XpESUeuXk+ePFnDhw9Xz549ddlll6m4uFiPP/64srKyNHr06FB3NQDU6c0339S+ffv0u9/9LuC/n3HGGcrIyNCcOXM0aNAg3+PHHnuszj77bF1//fUqKSnR1KlTddRRR/mGmH3zzTe64IILdMkll+jEE09UcnKyXn/9dRUXF+vSSy+VdGSo3Lhx4zRx4kT16dNHv/vd77R27Vo9/fTTOu2003TFFVfUuN1NmjTRn/70Jz355JNKSEhQp06d9NZbbwWcFyonJ0eSdNNNNyk/P19JSUm69NJL1bNnT1177bWaNGmSVq1apQsvvFD16tXTt99+q1deeUWPP/64Lr744jr34auvvqpGjRqptLRUW7du1cKFC/Xhhx+qa9eueuWVV+p8figiqY0Vpk2bprPPPlsnn3yyRo4cqY4dO6q4uFjLli3Tli1b9MUXX4S0Tenp6Xrsscd09dVX67TTTtPll1+uZs2a6YsvvtDBgwf1wgsvKDExUc8995z69u2rk046ScOHD1ebNm20detWvffee0pPT9e//vWvSHaNY23atEmzZs3Spk2b1Lp1a0lHhlouWLBAs2bN0oMPPhjya+bm5mr27Nk64YQT9MMPP2jixIk655xz9NVXX0V1zrOYsu1+gIipitt91nbrbMv69darr7zySlDPrbhl9UknneT3eE23Pf3222+tpKSkausI5K9//at17rnnWkcddZSVmppqderUybr99tutPXv2+C23cuVKKz8/32rUqJHVoEED67zzzrM++uijgO+r6i1lH3nkEatNmzZWamqqddZZZ1mfffaZ1bNnz2q39v7nP/9pnXjiiVZycrLfLWgD3fJ137591ujRo63WrVtb9erVs4477jjr4Ycf9rs1qmUFvgWsZVW/LTkAmCzQbbh79uxZrS5UKCsrs/785z9bLVq0sBo0aGDl5+db69atC3js27dvnzVu3Djr2GOPtVJSUqwWLVpYZ555pjVlyhSrtLS0xm1auXKlddlll1nt27e3UlNTrZYtW1q//e1vrc8++6za6wdzvA60bStWrLByc3OtlJQUq3379tajjz7qq5cbNmzwLVdUVGT169fPaty4sSXJV19qqksvv/yydeqpp1qpqalW8+bNrcGDB/vdkt2yjtSehg0bVnvfFbcuB4Bg9e/f30pLS7MOHDhQ4zLDhg2z6tWrZ+3cudPasGGDJcl6+OGHrUceecRq166dlZqaap1zzjnWF1984XvOzp07rVGjRlnZ2dlWw4YNrSZNmli5ubnW3//+92qv/9RTT1nZ2dlWvXr1rFatWlnXX3+99dNPP/ktE+ice8eOHdbAgQOtBg0aWM2aNbOuvfZa66uvvvI7V7csyzp8+LB14403WhkZGVZCQkK14+SMGTOsnJwcq379+lbjxo2tk08+2brjjjusbdu21brvKo65FT9paWlW27Ztrd/+9rfWzJkzrV9++aXacwK9j5raTTW1FYKpjZU/p0DWr19vDRkyxMrMzLTq1atntWnTxvrtb39rvfrqq75lamoD1lS/3nzzTevMM8+06tevb6Wnp1unn3669T//8z9+y3z++efWH//4R1/7rkOHDtYll1xiLV68OOB2upEk6/XXX/f9/tZbb1mSrIYNG/r9JCcnW5dccollWZa1evVqv+9aoJ8///nPNa7zp59+stLT063nnnsu1m8vahIsi1mXAQAAAAAAoikhIUGvv/6674ZgL7/8sgYPHqyvv/662kTzjRo1UmZmpkpLS/Xdd9/V+rpHHXVUrRP2n3baacrLy9OkSZMifg/xwFA+AAAAAACAGDv11FNVVlam7du365xzzgm4TEpKirKzs8Nex/79+7V+/XpdeeWVYb9GvBFMAQAAAAAARMH+/fu1bt063+8bNmzQqlWr1Lx5cx1//PEaPHiwhgwZokceeUSnnnqqduzYocWLF+uUU04J6+Ynt912m/r3768OHTpo27ZtKiwsVFJSki677LJovq2YYigfAAAAAABAFCxdulTnnXdetceHDh2q2bNn69ChQ7r//vv14osvauvWrWrRooXOOOMMTZw4USeffHLI67v00kv1/vvv68cff1RGRobOPvtsPfDAA466EVdiqE94//331b9/f7Vu3VoJCQl644036nzO0qVL9Zvf/Eapqak69thjNXv27DA2FQC8Ydq0acrKylJaWppyc3O1fPnyWpd/5ZVXlJ2drbS0NJ188smaP39+jcted911SkhI0NSpU6O81cGhhgBA7FFH/FFHAMRTr169ZFlWtZ+KY0+9evU0ceJEbdiwQaWlpdq2bZtee+21sEIpSZo3b562bdumkpISbdmyRfPmzQs7lArnGFtSUqK77rpLHTp0UGpqqrKysjRz5syQ1htyMHXgwAF17dpV06ZNC2r5DRs2qF+/fjrvvPO0atUq3XLLLbr66qu1cOHCUFcNAK738ssva8yYMSosLNTKlSvVtWtX5efnB7wdsiR99NFHuuyyyzRixAh9/vnnGjBggAYMGKCvvvqq2rKvv/66Pv74Y9+tae1ADQGA2KKO+KOOAEDwQj3GStIll1yixYsX6/nnn9fatWv1P//zPzrhhBNCW3Ekt/RTlVsfBnLHHXdUu4X0oEGDrPz8/EhWDQCudPrpp/vdKrisrMxq3bq1NWnSpIDLX3LJJdVuOZybm2tde+21fo9t2bLFatOmjfXVV19ZHTp0sB577LGob3uoqCEAEH3UEX/UEQAITzDH2H//+99WkyZNrB9//DGidcV88vNly5YpLy/P77H8/HzdcsstNT6npKREJSUlvt/Ly8u1a9cuHXXUUUpISIjVpgJwEMuytG/fPrVu3VqJiSF3/vT55ZdfVFpaGsUt82dZVrXjVmpqqlJTU6stW1paqhUrVmjcuHG+xxITE5WXl6dly5YFfP1ly5ZpzJgxfo/l5+f7dbstLy/XlVdeqdtvv10nnXRSBO8m/sKpIRJ1BEDdolFHYl1DJOpIpGiLAIgVN9aRUL355pvq3r27/vKXv+ill15Sw4YN9bvf/U733Xef6tevH/TrxDyYKioqUqtWrfwea9Wqlfbu3auff/454MZOmjRJEydOjPWmAXCBzZs3q23btmE995dfflH79g21Y0d5lLfqV40aNdL+/fv9HissLNSECROqLbtz506VlZUFPGauWbMm4OvXdIwtKiry/T558mQlJyfrpptuCvNd2CecGiJRRwAEL9w6Eo8aIlFHIkVbBECsuamOhOq7777TBx98oLS0NL3++uvauXOnbrjhBv3444+aNWtW0K8T82AqHOPGjfO7crNnzx61b99ety8+X6kNjdxkAFE2oPEXtf77/v3l6pW7Q40bNw57HaWlpdqxo1xLP2mpRo2ifwV0/35LvXK3a/PmzUpPT/c9Ho2rE8FasWKFHn/8ca1cudJTV3mpIwBiXUdiXUMk6ohdaqohvVoMUXJiio1bBsAkh8tLtXTni56uI+Xl5UpISNCcOXPUpEkTSdKjjz6qiy++WE8//XTQvaZifnaemZmp4uJiv8eKi4uVnp5e40bW1K0stWGy0hrVi8l2AjDLAqu7Lk5fWedy0ThJbtQoQY0ahz8csGZHrn6kp6f7FYKatGjRQklJSQGPmZmZmQGfU9MxtmL5//znP9q+fbvat2/v+/eysjLdeuutmjp1qjZu3BjKG4q7cGqIRB0BoKCP65HWkdjVEIk6ErlotkWSE1MIpgBU46Y6Eqqjjz5abdq08YVSktS5c2dZlqUtW7bouOOOC+p1YvXufXr06KHFixf7PbZo0SL16NEj1qsGAEdJSUlRTk6O3zGzvLxcixcvrvGYWdcx9sorr9T//d//adWqVb6f1q1b6/bbb3fEHYmoIQDC9ere39i9CXFHHamOOgIAsXPWWWdp27ZtfkMFv/nmGyUmJoY0vDHkHlP79+/XunXrfL9v2LBBq1atUvPmzdW+fXuNGzdOW7du1YsvvihJuu666/TUU0/pjjvu0FVXXaUlS5bo73//u95+++1QVw3AY17d+5ugek25yZgxYzR06FB1795dp59+uqZOnaoDBw5o+PDhkqQhQ4aoTZs2mjRpkiTp5ptvVs+ePfXII4+oX79+mjdvnj777DPNmDFDknTUUUfpqKOO8ltHvXr1lJmZGfptXKOAGgIAsUUdoY4AQLhCPcZefvnluu+++zR8+HBNnDhRO3fu1O23366rrroqtpOff/bZZzrvvPN8v1eMvx46dKhmz56tH374QZs2bfL9+zHHHKO3335bo0eP1uOPP662bdvqueeeU35+fqirBgDXGzRokHbs2KHx48erqKhI3bp104IFC3wTt27atMnvrh9nnnmm5s6dq7vvvlt33nmnjjvuOL3xxhvq0qWLXW+hVtQQAPHkxQsc1BHqCACEK9RjbKNGjbRo0SLdeOON6t69u4466ihdcskluv/++0Nab4JlWVZ03kLs7N27V02aNNHdH1/I3CCABwVqVOzfV67uJxVrz549YY+Xrji2fPZ1q5iM647GNiI6qCOAt8WijsS6hkjUEVNUfNZ5La9mjikAPofLS/Xu9ueoI1EQ8zmmAAAAAAAAgEAIpgAYz4sT2AIAooc6AgCAuQimAAAAAAAAYAuCKQCOwNVuAEAkqCMAAJiJYAoAAAAAAAC2IJgC4Bhc7QYARII6AgCAeQimAAAAAAAAYAuCKQCOwtVuAEAkqCMAAJiFYAqA49CoAAAAAAB3IJgCAACAp3CBAwAAcxBMAXCkN/Z1tXsTAAAAAAARIpgCAACA53CBAwAAMxBMAQAAAAAAwBYEUwAAAAAAALAFwRQAAAAAAABsQTAFAAAAAAAAWxBMAQAAAAAAwBbJdm8AUJtFRdlBL9s7c00MtwQA4ETUEQAAALMRTCGqQmkAmLpuGiYAYB+n1xFqCAAAQGgIphA0OxsL8RTM+6ThAQCh80IdoYYAAACEhmAK1Xih4RCp2vYRDQ4AXkcdqR01BAAA4FcEU6ABEWU17U8aGwDciBoSXYRWAADAawimPITGg72q7n8aGACchBpiP+oIAABwI4IpF6MRYbbKnw+NCwCmoYaYjzoCAADcgGDKZWhIOBONCwAmoIY4F3UEAAA4FcGUC9CQcJdAnyeNDACxRB1xF+oIAABwEoIph6IR4S0VnzcNCwDRQh3xFuoIAAAwFcGUg9CIAEM1AESCOgICKgAAYBqCKQegIYFAaFwACBZ1BFVxoQMAAJiCYMpgNCQQDAIqADWhjiAY1BEAAGAngikD0ZBAOGhYAKhAHUE4qCMAAMAOBFMGoSGBaKBhAXgXdQTRQB0BAADxRDBlCBoTiDYaFoB3UEMQC9QRAAAQD4l2b4DXLSrKpkGBmOL7BbgXNQTxwHcMAADEEsGUTWhMIJ74rgHuw9814onvGwAAiBWCqTgjkIJd+N4B7kAdgV343gEAgFggmIojTuhgN76DgLPxNwy78R0EAADRRjAVJ5zIwRT0tgCcib9bmILvIgAAiCaCqTjgBA4m4nsJOAd/rzANFzkAAEC0EEzFGCdtMBkNC8B8/I3CZHw/AQBApAimYoiTNTgF31XATPxtwgm4yAEAACJBMBUjnKDBafjOAmbhbxJOw3cWAACEI9nuDXAjTsxqt3FLhq3rz2q7w9b1m2xRUbZ6Z66xezMAz6OO1I46Yi7qCAAACBXBVJTRmDjC7kZDbWrbNhobNCoAu1FHjqCOOBd1BAAAhIJgKoq82pgwufEQqkDvxYuNDBoVgD2oI85X9b14sYYAAACEgmAqSrzUmHBTAyIYXm1kEE4B8UUdcSevXvCghgAAgGARTEWBFxoTXmpE1KXyvvBC4wJA7Lm9jlBD/HmljhBOAQCAYBBMISAaEcFxe+OCRgUQe24NpagjwaGOAAAAryOYipDbGhQ0JMLn1sYFjQoAoaCOhI86AgAAvIhgKgJuCqVoSESX2xoXNCqA2KCOoCZuqyMAAAA1SbR7A2CfjVsyfD+IHfYzQjVt2jRlZWUpLS1Nubm5Wr58ea3Lv/LKK8rOzlZaWppOPvlkzZ8/3+/fLcvS+PHjdfTRR6t+/frKy8vTt99+G8u3gCC4IZTi+BYfbtjHbvi+Owl1BAAQjvfff1/9+/dX69atlZCQoDfeeCPo53744YdKTk5Wt27dQl4vwVSYnHyC5YYTXKdy8n538nfeSV5++WWNGTNGhYWFWrlypbp27ar8/Hxt37494PIfffSRLrvsMo0YMUKff/65BgwYoAEDBuirr77yLfOXv/xFTzzxhKZPn65PPvlEDRs2VH5+vn755Zd4vS24DHXEHk7f79SR+KCOAADCdeDAAXXt2lXTpk0L6Xm7d+/WkCFDdMEFF4S1XoIpj+Cqtjn4HFCbRx99VCNHjtTw4cN14oknavr06WrQoIFmzpwZcPnHH39cffr00e23367OnTvrvvvu029+8xs99dRTko5c5Z46daruvvtu/f73v9cpp5yiF198Udu2bQvpCgiiy4kNdOqIOfgcUBvqCAAgXH379tX999+vP/zhDyE977rrrtPll1+uHj16hLVegqkwOKlBwcmruZz42Tjpu+9EpaWlWrFihfLy8nyPJSYmKi8vT8uWLQv4nGXLlvktL0n5+fm+5Tds2KCioiK/ZZo0aaLc3NwaXxOozInHKq9w4mdDHYkt6ggAIN5mzZql7777ToWFhWG/BpOfu5jTTla9auOWDCa2dbm9e/f6/Z6amqrU1NRqy+3cuVNlZWVq1aqV3+OtWrXSmjWBJ58vKioKuHxRUZHv3yseq2kZxJdTGubUEOegjrgfdQQAEIlg60iovv32W40dO1b/+c9/lJwcfrxEMBUiJzQoaEw4T8Vn5oSGhRvv0PfGvq5Ks+pF/XV/2X9I0jtq166d3+OFhYWaMGFC1NcHRAt1xHmoI/aJVQ2RqCMA4AVOrSNlZWW6/PLLNXHiRB1//PERvRbBVAgIpRBrTmpYIHibN29Wenq67/eark60aNFCSUlJKi4u9nu8uLhYmZmZAZ+TmZlZ6/IV/y0uLtbRRx/tt0w4d8xAZEyvI9QQ56OOuBN1BAAQiWDrSCj27dunzz77TJ9//rkKCgokSeXl5bIsS8nJyXrnnXd0/vnnB/VazDHlEk6cZwI1M/3zNL1xbZr09HS/n5oKQUpKinJycrR48WLfY+Xl5Vq8eHGNEwn26NHDb3lJWrRokW/5Y445RpmZmX7L7N27V5988knYkxPCnUw+5iB01BF3oY4A9irNbmv3JgARCbaOhPqaX375pVatWuX7ue6663TCCSdo1apVys3NDfq16DEVJJNPoEw+8URkmDfEe8aMGaOhQ4eqe/fuOv300zV16lQdOHBAw4cPlyQNGTJEbdq00aRJkyRJN998s3r27KlHHnlE/fr107x58/TZZ59pxowZkqSEhATdcsstuv/++3XcccfpmGOO0T333KPWrVtrwIABdr1NT6KOwA7UEe+hjgCRqSmEqunxlDVbYrk5QFzt379f69at8/2+YcMGrVq1Ss2bN1f79u01btw4bd26VS+++KISExPVpUsXv+e3bNlSaWlp1R6vC8GUw9GYcD9Th2W4bY4QUwwaNEg7duzQ+PHjVVRUpG7dumnBggW+SWc3bdqkxMRfO7ueeeaZmjt3ru6++27deeedOu644/TGG2/4FYM77rhDBw4c0DXXXKPdu3fr7LPP1oIFC5SWlhb39wezUEO8wdRwijoSG9QRIDSR9oYK9HzCKjjVZ599pvPOO8/3+5gxYyRJQ4cO1ezZs/XDDz9o06ZNUV9vgmVZVtRfNcr27t2rJk2a6O6PL1Rao9hMClYbE69y05jwJhMbFnY1Kn7Zf0j3n/GO9uzZ4zdeOhSxPrZEYxsRHdSR6qgj3mRaHbEzmIr0GB2P4wp1xAwVn3Vey6uVnJhi9+YgCuwamkdg5S6Hy0v17vbnqCNRQI8pB6Ix4V2mXvUG4CzUEe8yrY7QawpAvJgwT1TVbSCoAo4gmKqDaVe5aUyARgXgLCbVEWoIJPPqCADEiglhVG0YBggcQTDlEDQmUBmNCgChoo6gMpPqCBc4AEST6WFUXQir4EUEU7Uw6So3UBWNCsB8ptQRQikEYlIdAYBIOD2MqgtDAOF2iXUvArvRoEBN+G4AACJhSh0xJcQF4Byl2W19P17j1fcN96LHlOFMOWGEubjiDZjJlIY2dQR1oY4AcArCGH/0pIJbEEzVwIQGhVcbE6mbIrsNb0n70ihtiXOY0KhgOB9gHi/WEWpIeEyoIwAQCGFU8CrvK0IqOAnBlKHc3piItOEQ7mu7ucFBowJAZdSR2LwudSR2uMABoDICqcjQmwpOQjBlILc1JmIZQoUq0La4qZFhd6MCwBF297qljsRO1W1xUw2RqCMA7EcgFRsV+5WACiYimArA7gaF05nUgAiG2xoZdjYquNoN2M8NoZST6ogbL3gQTgGIN8Ko+CGggokIpgzj5AaFkxoStan8PpzauKBRAXgTNcQM1JHwcYED8BYCKfswHxVMQjBlECc2KNzUkAjEyY0LwikApnN7DZF+fY9OqyEAEEsEUmahFxXsRjBVhV3D+JwUSnmhIREIjQsAwaCO1M7rNURyTh3hAgeAaCOQMhsBFeySaPcGwBmNidRNKb4fr3PSfrDju8UcbUD8OamOwFn7gjoCIBpKs9sSSjkInxfijWDKZqY3Jpx08hxvTtk3pn/HAETG9L9xpxwr7eCUfWP6dwyAuQg4nI3PD/FCMGUj00/0nHCybAInNCxM/64BbhHvnh4m/2074dhoCvYVADci0HAPPkvEGsFUJXQdP4IT5PCw337F3xLgXRwLw2fyvjM5BAVgFnrZuBOfK2KJYMomJp7gmXxC7CSm7kcTv3MAwmfi37SJxz4noo5wgQNwIoILb+BzRixwVz5IojERC6mbUhxz5yUAzmJaKEUNiQ3qCAAnIKTwJu7gh2iix5QNTGpQmHpV1i1M278mffcAt/FqDw+TjnFuZNr+pY4AkH7tNUMoBSAaCKb+Py82KEw72XUzk/Z1vBoVXvybAuLBpGDApGObm5l2kQOAdxFGAYgFgqk4M6VBwQlu/LHPAbgFQYk9TNnnXOAAvIlACoHwvUA0EEx5DI0Je5my/00JSAGExoS/XROOYV5myv434bsIID7oJQUg1pj8PI7sPokz5WQWTGgLwJmoI2ao+ByoIwBigRAKQLzRYypOCKVQld2fid3fScBN4jHkyO6/WbuPWajO7s/E7u8kgOiiZxTCxfcGkSKYkvvnMLD7xBU1c/tn4/a/LcALTBmCjMD4bABEA8ECADuFFUxNmzZNWVlZSktLU25urpYvX17r8lOnTtUJJ5yg+vXrq127dho9erR++eWXsDbYiey6okhjwhns/Iy42g27UEdCY2cdgfncXEe4wIFAqCGI1J5OqbX+AIivkOeYevnllzVmzBhNnz5dubm5mjp1qvLz87V27Vq1bNmy2vJz587V2LFjNXPmTJ155pn65ptvNGzYMCUkJOjRRx+NyptAdTQmnIU5p+Al1BFnoI44C3UEXkENiT639ZaKRrBU12s0WV8S8ToA/CrkHlOPPvqoRo4cqeHDh+vEE0/U9OnT1aBBA82cOTPg8h999JHOOussXX755crKytKFF16oyy67rM4rG25BjxQEy65GIN9RxJvb6kise3TY8TdKKOVM1BF4gdtqiN3cFErFs7cTPa2qc9N3CfEXUjBVWlqqFStWKC8v79cXSExUXl6eli1bFvA5Z555plasWOE7+H/33XeaP3++Lrroogg2O3rc2EWcBoVzufGzc+PfGMLnxjriNm48DnkJnx/cjBoSXW4IEkwLhQirgPCENJRv586dKisrU6tWrfweb9WqldasWRPwOZdffrl27typs88+W5Zl6fDhw7ruuut055131riekpISlZT82j1y7969oWymMbjKjXDYMRxj45YMZbXdEdd1wpuoI6GhJwrC4bZhfYuKstU7M/DxAd5CDYkep4dSTgp8Am0rQwEBfzG/K9/SpUv14IMP6umnn9bKlSv12muv6e2339Z9991X43MmTZqkJk2a+H7atWsX6810BUIp9+CzBH5FHYkfjj3uEe/PkhAVpqKG+CvNbuvoUMotvZDc2qPKyd8t2CukHlMtWrRQUlKSiouL/R4vLi5WZmZmwOfcc889uvLKK3X11VdLkk4++WQdOHBA11xzje666y4lJlbPxsaNG6cxY8b4ft+7d6/jCkK8T9BoTLhPvK9402sK8eC2OhLLoarUEUTKbT2nALfVkHhzcmjgtgCnsqrvjd5U8KKQekylpKQoJydHixcv9j1WXl6uxYsXq0ePHgGfc/DgwWoH/KSkJEmSZVkBn5Oamqr09HS/n1hwy9w3NCbcyy2frVv+1hA5t9URt3DLsQbVxfOzpdcUYo0aEj6nhlJu7FVUF7f2pgJqE1KPKUkaM2aMhg4dqu7du+v000/X1KlTdeDAAQ0fPlySNGTIELVp00aTJk2SJPXv31+PPvqoTj31VOXm5mrdunW655571L9/f19RQPjc1pho/H3gE4RQ7euQEJXXMQFXvOE21JG6xbOBTx0JzE11xA2YZwoVqCGhc2IoRShzROX9QE8quFnIwdSgQYO0Y8cOjR8/XkVFRerWrZsWLFjgm4Rw06ZNflcl7r77biUkJOjuu+/W1q1blZGRof79++uBBx6I3rswDFcMgxOtxkOwr00jo24M50M8UEfM4fRQijoSnHhe4KCOINaoIaFxWihFIFUzp4RUpdltlbJmi92bAYcJOZiSpIKCAhUUFAT8t6VLl/qvIDlZhYWFKiwsDGdVqIUTGxSxbESEsm6nNS7oNQW3oY4gHHbWkKrrp44A9qGGBMdJoRSBVGicElIBwQormIL9nBRK2d2QCMSJjQsaFYBZYjV3Wrx63TqljphYQyTqCACzOSGUIoyKjor9SEAFJwtp8nPUjWF8RzT+3vL9mM5J2xqPhmSsvsNMgA6YwwmhlFOOy5Kz6kg8cC4E2ItQypuYMB1O5tlgysmNZJMbFE4/MXf69gOA0zn9OGz6tpt8DlEXJ5+7AfFieihFeBJ73NUPTuTZYMqpTD2hdHpDoiqT34uTe00BqF08/vaoI7Fn+nsx9TsAwN0ISuKPgApOQTAVRV5tzJt88h0J0xsWABAqEwMJNx9r3fze6uLVcyLATqb2liIcsV+8PwNTv4swF8GUg5jWoPDKCbeJ79O07wIAhMrEY2usmPheqSOAu5gaBBBImYXPA6YimHIIk04gTTzBjgfT3nesvxNc7QbiK9Z/c6bVES8yrY4AcAcTQyl6SQEIBcFUlHihEc8J9RHsBwBOm4TZlFCK4+cRpuwDLnAAzmdqKAVz8fnARARTDmBCg8KUk2iTmLBPTPhuhMJpjXkA0WPCMdMkpoR0TqsjAH5lWihFLynn4HOCaTwZTNE4Do0JJ86mcvu+4Wo3EB+x/FszIXhw+7EyEuwbAOEwMZQCgHB5MpiKNrc2KEy5mms6u/eRCY1OAAiEOhIcu/dRLOtItM+RuLgImBVK0UvKufjcYBKCKYPZHUoheHY3vginAARCHXEO9hcApyHYABAtBFOohpPj8LHvAITKjUNmORaGx86LHFzgAJzBlN5ShFLuEMvP0ZTvKpyBYCpCsWpQ2HWCSGMicm5rVLix0Qx4AXXEudiHAExGKAUg2gim4MOJcPSwLxFru3bt0uDBg5Wenq6mTZtqxIgR2r9/f63P+eWXXzRq1CgdddRRatSokQYOHKji4uKAy/74449q27atEhIStHv37hi8A+dijpuaceyLHjv2JRc4vIU64jx290BhPil34jNFZe+//7769++v1q1bKyEhQW+88Uaty7/22mvq3bu3MjIylJ6erh49emjhwoUhr9dzwZQTGhR2XOWmMRF97FPE0uDBg/X1119r0aJFeuutt/T+++/rmmuuqfU5o0eP1r/+9S+98sor+t///V9t27ZNf/zjHwMuO2LECJ1yyimx2HRU4qZetxzzoo99iliijiAUJoYX+zokxO0H8IoDBw6oa9eumjZtWlDLv//+++rdu7fmz5+vFStW6LzzzlP//v31+eefh7Te5HA2Fke45QogJ76x0/h7K67FLHVTikral8ZtfeFYVJSt3plr7N4MR1u9erUWLFigTz/9VN27d5ckPfnkk7rooos0ZcoUtW7dutpz9uzZo+eff15z587V+eefL0maNWuWOnfurI8//lhnnHGGb9lnnnlGu3fv1vjx4/Xvf/87Pm8KjkYdiR3qCGKBOuI8dvaWMimUsiskqmu91EG4Rd++fdW3b9+gl586darf7w8++KD++c9/6l//+pdOPfXUoF/Hcz2mTBfvq9wcRGPPDfvYLSGsXfbu3ev3U1JSEtHrLVu2TE2bNvU1JiQpLy9PiYmJ+uSTTwI+Z8WKFTp06JDy8vJ8j2VnZ6t9+/ZatmyZ77H//ve/uvfee/Xiiy8qMZES4UTUEfdxwz6mjkSGOuJthFIyvucSPaxgumjXkZqUl5dr3759at68eUjPo8eUh7nhRNcp4nnFm6vdoXuv+Hgl74/+idfhAyWS3lG7du38Hi8sLNSECRPCft2ioiK1bNnS77Hk5GQ1b95cRUVFNT4nJSVFTZs29Xu8VatWvueUlJTosssu08MPP6z27dvru+++C3sb4Q3UEdjBtJ63saohEnUE9jElkJLs6yUViUDbTM1ETZxYR2oyZcoU7d+/X5dccklIzyOYMgi3ana3eA/HgDk2b96s9PR03++pqYELz9ixYzV58uRaX2v16tVR3bbKxo0bp86dO+uKK66I2Trwq1j0IIlnHeEEO764wOFt1BHvsqO3lAmhlBvPmSu/J2oo4i3YOhKJuXPnauLEifrnP/9Z7eJHXQimwuT0LukcDN2NRoVZ0tPT/QpBTW699VYNGzas1mU6duyozMxMbd++3e/xw4cPa9euXcrMzAz4vMzMTJWWlmr37t1+V7uLi4t9z1myZIm+/PJLvfrqq5IkyzpynGjRooXuuusuTZw4sc73ACB2uMDhXdQRxIvdoZRXjnFV36fdbbM9nVLVZH1shnbBDMHWkXDNmzdPV199tV555RW/Id/BIpjyILsPfF7m5EbFxi0Zymq7w+7NcLWMjAxlZNQdevfo0UO7d+/WihUrlJOTI+lIY6C8vFy5ubkBn5OTk6N69epp8eLFGjhwoCRp7dq12rRpk3r06CFJ+sc//qGff/7Z95xPP/1UV111lf7zn/+oU6dOkb49xBi9pbwhXnWECxzORB1xl3j3lrIzlHLq+XG00JsKTvY///M/uuqqqzRv3jz169cvrNcgmDJEvBoUHOjs5+RwCmbo3Lmz+vTpo5EjR2r69Ok6dOiQCgoKdOmll/rupLR161ZdcMEFevHFF3X66aerSZMmGjFihMaMGaPmzZsrPT1dN954o3r06OG7k1LVRsPOnTt966s6p4hXLSrKtnsTbEcdsZ9T6wgXOMxBHTEfoZR3EVLBTvv379e6det8v2/YsEGrVq1S8+bN1b59e40bN05bt27Viy++KOnI8L2hQ4fq8ccfV25urm/Owfr166tJkyZBr5dbZXgIBzZzxOOzYM4yd5szZ46ys7N1wQUX6KKLLtLZZ5+tGTNm+P790KFDWrt2rQ4ePOh77LHHHtNvf/tbDRw4UOeee64yMzP12muv2bH5cCjqiLdQR9yNOoIKdoVS3MGubk7fR3beURLh+eyzz3Tqqafq1FNPlSSNGTNGp556qsaPHy9J+uGHH7Rp0ybf8jNmzNDhw4c1atQoHX300b6fm2++OaT1eqrHFFe6AbhF8+bNNXfu3Br/PSsryze3R4W0tDRNmzZN06ZNC2odvXr1qvYaiI5oz1MYjwCBUMosTu01BXNQR8wVz8a8naEUglexv6jFiLW6jtuzZ8/2+33p0qVRWS89psJAgwLR4MReU9H67hMSA0DkqO0AnMbpPYDsFsv9Z/fE9/A2gikP4MTVXHw2AJyAY5V3mXqBA3Art/aWIpCKLvYl3IZgymax7i1FYwLMEQK4G3XE27z6+dDzFohMvEMpAKgNwRRgM682KgCvoscIoi3WdYQLHEB8xKu3FKGUO7Bv4Saemvw8GpzUoHBy4NFkfUnIz3HyuGgmsQUQDnpL1cxrdQQAgkEo5S77OiQ4ulYDFQimbMQVyF+F04AI5jVoZByRuilFJe1Lo/JaG7dkKKvtjqi8FgBzOe1E1+t1hAscAOpCKOVO0Qyn9nRKjUo9BUJFMOVSTmhQxOOgV3kdpjcuaFQAQGioI/5iWUe4wAHEVjwnPY8lzmUBhINgCnFnVwpfsV7TGxYAzGTaZMux7HVr+sUN6ggAhCYexy1CKQDhYvJzm3ixQdFkfYkRXUNN2Y5ATP3sAMAEphy/TdmOQKgjgPPEurcUoRTs5JbegIgtz/SYMu1Kd6yYeEJq6sm7qVe+YzUUI5rDMACExwk30KCOBM/UOgIAFQilADgBPaZC4IQGhUlMvqJcmVO20yT8LQCIB6ccn03bxliFi9y0BYg+J/cm2dchgVDKEHwOcDqCKRvE6sTOpKvcpp2kB8OkbTbpswRgHuqIeZwSopmCCxxA7MWytxRBCIBoIphCVDn9xNzJ2+40XhleCyA01JHoMClkBBB/hFIIF8PTYQfPzDHldiacgJpyMh6pJutLjDggx2KuKeaZAlAT6kj0mFJHYoE6AkSPE4fxOSWUKmkXneNU6maGMAPxQDCFqHBLY6ICE9oCMJVb5/lxYx2xu4bE6mYaAMwWq2OPaceTaIVPkayD4AqIDobyxVksGhR2X+V2W2OiMje/t0gxPwgQGpP/ZqgjseH0YYmmY0g4UJ2bQ6mSdqV+Pyaouk12bpcJnxEQLoKpIJncoLCTF0647XyPsWgsurW3BQDn8Upw47Y6Ei2cWwGoiwmBT6hMCaoAJ2Eon8PZecLphcZEBROGZABALNhVR7xUQyR31RHmmQIiF4v5pdzQW8qNQU7l98TQPyAwekzFkVt6qnjlCndVdr1nk692A15hypAhN9URL6KOAHCSfR0SCKWijF5UQGAEU0AIvNqYAuBOdgQWXj+Oev39A4gNJ/fI9GJYE6v3yzxTcCqCKQejQWEPO/ZBtD9rt/S6AAAnckMtpY4A5ohFKBWPgMOLgVRlXn//QGUEUwiaG06ko4V9ASAUTPJ8BMfOX8V7XzCcDzBHLOaXiqZYhlJMCl6difvCyT3w4EyeCKYinRvExAZFvE8waUxUxz4x828DcKto91ChjtiPfQIgUtEOEGIdSiEwwjp4nSeCKRM4ucs7J841i+e+YTgfAKeijpiBXlMA7ELoAqA2BFOoFY0JxJIpdzoDEDvUkdo5ef9E4wIHPW/hNdEcxueU3lKEUsGLxr4ycQJ004evwn4EUw7EFU+zOLlRAcCbqCNmoY4AsBuhFAA7EUyhRpwoBy9e+4rGJOBNTh16Sx0JHnUEQCii2VuKUAqA3Qim4sCJDQoaE6Fz4j5z4ncTcBqvDlVy4jERzsOQcCAyhFIATEAwVQfTGhRc6QQARCIedYRQKjxO3G9c4ADiL9pzS0UboRSAUBFMoRonnhibIh77zrRw0rTwFgCczIt1BPAK0yaAjnZvqZJ2pYRSUcA+hBcRTMEPoRQAIBLUEQCIHVN7SxGmAIgEwZSDcIXTGWiUAYimaA6Voo44g9fqCD1vgfiLZm8pQinzROPzNTUEhTsRTMWYk+Ze8NqJcCzFel9Gs3HppO8oALNRR5yDkBJwnmgFBYRSAExDMAUAAGAYJ4V8XOAA6mba/FLRQCgFIFoIphwi1lc2nXQC7BTsUwAmoY4AgHOZ2FsKAKKFYApwKIZhAOZj7hxEwknDwgGYjyF8zsH+hdck270BsB9XuWOnyfoSJg4EEDanDJGijgCAdzghNMlqtyMmr7txMxecgFggmKpFpFe6o9Wg4IomANhnUVG23ZsQsVjWEUKp2OICB4BoHAOi1VvKxFAqViFUqOsitALCx1A+j6NBEXux3MfRamxGGqIyXAkAYscJdQRAzeye+NxtoVRWux1+P6YwaVsAp6HHFAAACAsXN1AhdVOKStqb0WgFEH12h1KEPqHb1yGBiw9wDNf3mHLDEIxYoUERP+xrAHbhpNQd3F5H6HkLBBbpMD4n34XPxF5RdXHStgImoceU4WhQAABM5PagBABwRLx7SxHuHFHSrlSpm51xExQgUq7vMYXAaFDEX6z2OeEl4E5OuSMfAKBmds4vFY3eUvEMpZzWOwpA9BBMxQgNCgAAEE1c4AC8xe47csYrlCKQAkAw5UH0lrKPyfverjCVeeAA5zH5WAYAcMbcUm4NpNz4noBYI5gCAMCl6AnjPiaHgvQWB9wjlr2l3BpIuZHdvfbgHQRTBqNBgWDxXQHM49a7jJkcjACAW0QSCJjcW4pACkAg3JXPY5zWoEhZsyWo5eycWDJUTdaXcPUBAOIkmDripBoSK42/t4xuzAJO5NRjS6x6SxFKAagJwVQN3Hql2ymCDaSqLu/UEwAAqIwhUZELpY5UXtYJdYQLHABqEmnAHItQyouBVFa7Hdq4OfL2ZEm7UqVudsc5QWl225DbePAOgikPcUJvqUgPVgRUAHBELIb4UkcAIPbcFDx7MZQyyb4OCUz5AUdgjqkYiMaVbi8dQFLWbPH9RPs1TWVq4y6S7y69DONr165dGjx4sNLT09W0aVONGDFC+/fvr/U5v/zyi0aNGqWjjjpKjRo10sCBA1VcXOy3zKeffqoLLrhATZs2VbNmzZSfn68vvvgilm8FiBh1xNu4u2t4qCPuY1pvKUIpwHnef/999e/fX61bt1ZCQoLeeOONOp+zdOlS/eY3v1FqaqqOPfZYzZ49O+T1EkzBNvE46Te9YRFNXgozIQ0ePFhff/21Fi1apLfeekvvv/++rrnmmlqfM3r0aP3rX//SK6+8ov/93//Vtm3b9Mc//tH37/v371efPn3Uvn17ffLJJ/rggw/UuHFj5efn69ChQ7F+S0DIqCNA+KgjqIxQKvrYB3CiAwcOqGvXrpo2bVpQy2/YsEH9+vXTeeedp1WrVumWW27R1VdfrYULF4a0XobyeYRpV1bjfZLP0Ay4yerVq7VgwQJ9+umn6t69uyTpySef1EUXXaQpU6aodevW1Z6zZ88ePf/885o7d67OP/98SdKsWbPUuXNnffzxxzrjjDO0Zs0a7dq1S/fee6/atWsnSSosLNQpp5yi77//Xscee2z83iSMQx3Z4voaEo0J0FM3paikfXgN3I1bMpTVloZcPFBH3CeSv11CKQAV+vbtq759+wa9/PTp03XMMcfokUcekSR17txZH3zwgR577DHl5+cH/Tr0mELc2Xnl2aSr3qY18hA7e/fu9fspKYnss1+2bJmaNm3qa0xIUl5enhITE/XJJ58EfM6KFSt06NAh5eXl+R7Lzs5W+/bttWzZMknSCSecoKOOOkrPP/+8SktL9fPPP+v5559X586dlZWVFdE2A9Fk17HcpBoiUUe8hDriHU6fX4pQCjBTtOtIhWXLlvnVBUnKz8/31YVg0WMKcWXCSb0XrnojNJu2tVBi/bSov275z79Iku+qcYXCwkJNmDAh7NctKipSy5Yt/R5LTk5W8+bNVVRUVONzUlJS1LRpU7/HW7Vq5XtO48aNtXTpUg0YMED33XefJOm4447TwoULlZxMuXASNw/ttbuO0AMXVcWqhkjUEcReNHtLEUpVF62788HdNm11Xh2pUFRUpFatWvk91qpVK+3du1c///yz6tevH9Tr0GPKQNFuUJhyRdXuxkRlJm1LNLm5Mepkmzdv1p49e3w/48aNC7jc2LFjlZCQUOvPmjVrYradP//8s0aMGKGzzjpLH3/8sT788EN16dJF/fr1088//xyz9cJ81JHqTNoWuB91BLWJdAhuNBBKAWYLto7YhUsXiAsTT+BN6DnVZH2J47tso27p6elKT0+vc7lbb71Vw4YNq3WZjh07KjMzU9u3b/d7/PDhw9q1a5cyMzMDPi8zM1OlpaXavXu339Xu4uJi33Pmzp2rjRs3atmyZUpMTPQ91qxZM/3zn//UpZdeWud7QOSicWdXN6KOBBbtOhKNeaYQfdQR5wnn2BDvc8Jo9ZYilIqtknalSt3MuQEiE2wdCVVmZma1u7MWFxcrPT096N5SEsEU4sDExkQFExoVQIWMjAxlZNTd3btHjx7avXu3VqxYoZycHEnSkiVLVF5ertzc3IDPycnJUb169bR48WINHDhQkrR27Vpt2rRJPXr0kCQdPHhQiYmJSkj4tVFa8Xt5eXmkb89TNm6h2340UUeA4FBHvMfuIJlQCvC2Hj16aP78+X6PLVq0yFcXgsVQvijjSrc/kxsTFZywjUBlnTt3Vp8+fTRy5EgtX75cH374oQoKCnTppZf67qS0detWZWdna/ny5ZKkJk2aaMSIERozZozee+89rVixQsOHD1ePHj10xhlnSJJ69+6tn376SaNGjdLq1av19ddfa/jw4UpOTtZ5551n2/uFvewexueEY7QTthGojDqCaPSWcmoo1TtzjXpnxm5Ia1VO3U/wpv3792vVqlVatWqVJGnDhg1atWqVNm3aJEkaN26chgwZ4lv+uuuu03fffac77rhDa9as0dNPP62///3vGj16dEjrpceUy9nZoHDSiTpXvH8Vya2+ET9z5sxRQUGBLrjgAiUmJmrgwIF64oknfP9+6NAhrV27VgcPHvQ99thjj/mWLSkpUX5+vp5++mnfv2dnZ+tf//qXJk6cqB49eigxMVGnnnqqFixYoKOPPjqu788Ui4qy7d6EkLlprjkn1RHAaagj3uWFUCqY4KliGSfWeiBWPvvsM78LCWPGjJEkDR06VLNnz9YPP/zgC6kk6ZhjjtHbb7+t0aNH6/HHH1fbtm313HPPKT8/P6T1EkwZxk0NCgSHeaYQjubNm2vu3Lk1/ntWVpYsy/94kpaWpmnTpmnatGk1Pq93797q3bt31LYTCJfTQikucMBpqCNmCedc0O5hfCaJZw8owM169epV7dhf2ezZswM+5/PPP49ovQzlC4C5QSLntAaF5MxtBgCYw646Yvdwy6qY1gAwl5t6S1UMyYs0lCLUAuwXVjA1bdo0ZWVlKS0tTbm5ub6x5zXZvXu3Ro0apaOPPlqpqak6/vjjq02QBfcg4LGXnb3uCHURLOoIakMdAVAb02uIm3tP2h1KRSuMCvS6AOwT8lC+l19+WWPGjNH06dOVm5urqVOnKj8/X2vXrlXLli2rLV9aWqrevXurZcuWevXVV9WmTRt9//33freZRWyYdgXVCRiKAcQedcQ5qCOhc0Mdafy9xRAhGMuNNSRew/gi7S1lZyhFcAS4W8jB1KOPPqqRI0dq+PDhkqTp06fr7bff1syZMzV27Nhqy8+cOVO7du3SRx99pHr16kk6MmY9HpjILv7ccJXbjkYF80zBS5xURxB/bqgjAGKHGuI98QqlemeucWX7cV+HBOYxhvFCGspXWlqqFStWKC8v79cXSExUXl6eli1bFvA5b775pnr06KFRo0apVatW6tKlix588EGVlZVFtuUAAMehjsQWJ55msCNco3cbvIAaEj6n9ZaK1ZC9YNYLf1w8RzyE1GNq586dKisrU6tWrfweb9WqldasCfxH/N1332nJkiUaPHiw5s+fr3Xr1umGG27QoUOHVFhYGPA5JSUlKin59QRr7969oWwmbOKmq9xuGIoBmIg6UjuvTxrtpjqC8GzckqGstmZMrAzzUEOOiPdQ23iGUgRDgDfF/K585eXlatmypWbMmKGcnBwNGjRId911l6ZPn17jcyZNmqQmTZr4ftq1axfrzTQCV7oBoDrqiD3ogRMZQjbADNQQ5zAllIrFdtg9aTxgupCCqRYtWigpKUnFxcV+jxcXFyszMzPgc44++mgdf/zxSkpK8j3WuXNnFRUVqbQ0cJfScePGac+ePb6fzZs3h7KZtjHpSne8GxRuPAGP93uK5mdGyAlTUUdQEzfWEQDR5cYaEo9hUpEM44tXoGJKKFXBtO2JdCgmYLqQgqmUlBTl5ORo8eLFvsfKy8u1ePFi9ejRI+BzzjrrLK1bt07l5eW+x7755hsdffTRSkkJHOSkpqYqPT3d7wdA/JgUssJdqCPwEidf4ABMRA2J/zC+WLNjHqlgmbpdgBuFPJRvzJgxevbZZ/XCCy9o9erVuv7663XgwAHfnTGGDBmicePG+Za//vrrtWvXLt1888365ptv9Pbbb+vBBx/UqFGjovcuYCs3X+V283sziRvvgIKaUUdQFcda89DzFqaihoTG5N5STgh+nLCNgBuENPm5JA0aNEg7duzQ+PHjVVRUpG7dumnBggW+SQg3bdqkxMRf86527dpp4cKFGj16tE455RS1adNGN998s/785z9H710AAByDOmK+ePa8cXso5dWbaaRuSlFJe4aeIPqoIfFBKPWr3plruIgKxFjIwZQkFRQUqKCgIOC/LV26tNpjPXr00McffxzOqhAGGhTRFc9GRZP1JdySFZ5AHYGXeDWcAmLFqzUk1GF8Js5L5KRACkD8xPyufAAAIPacOPTKCxc34o15pgBEKla9pQilANSEYMoQNCjM5qX3CiAyG7dk2L0JAAAH8FIveUIpALUhmAJcyolhJwC4FRc4AG+I1bDdeA3ji0VvKUIpAHUhmAIAAD7xGgpGUAMA7kcoBSAYBFMuQ4Midrz4ngHET+qmFLs3AfBDz1vAW6LdW4pQyl+s73QIOBnBFGAYJq4FAHfiAgeAeDDhbnyEUgBCQTAFAADiioAmtrjAAbhLqPNLhSOavXkIpQCEimCqinDvpuSlIRheblB4+b0DAADAbCb0lgKAUBFMAQAAxAkXOABI0p5OqXZvgg+9perm1vcFmIJgCkBAXuoFCOAIhoABAMJFeAMgXARTAKIq3OGwAMLnpLun0WMIsbCoKNvuTQBiIpT5pcIZxhet3lKEUgAiQTDlIvG40k2Dgn0AAAAAAEC0EEwZwElXuhEfDKcBAPfyygUOhoQDZqO3FABTEEwBLkboCQDexAUOwFvsuhsfoRSAaCCYQtC8coUXABAb1BEAblaa3Tao5YK9I18o80uFKpp34kN82BU+AvFAMAWEgcYVAMAL7Oh5y000AGegtxSAaCGYAgAADP2KMy5wAHAyQilniWXvOyAaCKYAAAAAwKFCHeLFMD4ApiGYcgmudAMATEYPIQAAAARCMIWg0KCIP8JGAAAAb2LoFQAvIZgCwkRYBwAAgFAEe0c+0y0qyrZ7EwC4CMEUAAAAADhQqPNLAYCJCKYAAHCwxt9bdm8CwkTPWwDxxsTnAEyUbPcGAAAAIPqarC9xzbAhAGaqbUhf78w1Eb32xekrJUmv7v1NRK8DwHz0mAIAwGapm1Ls3gQAgCGCnfjc7mF8dc0ztagoO6K5qCoCqYvTV/pCKqejxxoQGMEUAACIKYasAYB3RRpQVbA7nIq0BxiAmhFMAQAAAABiKpyAquowPrvDKQCxQTBlMydMWsuVbgAAAMDZTBlGFmkPKsIpZ6JNidoQTAER4AALAACAeLN7fqloiCSgIpwC3IVgCnA5J/TKAwAAQPATn4fKlN5SgdQVTtV0Vz43TYoOeB3BFAAAHtdkfYndm+BZ9LwFAHpPAV5HMBUF3OYbAAAAQG32dEq1Zb0m95aqqqZwqqZeUxUIpwBnI5gCAAAAAIdww/xSsUA4BTgXwZQLMAQDAGAqhqoBgH2c1FuqQri9piTCKcCpCKYAAABQI26iAQAAYolgCgAAAABcaOPmDLs3IWS9M9cEfJzeUIB7EUwBAAAAgEOkbg7txksbN2c4MqAC4B3Jdm8AAAAAALhdk/Ultt2ZT/q195Sp807V1FNKorcU4Hb0mAIAB9q1a5cGDx6s9PR0NW3aVCNGjND+/ftrfc6MGTPUq1cvpaenKyEhQbt37/b7940bN2rEiBE65phjVL9+fXXq1EmFhYUqLeXuPwDgNtQRZwu111RlJvWe6p25xvdTE0KpyNk9VyA3QnGWadOmKSsrS2lpacrNzdXy5ctrXX7q1Kk64YQTVL9+fbVr106jR4/WL7/8EtI6CaYAwIEGDx6sr7/+WosWLdJbb72l999/X9dcc02tzzl48KD69OmjO++8M+C/r1mzRuXl5frrX/+qr7/+Wo899pimT59e4/IAAOeijpgplAAh0nAqVgFVxWvXto66wii3MikUBAJ5+eWXNWbMGBUWFmrlypXq2rWr8vPztX379oDLz507V2PHjlVhYaFWr16t559/Xi+//HLIx32G8gGAw6xevVoLFizQp59+qu7du0uSnnzySV100UWaMmWKWrduHfB5t9xyiyRp6dKlAf+9T58+6tOnj+/3jh07au3atXrmmWc0ZcqUqL4HAIB9qCPukbo5RSXtwu+RVjUoCWWYX7Ahy8bNGWEPH6S3FBBfjz76qEaOHKnhw4dLkqZPn663335bM2fO1NixY6st/9FHH+mss87S5ZdfLknKysrSZZddpk8++SSk9dJjCgAcZtmyZWratKmvMSFJeXl5SkxMDLkI1GXPnj1q3rx5VF8TAGAv6oi7RNJzqqqqvZ1q+wHgLqWlpVqxYoXy8vJ8jyUmJiovL0/Lli0L+JwzzzxTK1as8A33++677zR//nxddNFFIa2bHlMAEGN79+71+z01NVWpqeFPflpUVKSWLVv6PZacnKzmzZurqKgo7Netat26dXryySe5yg0ANqOOwItM6y21qCjb1vVHM4CE9wRTR3bu3KmysjK1atXK7/FWrVppzZrAQ28vv/xy7dy5U2effbYsy9Lhw4d13XXXMZQPAEKVsjlFSWnRL/Zlv5RLktq1a+f3eGFhoSZMmFBt+bFjx2ry5Mm1vubq1aujtn212bp1q/r06aM//elPGjlyZFzWCQBOFKsaIlFHnCZlzRaVZreN+HUaf29pX4eEkJ4T6ZA+IFaY+LxuKVvMqSOhWrp0qR588EE9/fTTys3N1bp163TzzTfrvvvu0z333BP06xBMAUCMbd68Wenp6b7fa7rKfeutt2rYsGG1vlbHjh2VmZlZbQLCw4cPa9euXcrMzIx4e7dt26bzzjtPZ555pmbMmBHx6wEAIkMdcY8m60u0p1P4vd1q46ZwyrTeUoDTBVNHWrRooaSkJBUXF/s9XlxcXGNtuOeee3TllVfq6quvliSdfPLJOnDggK655hrdddddSkwMbvYogikAiLH09HS/QlCTjIwMZWTUPWdDjx49tHv3bq1YsUI5OTmSpCVLlqi8vFy5ubkRbevWrVt13nnnKScnR7NmzQq6mAAAYoc6gmA5JZxaVJRd4135CKWA6AumjqSkpCgnJ0eLFy/WgAEDJEnl5eVavHixCgoKAj7n4MGD1Y7zSUlJkiTLCv4uo1QKAHCYzp07q0+fPho5cqSWL1+uDz/8UAUFBbr00kt9d1LaunWrsrOzfRMRSkfmFFm1apXWrVsnSfryyy+1atUq7dq1y/ecXr16qX379poyZYp27NihoqKiqM43AgCwH3UEbvXq3t/YvQmAo40ZM0bPPvusXnjhBa1evVrXX3+9Dhw44LtL35AhQzRu3Djf8v3799czzzyjefPmacOGDVq0aJHuuece9e/f3xdQBYMeUwDgQHPmzFFBQYEuuOACJSYmauDAgXriiSd8/37o0CGtXbtWBw8e9D02ffp0TZw40ff7ueeeK0maNWuWhg0bpkWLFmndunVat26d2rb1nyMjlCseAADzUUfMFs48UxWc0msqEHpLAfYaNGiQduzYofHjx6uoqEjdunXTggULfBOib9q0ya+H1N13362EhATdfffd2rp1qzIyMtS/f3898MADIa2XYAoAHKh58+aaO3dujf+elZVVrREwYcKEWic5HDZsWJ1zkwAA3IE64m5ODKcIpdyHic+dqaCgoMahe0uXLvX7PTk5WYWFhSosLIxonQzlAwAAAACXSd2cotTNsbnTl53rAuA+9JgCAAAAgDiJ5Z35AgkUGEXSmyqcAGrj5gxltdsR9joBuBvBFAAAAGoU7jw3ACITyTxTdYl37yZCKedqsr7E7k2ABzCUzwXiecUFAIBQlGa3rXshAIDjhdILq3fmGr/fmV8K8DaCKQAAAACIgmhP9tz4e+ffzZDeUt7ExOcIBcFUFJS0d9YdLwAAAAA4gynhVEm70pDnpgoUStFbCkBVBFMAAAAAYDBTwikpvIAqHl7d+xu7NwFAmAimAJdj0loAdWGuQvswBxfgTU6cULrqhOmVA6pAQRVD+JzPid9TOBPBFAAAAAAYzqReU5WFEkoxjA9AIARTQAS40g0AAIB4MTWcAipj4nOEimDKZk4YZkX4AgAAAJjBznCq6nA+r1hUlG33JgCulmz3BgAAAAAAghconDL9gncsh/Ex8TngbPSYAgDAZiXtzbu7UTTR8xaAl9g1jKnx91bUelNVvFag16ur11Sg+aWqhlJetHFzht2bEBImPkc80WMKAAAAAOKsyfqSmNwVtSJMCrYHVV1hVuPvLeN7YwFwNoIpAAAcbF+HBCbDRUCxaPACcA5TagO9pbyFic8RDobyAQAA2IAhjgCcMlyqasjl1UnQ7RLJ/jYloARqQzAFAAAAAAAAWxBMAWGK9ZVuhmAAAAC4n1N6TVVlSq8p7sgHOB/BFILCcAMAAADAu8IZElbb/FIESuYKNyxlfimEi2DKJehdAwAwGRc4AKBmTu01VVVWux12bwIAByKYAgAAXOAAAJu5JZxym0VF2XZvAuB6BFMAAABx5pQeZPs6JNi9CQAMEerxoLZhfNHCcEDAHQimEDSnnETHA/sCAIDYyGrLUCA4X7hz7TRZX+L7MV1Ju1K/3xnGZ55w5gVjfinYgWAKAADEBaE+AATPKQEV6rZxc4bdmwAYjWAKcDGGYACAN5kwZ1hJ+9K6FwJQJxMCqqrnlFV7S1UV7DC+SIbiMYwPcA+CKQMQHqAqExoUAIDYoOcYgHCYEFDVhGF8ACJBMOUi8QgzOJlmHwAAYJp4TLIMmCKW81Dt65Dg+6n8mFd57Y58zC8FuyTbvQEAACAy+zokhDXBaVV7OqXG/Gp8aXZbTmABIErqOmZHcuG6pkAqWsP4IuGlYXypm1PCel40zguAeKHHFICAwp0bhLspAQAAmCEew/8YxgcgUgRTQAgYxgcAiAR1BPAOk3qHBhtOOaGXjZd6SwFewVC+KrLa7tDGLaHfzrOkfalSN4XXzdJpGIYBAIC5uIEGgECarC+J+PhQdRhf1d5StQ3juzh9ZbXHXt37G7269zcB/w3OQLsQ0UCPKQAAEFf0GgLgFaY12iv3nDLhDn8Xp68MKZSKZ2+paE18vnFz6J0e7GDC9wHeRY8pwDBc6Qa8yUs9b73KaYGcl+/EBbgZAYT7OWFIJlAZPaZcJl6hhtNOrqPBi+8ZAAAAkTGt11RloYRUkQzjiwbmlgLci2AKcCmudAMIBxc4AAAAEE8EU4YgRAAAwL0I4gA4RaBhYFV7S4Uq0snN6S0VW+EO7zS5NyCchWAKYfPSSbaX3iuAyGS13VH3QpDEsTUWmKcQMJPbGvBVh/G5TbQmPgcQHIIpwCA0KACEi5635iKAAyA5J5wKp57Ecn4pL/eWSt0c+k1RmPgcThRWMDVt2jRlZWUpLS1Nubm5Wr58eVDPmzdvnhISEjRgwIBwVosgxTPc8MLJthfeo91iPVkmzEMdQQWOse5U0j6yYT9AXagj0RGPdkMkw/icHkpt3Jxh9yYAjhByMPXyyy9rzJgxKiws1MqVK9W1a1fl5+dr+/bttT5v48aNuu2223TOOeeEvbFAvNFgAqKPOmI+em9GD3UEiD4n15GUNVsc03OqQl1344OzMb8UTBByMPXoo49q5MiRGj58uE488URNnz5dDRo00MyZM2t8TllZmQYPHqyJEyeqY8eOEW0wzMNJt/twpRuxRB1BVdQR8zA0FCZzQx0xoVEf6CJEtIfxebm3VKTCGcYHOFVIwVRpaalWrFihvLy8X18gMVF5eXlatmxZjc+799571bJlS40YMSL8LQ1DvIcH0Zh3l3g3lOihAC9wWh0BIkEdAaLPTXXEhHCqLpHejS9cdoZSTp74nPml4FTJoSy8c+dOlZWVqVWrVn6Pt2rVSmvWBA6BPvjgAz3//PNatWpV0OspKSlRScmvXQr37t0bymY61r4OCVE7mOzplBp2t8xwlGa3dURx9QqudMNU1BHUhDoCIBjxqCPxrCEVx72KILvq7zU9JxbBdzDnj/EYxuf1nlKAF8X0rnz79u3TlVdeqWeffVYtWrQI+nmTJk1SkyZNfD/t2rWL4VYC1TGsBDCD1+pIpD1voxlK0/smMtQRwAzh1BE7akjVuacqfg/0U/HvkTDxGP/q3t+4KpRywsTnzC8FU4TUY6pFixZKSkpScXGx3+PFxcXKzMystvz69eu1ceNG9e/f3/dYeXn5kRUnJ2vt2rXq1KlTteeNGzdOY8aM8f2+d+9exzQqvIyr3QDqQh1BbagjyGrLpMqoXTzqCDXkiGCG8dU1dcqre39T5zxTbgqjoiWc+aUYxgcnCymYSklJUU5OjhYvXuy7xWp5ebkWL16sgoKCastnZ2fryy+/9Hvs7rvv1r59+/T444/XeIBPTU1Vaqp5KT7q5oZGBVe5gdihjsAL7KgjJvZ+AGIhHnXEKTUkmkP6YjmML1A4ZWoY5eT5pQAnCymYkqQxY8Zo6NCh6t69u04//XRNnTpVBw4c0PDhwyVJQ4YMUZs2bTRp0iSlpaWpS5cufs9v2rSpJFV7HPAyGhTwEuoIauOGCxwAYos68qtwwik7zjtNDaIAmCHkYGrQoEHasWOHxo8fr6KiInXr1k0LFizwTUC4adMmJSbGdOoqBCneE6BXcHKjgt5SkWEIBoJBHXEO6kjo3FBHuIEGTEcdiT277saH+GF+KZgk5GBKkgoKCgJ2lZWkpUuX1vrc2bNnh7NKAICLOKWO9M5cQ7d+BM0NoVQ0RDqJPxAMp9SReAil11S4vaUCDeOra34pp4lmvY/3xOfMLwWn41JCAHb2+nDLVUonnpw7cZsDcct3CIC3ueWYHA8MBwcQCc4dzRLOxOeA0xFMIWac1Kiwc1tpUACIJhoY9nBSzQPgPtEcXsUwPgDxRjDlcoQedaMx4Y8hGM6wa9cuDR48WOnp6WratKlGjBih/fv31/qcGTNmqFevXkpPT1dCQoJ2794dcLm3335bubm5ql+/vpo1a+a76xG8yc464pTjs1O2E6iMOuI90Tyeu20Yn9cwvxRqM23aNGVlZSktLU25ublavnx5rcvv3r1bo0aN0tFHH63U1FQdf/zxmj9/fkjrJJiKMhr1/jhZB2Jj8ODB+vrrr7Vo0SK99dZbev/993XNNdfU+pyDBw+qT58+uvPOO2tc5h//+IeuvPJKDR8+XF988YU+/PBDXX755dHefNfjRgDRY3odMX37gJpQR9wnnNAgmF62geaXchsnzyfJ/FKIppdfflljxoxRYWGhVq5cqa5duyo/P1/bt28PuHxpaal69+6tjRs36tVXX9XatWv17LPPqk2bNiGtN6zJz4FQmHx3JRoUcKLVq1drwYIF+vTTT9W9e3dJ0pNPPqmLLrpIU6ZMUevWrQM+75ZbbpFU86Swhw8f1s0336yHH35YI0aM8D1+4oknRnX7gVCZWkdMqCHR7tHGUFBvoI6gJgzjA7zt0Ucf1ciRIzV8+HBJ0vTp0/X2229r5syZGjt2bLXlZ86cqV27dumjjz5SvXr1JElZWVkhr5ceUx5gwnA+E07eKyvNbmvENtGg8Ia9e/f6/ZSUhNd9usKyZcvUtGlTX2NCkvLy8pSYmKhPPvkk7NdduXKltm7dqsTERJ166qk6+uij1bdvX3311VcRbS9CQ8/bwEw4Zldm2vbA3agjCEZtxyUT2gNeEckd+eIx8Xm4w/jgbMHUkdLSUq1YsUJ5eXm+xxITE5WXl6dly5YFfN0333xTPXr00KhRo9SqVSt16dJFDz74oMrKykLaPnpMGWhfhwRXdsmsKJZ2X/WmMYGqGm+2lJQS/b+5stIjr9muXTu/xwsLCzVhwoSwX7eoqEgtW7b0eyw5OVnNmzdXUVFR2K/73XffSZImTJigRx99VFlZWXrkkUfUq1cvffPNN2revHnYrw1n29Mp1YgTWVN6TlFHUFmsaohEHUFshTuMz23zSzGMD3YzpY7s3LlTZWVlatWqld/jrVq10po1gf/uv/vuOy1ZskSDBw/W/PnztW7dOt1www06dOiQCgsLg95OginEnZ0NCxoTsMPmzZuVnp7u+z01NfBVy7Fjx2ry5Mm1vtbq1aujum2VlZeXS5LuuusuDRw4UJI0a9YstW3bVq+88oquvfbamK0b0eXWCxyS/Rc5TKoj9IDwDuoI6mLSscnLTO8tFQkTLgwhfMHWkVCVl5erZcuWmjFjhpKSkpSTk6OtW7fq4YcfJphCdaZc7a5gRzhlWsGmQeEd6enpfoWgJrfeequGDRtW6zIdO3ZUZmZmtQkIDx8+rF27dikzMzPs7Tz66KMl+c8Fkpqaqo4dO2rTpk1hvy4QC9QRMzEUNTaoI4hEsOeczC8FuFcwdaRFixZKSkpScXGx3+PFxcU11oajjz5a9erVU1JSku+xzp07q6ioSKWlpUpJCS5wJZiCbeLZqKAxERwaFPbKyMhQRkbdV9p69Oih3bt3a8WKFcrJyZEkLVmyROXl5crNzQ17/Tk5OUpNTdXatWt19tlnS5IOHTqkjRs3qkOHDmG/LtzBtAscEnUk2uyep5C7WUaOOuJN4RyfGMZ3hJOH8YXDtDoOs6SkpCgnJ0eLFy/WgAEDJB3pEbV48WIVFBQEfM5ZZ52luXPnqry8XImJR6Yw/+abb3T00UcHHUpJTH4eE9Fo3Nt9chgvsZ6E3JRJzuPBK98ZHLkK0adPH40cOVLLly/Xhx9+qIKCAl166aW+Oylt3bpV2dnZWr58ue95RUVFWrVqldatWydJ+vLLL7Vq1Srt2rVL0pErKdddd50KCwv1zjvvaO3atbr++uslSX/605/i/C6B4MTyOF/x2ibWEXrdIhLUEcAs4Qzji+eQfYbxeceYMWP07LPP6oUXXtDq1at1/fXX68CBA7679A0ZMkTjxo3zLX/99ddr165duvnmm/XNN9/o7bff1oMPPqhRo0aFtF56THmIiVe7K0RzzhATGxBV0aBApObMmaOCggJdcMEFSkxM1MCBA/XEE0/4/v3QoUNau3atDh486Hts+vTpmjhxou/3c889V9KR+T8qhn48/PDDSk5O1pVXXqmff/5Zubm5WrJkiZo1axafN4aocfM8U4F4rY4AkaKOeAPD+OInkvmlAFMMGjRIO3bs0Pjx41VUVKRu3bppwYIFvgnRN23a5OsZJR2ZVH3hwoUaPXq0TjnlFLVp00Y333yz/vznP4e0XoIpGKVyYyDUxgUNCXsxBCO+mjdvrrlz59b471lZWbIs/1BiwoQJdd7FqV69epoyZYqmTJkSjc1EmEralyp1k3mToJp8gaMCdQQIDnXEuxjGdwTD+IDACgoKahy6t3Tp0mqP9ejRQx9//HFE6ySYqkFW2x3auIXU205ubSDQWwoA4oM6EjyGgwPO5NbjnJeYPowPiAfmmPIYQhF3okEBIF6oIwDgDNEcxkdvKe9ifinEA8GUwQgbAMC5GN6KWDA5GLTrzq5uazADsUT7AoCJCKY8yOSTWrdj3wOIJxogAIBQBZpfyk1i0Vsq3InP4zWMj/mlYDqCqRix66ohEC6+swCCRcgOAParbX6paB6n6ZXoXQzjQ7wQTAFxQkMOABCJWNURetYB3hDobz2Y+aXchLmlADMRTHkUIYl7OL1BwVU4IDDTezFSRwDATJEcn90+jC8WTB/GBzgBwZThnB46AACAyLk9CORmAQAQG8wvBSdwfTBFb4yauf0k1yTsawB2ieUFDo5tAGCP2uaXCiTcYXxuaksxjC80zC+FeHJ9MAUg9rjSDQCxE8sAMFrBpelDTwE3idWk5wzjCx3D+IDoIJiqhSmNba52OxsNCgBuRh0BADPUdDymtxS9pQDTEUzFEI19AHA+N5yYM1+hcxH8Aags1CF8iF0oFc/eUuFifik4BcEUOOmNIfYt4G2R9rx1ygUOjnXORGAJOEs4Q/iC/TtnGF9owg2lwhXvYXzML4V4I5hyCE4eURXfCQBwNwI/ABWiOa9UMMP43IIhfIAzEExBEie/scA+BWCSWIfZHPO8yyk9+wCvifS4T6gTWCS9pRjGBwRGMBVjTjpZo1ERPU7al076jgKAV8S6jpjU69aUm80ApormEL5AvaXcOozPLcEad+OLvdLstr4f2INgykFMOomEvfguAN7hpPDYSaE8ADiB3Q1lp4Y7pk14LsW3t1QkvDC/VOUgqurfGAGVPQim4IdGReS8tg+50g04B6G2M3itjgAIrK7GMb2lUBOG8dUs2NCJcCq+CKYAAEDUEKpEJh77z00BZe/MNXZvAmCLeB5rndZrysTeUuFiGF90ETaZi2CqDqb1BonHySSNivDRoAAA6oiXOGmoKeAk4TagQ+kt5UamhmhOGcbnZoRSZiOYigMnnrTRqAidE/eZE7+bACJDuG0uJ9YRANEXjyF8UmjD+EwNfCpzwjbGQyTD+Lwwv1QoCLPih2AKAIAYikbPWyeGyIQsoYnX/iKYBMwWbigVDyYHP7HeNjsmPWcYH7yEYMqB4nVSSaMieDQowsPcIIC7UUcAIHiR9M6IZW8pAIg1ginUikZF3dhHAJwmniE3x8i6sY8ABMOEY4WJvaboLfUrhvHBqQimgAiYcIIAAHCueNaRaAeS0RhiatpNZgC7RDKEL969pUwKp0zaFgDh80QwZcJwoWjPD8LVbu+hQQHAqagjgbFfAEhMsByueIRSkfSWAhA8TwRTkaLxzclzIOwTAPHk5AscEsfMqtgfAKTgQikTjxeLirJt7a3khJ5SThrGB9iNYMrBaFTYh30BAKHj2Gkft908A8ARdk96Hu+AKJ6BmJd6S7l9fil6JZov2e4NgLPs6ZTq+TTejoYVDQrA2bLa7tDGLead4O7rkBD321FTR9wR0EW7Bx/gRU7tLVVV5aAoFlOo2NE7KtJQKt69pQCnI5iKo5L2pUrdFN5ByiReblQ44eQgGDQoANiJOgLA66LRg8PEC5fRDKmcMFzPFF6tqcGgt5QzEEw5nB1XuyVvNirsakyYeNIBeE3vzDVGnCDH4gIHdSR+qCMAQhHOnfhMUldIZUJdDcSu3lKAlxFMIWxealRwhRsAoo864m3cXAbwDlNDqKrsnFfKrmF8bp9fCs7A5OcuYOcVEy+caNv5Hp1wNQwAIuH2OrKnU6rr6gjDwYHIRDq3VF1/1/Ga+NxtohFK2dFbyisXeMLBMD7nIJgKUrSu6rnxZM6tjQq7GxOxEq3vIFe6AfewOwR347FWcu/7MkUsJlkGADsw6Tm8jmDKJUxoVLjpBNyE92L3ZwogurjAUTvqSPRRRwBncvrcUk7k1N5SgFsQTCGqTDgRj5Qb3oMTcKUbMIspjR03HIPd8B5q4tZgFIgXO4cW2Tl/ksmcvF8iHcbH/FIwBcGUDWJ1UkejInKmbHusPksaFACcwJRjcahM6vVlyjkBgNDQWyq+ohVKRdJbimF8ZmOeqvggmEJMmHRyHgynbS8Ab3P7BQ7JecdlJ22rCZinEF5UVwM3GseRmiY+r+Dk3kHRxr4AzOGZYCoaw4accBJlUqNCMr9hYeL2mfYZBuKEvwUA7mDicboyE7ePXreA+wTzd11XKFWBQCa6+8Cu3lLcja929HRyFs8EU7CXaSfupm1PPNCgAOznhFDX1HDctOO2adtTwdTPD/C6WDeSgw2lYE4oBeBXBFM2iWVIYPJJqd0n8navvy4mf3YAzOLVsNnuY7jpdQSAM0Uyt1Q4oRS9ppwtGr2lmPgcJiGYgi3ifWJPQwJApLx0J0nTQ3I7julOqCOmf26AV8WytxQ9pUJjUm8pJj0HfpVs9wYgNvZ1SHDEwa7qSX60xkqb3ngIJJYNCq/2rAAQPifUkUDHei/XkViKZh1xwpBWwAs2bs5QVjvv/D2aFEohtphfynkIpkKU1XaHNm6JzkGtpH2pUjfF7qDmhEZFVeEGVU5vQDjpKjcNCsAcsa4jThROHXF6DZGcVUcALwmmgWznMcgr4ZRpQxftnvScYXwwDcEUjOaGxgIAVBbNCxyx5sQLHFVRRyJHr1vA3dweTkU7lGIIHxB9zDFls1if7HEF1Xyx/oxoUACIBHXEfHxGgHdFa0iZaT2KosW0UAqxxzA+ZyKY8gBOWAHAvQifvc1pNT5aw8G9dDMCOJfpw/iqclM4tXFzhpGhVKS9paI1jyJgGoIpwEZebVAAcBanHasQPQSfgLe4IZyKxXugp5Qz0FvKuTwVTEXr6lq0G+fxOOmjUWGeeHwmpjYouNINOA91xDx8JoC5nNxAdnI4ZXIoZcrcUkx8DhN5KpjyOk5gzcFnAXibEy9wwCzUEcD5TBrGV5XTwqlYDN2LJlNCKTdzchgMgikg7uLVmKCh6m67du3S4MGDlZ6erqZNm2rEiBHav39/rcvfeOONOuGEE1S/fn21b99eN910k/bs2eO33KZNm9SvXz81aNBALVu21O23367Dhw/H+u04htd7+xGGmIE6gmigjjhHXaFGrIaZmRz0VIh1IGXSED7ml0K8TJs2TVlZWUpLS1Nubq6WL18e1PPmzZunhIQEDRgwIOR1EkwZIl4nfzQqAHcYPHiwvv76ay1atEhvvfWW3n//fV1zzTU1Lr9t2zZt27ZNU6ZM0VdffaXZs2drwYIFGjFihG+ZsrIy9evXT6Wlpfroo4/0wgsvaPbs2Ro/fnw83hIiRB3xBifvf+YpNAt1xDns/Ls3OZyK9ba5bQgfEIyXX35ZY8aMUWFhoVauXKmuXbsqPz9f27dvr/V5Gzdu1G233aZzzjknrPUSTIXJySdXTj6pdTon73snf+fdZvXq1VqwYIGee+455ebm6uyzz9aTTz6pefPmadu2bQGf06VLF/3jH/9Q//791alTJ51//vl64IEH9K9//ct3Jfudd97Rf//7X/3tb39Tt27d1LdvX913332aNm2aSkvpOYFfOflYhuDQW8rdqCMIhUnD5Cq2hVAKlTGML3oeffRRjRw5UsOHD9eJJ56o6dOnq0GDBpo5c2aNzykrK9PgwYM1ceJEdezYMaz1EkwZJJ4ngTQq4i+e+5wGhbstW7ZMTZs2Vffu3X2P5eXlKTExUZ988knQr7Nnzx6lp6crOTnZ97onn3yyWrVq5VsmPz9fe/fu1ddffx29NwBJzg97qSPxxz5HtFBH7Bfs/FLB/t3HY8iZnQFVPNdt0vC9CtEaxsfE56hNaWmpVqxYoby8PN9jiYmJysvL07Jly2p83r333quWLVv69aANVXLYz4Tj7euQQIofJzQmvG3v3r1+v6empio1NfwJT4uKitSyZUu/x5KTk9W8eXMVFRUF9Ro7d+7Ufffd5zdso6ioyK8xIcn3e7CvC3uVtC9V6qb4nVBTR+KHOuJt1BFncXvvjYqAKKtd7C+wxDsIi2YoFa36yNxStXP731u0BFNHdu7cqbKysoDH8TVrAs+z+sEHH+j555/XqlWrIto+ginD0Khwn3g3JugtFbr0DSVKTo7+53T48JETiXbt2vk9XlhYqAkTJlRbfuzYsZo8eXKtr7l69eqIt2vv3r3q16+fTjzxxIDbAcAsbqgj0ewhaNpNCGJVQyTqCEKTujlFJe3idx4Y64CKUApeYVIdCcW+fft05ZVX6tlnn1WLFi0iei2CKRBOxZBbrnA7fciR3TZv3qz09HTf7zVd5b711ls1bNiwWl+rY8eOyszMrDYB4eHDh7Vr1y5lZmbW+vx9+/apT58+aty4sV5//XXVq1fP92+ZmZnV7rpRXFzs+zc4Axc43MUtdQSRoY64S7SH8dmtcoAUjZDKjuGCpoZS9JaqHb2lghdMHWnRooWSkpJ8x+0KxcXFAY/h69ev18aNG9W/f3/fY+Xl5ZKO9MJdu3atOnXqFNT2EUxFIKvtDm3cEv0DZ7wbFYgNO04mnNBbyrQr3fGQnp7uVwhqkpGRoYyMuo8pPXr00O7du7VixQrl5ORIkpYsWaLy8nLl5ubW+Ly9e/cqPz9fqampevPNN5WWllbtdR944AFt377dN8Rj0aJFSk9P14knnljndiF0saoj8UY4FRvUEVSgjiBY8e41VVVtoVJFaGXKROoVTA2lgGgKpo6kpKQoJydHixcv1oABAyQdCZoWL16sgoKCastnZ2fryy+/9Hvs7rvv1r59+/T4449X66VVG89Nfu7FRnEwnHJFBoDUuXNn9enTRyNHjtTy5cv14YcfqqCgQJdeeqlat24tSdq6dauys7N9V6737t2rCy+8UAcOHNDzzz+vvXv3qqioSEVFRSorK5MkXXjhhTrxxBN15ZVX6osvvtDChQt19913a9SoURHNZYL4syNcoI5EF/sTsUQdMZ8bjwEm3d2vgokTnVeIdm8pt018Tm+p2BgzZoyeffZZvfDCC1q9erWuv/56HThwQMOHD5ckDRkyROPGjZMkpaWlqUuXLn4/TZs2VePGjdWlSxelpAT/90WPKUPZ0WuKK97R46ar3AzjM9OcOXNUUFCgCy64QImJiRo4cKCeeOIJ378fOnRIa9eu1cGDByVJK1eu9N1p6dhjj/V7rQ0bNigrK0tJSUl66623dP3116tHjx5q2LChhg4dqnvvvTd+b8wBemeu0aKibLs3w0jUkeiwq0FKHfEW6oh72d1ryimiHUoxhA9uMGjQIO3YsUPjx49XUVGRunXrpgULFvgmRN+0aZMSE6Pfv4lgKkJuGYZRgUZFZNx4dQtmat68uebOnVvjv2dlZcmyfv1b7tWrl9/vNenQoYPmz58flW1EcNw2LJw6EhnqCOKFOhIb0erF0fh7K6LjAeFU7UwOpQC7FRQUBBy6J0lLly6t9bmzZ88Oa52eG8rnJHbN87CvQwInxmGwc58xJwgAk1BHwkMdAYDYSt2cYnwoFYveUgzjg+kIplAjGhXBc+u+YvgF4Gx2hw1uPTbGgltDqWjXEeYKBYJn8vxJdmB/AOYimIqCWDbeaVSYz+59ZPd3JBQ0KIDAqCPeRe8ywHuC7RETjZ44hDFHxGo/OKG3lNvQW8qdPBlM0TgODSfMgdGYAIDgcKwMzIT9YndwCQCx5pRQKlbcNowP7uTJYMppTDhpJITxZ8q+cNLwC8BNnHaBgzpiHi/sC+oIYAYv95pyUihFb6m60VvKvQimoiTWJ18mNCokb5xI18akhpUp3wkA0eGVRrwpx1C7UEcAhCJaAYgXwykvvueq6C0Fp0i2ewPgPBUn1E7pvhotpjQkACAcJe1LlbrJjJN0L9YR02oIoRQAt4p1IEVvKXvQW8rd6DHlIKadRJp01TeWTHyfsf4ueKXnBuA11JH488J7DIQ6ApjHCz2InBhKASCYiqp4nISZ1qiQ3HvSber7MvE7ACA6vNqYN/V4GwmT35NT64jT5nYDoiWaYYibwymnhlKx6i3FMD44CUP5EDVuGZphakPC6WhQAGYwaUhfVdSR2HNqKAU4gVOGGqVuTlFJO/ccC9wctuEIp/xtIXye7THl5Eay6SeVJl8lro0Ttjsen71Xe2wAXkIdiQ2nbne0UUeA6It2YO+GMCd1c0rc3ge9pYDY8mwwFSvxOhkzvVEh/XqCbvJJuhO2sYITPnPAS2J1gYNG/a+ccIx2wjZWoI4AqMzJ4VQ8t91poZTb0FvKGxjKh7iofMJu9xANJzQeACDWTB7SFwh1JDKEUgACqQh4nDK0L95hmt31Bvaj91l8EEw5mNMaFRXsaFw4sRFRWbwaFPTUALyFOhLeOlEz6ggQO42/t2J2LDJ93ik7enfFsr7EsrcUQQqcKKyhfNOmTVNWVpbS0tKUm5ur5cuX17jss88+q3POOUfNmjVTs2bNlJeXV+vybhDPkzKnXwGtPAQiGsMhov16JnD6ZwwEQh2pHY374EXzuF/Ta1FH7OfkuUERG9SR6DNxaF8855GqjJ5SZmAYn3eEHEy9/PLLGjNmjAoLC7Vy5Up17dpV+fn52r59e8Dlly5dqssuu0zvvfeeli1bpnbt2unCCy/U1q1bI954uFdtjQO3NhwAr6COmMUNoUUg1JAj3Pr5wtu8XEdiHZjYFQSZtB2x3sf0lgKqCzmYevTRRzVy5EgNHz5cJ554oqZPn64GDRpo5syZAZefM2eObrjhBnXr1k3Z2dl67rnnVF5ersWLF0e88ZFyy9U3TjrdK56fbSx7aLjlbw3RQR0xD3XEneL9udLTD/Hihjpi+sTXdoVCdgdj9JQyB72lvCWkYKq0tFQrVqxQXl7ery+QmKi8vDwtW7YsqNc4ePCgDh06pObNm9e4TElJifbu3ev34zTxPjmjUeE+fKZwI+pI8KgjiASfJ9wqHnXEDTUkGuIVElWsx+6eWvEIpegtBQQWUjC1c+dOlZWVqVWrVn6Pt2rVSkVFRUG9xp///Ge1bt3ar5hUNWnSJDVp0sT3065du1A207M4CXUPrnLDragjZqOOuIMdnyN1BPESjzpieg2Jd6+eWARHpoRRFZweSrkNvaW8J6zJz8P10EMPad68eXr99deVlpZW43Ljxo3Tnj17fD+bN2+O41ZGjx0naTQqnK2kfSmfIVAL6kjscQxyNj4/oHbB1BEn1BC7hpyFEiZVDp+q/pjEDcP36C0VG+zX+EkOZeEWLVooKSlJxcXFfo8XFxcrMzOz1udOmTJFDz30kN59912dcsoptS6bmpqq1NTUUDYtbL0z12hRUXbMXj+r7Q5t3JIRs9cPxKm3//Y6GhPwAjfWETeijjiTXXUk1gGqW+ZyQ3TEo46EW0NS1mwJqadHk/Ul2tPJmbXKtHApXPEKpegtFTx6S3lTSD2mUlJSlJOT4zdRYMXEgT169KjxeX/5y1903333acGCBerevXv4W4ugEXI4i52fF8MvEE9urCOxbjTb9TdKHXEWPi94hRvrSLjc0NPHTm4JpejVExvs1/gKqceUJI0ZM0ZDhw5V9+7ddfrpp2vq1Kk6cOCAhg8fLkkaMmSI2rRpo0mTJkmSJk+erPHjx2vu3LnKysryjf1u1KiRGjVqFMW3Yi47ek1JXPF2Crc3JrjSjaqoI85BHXEGLm7Aa6gjiBShHmCWkIOpQYMGaceOHRo/fryKiorUrVs3LViwwDcB4aZNm5SY+GtHrGeeeUalpaW6+OKL/V6nsLBQEyZMiGzrUScaFWazO5SiQQE7UEdCZ9cFDok6Yjq76whgBzfVkUiH8zX+3tK+DglR3CL3i2coxRC+0DCMz7tCDqYkqaCgQAUFBQH/benSpX6/b9y4MZxVxFWs55mSaFSgOhoT8DK31RG3o46YiToCL6OOIBxuC6UYbga3iOtd+WAfTl7NYsLnQW8pIHriMWTV7r9ZE45b+JUJn0c8vpMMBwfqxrC04LgtlHIbk3pLEfjFH8FUHJnQqDDhRNbrvPQZ0KAAosuEOgL7mfA52P1dBOCPcKp2btw/bgpPTAqlYA+CKQ8y4YTWq0zZ9zQoAISLixz2Yt8D7hOt3jVuDF8i1fh7K+77hd5SQOgIpuLMlECAE9v4M2Wfm/IdBBAeU/6GTTmmeYVJgaAp30EA1RFO/cqOfRGvUIreUrHjpn3rJART/58XhxyZdJLrZl7dz178mwK8xovHNjt4dT9TRwCEw45eUgAiQzBlA9OuNHr1hDceTNu3pn33ADeJZyPapL9lr4bv8WLavjXpuweYyITeFl4OZex87/SWAsJHMAVJNCyizcT9SWMCQCyZdsxzOuoI4C3RDjW8Fk7Z3UuKeaXcgdDPPgRTNjH15M7EE2EnYf8BiBcT6wjHwMixD49gGB8QOS+EU3YHUlJ8Qym3BSemzS8F+xBM2cjERkUFToxDY/r+ivd3jQYFEB+m1hHTj4kmMn2fmfpdA1A7u0ObWHLzewO8hmCqEhrT1Zl+omw3J+wfGhMA7OKEY6TdnLCPqCNAaMLt1RKrnjcm9CqKJpPeD72l3IP9ay+CKZs55WTPCSfO8cT+ABCIHRc4nFBHOGZW55R94oTvF4DgmBLmhMukQEpiXqlImTSMj1DKfgRTBnDSSZ9TTqRjxWnv30nfLQDhc8rfutOOobHgpH1g1/eKHuxA7JgU7ATLtEBKin8oRXACtyOYqsKukyGnNCoqOOnEOhqc+H5pUAD24G+gbhXHVKcdV8PltfcLIHzxCDxMDHqqqthGE7eTnlKRo7cUqkq2ewPgbJVPslM3pdi4JdHn5AaE04JOAJHLartDG7dk2L0ZIaOOmIk6Arhf4+8t7euQYPdm+DExiLIbwQm8gGDKIE5tVFRwQ+PCyY2ICnY2JugpAtiLOmIvN9QQiToCREPKmi1h9wppsr5EezqlRnmLAqsIguwMqJwURtFbyl0I/cxBMBVA78w1WlSUbcu6nd6oqBDo5Ny0RoZbGhCVcYUbMAN1JHKm1xE31hCJOgJ4VeVwKNYhlZOCqMrsCKXcGJyYNIwP5iCYMpBbGhVVVT2Jj2cDw60NiMpoTACoQB2J/brdyO46Qm8p4Ffx7DVVVdXgKJygyqnhUyD0knInN4Z+TkYwVQM7r3ZL7m1UVOaFk3wvoUEBmIU6glDYHUoBqM7OcKoyN4VMobIzlHJjcGJKbyk37lun4658BuMkEcHiuwKYx4SwlmMDgsH3BACqI5Ryn5Q1W9i3hiKYMhwni6iLCd8RExrgAAIz4RgB1IU6AgTGMDJ7sN+B+CKYqoUpJ0k0KlATvhuA2agjMB3fDQDwZ3co5dYePaYM44OZCKYcghNHVMV3AkAoOGagKr4TQGxFK2CwOyjxErv3tVtDKaAuBFN1MOVqt8QJJH5l0nfBpL8RALUz6dgBe5n0XaCOADCB3aGUm5nQW4rQz2wEUw5j0okk4i+r7Q6+A4DDmNbo5hjibdQRwJkITWKnyfoSI/YvwQm8jGAqCCY2Kjip9B4TP3PT/jYABIc64k0mfubUESB4JoQnbmPKPiWUgtcRTDmYiSeYiA0+a1S1a9cuDR48WOnp6WratKlGjBih/fv317r8jTfeqBNOOEH169dX+/btddNNN2nPnj0Bl//xxx/Vtm1bJSQkaPfu3TF6F95hauObY4t38FmjKuoIvM6UUAowzbRp05SVlaW0tDTl5uZq+fLlNS777LPP6pxzzlGzZs3UrFkz5eXl1bp8TQimgkSjAnYx9TM29W/CKwYPHqyvv/5aixYt0ltvvaX3339f11xzTY3Lb9u2Tdu2bdOUKVP01Vdfafbs2VqwYIFGjBgRcPkRI0bolFNOidXmwyCmHmMQHSb3jqOO2Is6Eh/R7glDmBI5U4buVaC3FEzy8ssva8yYMSosLNTKlSvVtWtX5efna/v27QGXX7p0qS677DK99957WrZsmdq1a6cLL7xQW7duDWm9BFMuYPJJJ8LH54qarF69WgsWLNBzzz2n3NxcnX322XryySc1b948bdu2LeBzunTpon/84x/q37+/OnXqpPPPP18PPPCA/vWvf+nw4cN+yz7zzDPavXu3brvttni8Hc8wuRHOscad+FxRE+qIs5kUqjiNafvOC6GUCROfI3iPPvqoRo4cqeHDh+vEE0/U9OnT1aBBA82cOTPg8nPmzNENN9ygbt26KTs7W88995zKy8u1ePHikNZLMBUCkxsVEiegbmL6Z2n634LbLVu2TE2bNlX37t19j+Xl5SkxMVGffPJJ0K+zZ88epaenKzk52ffYf//7X91777168cUXlZhIifASwnD3cMJnSR2xF3XE+UwLWExnWi8pyRuhFJyltLRUK1asUF5enu+xxMRE5eXladmyZUG9xsGDB3Xo0CE1b948pHVTLVzGCSejqBmfnzvt3bvX76ekJLITo6KiIrVs2dLvseTkZDVv3lxFRUVBvcbOnTt13333+Q3bKCkp0WWXXaaHH35Y7du3j2gbEZgTGuMcg5zNCZ+fE/4OTEMdQSCmBS2mYj8BwdWRnTt3qqysTK1atfJ7vFWrVkHXhj//+c9q3bq1X7gVjOS6F0FlvTPXaFFRtt2bUaestju0cUuG3ZuBEDihMSG5s0GR8s02JSemRP11E8tLJUnt2rXze7ywsFATJkyotvzYsWM1efLkWl9z9erVEW/X3r171a9fP5144ol+2zFu3Dh17txZV1xxRcTrQM2cUEcqjkfUEWdxSh1xm1jVEIk64hYpa7bEbDhTk/Ul2tMpNSav7QamhlL0lkJlJtWRSDz00EOaN2+eli5dqrS0tJCeSzDlYjQsnIGGhPtt3rxZ6enpvt9TUwOfQN56660aNmxYra/VsWNHZWZmVpuA8PDhw9q1a5cyMzNrff6+ffvUp08fNW7cWK+//rrq1avn+7clS5boyy+/1KuvvipJsixLktSiRQvdddddmjhxYq2vDfehjjiDk+qIGy9uxAN1BLUhnKrO1EBKIpSyA/s8uDrSokULJSUlqbi42O/x4uLiOmvDlClT9NBDD+ndd98N68YXBFNhcMLV7spoWJjJSQ2JCjQowpOenu5XCGqSkZGhjIy6/0579Oih3bt3a8WKFcrJyZF0pDFQXl6u3NzcGp+3d+9e5efnKzU1VW+++Wa1Kxn/+Mc/9PPPP/t+//TTT3XVVVfpP//5jzp16lTndiF41BFEyok1BOGjjgDBMTmQkrwXkNg18bnX9nMwgqkjKSkpysnJ0eLFizVgwABJ8k1kXlBQUOPz/vKXv+iBBx7QwoUL/eYuDAXBlIdUPomlcWEfGhOIVOfOndWnTx+NHDlS06dP16FDh1RQUKBLL71UrVu3liRt3bpVF1xwgV588UWdfvrp2rt3ry688EIdPHhQf/vb33zjy6UjDZmkpKRqjYadO3f61te0adO4vkeYiYDKfk6uIVzcMAd1JP5iOZxP+jWQ8WrPKdMDKcQPoVRkxowZo6FDh6p79+46/fTTNXXqVB04cEDDhw+XJA0ZMkRt2rTRpEmTJEmTJ0/W+PHjNXfuXGVlZfnmomrUqJEaNWoU9HoJpsLktKvdVdG4iD8nNyYkGhSmmTNnjgoKCnTBBRcoMTFRAwcO1BNPPOH790OHDmnt2rU6ePCgJGnlypW+Oy0de+yxfq+1YcMGZWVlxW3bcYST6wgXOuLP6TUE5qGOuJPXAionBVIEJrHHPo7coEGDtGPHDo0fP15FRUXq1q2bFixY4JsQfdOmTX53XH3mmWdUWlqqiy++2O91Qp3DimDK42hcxJZbGhKEUuZp3ry55s6dW+O/Z2Vl+eb2kKRevXr5/R6McJ4D7+FCR2xRRxAr1BF3c/O8U04KoyoQmMQe+zh6CgoKahy6t3TpUr/fN27cGJV1EkxFwMlXuwOhcREdbmlEAIg9N9URakj0UEcAREPlAMfpIZUTw6gKBCZA3QimIuSmRkUFGhehc3MjgqvcQGy5rY7QEzc81BHA/WI9z1RtnDjEz8lhVAUvh1Lx/K57eT+7BcFUFLitUVGh6kkyDQx/bm5EVKAxAcQHdcSbqCMA4s0JvajcEEhJhCXxwn52B4KpKHFro6IyrzcwvNCAqIzGBIBo83pvKuoIAJNUDYDsCKrcEkJV5fWwxK6egXAugqko8kI4VZnbgyqvNSAqozEBxJ/Xa4hEHXET6ggQmJ3D+eoSKCSKZljl1hCqKq+HUvHEvnYPgqko81rDorKaTsBNb2h4ueEQCI0JwD5eriESdcQtqCOAe3glTIoWghIgPARTiLm6Tthj3eCgwRA8GhOA/bweTgVCHXEO6ghQN5N7TQGR4ruNcBBMxQCNitBwwm8GGhOAOagjoaGOmIE6AgSPcMpd6CkFRCbR7g1wK07OAACRoI7ASfi+AvAqQikgcgRTMcRJGpyC7ypgJv424QR8T4HwEGg4H59h/KSs2cL+djGCqRjjZA2m4zsKmI2/UZiM7ycQGRrazkRIEr5w9lvl57Dv3YlgKg44aYOp+G4CzsDfKkzE9xKAFxGKRC7YcKm25fgc3IVgKk44eYNp+E4CzsLfLEzC9xGIHhrYzsFnFZmq+y9Q8FTxGPvaW7grXxxVnMRxpyXYicYE4FzUEZiAOgJEH3fpMx9BSeywb0EwZQMaFrADDQnAPXpnrqGGIO6oIwC8iNAkeISrCBdD+WzECR7ihe8a4D69M9fwt4244LsGxAcBiHn4TEJT2/5iX6I29JiyGb2nEEs0JAD3o/cUYoUaAsDLCFLCw35DOOgxZQhO/hBNXN0GvIW/eUQb3yfAHjTq7cfE20D80WPKIPSeQqRoSADeRh1BpKgjgP2YCN0ehFGAfQimDFT5pJDGBYJBQwJAZQRUCBV1BDBLRUhCQBV7BFKA/QimDEfjArWhIQGgNtQQ1IU6ApiN3lOxQyAFmINgyiFoXKAyGhIAQkFPXFRGDQGchXAqegijADMRTDkMjQvvoiEBIBqoI95FHQGci6F9kSGQAsxGMOVgNC7cj0YEgFiijrgfdQRwF3pPhYZACnAGgimXoHHhDjQgANiFOuIO1BHA/eg9VTvCKMB5CKZciMaFs9CIAGAa6oizUEcAbyKg+hVhFP5fe/cfW1V9/3H81R/ce1noL8bawtJKQAFlqBOkVmb4zjUrgbh1yTcyxAaXOrbJXCLLtipb6oZTZpplCWMzU5QtUTsxwgw0CCKNASsiFAO0ujHqGMFbv9XRFhD66/39g/SOym3tvbf3nns/fT6S+0fP/Zx73+/e9rzued/bXqQ2BlOOG+rJKica3uEEAkAqCXfMIkO8RY4AuNzlQ5mxMqRiEAW4hcHUGPXpJ7WcZMQPJxAAXMOwKrHIEQAj5eKQiiEU4D4GU5DESUYsOGEAAHIkFuQIgHgYaqCTzAMrhlDA2MRgCkP6rCfKY+mEg5MGAIgcOfJf5AiAZDHc8CcRQyuGTwA+jcEUohbpk+xkOwHhJAEAvJXKOUKGAHARQyMAXmAwhYThSTwAIBbkCAAAgHvSvS4AAAAAAAAAYxODKQAAAAAAAHiCwRQAAAAAAAA8wWAKAAAAAAAAnmAwBQAAAAAAAE8wmAIAAAAAAIAnGEwBAAAAAADAEwymAAAAAAAA4ImUGkxVZL3jdQkAAAAAAAAYJSk1mAIAIBa8wAEAAAAkl5QbTP1v9iGvSwAAAAAAAMAoSLnBFAAAseAFDgAAACB5pORgipMKAEAsyBEAAAAgOaTkYAoAAAAAAACpL2UHU7zaDQCIBTkCAAAAeC9lB1MAAAAAAABIbSk9mOLVbgBALMgRAAAAwFspPZgCAAAAAABA6kr5wRSvdgMAYkGOAAAAAN6JajC1YcMGTZ06VYFAQCUlJXrrrbeGXb9582bNmjVLgUBAc+bMUX19fVTFAgAu+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<p>We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem.</p>
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<h2 id="What's-next?">What's next?<a class="anchor-link" href="#What's-next?"></a></h2><p>Congratulations on completing the two dimensional Wave tutorial of <strong>PINA</strong>! There are multiple directions you can go now:</p>
<ol>
<li><p>Train the network for longer or with different layer sizes and assert the finaly accuracy</p>
</li>
<li><p>Propose new types of hard constraints in time, e.g. $$ u_{\rm{pinn}} = xy(1-x)(1-y)\cdot NN(x, y, t)(1-\exp(-t)) + \cos(\sqrt{2}\pi t)sin(\pi x)\sin(\pi y), $$</p>
</li>
<li><p>Exploit extrafeature training for model 1 and 2</p>
</li>
<li><p>Many more...</p>
</li>
</ol>
</div>
</div>
</div>
</div>
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