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PINA/docs/source/tutorials/tutorial3/tutorial.html
github-actions[bot] 4357f8681f export tutorials changed in 5aec5f5 (#540)
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This file contains invisible Unicode characters
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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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/* Icons urls */
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,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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,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
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<h1 id="Tutorial:-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 0x7f48440f4be0&gt;</pre>
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"/>
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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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uwYLCwscOzYMb1ud8KECfDz89PrNomISPr8/PwwYcIEvW7TUNcqIiIxXL16FRYWFli8eLFR92sqn8+1HUdhYSEmTZoET09PWFhYYMaMGQCA7OxsDB8+HI0bN4aFhQWWL19u9P4SiYHhlAxt3LgRFhYW6oednR2efPJJREVFITs7W92u/IPxrl27Kq1rZ2eHrKysStvu1asX2rZtq7HMz88PFhYWeP311yu117YPKbt58yZiY2ORmpoqdleIiCSp4vWlugdDl0dWrVqFjRs3it0NIqJ6WbVqFSwsLBAaGip2V/TmwIEDiI2N1ft2Y2NjNa6HDg4O8PHxweDBg7FhwwYUFRXVajsfffQRNm7ciGnTpuHrr7/G2LFjAQAzZ87EwYMHMXfuXHz99dfo37+/3o+BSIqsxe4A1d2CBQvg7++Phw8f4qeffsKXX36JAwcO4Ny5c3BwcKh23aKiInz88cdYsWJFrfe3Zs0azJ07F15eXvXtumhu3ryJ999/H35+fggODtZ4bs2aNVCpVOJ0jIhIIr7++muNf2/evBmJiYmVlgcFBRmzW5K1atUqNGnSpFLlVY8ePfDgwQPY2NiI0zEiIh1s3boVfn5+OH36NC5fvoyWLVuK3aV6O3DgAOLi4gwSUAHAl19+CUdHRxQVFSErKwsHDx7EK6+8guXLl2Pfvn3w9vZWt9X2PePIkSPo0qULYmJiKi0fMmQI3nzzTYP0m0iqGE7J2IABA9CxY0cAwKRJk9C4cWMsXboU33//PUaPHl3tusHBwTqFTW3atEF6ejo+/vhjfPHFF3rpv9Q0aNBA7C4QEYnu5Zdf1vj3yZMnkZiYWGn54+7fv1/jDyPmxNLSEnZ2dmJ3g4ioRhkZGThx4gS+++47vPrqq9i6dWulwIQqGz58OJo0aaL+9/z587F161aMGzcO//rXv3Dy5En1c9q+Z9y6dQutW7fWutzFxUVv/SwtLYVKpeKPJSR5HNZnQp599lkAjy4wNXnnnXdQVlaGjz/+uFbb9vPzw7hx47BmzRrcvHmzTv1bsWIF2rRpAwcHBzRq1AgdO3bEtm3bNNr8+uuvGDBgABQKBRwdHdGnTx+NN/bq+qdtvpBevXqhV69eAB4NQezUqRMAIDIyUl2KWz4cQ9tY8Hv37mHWrFnw9vaGra0tWrVqhcWLF0MQBI12FhYWiIqKwp49e9C2bVvY2tqiTZs2SEhIqN3JISKSkfIh4CkpKejRowccHBzwzjvvAHj0fqjtV2pt79O5ubmYMWOG+j22ZcuW+OSTT2pVxfrLL78gIiICTZo0gb29Pfz9/fHKK69otKnte/jjyodsPK58aPzVq1fVx3T+/Hn8+OOP6mtKxWuOtuGPO3fuREhICOzt7dGkSRO8/PLLlYbZT5gwAY6OjsjKysLQoUPh6OgINzc3vPnmmygrK6vx3BAR6WLr1q1o1KgRBg0ahOHDh2Pr1q3Vtl+2bBl8fX1hb2+Pnj174ty5cxrPK5VKREZGonnz5rC1tUXTpk0xZMgQ9XtnuVWrVqFNmzawtbWFl5cXpk+fjtzc3Gr3XdV7a/mcWBU/18fFxQHQHK5eTqVSYfny5WjTpg3s7Ozg4eGBV199FXfv3q12/zUZM2YMJk2ahFOnTiExMVG9vOL3jPJjyMjIwP79+zW+k1hYWEAQBMTFxVXqc22umRXnBlu+fDkCAgJga2uLP/74AwBw4cIFDB8+HK6urrCzs0PHjh2xd+9ejWMo78fPP/+M6OhouLm5oWHDhnjhhRdw+/btSsf8n//8Bz179oSTkxMUCgU6depU6TveqVOn0L9/fzg7O8PBwQE9e/bEzz//XK9zTaaHlVMm5MqVKwCAxo0b19jW399fHTbNmTOnVtVT7777LjZv3lyn6qk1a9bg3//+N4YPH4433ngDDx8+xP/+9z+cOnUKL730EgDg/Pnz6N69OxQKBd566y00aNAAX331FXr16oUff/yx3mPgg4KCsGDBAsyfPx9TpkxB9+7dAQBdu3bV2l4QBDz//PM4evQoJk6ciODgYBw8eBCzZ89GVlYWli1bptH+p59+wnfffYfXXnsNTk5O+OKLLzBs2DBkZmbW6jUhIpKTv//+GwMGDMCoUaPw8ssvw8PDQ6f179+/j549eyIrKwuvvvoqfHx8cOLECcydOxd//fVXtRPA3rp1C/369YObmxvmzJkDFxcXXL16Fd999526ja7v4XWxfPlyvP7663B0dMS7774LANWeh40bNyIyMhKdOnXCokWLkJ2djc8//xw///wzfv31V41fysvKyhAREYHQ0FAsXrwYhw8fxpIlSxAQEIBp06bVu+9EROW2bt2KF198ETY2Nhg9ejS+/PJLnDlzRv2jbkWbN29GQUEBpk+fjocPH+Lzzz/Hs88+i99//139/jds2DCcP38er7/+Ovz8/HDr1i0kJiYiMzNTHdDExsbi/fffR3h4OKZNm4b09HT1fn/++ed6j2h49dVXcfPmTa3D0sufL39P/ve//42MjAysXLkSv/76a733P3bsWKxevRqHDh1C3759Kz0fFBSEr7/+GjNnzkTz5s0xa9YsAECHDh3Uc0/17dsX48aNU6+j6zVzw4YNePjwIaZMmQJbW1u4urri/PnzeOaZZ9CsWTPMmTMHDRs2xDfffIOhQ4fi22+/xQsvvKCxjddffx2NGjVCTEwMrl69iuXLlyMqKgo7duxQt9m4cSNeeeUVtGnTBnPnzoWLiwt+/fVXJCQkqL/jHTlyBAMGDEBISAhiYmJgaWmJDRs24Nlnn8V///tfdO7cuc7nmkyMQLKzYcMGAYBw+PBh4fbt28L169eF7du3C40bNxbs7e2FGzduCIIgCEePHhUACDt37qy07pkzZ4QrV64I1tbWwr///W/18z179hTatGmjsT9fX19h0KBBgiAIQmRkpGBnZyfcvHmzyn1oM2TIkErbfdzQoUMFGxsb4cqVK+plN2/eFJycnIQePXqol5Xv8+jRoxp9HD9+fKVt9uzZU+jZs6f632fOnBEACBs2bKjUdvz48YKvr6/633v27BEACAsXLtRoN3z4cMHCwkK4fPmyehkAwcbGRmPZb7/9JgAQVqxYUe1xExFJ2fTp04XHPy707NlTACDEx8dXag9AiImJqbT88ffpDz74QGjYsKFw8eJFjXZz5swRrKyshMzMzCr7tHv3bvW1rCq6vIc/3reYmJhKxywI/1xDMzIy1MvatGmjcZ0p9/i1qri4WHB3dxfatm0rPHjwQN1u3759AgBh/vz56mXjx48XAAgLFizQ2GaHDh2EkJCQKo+ZiEhXv/zyiwBASExMFARBEFQqldC8eXPhjTfe0GiXkZEhAND4riEIgnDq1CkBgDBz5kxBEATh7t27AgDhs88+q3Kft27dEmxsbIR+/foJZWVl6uUrV64UAAjr169XL3v887m27wEV+1fxM76265cgCMJ///tfAYCwdetWjeUJCQlalz+u/Bpx+/Ztrc+Xn4MXXnihyuMQBM3vWBUBEKZPn66xrLbXzPLzoFAohFu3bmm07dOnj9CuXTvh4cOH6mUqlUro2rWr8MQTT6iXlV/rwsPDBZVKpV4+c+ZMwcrKSsjNzRUEQRByc3MFJycnITQ0VOO6Vr7d8v994oknhIiICI1t3b9/X/D39xf69u1b6fjJfHFYn4yFh4fDzc0N3t7eGDVqFBwdHbF79240a9asVuu3aNFCnez/9ddftVpn3rx5KC0trfVwwHIuLi64ceMGzpw5o/X5srIyHDp0CEOHDkWLFi3Uy5s2bYqXXnoJP/30E/Lz83XaZ30dOHAAVlZW+Pe//62xfNasWRAEAf/5z380loeHhyMgIED976eeegoKhQJ//vmnUfpLRGRMtra2iIyMrPP6O3fuRPfu3dGoUSPcuXNH/QgPD0dZWRmOHz9e5brlFUb79u1DSUmJ1ja6vocb2i+//IJbt27htdde05iLatCgQQgMDMT+/fsrrTN16lSNf3fv3p3XFCLSq61bt8LDwwO9e/cG8GgI3MiRI7F9+3atw4iHDh2q8V2jc+fOCA0NxYEDBwAA9vb2sLGxwbFjx6ocInf48GEUFxdjxowZsLT85+vo5MmToVAotL4f6tPOnTvh7OyMvn37alx/QkJC4OjoiKNHj9Zr+46OjgCAgoICfXQXgO7XzGHDhsHNzU3975ycHBw5cgQjRoxAQUGBev2///4bERERuHTpUqUh5lOmTNEYVti9e3eUlZXh2rVrAIDExEQUFBRgzpw5leZYLF8vNTUVly5dwksvvYS///5bvd979+6hT58+OH78OG9IRWoMp2QsLi4OiYmJOHr0KP744w/8+eefiIiI0GkbuoZNdQm0AODtt9+Go6MjOnfujCeeeALTp0/XGGd8+/Zt3L9/H61ataq0blBQEFQqFa5fv17r/enDtWvX4OXlBScnp0r9KX++Ih8fn0rbaNSoUb3HrhMRSVGzZs3qNbnqpUuXkJCQADc3N41HeHg4gEdD96rSs2dPDBs2DO+//z6aNGmCIUOGVLp9t67v4YZWvj9t17nAwMBK/bGzs9P4YgHwmkJE+lVWVobt27ejd+/eyMjIwOXLl3H58mWEhoYiOzsbSUlJldZ54oknKi178skn1fNJ2dra4pNPPsF//vMfeHh4oEePHvj000+hVCrV7at6P7SxsUGLFi0M/v586dIl5OXlwd3dvdI1qLCwsNrrT20UFhYCQKXrT33oes309/fX+Pfly5chCALee++9Stson/z+8W08/t2mUaNGAKC+DpVPKdO2bdtq+w0A48ePr7TftWvXoqioCHl5eTqdCzJdnHNKxjp37qy+W19dtWjRAi+//DJWr16NOXPm1Gqdd999F19//TU++eQTDB06tFbrBAUFIT09Hfv27UNCQgK+/fZbrFq1CvPnz8f7779fjyN4RNvEtcCji66VlVW9t18bVe1HqGHiXSIiObK3t9ep/eO/wKtUKvTt2xdvvfWW1vZPPvlklduysLDArl27cPLkSfzwww/q23cvWbIEJ0+eVP9qXVfVXVOMxVjXLiIyX0eOHMFff/2F7du3Y/v27ZWe37p1K/r166fzdmfMmIHBgwdjz549OHjwIN577z0sWrQIR44cQYcOHerVZ328P6tUKri7u1c58fvjPwzoqnyC+JYtW9ZrOxXpes18/BpdXp305ptvVlnM8Hh/9fHdpny/n332GYKDg7W2qe81m0wHwynCvHnzsGXLFnzyySe1ah8QEICXX34ZX331lU6TlDds2BAjR47EyJEjUVxcjBdffBEffvgh5s6dCzc3Nzg4OCA9Pb3SehcuXIClpSW8vb2r3HajRo203t3j2rVrGsMEq7qgaePr64vDhw+joKBA45ePCxcuqJ8nIiJN2t6Pi4uLK1XbBgQEoLCwUP2rb1106dIFXbp0wYcffoht27ZhzJgx2L59OyZNmlSv9/DyX4dzc3M1JinX9mt+ba8r5ftLT09X3123XHp6Oq8pRGR0W7duhbu7u/qudhV999132L17N+Lj4zWCjvJKmIouXrxY6Y7XAQEBmDVrFmbNmoVLly4hODgYS5YswZYtWzTeDyt+Ti8uLkZGRka114WK788V6fL+HBAQgMOHD+OZZ57R+YeW2iifgF3XES3Vqe81s/w8N2jQoF7X3cf7BDwK46oK4srbKBQKve2XTBeH9ZFG2FSx5LY68+bNQ0lJCT799NNatf/77781/m1jY4PWrVtDEASUlJTAysoK/fr1w/fff69xm9ns7Gxs27YN3bp1g0KhqPYYTp48ieLiYvWyffv2VRoK2LBhQwCVL2jaDBw4EGVlZVi5cqXG8mXLlsHCwgIDBgyocRtEROYmICCg0twXq1evrvSr9ogRI5CcnIyDBw9W2kZubi5KS0ur3Mfdu3cr/XJb/ots+dC++ryHl3+Yrngc9+7dw6ZNmyq1bdiwYa2uKR07doS7uzvi4+M1hh/+5z//QVpaGgYNGlTjNoiI9OXBgwf47rvv8Nxzz2H48OGVHlFRUSgoKMDevXs11tuzZ4/G3ESnT5/GqVOn1O+p9+/fx8OHDzXWCQgIgJOTk/q9Lzw8HDY2Nvjiiy803svXrVuHvLy8at8PfX19YWVlVek6s2rVqkptq/rcP2LECJSVleGDDz6otE5paWmt3tOrsm3bNqxduxZhYWHo06dPnbfzuPpcMwHA3d0dvXr1wldffaV1apbbt2/r3Kd+/frByckJixYtqvSal7+uISEhCAgIwOLFi9XDHeu7XzJdrJwiAP8M1UtPT0ebNm1qbF8eaGn7oK5Nv3794OnpiWeeeQYeHh5IS0vDypUrMWjQIPUv2gsXLkRiYiK6deuG1157DdbW1vjqq69QVFRUYwg2adIk7Nq1C/3798eIESNw5coVbNmyRWOC8vJ+u7i4ID4+Hk5OTmjYsCFCQ0MrjcsGgMGDB6N379549913cfXqVbRv3x6HDh3C999/jxkzZlTaNhERPXo/njp1KoYNG4a+ffvit99+w8GDB9GkSRONdrNnz8bevXvx3HPPYcKECQgJCcG9e/fw+++/Y9euXbh69Wqldcpt2rQJq1atwgsvvICAgAAUFBRgzZo1UCgUGDhwIID6vYf369cPPj4+mDhxImbPng0rKyusX78ebm5uyMzM1GgbEhKCL7/8EgsXLkTLli3h7u5eqTIKePRr9SeffILIyEj07NkTo0ePRnZ2Nj7//HP4+flh5syZup5qIqI627t3LwoKCvD8889rfb5Lly5wc3PD1q1bMXLkSPXyli1bolu3bpg2bRqKioqwfPlyNG7cWD3c7OLFi+jTpw9GjBiB1q1bw9raGrt370Z2djZGjRoF4NGwublz5+L9999H//798fzzzyM9PR2rVq1Cp06d8PLLL1fZb2dnZ/zrX//CihUrYGFhgYCAAOzbt0/rPFEhISEAgH//+9+IiIiAlZUVRo0ahZ49e+LVV1/FokWLkJqain79+qFBgwa4dOkSdu7cic8//xzDhw+v8Rzu2rULjo6OKC4uRlZWFg4ePIiff/4Z7du3x86dO2tcXxf1uWaWi4uLQ7du3dCuXTtMnjwZLVq0QHZ2NpKTk3Hjxg389ttvOvVJoVBg2bJlmDRpEjp16oSXXnoJjRo1wm+//Yb79+9j06ZNsLS0xNq1azFgwAC0adMGkZGRaNasGbKysnD06FEoFAr88MMP9Tk1ZEpEu08g1Vn57T2ru4W2IPxzq9WdO3fWat3yW1e3adNGY3lVtzm9dOmSYGVlVWkf2nz11VdCjx49hMaNGwu2trZCQECAMHv2bCEvL0+j3dmzZ4WIiAjB0dFRcHBwEHr37i2cOHFC63E9fgvZJUuWCM2aNRNsbW2FZ555Rvjll1+Enj17VrrF9/fffy+0bt1asLa21rjlrLZbvBYUFAgzZ84UvLy8hAYNGghPPPGE8Nlnn2ncClUQtN/yVRAq356ciEhutN2Ku2fPnpWuFeXKysqEt99+W2jSpIng4OAgRERECJcvX9b6flhQUCDMnTtXaNmypWBjYyM0adJE6Nq1q7B48WKhuLi4yj6dPXtWGD16tODj4yPY2toK7u7uwnPPPSf88ssvlbZfm/dwbX1LSUkRQkNDBRsbG8HHx0dYunSp+hqakZGhbqdUKoVBgwYJTk5OAgD1Naeqa9WOHTuEDh06CLa2toKrq6swZswYjduyC8Kj61HDhg0rHXf57cuJiOpr8ODBgp2dnXDv3r0q20yYMEFo0KCBcOfOHSEjI0MAIHz22WfCkiVLBG9vb8HW1lbo3r278Ntvv6nXuXPnjjB9+nQhMDBQaNiwoeDs7CyEhoYK33zzTaXtr1y5UggMDBQaNGggeHh4CNOmTRPu3r2r0Ubb5/Pbt28Lw4YNExwcHIRGjRoJr776qnDu3DmNz/WCIAilpaXC66+/Lri5uQkWFhaV3j9Xr14thISECPb29oKTk5PQrl074a233hJu3rxZ7bkrfy8uf9jZ2QnNmzcXnnvuOWH9+vXCw4cPK62j7Tiq+o5V1feK2lwzK75O2ly5ckUYN26c4OnpKTRo0EBo1qyZ8Nxzzwm7du1St6nq+2JV17W9e/cKXbt2Fezt7QWFQiF07txZ+L//+z+NNr/++qvw4osvqr8L+vr6CiNGjBCSkpK09pPMk4UgcLZmIiIiIiIiIiISB+ecIiIiIiIiIiIi0TCcIiIiIiIiIiIi0TCcIiIiIiIiIiIi0egcTh0/fhyDBw+Gl5cXLCwssGfPnhrXOXbsGJ5++mnY2tqiZcuW2LhxYx26SkQkTXFxcfDz84OdnR1CQ0Nx+vTpatvv3LkTgYGBsLOzQ7t27XDgwAGN5wVBwPz589G0aVPY29sjPDwcly5d0miTk5ODMWPGQKFQwMXFBRMnTqx0i97//e9/6N69O+zs7ODt7a31rpc19cVYeG0hIqpM39eXwsJCREVFoXnz5rC3t0fr1q0RHx9vyEMQHa8vRESadL225ObmYvr06WjatClsbW3x5JNPVvmd4eOPP4aFhQVmzJihc790Dqfu3buH9u3bIy4urlbtMzIyMGjQIPTu3RupqamYMWMGJk2ahIMHD+rcWSIiqdmxYweio6MRExODs2fPon379oiIiNB6S2MAOHHiBEaPHo2JEyfi119/xdChQzF06FCcO3dO3ebTTz/FF198gfj4eJw6dQoNGzZEREQEHj58qG4zZswYnD9/HomJidi3bx+OHz+OKVOmqJ/Pz89Hv3794Ovri5SUFHz22WeIjY3F6tWrdeqLsfDaQkSkyRDXl+joaCQkJGDLli1IS0vDjBkzEBUVhb179xrrsIyO1xcion/oem0pLi5G3759cfXqVezatQvp6elYs2YNmjVrVqntmTNn8NVXX+Gpp56qW+fqc6s/AMLu3burbfPWW29Vut30yJEjhYiIiPrsmohIEjp37qxxu9+ysjLBy8tLWLRokdb2I0aMqHTb4NDQUOHVV18VBEEQVCqV4OnpqXEL4NzcXMHW1lZ9W94//vij0i1+//Of/wgWFhZCVlaWIAiCsGrVKqFRo0ZCUVGRus3bb78ttGrVqtZ9EQuvLURE+r++CIIgtGnTRliwYIFGm6efflp499139dhz6eL1hYjMna7Xli+//FJo0aKFUFxcXO12CwoKhCeeeEJITEwUevbsKbzxxhs69826bpFW7SUnJyM8PFxjWURERLVlXkVFRSgqKlL/W6VSIScnB40bN4aFhYWhukpERiAIAgoKCuDl5QVLS92nvXv48CGKi4sN0LNHBEGo9D5ja2sLW1vbSm2Li4uRkpKCuXPnqpdZWloiPDwcycnJWrefnJyM6OhojWURERHqYQYZGRlQKpUa75vOzs4IDQ1FcnIyRo0aheTkZLi4uKBjx47qNuHh4bC0tMSpU6fwwgsvIDk5GT169ICNjY3Gfj755BPcvXsXjRo1qrEvUsZrCxFVJPVrCyD+9QUAunbtir179+KVV16Bl5cXjh07hosXL2LZsmX1ODLTwusLEVVkSteXulxb9u7di7CwMEyfPh3ff/893Nzc8NJLL+Htt9+GlZWVut306dMxaNAghIeHY+HChXU6DoOHU0qlEh4eHhrLPDw8kJ+fjwcPHsDe3r7SOosWLcL7779v6K4RkYiuX7+O5s2b67TOw4cP4e3TEHduqwzUK8DR0bHS3E0xMTGIjY2t1PbOnTsoKyvT+h534cIFrduv6j1RqVSqny9fVl0bd3d3jeetra3h6uqq0cbf37/SNsqfa9SoUY19kTJeW4hIm7peW3x8G+L2LcNdWwDxry8AsGLFCkyZMgXNmzeHtbU1LC0tsWbNGvTo0aOOR2V6eH0hIm3qfH3xaYjbBvzuAtT++lKXa8uff/6JI0eOYMyYMThw4AAuX76M1157DSUlJYiJiQEAbN++HWfPnsWZM2fqdRwGD6fqYu7cuRq//OTl5cHHxwcHT3qioSNvMEgkde2806p8Lj8/H97e3nByctJ5u8XFxbhzW2Ww94J7hSpEdFHi+vXrUCgU6uXaftUm+anq2hJ79BnYOUryckhEFfRrqP2DMwAUFqrQo/PtOl9bbt9S4fhpdzg6GqbKpbBQQI/Ot0S/vqxYsQInT57E3r174evri+PHj2P69Onw8vKqVC1EtVfV9aVXk3GwtrSpZk0ikrpSVTGO3dlc9+vLbRWOnTLs9aVXqOGuLyqVCu7u7li9ejWsrKwQEhKCrKwsfPbZZ4iJicH169fxxhtvIDExEXZ2dvXal8E/jXt6eiI7O1tjWXZ2NhQKhdZfHoCqS5wbOlrC0YnhFJHUZeS2QXuf69W2qU+Zu6HfCxQKhcabe1WaNGkCKysrre9xnp6eWtep6j2xvH35/2ZnZ6Np06YabYKDg9VtHp+0sLS0FDk5ORrb0bafivuoqS9Sps9ri52jNcMpIhlwrMWPEvW5tjg6Whjw2vLoV3Mxry8PHjzAO++8g927d2PQoEEAgKeeegqpqalYvHgxw6n/T5/XF2tLG4ZTRCbCFK4vdbm2NG3aFA0aNNAYwhcUFASlUqkeJnjr1i08/fTT6ufLyspw/PhxrFy5EkVFRRrrVsfgSU9YWBiSkpI0liUmJiIsLMzQuyYiMigbGxuEhIRovMepVCokJSVV+R5X03uiv78/PD09Ndrk5+fj1KlT6jZhYWHIzc1FSkqKus2RI0egUqkQGhqqbnP8+HGUlJRo7KdVq1Zo1KhRrfoiZXLuOxHVzYHC1mJ3wWgMcX0pKSlBSUlJpTlTrKysoFIZdsiJnPD6QkSmqi7XlmeeeQaXL1/WuE5cvHgRTZs2hY2NDfr06YPff/8dqamp6kfHjh0xZswYpKam1jqYAuoQThUWFqp3CjyavDc1NRWZmZkAHpW1jhs3Tt1+6tSp+PPPP/HWW2/hwoULWLVqFb755hvMnDlT110TkYz8luktdheMIjo6GmvWrMGmTZuQlpaGadOm4d69e4iMjAQAjBs3TmPSwTfeeAMJCQlYsmQJLly4gNjYWPzyyy+IiooC8OhXmRkzZmDhwoXYu3cvfv/9d4wbNw5eXl4YOnQogEe/VvTv3x+TJ0/G6dOn8fPPPyMqKgqjRo2Cl5cXAOCll16CjY0NJk6ciPPnz2PHjh34/PPPNYYd1NQXY+K1hYhIk76vLwqFAj179sTs2bNx7NgxZGRkYOPGjdi8eTNeeOEFUY7RGHh9ISL6h67XlmnTpiEnJwdvvPEGLl68iP379+Ojjz7C9OnTAQBOTk5o27atxqNhw4Zo3Lgx2rZtq1PfdB7H8Msvv6B3794aBwcA48ePx8aNG/HXX3+p3+yBR1UA+/fvx8yZM/H555+jefPmWLt2LSIiInTdNRGR5IwcORK3b9/G/PnzoVQqERwcjISEBPVEg5mZmRq/Unft2hXbtm3DvHnz8M477+CJJ57Anj17NN6833rrLdy7dw9TpkxBbm4uunXrhoSEBI1x3Fu3bkVUVBT69OkDS0tLDBs2DF988YX6eWdnZxw6dAjTp09HSEgImjRpgvnz52PKlCk69cVYeG0hoto4UNgaAx3/ELsbRmGI68v27dsxd+5cjBkzBjk5OfD19cWHH36IqVOnGv34jIXXFyKif+h6bfH29sbBgwcxc+ZMPPXUU2jWrBneeOMNvP3223rvm4UgCILet6pn+fn5cHZ2xk/nvDjnFJHMPD73VPl/z3l5ebWad0PbuoZ6LygsUKFb25t16hvJT/nf08dnenLOKSIZ0RZOFRao8HTr7HpdW87+4WGwz5n16R/JT/nfVLj7JM45RSRzpapiHL61tl7Xl1/OG/b60rGNaVxfmPQQERERkWyY09xTRERE5sIkfyr++m7XOq87ttEJPfaEiH7L9K7xzn1ERERERERkvkyucqo+wZQ+1iciIiIiw2L1FBERkWkxqXBKX8ESAyoi/TKXO/cRERERERGR7mQ9rM+QIdLXd7tyiB8RERGRRJnTnfuIiIhMnazDKUN7PPxiWEVUd5x7ioiIiIiIiLQxqWF9hsbhfkRERETSwbmniIiITINZVE4dymxVq3b9fNJrbMPhfkR191umN/xdzovdDSIiIiIiIpIQk6+cqm0wpWtbIiIiIhIfq6eIiIjkz6Qrp+oSNh3KbFVjBRWrpwxDzsMm+fdQe79fDxK7C0RERERERCQhJh1O1VV5qFVdSMWAqmpyDpnqSpdj5t8NERGRfh26FwggW+xuEBERUR2ZbDhV3yF6tZl/ylyZY/ikT1WdP4ZWREREREREZI5MNpyqDwZTjzCEMi5t55uBFREREREREZk6kwynOLG5bhhCSRcDKyIiIiIiIjJ1JhlO1YcuVVNynXeKYZS8Pf76yfFvkIiIiIjIGIoDm4vdBZ3ZXLghdheIjE624VRVAQurpipjGGXayl9fhlREJGdH7gSK3QWdPdvkgthdICIyW3IMnWqrNsfGAItMjWzDKUMwlbmmGEaZp4qvO4MqIpISOQZPtVGb42KARURUP6YcQtVHTeeF4RXJjdmGUxWDKLlXWzGMosexmoqIxGCqIVR91HROGF4REf2DQZT+VHUuGVqRVJlUOFXXkEmOFVMMpKg2GFIRkaEwiNKPqs4jQysiMmUMocRT8dwzqCIpMalwyhwwlKK6kOvk/UQkPoZQ4qh43hlUEZEpYCAlPQyqSEoYTumg/Mt9xYDIWF/6GUpRfTGgIqLaYiAlLQyqiEiuGEjJB4cBkthMJpwy9LxRFb/UV/z/xgiNGEyRvnCYHxFVhYGUPHAYIBFJHQMp08LqKjIWkwmnDKm6L/KG+pLPQIoMiVVURAQwkDIl5a8lQyoiEgtDKdNX/hozpCJDYDglIQykyJgYUBGZL4ZSpotDAInIWBhGmS+GVGQIDKdqwPmkyJQxoCIyDwyjzBOrqYjIEBhKUTmGVKRPZhtOHcpshX4+6WJ3g8GUntVn7jEp/D2IgQEVkeliKEUAQyoi0g+GUlQVhlSkD2YbTtWGIb+wM5SqnqEnuNfHPk0pzGJARWRaGEqRNgypiKguGEpRbTGkovow23BKzGCBwdQjYgRQ+lRV/+UaWvFOfkTyx1CKaoMhFRHVFoMpqguGVFQXZhlOMZgyPrkHUbrQdqxyDayISD4YTJGuGFIRUVUYSpE+FAc2Z0BFtWaW4ZQYzCmUMqcgqrYqnhOpB1Uc4kckLwylqL6O3AlkQEVEABhKkf6xiopqy6TCqYpf+vUx5EpfX9JNPZhiGKUbOQVVRCRdDKVInxhQERGDKTIkVlFRTUwqnKqo/Eu/2EGAKQdTDKXqT+y/z6qweopI2hhMkSFwmB+ReWIoRcbCgIqqYzLhVFVf7Dm/lH4xkDIcqQZVRCQtDKbI0FhFRWQ+GEyRsXGYH1XFZMIpqTGlYIqBlPGVn3Oxw1VWTxFJC4MpMhZWURERkSGxiooeZyl2B8RkqC/ephBMHcpspX6QePgaEFE5BlMkBv7dEZkuVk2R2Pg3SBWZbeVUeTBV/r+mECjpA4MQaTqU2Uq0OdNYPUUkPgYEJCYO8yMyPQwF/pEXYCvKfp2vFImyX6lhBRWVM8twStuX7apCKl2/nMs15GIoJX1SGOpHRMbHYOqRNKWHKPsN8swWZb9Sw4CKyHSYYzAlVgBVner6ZG7BFQMqAswwnKopaBrb6ESdAyY5BlMMpeTH2FVUrJ4iEo85BlNihVBVqa4/DK6ISG7MIZiSYhClq6qOwZRDKwZUZFbhVG2/YJv6UD8GUvIn1jA/IjIecwimpBZE6Upb/005sGL1FJG8mWIwZQpBlC4eP15TC6sYUJk3kw+n6hM06VJFJYcgi6GUaTHmMD9WTxEZlykGU3IPomrL1AMrBlRE8mRKwZS5BVLVqXguTC2oIvMj23CqNsFRxS/Tdf1iXZv1pB5MMZQybZyLisi0mFIwZS6BVE0qngdTCKoYUBHJi9yDKYZRtWMqQRWrp8yXbMMpqWAwRVJh6KF+rJ4iopowjKqZqQRVDKiIyJAYSNWP3IMqBlTmieGUiWIoZZ44FxWRvMmxaoqBVN3JPahiQEUkfXKrmmIopX9yDaoYUJkfS7E7IGdSrZpiMGXeDPn6S/VvnsgUyC2YSlN6MJjSI7meT7n93cpNXFwc/Pz8YGdnh9DQUJw+fbra9jt37kRgYCDs7OzQrl07HDhwQON5QRAwf/58NG3aFPb29ggPD8elS5cMeQgkIjkFU3kBtgymjEBu51lOf8NUfyZbOWXo4UdS/ZJujsFUYYazXrfn6J+n1+2JgRVURPIipy/4cgxQ5ESO1VSsoDKMHTt2IDo6GvHx8QgNDcXy5csRERGB9PR0uLu7V2p/4sQJjB49GosWLcJzzz2Hbdu2YejQoTh79izatm0LAPj000/xxRdfYNOmTfD398d7772HiIgI/PHHH7CzszP2IZIByeVLvZyCElNSft7lUEnFCirzwcopE3Eos5XJB1OFGc5aH8bajyH2ZUim/vdAZCrkEkzJtbJHznjOzdvSpUsxefJkREZGonXr1oiPj4eDgwPWr1+vtf3nn3+O/v37Y/bs2QgKCsIHH3yAp59+GitXrgTwqGpq+fLlmDdvHoYMGYKnnnoKmzdvxs2bN7Fnzx4jHhmR/Cp4TBVfB5IShlN1ILWqKVMMIaQaDEm1X1Uxxb8NIjIuBiTik8NrIJeQVS6Ki4uRkpKC8PBw9TJLS0uEh4cjOTlZ6zrJycka7QEgIiJC3T4jIwNKpVKjjbOzM0JDQ6vcJsmTlKumGIZIk9RfFyn/TZP+mOywPnNhKuGD1EOe6lTsuykMCSQi45HyF3qphyHmKE3pIZuhfqRdfn6+xr9tbW1ha1v5C+GdO3dQVlYGDw/N/w49PDxw4YL2IZRKpVJre6VSqX6+fFlVbYgMRcrBB/1DTsP9yPSYbDhlqNveS6lqSu7BlJwDqapIMajS9/xThvpvi4ikgaGUtJW/PlIMqeQ+99She4GwszDMR+OH90oBZMPb21tjeUxMDGJjYw2yTzJPUqwwYTAlP3kBtpILqDj3lOkz2XDK1Mk1mDLFQKoqUgqqOEE6kfRIsWqKwZR8SDmkoqpdv34dCoVC/W9tVVMA0KRJE1hZWSE7W/P1zc7Ohqenp9Z1PD09q21f/r/Z2dlo2rSpRpvg4GCdj4WoJgyl5I1VVGRssp5zylyrN+QWTMllfiZDMvfjJyJpk8OcRqSd1F47KYauUqJQKDQeVYVTNjY2CAkJQVJSknqZSqVCUlISwsLCtK4TFham0R4AEhMT1e39/f3h6emp0SY/Px+nTp2qcpskL1KqmmIwZTqkPh8V6S4uLg5+fn6ws7NDaGgoTp8+XWXbjRs3wsLCQuOh7e6uaWlpeP755+Hs7IyGDRuiU6dOyMzM1Klfsg6ngEcBlbFCKikM6ZNLMMVASjsxz4lc/naIzIGUvsBLKdiguuPraHqio6OxZs0abNq0CWlpaZg2bRru3buHyMhIAMC4ceMwd+5cdfs33ngDCQkJWLJkCS5cuIDY2Fj88ssviIqKAgBYWFhgxowZWLhwIfbu3Yvff/8d48aNg5eXF4YOHSrGIZKJYpBhmqTwukopgJWrHTt2IDo6GjExMTh79izat2+PiIgI3Lp1q8p1FAoF/vrrL/Xj2rVrGs9fuXIF3bp1Q2BgII4dO4b//e9/eO+997SGWNUxmWF9FQMqKYRIhiCHcIFhVO2Unyexh/sRkXljoGFapDLUT+5zT0nFyJEjcfv2bcyfPx9KpRLBwcFISEhQT2iemZkJS8t/fmfu2rUrtm3bhnnz5uGdd97BE088gT179qBt27bqNm+99Rbu3buHKVOmIDc3F926dUNCQoLOXyBIeqTwpV0K4QUZlhTnoiLdLF26FJMnT1b/0BEfH4/9+/dj/fr1mDNnjtZ1LCwsqhxSDgDvvvsuBg4ciE8//VS9LCAgQOe+yb5yShtDVFOZauClL6ySqhtjnzN9BZz874Go7qRSNcVgynTxtTUdUVFRuHbtGoqKinDq1CmEhoaqnzt27Bg2btyo0f5f//oX0tPTUVRUhHPnzmHgwIEaz1tYWGDBggVQKpV4+PAhDh8+jCeffNIYh0ImjsGU+RD7tZZCECtXxcXFSElJQXh4uHqZpaUlwsPDkZycXOV6hYWF8PX1hbe3N4YMGYLz58+rn1OpVNi/fz+efPJJREREwN3dHaGhodizZ4/O/TPJcMoUSbVqiqFU/fEcEpExSW2OIjIMsV9jqYSwROZA7C/rYocVZHx8zaUnPz9f41FUVLnC7c6dOygrK1NX4Jbz8PCAUqnUut1WrVph/fr1+P7777FlyxaoVCp07doVN248unPirVu3UFhYiI8//hj9+/fHoUOH8MILL+DFF1/Ejz/+qNMxmMywPlMmxWCKYYr+GWuoH+/cRyQesb+wix1YkHGlKT1EH+JHRKaLAYV5E/NufsWBzWFz4YbR91tXewraw05oYJBtPywsAXAI3t7eGstjYmIQGxtb7+2HhYVp3DSja9euCAoKwldffYUPPvgAKpUKADBkyBDMnDkTABAcHIwTJ04gPj4ePXv2rPW+GE7VgphDmBhMmZ/CDGfORUVEesdgyjyJGVBx7ikiwxOraorBFJXjPFTScP36dSgUCvW/td0NtkmTJrCyskJ2tubnguzs7GrnlKqoQYMG6NChAy5fvqzeprW1NVq3bq3RLigoCD/99JNOx8BwSsKkFkwxlDIeQwdU+qie+vpuV6PdKZPIFIhZNcVgyryxgoqI9MlUg6kCXwuj7MfpmmCU/RiTGAGV3KqnDE2hUGiEU9rY2NggJCQESUlJ6ru0qlQqJCUlqe/sWpOysjL8/vvv6jkNbWxs0KlTJ6Sna363vHjxInx9fXU6BoZTVCsMpoyPFVREpA+mGEyV3nQw+D6sve4bfB/GxICKyPSIPdeUnBkriKrtvk0xsCJpio6Oxvjx49GxY0d07twZy5cvx71799R37xs3bhyaNWuGRYsWAQAWLFiALl26oGXLlsjNzcVnn32Ga9euYdKkSeptzp49GyNHjkSPHj3Qu3dvJCQk4IcffsCxY8d06hsnRK+BWEP6pFI1xcm6xWXIcy+VvzFzkpOTgzFjxkChUMDFxQUTJ05EYWFhtes8fPgQ06dPR+PGjeHo6Ihhw4ZVKsXNzMzEoEGD4ODgAHd3d8yePRulpaUabY4dO4ann34atra2aNmyZaU7PAFAXFwc/Pz8YGdnh9DQUJw+fVprnwRBwIABA2BhYVGnO3GQ8cg9mCq96aD1Ida+5U6Mvwex51kjIv2SY9VUga9FpYfUyKGPNRHjb4MBre5GjhyJxYsXY/78+QgODkZqaioSEhLUk6RnZmbir7/+Ure/e/cuJk+ejKCgIAwcOBD5+fk4ceKExjC+F154AfHx8fj000/Rrl07rF27Ft9++y26deumU99YOSVBUggNGEhJhyEnSufk6MY1ZswY/PXXX0hMTERJSQkiIyMxZcoUbNu2rcp1Zs6cif3792Pnzp1wdnZGVFQUXnzxRfz8888AHpXWDho0CJ6enjhx4gT++usvjBs3Dg0aNMBHH30EAMjIyMCgQYMwdepUbN26FUlJSZg0aRKaNm2KiIgIAMCOHTsQHR2N+Ph4hIaGYvny5YiIiEB6ejrc3d01+rR8+XJYWMjvQ5OYxPiCLsdgSuoB0OP9k2N1FSuoiEyDGF/K5RRMyTHceVzFY5BLZRXnn5KHqKioKofxPV7ttGzZMixbtqzGbb7yyit45ZVX6tUvVk5VQ4yqKQZTRKYpLS0NCQkJWLt2LUJDQ9GtWzesWLEC27dvx82bN7Wuk5eXh3Xr1mHp0qV49tlnERISgg0bNuDEiRM4efIkAODQoUP4448/sGXLFgQHB2PAgAH44IMPEBcXh+LiYgBAfHw8/P39sWTJEgQFBSEqKgrDhw/XuNAsXboUkydPRmRkJFq3bo34+Hg4ODhg/fr1Gn1KTU3FkiVLKi0naZFTMCXnyiS5VlYZ+++D1VNE8ieHYErOVUc1kdOxGftvhdVTpoPhFGlgMCVdUnxtxLyTpSHl5+drPIqK6v8LUHJyMlxcXNCxY0f1svDwcFhaWuLUqVNa10lJSUFJSQnCw8PVywIDA+Hj44Pk5GT1dtu1a6cuxQWAiIgI5Ofn4/z58+o2FbdR3qZ8G8XFxUhJSdFoY2lpifDwcHUbALh//z5eeuklxMXF1fqOHmT8L+ZyCKbkGOjUhpyOSw5/J0QkDVIOpuQU2uiLHI5Xyn8zJF0c1ichYldNSTH8IE2GmCRdjkP7tueGwra0gd63W1RYAmA3vL29NZbHxMQgNja2XttWKpWVhsdZW1vD1dUVSqWyynVsbGzg4uKisdzDw0O9jlKp1Aimyp8vf666Nvn5+Xjw4AHu3r2LsrIyrW0uXPjnNvAzZ85E165dMWTIkFoeNRmblAMHOQQ2+lR+vFIe+schfkRUE6mGDFIPZ4yh/BxIdcgfh/iRrhhOEQAGU3LCu/gZ3vXr1zVuxWprW/UHszlz5uCTTz6pdntpaWl665tY9u7diyNHjuDXX38VuytUBakGU+YWSj1ODiGVMRy5E4hnm1youSER1chYw5ikGEwxlKpMjnNTEWnDcKoKxh6uJFbVFEMpedJ3QCXH6ilDUigUGuFUdWbNmoUJEyZU26ZFixbw9PTErVu3NJaXlpYiJyenyiFynp6eKC4uRm5urkb1VHZ2tnodT0/PSnfVK7+bX8U2j9/hLzs7GwqFAvb29rCysoKVlZXWNuXbOHLkCK5cuVKpimvYsGHo3r27zreKJf2SYjBl7qHU46QaUrF6ioi0kVowxVCqdqRWTWWs6qniwOawuXDD4Pshw2I4ZcZMPZhyulL7KdUKAlQG7IlhsIJKGtzc3ODm5lZju7CwMOTm5iIlJQUhISEAHgU+KpUKoaGhWtcJCQlBgwYNkJSUhGHDhgEA0tPTkZmZibCwMPV2P/zwQ9y6dUs9bDAxMREKhUJ9i9ewsDAcOHBAY9uJiYnqbdjY2CAkJARJSUkYOnQoAEClUiEpKUl9J485c+Zg0qRJGtto164dli1bhsGDB9d4/ObKHCeCZihVPSmGVAyoiEiqGErVjZRCKg7vo9piOCUBYs81JWe6BFB12Y4cQytj+/puV4xtdELsbkheUFAQ+vfvj8mTJyM+Ph4lJSWIiorCqFGj4OXlBQDIyspCnz59sHnzZnTu3BnOzs6YOHEioqOj4erqCoVCgddffx1hYWHo0qULAKBfv35o3bo1xo4di08//RRKpRLz5s3D9OnT1cMRp06dipUrV+Ktt97CK6+8giNHjuCbb77B/v371f2Ljo7G+PHj0bFjR3Tu3BnLly/HvXv3EBkZCeBR9ZW2Ci8fHx/4+/sb+vRRNaRSNcVQSjdSC6kYUBHJgzGG9EmhaoqhlH4U+FowoCLZYDhlpuRaNaWvMKqu+5NaWKXP6ikO7TO8rVu3IioqCn369IGlpSWGDRuGL774Qv18SUkJ0tPTcf/+P19Wly1bpm5bVFSEiIgIrFq1Sv28lZUV9u3bh2nTpiEsLAwNGzbE+PHjsWDBAnUbf39/7N+/HzNnzsTnn3+O5s2bY+3atYiIiFC3GTlyJG7fvo358+dDqVQiODgYCQkJlSZJJ2mRQjDFUKp+pBZSGRLnnSKSPgZTpkcqVVSGDqg4tE/+GE5pYcz5psSompJTMGXsMKomFfsjlaCKw/vkw9XVFdu2bavyeT8/PwiC5gcHOzs7xMXFIS4ursr1fH19Kw3be1yvXr1qnMw8KipKPYyvNh7vK2ky9JA+BlOmRQohFauniEhMDKUMSypVVERVkdY3fzI4uQRTTlcsJRdMPa68j1LvJxGRvpXedGAwZSBin1cphJ5EJA4xq6YYTBlHga+FqOdaCpV5JF38Vi0iY1dNST2YknPYI3a/pf7aEpF+iRkgiB2emANTPsfmeJMAIn0x5HxTDKbMC885SZH8UgCqEymHF2IHO/ok92Ph5PxEVB1TDk2kRszqNFZPEZExiF3FY+7EOv+GDEKNccMAMhz5fouWOWOGAFINpuQe5FRHjGOT6utMZI4MWR0iVnDAYEocPO9EZGhiVE0xlJIOvhYkFaaZDNSDMSdDN1emHEo9zlyOk4hMF+eXEp8Y55/VU0RkKAxDpMfYrwnnniJt+M1ZBOZaNWVOoVRFxjxuKb3eRKR/xg4MGEpJhym9Fpx3ikh3hhquZOyQgMGUdJnKa8OhffJlfkmBGZFSUGGOodTj5HIOOO8UUf2YyhdvUwpDTIWxq9hYPUVE+mQq4YcpM+ZrxOopepw8vi2bEGN98ZdKMGWu1VJVMca5kMprT0T6ZcyggMGUtPH1ISJ9MGY4wGBKPvhakViYGpDBMJTSzhiBnbEDKs7VRmQ6GHzIg7FeJ0OFoqZSYUhkDHIfpsSwQ36M9ZqxeooqYnpQgal8wRa7cobVUrXDc0REtWWsqikGU/LC14uIpI7BlHzxtSNjsxa7A+bEGEP6pBBMUe05XbFEQYBK7G5UciizFfr5pIvdDSIyIgYd8lR60wHWXvcNuo80pQeCPLMNug8iMi5jVKyYQrhR5FNc53VtM2302BNxFPhawOmaYNB95AXYwvlKkV63WRzYHDYXbuh1m2R4DKdIbxhM1Y2hAqrCDGc4+ufpfbtEVDVDDFUyRtWUKQRTDjfqdg2631x6PxAQEcmd3IKp+oRQddmmnIIrYwRURADDKZMiZtUUg6n6kWoFFRGZPrkFU3UNoeqyPbkEV6yeIiJdGLpqSg7BlCHCqLruXw5BlaEDKkNUT5H8MJwyEmPdpU8MDKaki9VTRFQdOQRT+g6j6rpvqQdVxgioiIhqIuVgSuxAqiqP90sOYRWRITBVMBFiVU0xmNIfnksiepwhh/RJOZhyuGGpfkiFFPv0OEO/pvr+e+Qd+4hqJqc79Uk1mCryKZZsMKWNVPtr6NeXd+4j6X7CIsljmKJ/PKdEZM6kHv6Uk3JQJeXQkYjEZy4BQHnAI8WQp7akeAxSDSC1kVOwS49I71OVSL6+21XsLtSZGFVTDFHko65/H6Y8FJVIDsylakrKQU9tSLHvhnx9jTFBPxHJj1RCC6mFOfoipeMy5GttLuEpaVenT1NxcXHw8/ODnZ0dQkNDcfr06WrbL1++HK1atYK9vT28vb0xc+ZMPHz4sE4dliNT+5LPYMqweH7JnMn5+iKXIUpSCaakGOrUh6kdD5EpkfO1RZ8M9cVfCsGUlMIbQzKX4yTzpPOnqB07diA6OhoxMTE4e/Ys2rdvj4iICNy6dUtr+23btmHOnDmIiYlBWloa1q1bhx07duCdd96pd+fJ+FVTDE6MQ47nWc7VhyQNvL6YB1MPcaRyfFIJIYnExmuL6TPHsEbsY5ZCIEmmR+dPT0uXLsXkyZMRGRmJ1q1bIz4+Hg4ODli/fr3W9idOnMAzzzyDl156CX5+fujXrx9Gjx5d4y8WJD1yDEzkTJ/nW6wJ84l0wevLPww1dErswEIKoY2xSCGkMtTrrc+/T7lUHJJ88dpiWGKGFOZeRST28RvqtefQPvOl06em4uJipKSkIDw8/J8NWFoiPDwcycnJWtfp2rUrUlJS1G/of/75Jw4cOICBAwfWo9vyYcghfcYMHBhMEZEh8fpieGIGU1IIasRizsdOJDa5X1v0OaGzIb7wix1M0SM8F2QqrHVpfOfOHZSVlcHDQ/MXMw8PD1y4cEHrOi+99BLu3LmDbt26QRAElJaWYurUqdWWxhYVFaGoqEj97/z8fF26SWQynK5YoiBAJdr+D2W2Qj+fdNH2T+bDGNcXuVxbDFE1JXYwRY/Ow/3mxn8/L73pAGuv+0bfL5EU8LuL6WEQo135ebHNtDHqfgt8LeB0TTDqPnVRHNgcNhduiN0NqiWDf2I8duwYPvroI6xatQpnz57Fd999h/379+ODDz6ocp1FixbB2dlZ/fD29jZ0N2WHVVPmQ1/nn0P7yNToen0x5LWFQ5MqY8VQZaZ0PnjXPjJVpvjdxVSqphhM1YzniORMp09JTZo0gZWVFbKzszWWZ2dnw9PTU+s67733HsaOHYtJkyahXbt2eOGFF/DRRx9h0aJFUKm0/4I4d+5c5OXlqR/Xr1/XpZs6M9REzqZwlz4GU0RkDMa4vhj72iIVYlRNmVIIo29ihHZizzVGJBZT/e5ibsSeW0lujH2+DBFUct4p86TTpyMbGxuEhIQgKSlJvUylUiEpKQlhYWFa17l//z4sLTV3Y2VlBQAQBO0lgLa2tlAoFBoPMj45B1ON0ovr9JAqOb8WRLVhjOuLHK4tcq9GYbVU7TGgIjI8fncxDGNWTTGUqju5B1RkfnSacwoAoqOjMX78eHTs2BGdO3fG8uXLce/ePURGRgIAxo0bh2bNmmHRokUAgMGDB2Pp0qXo0KEDQkNDcfnyZbz33nsYPHiw+o2edMPhWZr0GSo9vq27rYw7brs6+ph/qjDDGY7+eXrqEZF+8fqif8YMJBhK6U6seaj0JU3pgSDP7Job1uDInUA820T7/D9E9SXXa4u+JkOXcwUKg6n6K/IpNvo8VPqSF2AL5ytFNTckk6FzODVy5Ejcvn0b8+fPh1KpRHBwMBISEtQTDWZmZmr82jBv3jxYWFhg3rx5yMrKgpubGwYPHowPP/xQf0dBeieHSh1jVDpV3IeUgipj4qToZCy8vsgXg6m6M2ZAxcnRyRzx2qJfxqqQYTClP8YKqKQ+OTpJn87hFABERUUhKipK63PHjh3T3IG1NWJiYhATE1OXXcmWnOebknowJdbwu0bpxWYbUBEZizlfX/Q9pM9YVVMMpuqv/BzKuYqKSMrM+doiRwym9E/OFVT1xTv2yQc/UcqMOQ/pk8K8UGL3QerBIZE5Muc79TGY0i9jnE99h5ZynyfNFOTk5GDMmDFQKBRwcXHBxIkTUVhYWO06Dx8+xPTp09G4cWM4Ojpi2LBhlSYNP3PmDPr06QMXFxc0atQIERER+O233wx5KKRn+h7SZ4yqKQZThmOMc8u5p6g++KmSNEgx/BA7ENJGin2qLXMOOInMhTGqphhMGYYcAyoS15gxY3D+/HkkJiZi3759OH78OKZMmVLtOjNnzsQPP/yAnTt34scff8TNmzfx4osvqp8vLCxE//794ePjg1OnTuGnn36Ck5MTIiIiUFJSYuhDIjPFYMrw5BZQyXnONNJdnYb1ERmDHMKf8j4ac7ifPiZGJyJpkVv1CYMpw5L7ROlkPGlpaUhISMCZM2fQsWNHAMCKFSswcOBALF68GF5eXpXWycvLw7p167Bt2zY8++yzAIANGzYgKCgIJ0+eRJcuXXDhwgXk5ORgwYIF8Pb2BgDExMTgqaeewrVr19CyZUvjHSRJgqErYhhMGY85D/EjaeOnSxkxdMWLlKqm5BBMVSS3/hKR6TJ0VQyDKaK6y8/P13gUFdXvTlTJyclwcXFRB1MAEB4eDktLS5w6dUrrOikpKSgpKUF4eLh6WWBgIHx8fJCcnAwAaNWqFRo3box169ahuLgYDx48wLp16xAUFAQ/P7969ZnocQymjI/nnKSIlVMkOXINekx5wvTa3rHv67tdMbbRCSP0iIjEwGDKeAxdPaXPO/elKT0Q5Jldc0OJ+/HvJ9HgoWGu4yX3igH8qK5CKhcTE4PY2Ng6b1epVMLd3V1jmbW1NVxdXaFUKqtcx8bGBi4uLhrLPTw81Os4OTnh2LFjGDp0KD744AMAwBNPPIGDBw/C2ppfHwytOLB5vbehz+FQhqyaYkhimnjnPqoLs/+U+fXdrnrfphzv1CeFqik5z+NUzlj9r+/rxXmniEyTIaumGEwZnzmdc3O5scD169eRl5enfsydO1druzlz5sDCwqLax4ULFwzWzwcPHmDixIl45plncPLkSfz8889o27YtBg0ahAcPHhhsv0RkPHIJBjnvlPngTx8yYephgtxDqYpMuYKKiPRPbvNNkXEZsoJKn9VTVDsKhQIKhaLGdrNmzcKECROqbdOiRQt4enri1q1bGstLS0uRk5MDT09Pret5enqiuLgYubm5GtVT2dnZ6nW2bduGq1evIjk5GZaWlupljRo1wvfff49Ro0bVeAxkGlg1ZdoMOf8Uq6dIVwynSPSqKVMKpsoZI6DixOhEVBGrpkyXHCZIN5WhfVLh5uYGNze3GtuFhYUhNzcXKSkpCAkJAQAcOXIEKpUKoaGhWtcJCQlBgwYNkJSUhGHDhgEA0tPTkZmZibCwMADA/fv3YWlpCQuLf4KJ8n+rVNL+WyR5VJowmJIOc5ggvTiwOWwu3BC7G1QDhlMkKlMMpsqxgorI9JnDUCQGU6aN1VPyFhQUhP79+2Py5MmIj49HSUkJoqKiMGrUKPWd+rKystCnTx9s3rwZnTt3hrOzMyZOnIjo6Gi4urpCoVDg9ddfR1hYGLp06QIA6Nu3L2bPno3p06fj9ddfh0qlwscffwxra2v07t1bzEMmIzJU1ZTcgim/5rfrtN7VGzUHzKZOX9VTeQG2cL5SvxtIkPQxnJIBQw7pE7NqypSDqXIMqIioOvoa0meoqik5BVOOWXWr5ihsJo9jlEP1FIlj69atiIqKQp8+fWBpaYlhw4bhiy++UD9fUlKC9PR03L//Twi5bNkydduioiJERERg1apV6ucDAwPxww8/4P3330dYWBgsLS3RoUMHJCQkoGnTpkY9PiJjqmsQVdttSTWwMofqKZI+hlN6JsfJ0MVgDsFUOUMGVPUZ2leY4QxH/zw994iIyPDqGkTVdltSDawYUJE2rq6u2LZtW5XP+/n5QRA0Kxfs7OwQFxeHuLi4Ktfr27cv+vbtq7d+kryYS9WUPsMoXfcntaCKARWJTZqfvsgoxKqaMqdgqpw5HjMRyZvUqqYcs1TqhyntS1eGeF30VXnHyf2JqDpSCqb8mt82ejBVVR/E7kdFhniNDDmpPpkWaX3ypEpM7S595hzSGOrYjRUysiqQSJoMMaRPSsGU2CGR2PsnItKFVCdDl0owJbUwqJxU+0VkTNL59ElGJUbVlDkHU0REciGFYEqKlUtS6pOUq6fqwxxuMEBUk+LA5mJ3wSQrXeQS/kihn1KtnpJq8Er6I/4nUDILDKYe4XkgonL6GAIlhUBB36QQ/tRECiGVFEJEIqLaELNqSgphT12I3W+pVLqRYcTFxcHPzw92dnYIDQ3F6dOna7Xe9u3bYWFhgaFDh2osLywsRFRUFJo3bw57e3u0bt0a8fHxOveLn2zI4BjIaDLE+ahrJZypDRslovoRM/CQQuCjK7n1l4jI2MQOpuTOFI6BpGXHjh2Ijo5GTEwMzp49i/bt2yMiIgK3bt2qdr2rV6/izTffRPfu3Ss9Fx0djYSEBGzZsgVpaWmYMWMGoqKisHfvXp36Ztbh1Nd3u+p1e/qek8dQwYFYE6ETERFpI+eQR8xQTd9hoilW4hGRbkxhSJ/YVUf6Jtbx6DtYNIW/LVOwdOlSTJ48GZGRkeoKJwcHB6xfv77KdcrKyjBmzBi8//77aNGiRaXnT5w4gfHjx6NXr17w8/PDlClT0L59+1pXZJVjSkEGxaop7XheiEhqxKiakmO1VFVMJaCqL96xj0g8UpuTR4yqKVMKpR5nCgEVGU5+fr7Go6ioqFKb4uJipKSkIDw8XL3M0tIS4eHhSE5OrnLbCxYsgLu7OyZOnKj1+a5du2Lv3r3IysqCIAg4evQoLl68iH79+ul0DNY6tSbSAQOY6jVKL8bdVjZid4OIZEruVS6mEkpVVH5Mhc2kFRgREZk6Uw6lKvJrfhtXb7iJ3Q1ZKg5sDpsLN0TZ99HsJ2FdaJjwuPReEYBD8Pb21lgeExOD2NhYjWV37txBWVkZPDw0f0jy8PDAhQsXtG7/p59+wrp165CamlplH1asWIEpU6agefPmsLa2hqWlJdasWYMePXrodCwMp8wMh/SZLqcrligIML0ve0RSZUp3FjNm9Y0phlKPc8xSGTWgcrhhifvN9XNeS286wNrrvl62RUTyos9hV8asuDGXYKpc+fEaK6Qq8imGbaZ+flAv8LWA0zWhzuvnBdjC+UrliiACrl+/DoVCof63rW39w7CCggKMHTsWa9asQZMmTapst2LFCpw8eRJ79+6Fr68vjh8/junTp8PLy0ujSqsmDKfIIFg1VTusniIyT+Y69Mkcgqlyxg6oiIjMkbkFU0RVUSgUGuGUNk2aNIGVlRWys7M1lmdnZ8PT07NS+ytXruDq1asYPHiweplK9eiznLW1NdLT0+Hl5YV33nkHu3fvxqBBgwAATz31FFJTU7F48WKdwil+apIo3kWNjIV/a0Tyo88hfcaqmjKnYKqcMY9Zn6+j3IeMEpG4jFU1Ze7BlDGPn3NPmQYbGxuEhIQgKSlJvUylUiEpKQlhYWGV2gcGBuL3339Hamqq+vH888+jd+/eSE1Nhbe3N0pKSlBSUgJLS83PIVZWVuogq7ZYOWVGjDWkj1VTupFT9dShzFbo55MudjeISGbMMZgqxwoqIpIDud1JzdyDqXJynIOqvkP7qH6io6Mxfvx4dOzYEZ07d8by5ctx7949REZGAgDGjRuHZs2aYdGiRbCzs0Pbtm011ndxcQEA9XIbGxv07NkTs2fPhr29PXx9ffHjjz9i8+bNWLp0qU59YzhFZEI47xQR6cIYVVPmHEyVM1ZApc+5p+ojTemBIM/smhsSkd5I5U59xqiwYTClyVgBlT7nniLxjBw5Erdv38b8+fOhVCoRHByMhIQE9STpmZmZlaqgarJ9+3bMnTsXY8aMQU5ODnx9ffHhhx9i6tSpOm2H4ZSeHMpsJXYXJIFVU3Ujp+opIhKXnIZcMZj6ByuoiIjqj8GUdnKsoKorTopef1FRUYiKitL63LFjx6pdd+PGjZWWeXp6YsOGDfXuFz8lmQnepY+ISBqkMhm6oaumGExVZoxzoq/XVawQ1JTugkkkF/oa0mfoqikGU9Xza37b4OeIc0+RITGxkCC5TlDNqqn6MZXz9/XdrmJ3gYhExmCqajw3RFSV4sDmYndBshhM1Z4czpXc5jgj42A4RWRi6lIlJ9dAlMjc6KuaxZBVUwxfamboc2SsOzASEZUzZEWNHMIWqeE5IznipxfSC1Op+iEiorpjMFV7pn6upDJ8lcgcSGUydENgyCI9ch7ax+pEaWM4ZQYMPd8Ugyn94bkkIkNjVY10SD2gktPk+0RUN/oYXiXnsMKUST3Yq8/fnikHsuaMn1CJiIh0VNdJm+tTTSL1oEDqQYtUGeq8MYQkIrmTergiB4Y6hwwkyRD4yYXqhZU++qePc8q7M0pTTk4OxowZA4VCARcXF0ycOBGFhYXVrvPw4UNMnz4djRs3hqOjI4YNG4bs7GyNNpmZmRg0aBAcHBzg7u6O2bNno7S0VKPNsWPH8PTTT8PW1hYtW7bUehvYuLg4+Pn5wc7ODqGhoTh9+rTG80qlEmPHjoWnpycaNmyIp59+Gt9++23dTgaJwlCBBYMpIiLSJwZTROaH32AlRt8TUzOkoNripOiGN2bMGJw/fx6JiYnYt28fjh8/jilTplS7zsyZM/HDDz9g586d+PHHH3Hz5k28+OKL6ufLysowaNAgFBcX48SJE9i0aRM2btyI+fPnq9tkZGRg0KBB6N27N1JTUzFjxgxMmjQJBw8eVLfZsWMHoqOjERMTg7Nnz6J9+/aIiIjArVu31G3GjRuH9PR07N27F7///jtefPFFjBgxAr/++qsezxLJDYOp+pNy9ZTUK/aIqO44pM88sHqK5ILJBRGREaSlpSEhIQFr165FaGgounXrhhUrVmD79u24efOm1nXy8vKwbt06LF26FM8++yxCQkKwYcMGnDhxAidPngQAHDp0CH/88Qe2bNmC4OBgDBgwAB988AHi4uJQXPzoQ0N8fDz8/f2xZMkSBAUFISoqCsOHD8eyZcvU+1q6dCkmT56MyMhItG7dGvHx8XBwcMD69evVbU6cOIHXX38dnTt3RosWLTBv3jy4uLggJSXFgGeO9IXDvKTNFEM+TopORHXBqin9k+o51UdASqaDn1T14FBmK7G7IAoO6TMcnlvTk5ycDBcXF3Ts2FG9LDw8HJaWljh16pTWdVJSUlBSUoLw8HD1ssDAQPj4+CA5OVm93Xbt2sHD458vgREREcjPz8f58+fVbSpuo7xN+TaKi4uRkpKi0cbS0hLh4eHqNgDQtWtX7NixAzk5OVCpVNi+fTsePnyIXr161fGskNyZYqBCRCQnYk4MbYjKGamGKERkeGYbTn19t6vYXSAyKA7prLv8/HyNR1FRUb23qVQq4e7urrHM2toarq6uUCqVVa5jY2MDFxcXjeUeHh7qdZRKpUYwVf58+XPVtcnPz8eDBw9w584dlJWVaW1TsW/ffPMNSkpK0LhxY9ja2uLVV1/F7t270bJly1qeBaorKQ6tYjClf4Y4p6yYIyIiwDDBn5hD+3jHPtNjLXYHiEheDmW2Qj+fdFH7cOTGE7By0P8Fqez+oxDK29tbY3lMTAxiY2O1rjNnzhx88skn1W43LS1NL/0T23vvvYfc3FwcPnwYTZo0wZ49ezBixAj897//Rbt27cTuHlWDAYV8OGapUNiMrxeRuSkObC52F0THqinD82t+G1dvuIndDSKtGE6ZMENWzshx2Jn9uawqn3vQtpkRe1I7jdKLcbeVjdjdMEvXr1+HQqFQ/9vWtuogbNasWZgwYUK122vRogU8PT01JhcHgNLSUuTk5MDT01Prep6eniguLkZubq5G9VR2drZ6HU9Pz0p31Su/m1/FNo/f4S87OxsKhQL29vawsrKClZWV1jbl27hy5QpWrlyJc+fOoU2bNgCA9u3b47///S/i4uIQHx9f7Tkg05p/h1VT8uJwwxL3m9f9NSu96QBrr/t67BERia2+c/3ou2KGwRQRMZwik1ZdIKWtnRRDKjI+hUKhEU5Vx83NDW5uNf8CFRYWhtzcXKSkpCAkJAQAcOTIEahUKoSGhmpdJyQkBA0aNEBSUhKGDRsGAEhPT0dmZibCwsLU2/3www9x69Yt9bDBxMREKBQKtG7dWt3mwIEDGttOTExUb8PGxgYhISFISkrC0KFDAQAqlQpJSUmIiooCANy//+iLqaWlZuhtZWUFlYpBBZE+sXqKiEia+npeqPb5RGWgkXpSd/quniryKYZtZt1/UC/wtYDTNUFv/SH5YjhFJqm2oVRV6zGkIn0LCgpC//79MXnyZMTHx6OkpARRUVEYNWoUvLy8AABZWVno06cPNm/ejM6dO8PZ2RkTJ05EdHQ0XF1doVAo8PrrryMsLAxdunQBAPTr1w+tW7fG2LFj8emnn0KpVGLevHmYPn26uuJr6tSpWLlyJd566y288sorOHLkCL755hvs379f3b/o6GiMHz8eHTt2ROfOnbF8+XLcu3cPkZGRAB5NxN6yZUu8+uqrWLx4MRo3bow9e/YgMTER+/btM/LZNC/1nW9K30P65FA15ZTxoNrnC/ztjdQTqosjdwLxbJPqvwASkemQctVUTWFUTe3lEFYRSQXDKQkpzHAWuwu1IuUhfXUNparbjpyDKqcrligIkP4XSXOxdetWREVFoU+fPrC0tMSwYcPwxRdfqJ8vKSlBenq6ukoJAJYtW6ZuW1RUhIiICKxatUr9vJWVFfbt24dp06YhLCwMDRs2xPjx47FgwQJ1G39/f+zfvx8zZ87E559/jubNm2Pt2rWIiIhQtxk5ciRu376N+fPnQ6lUIjg4GAkJCepJ0hs0aIADBw5gzpw5GDx4MAoLC9GyZUts2rQJAwcONORpIwmRajBVUxhVU3sphlX6rp6q79C+ukpTeiDIM7vmhkQkG2JOgm1ouoZRumxPSkEV554iKWI4RSZBX6FUVdsWK6Ay9rxThRnOcPTPM9r+zI2rqyu2bdtW5fN+fn4QBM2yZjs7O8TFxSEuLq7K9Xx9fSsN23tcr1698Ouvv1bbJioqSj2MT5snnngC3377bbXbIDIWXQOp2m5LSkGVlIb3cd4pIukxhbuVSaVqSt+hVE37kEJQpc+Aqr5D+4gAQBqfeEjvDDkZutQYMpgy5j6IiPRNn0P6pFI15ZTxQK/BVFXbN+Q+iIjEVN/J0E1JX88LRgmmtO2X/sG/SQIYTpGOpDakz5ihEQMqIgIezYdDxidGYCSFgEqfoaC+5x8jIvOjzyF9YldNiR0QiRWMVST2a1BfplA9SP/gpxSSLTHCIjH2KbVAkIiMp76ToeuL2FVTYoZErKIiIjItUgiFKpJaf+pKLnORFQc2F7sLVAWGUyRLYlYxya2CypyGeBJJVZrSw+j7NIUqGSkFQ2L2RexwkIhI38So2JF6CCRW3+RePUWmQ/6fXMnsSCEckkIfiIiMQYxgREqh1OOk2i9jkEolHxHVnbnO7SPlUKoiqQdoRIbEcMoEGapSRgrDy6QUCkmpL0REpkIO4Y8Y4Zm+QkJTqKgjInHoa9iWsSt15Bj2yLHPRPXFTyj1dCizldhdMBtSDIOM1ScpBINEZH6MXTUlh2CqIrn1VyxiDGslIgLkHfIYs+/6CgzrE2Caa1Uf/YPhlEQUZjiL3QUiIpKQ+gyhkmN1jFyDHrn2m4jkTa53KTNm1ZScg6lypnAMRLUlv0+vZJakWDVVTsp9qwsGpURkbAx4aocToxMR1Y4phTrGOhZOjE5iYzhFtcJhZfKm73nIOJyVyPQZKwgxhWBKbscgx8o6IhKXvuabMgZTCqbKmeIxET2On05I8uRQmWSMPjIgJCJTI7dQpzrGOhaxq6eMdce+I3cCjbIfIjIeY1TmMMSpH328RsYOMuU6xJUqYzhFkiaHYIqISEr0URVjjADElIKpcqZ4TERkOkx9wmlTD6ZM/fiIGE4R6QmDNCLShncqq8yUQxxTPjaSppycHIwZMwYKhQIuLi6YOHEiCgsLq11n9erV6NWrFxQKBSwsLJCbm6u13f79+xEaGgp7e3s0atQIQ4cO1f8BkFkwdNWUuQQ35nKcZJ4YTpFkMewhIiI5MnRApY/KNs47ZTrGjBmD8+fPIzExEfv27cPx48cxZcqUate5f/8++vfvj3feeafKNt9++y3Gjh2LyMhI/Pbbb/j555/x0ksv6bv7JANymm/KHBg6oBJzYnRTr+6j6lmL3QHSL31PfE3S0ii9GHdb2YjdDSIyMGPN66ONoYf0mUtlkVPGAxT424vdDTJxaWlpSEhIwJkzZ9CxY0cAwIoVKzBw4EAsXrwYXl5eWtebMWMGAODYsWNany8tLcUbb7yBzz77DBMnTlQvb926tV77T6QP5lhN1NfzAhKV0p2br8inGLaZ/M5CumGSQTXiRNy1J+VqLwaXRKZP6tUw5hJMGYPYE6OTNCQnJ8PFxUUdTAFAeHg4LC0tcerUqTpv9+zZs8jKyoKlpSU6dOiApk2bYsCAATh37pw+uk1mxpCVOOYYTBGZKml/iiWzJeWQh4iIdGeOwZQ5HnNVOPfaI/n5+RqPoqKiem1PqVTC3d1dY5m1tTVcXV2hVCrrvN0///wTABAbG4t58+Zh3759aNSoEXr16oWcnJx69ZmI9MOQwZyYQ/vIfHFYH5Ge2Z/LwoO2zcTuBhGRzliNo39SHt7ncMMS95vr/pqX3nSAtdd9A/RIXOnZbrBysDPItsvuPwQAeHt7ayyPiYlBbGxspfZz5szBJ598Uu0209LS9Na/x6lUj/4u3n33XQwbNgwAsGHDBjRv3hw7d+7Eq6++arB9m6PiwOYG30dd5/KR8nxTrJqS/vA+Il0wnCLJYdVU9TjvFBHJDSuIDMMxS4XCZiyCl5Pr169DoVCo/21ra6u13axZszBhwoRqt9WiRQt4enri1q1bGstLS0uRk5MDT0/POvezadOmADTnmLK1tUWLFi2QmZlZ5+2SfuQFaP+7kSJDVeAwmJI+zjtFumI4RWQArJ4iImOT+nxT5kzK1VNkXAqFQiOcqoqbmxvc3NxqbBcWFobc3FykpKQgJCQEAHDkyBGoVCqEhobWuZ8hISGwtbVFeno6unXrBgAoKSnB1atX4evrW+ftEpH+Gap6yq/5bVy9UfP7kL4V+FrA6Zpg0H0UBzaHzYUbBt0H6Y6fZKlanAydiMi4xLpTn6GG9LFq6hGeBzKEoKAg9O/fH5MnT8bp06fx888/IyoqCqNGjVLfqS8rKwuBgYE4ffq0ej2lUonU1FRcvnwZAPD7778jNTVVPZ+UQqHA1KlTERMTg0OHDiE9PR3Tpk0DAPzrX/8y8lESaWLVVGXmfk7kVE1IVWM4RZLCIX3SUJjhLHYXiMgEMJAxvPqGiqy4k7+tW7ciMDAQffr0wcCBA9GtWzesXr1a/XxJSQnS09Nx//4/84TFx8ejQ4cOmDx5MgCgR48e6NChA/bu3atu89lnn2HUqFEYO3YsOnXqhGvXruHIkSNo1KiR8Q6ORFXf+aY4qTYR6YLD+ogMxJBD+zjvFJE88A5lVBGH95EhuLq6Ytu2bVU+7+fnB0HQHCITGxurdSL2iho0aIDFixdj8eLF+ugmkV6Ye4VQdQwxvE+soX1knvhzmQSwSuURVk0ZntMV/idPVB9H7pjmHXEMMaSPVVOmTazhp0Sku7reqU9qGEzJj5Tv9kjSw2+qREREMsehWfLB0I6IiAyFAR7JGT/NEhkQq8GIyBwxgDEuQ01mT0RUV/qeb4qhi3kxlWo/0g3DKRPCIVskFV/f7Sp2F4hkiUOlzAPDOyIiMhR9B3mc2J6MhWkGERGRGdN31Q2DF/nhsFAiepyU5gpi1RSReeCnEapSo3TjXZRMefiboY7NmK8PERHpl75DPDkM7ePdK4nMAyttxMdAj+SI4RQRERHpBaumiIiITE99AkcpVeGRtDGcIiIikrH6DMmSQ7WNKWOYR0RUPVYAEZkPhlNERERUbwxazA8n8CcyvrwAW7G7QDLBYI/khuEUERERkQlgJRwRPa7A18Ko+9PnfFMMV4jMC8MpEp0pT4ZezhyOkYiIdCeVijPesY+IynGOINNhTgEfqwprLy4uDn5+frCzs0NoaChOnz5dq/W2b98OCwsLDB06VGO5IAiYP38+mjZtCnt7e4SHh+PSpUs694ufRIhkrC537HO6wv/siUi/pBKwEBERkWEY+y6Mxq76Mxc7duxAdHQ0YmJicPbsWbRv3x4RERG4detWtetdvXoVb775Jrp3717puU8//RRffPEF4uPjcerUKTRs2BARERF4+PChTn3jt1QiIiIzxCFg0sFwj4hIkzlV/JgDVuNJx9KlSzF58mRERkaidevWiI+Ph4ODA9avX1/lOmVlZRgzZgzef/99tGjRQuM5QRCwfPlyzJs3D0OGDMFTTz2FzZs34+bNm9izZ49OfWM4RUR1ciizldhdIJK0NKWHTu3rMrk0h2IRERFRdRj0mYf8/HyNR1FRUaU2xcXFSElJQXh4uHqZpaUlwsPDkZycXOW2FyxYAHd3d0ycOLHScxkZGVAqlRrbdHZ2RmhoaLXb1MZap9ZEVGf257LwoG0zsbtBRKRXrPqRFscsFQqbMbQkIuMz9rAvIrnIvNkElvZ2Btm26sGjoXPe3t4ay2NiYhAbG6ux7M6dOygrK4OHh+YPqB4eHrhwQXuI+dNPP2HdunVITU3V+rxSqVRv4/Ftlj9XWwynSFScKFy6CjOc4eifJ3Y3iIjMglPGAxT424vdDUk5cicQzzbhL/5E5kaKlT7DFWdr3XZX/tMG7Im4/JrfxtUbbmJ3g7S4fv06FAqF+t+2tvWfIL6goABjx47FmjVr0KRJk3pvryYMp4hkrlF6Me62shG7G0QkI/qab0qKVVPWV2r3o0dpACtZH+dwwxL3m3MuMiJzxrmBKtMlmKrYXkohVV/PC0hUBordDTIghUKhEU5p06RJE1hZWSE7O1tjeXZ2Njw9PSu1v3LlCq5evYrBgwerl6lUjz4nWFtbIz09Xb1ednY2mjZtqrHN4OBgnY6Bdd9ERsRKMSIiw7C+klXrYKou7Um7usyVRkQkB8MVZ3UOpvS5PpG+2djYICQkBElJSeplKpUKSUlJCAsLq9Q+MDAQv//+O1JTU9WP559/Hr1790Zqaiq8vb3h7+8PT09PjW3m5+fj1KlTWrdZHVZOERERkazVJ2QqX1cKlVQc2kdkfooDm4vdBUmRwpA+fQdKwxVnJVVFReYtOjoa48ePR8eOHdG5c2csX74c9+7dQ2RkJABg3LhxaNasGRYtWgQ7Ozu0bdtWY30XFxcA0Fg+Y8YMLFy4EE888QT8/f3x3nvvwcvLC0OHDtWpbwyniIiISJb0WflkfSVLEgGVPnBSdCICgAJfC6Pty1QmQzdUpZMUhvqJPbSvyKcYtpmcikRsI0eOxO3btzF//nwolUoEBwcjISFBPaF5ZmYmLC11+wzx1ltv4d69e5gyZQpyc3PRrVs3JCQkwM5Ot0ngGU4RERHJkMMNccMHseebMsSQPClVURERkXEZYwieKVRRcVJ0+YuKikJUVJTW544dO1btuhs3bqy0zMLCAgsWLMCCBQvq1S/+rEZkZJx3ioiofgw9VxTnojKcNKVHzY2IiIzMmHNDcR4qIu3qFE7FxcXBz88PdnZ2CA0NxenTp6ttn5ubi+nTp6Np06awtbXFk08+iQMHDtSpw0REZLp4fTE8fd2pTyzGCo7ECqjErkgjMkW8tsiDWPNNiREWMaCqHWMOTSXx6Tysb8eOHYiOjkZ8fDxCQ0OxfPlyREREID09He7u7pXaFxcXo2/fvnB3d8euXbvQrFkzXLt2TT2RFhEREcDrC9WMFU1EpCtTurbkBdiK3QWTY24hkdjzThFVR+dwaunSpZg8ebJ6Nvf4+Hjs378f69evx5w5cyq1X79+PXJycnDixAk0aNAAAODn51e/XhPJnP25LDxoyzlNiCoy5+tL6U0HsbugEzGqe8QIpuQ8SXpdJ0V3uGGJ+83lXV1HVJE5X1t0VeRTLHYXzIopzD9FpE86fWopLi5GSkoKwsPD/9mApSXCw8ORnJysdZ29e/ciLCwM06dPh4eHB9q2bYuPPvoIZWVl9es5Eak1Stftw4TTFU43R9LC6wtJlRihGIf2EekHry3GoY879YkxpE8KVVNS6AORVOj0DfXOnTsoKytT32awnIeHB5RKpdZ1/vzzT+zatQtlZWU4cOAA3nvvPSxZsgQLFy6scj9FRUXIz8/XeBARkekyxvWF1xb5Ens4n9j7J6K64XcXqoqUQiFj90UfQaA+Akmixxm8fEKlUsHd3R2rV69GSEgIRo4ciXfffRfx8fFVrrNo0SI4OzurH97e3obuJhERyYyu1xdeW/QzGbqxq3oYDBGRMfG7i+mTUjBlbjh0lKqjUzjVpEkTWFlZITs7W2N5dnY2PD09ta7TtGlTPPnkk7CyslIvCwoKglKpRHGx9j/OuXPnIi8vT/24fv26Lt0kkgX7c/zCRVTOGNcXU7q2ONwwj6G5UgqmpNQXIqodfnehx0k1mJJqv4iMSadPtzY2NggJCUFSUpJ6mUqlQlJSEsLCwrSu88wzz+Dy5ctQqf75tfbixYto2rQpbGxstK5ja2sLhUKh8SAiItNljOsLry1UX8YMqDjvFFH98buLPIgx35QUMaCqP13uaFkc2NyAPaG60Pmn1+joaKxZswabNm1CWloapk2bhnv37qnvgDFu3DjMnTtX3X7atGnIycnBG2+8gYsXL2L//v346KOPMH36dP0dBckSK4eIqCJeX6giqVYqSbVf2uhjGGdtyO1uk2ReeG2hcnIIf4zVRwaCJEXWuq4wcuRI3L59G/Pnz4dSqURwcDASEhLUEw1mZmbC0vKfzMvb2xsHDx7EzJkz8dRTT6FZs2Z444038Pbbb+vvKIiISPZ4fZE+Y1XzyCkAIiJp47XFsOQyMbYcgikic6dzOAUAUVFRiIqK0vrcsWPHKi0LCwvDyZMn67Irs+Don4fCDGexu0EisD+XhQdtm4ndDSLJMJXrS5rSo+ZGpJUcginrK1koDeB7N5FcmMq1RRcFvhZid4HqaLjiLHblPy12N2rk1/w2rt5wE7sbZELMY0ZVIiIiM2esIV7mwhghmljzTpnLhPtEJD5jDC9j1RSRPPDTBxEREREREZFEMFAjc8RwikTD4Wz61Shd++2NiYjkQg5D+oiITFGRDz9HmhtOik5Sw3CKiMhIcnJyMGbMGCgUCri4uGDixIkoLCysdp2HDx9i+vTpaNy4MRwdHTFs2DBkZ2drtMnMzMSgQYPg4OAAd3d3zJ49G6WlpRptjh07hqeffhq2trZo2bIlNm7cqPH88ePHMXjwYHh5ecHCwgJ79uzReL6kpARvv/022rVrh4YNG8LLywvjxo3DzZs363w+5ObInUCxuyAqsYaYSRnDNCIiaWMFEpF8MJwiEpn9OX65MRdjxozB+fPnkZiYiH379uH48eOYMmVKtevMnDkTP/zwA3bu3Ikff/wRN2/exIsvvqh+vqysDIMGDUJxcTFOnDiBTZs2YePGjZg/f766TUZGBgYNGoTevXsjNTUVM2bMwKRJk3Dw4EF1m3v37qF9+/aIi4vT2o/79+/j7NmzeO+993D27Fl89913SE9Px/PPP1/Ps0JEREREj2Ow9ggn9zcfdbpbnykY2+gEvr7btd7b6eeTjkOZrfTQo/orCFDB6QrzRiIpSktLQ0JCAs6cOYOOHTsCAFasWIGBAwdi8eLF8PLyqrROXl4e1q1bh23btuHZZ58FAGzYsAFBQUE4efIkunTpgkOHDuGPP/7A4cOH4eHhgeDgYHzwwQd4++23ERsbCxsbG8THx8Pf3x9LliwBAAQFBeGnn37CsmXLEBERAQAYMGAABgwYUGX/nZ2dkZiYqLFs5cqV6Ny5MzIzM+Hj46OX80Tmi1VIhuGYpUJhM342ICIiImnjpxUiIiNITk6Gi4uLOpgCgPDwcFhaWuLUqVNa10lJSUFJSQnCw8PVywIDA+Hj44Pk5GT1dtu1awcPDw91m4iICOTn5+P8+fPqNhW3Ud6mfBt1lZeXBwsLC7i4uNRrO+au9KaD2F2gemCoRkQkTaw8kibOb0ZVYThFRPSY/Px8jUdRUVG9t6lUKuHu7q6xzNraGq6urlAqlVWuY2NjUyn88fDwUK+jVCo1gqny58ufq65Nfn4+Hjyo2zxCDx8+xNtvv43Ro0dDoVDUaRtUNw43eOk2J5zri4ikyq/57Xqtzwm5a8aAjcyJ2Q7rIyL5undNAUs7O71vV/XwIQDA29tbY3lMTAxiY2O1rjNnzhx88skn1W43LS1NL/2TipKSEowYMQKCIODLL78UuztkAlh9RERERGTeGE4RET3m+vXrGtVAtra2VbadNWsWJkyYUO32WrRoAU9PT9y6dUtjeWlpKXJycuDp6al1PU9PTxQXFyM3N1ejeio7O1u9jqenJ06fPq2xXvnd/Cq2efwOf9nZ2VAoFLC3t6+2748rD6auXbuGI0eOsGqKCI/CtdKAZmJ3g4iI/j9WHNVOX88LSFTW/W7Efs1v4+oNNz32iMwZwykioscoFIpahy5ubm5wc6v5ohwWFobc3FykpKQgJCQEAHDkyBGoVCqEhoZqXSckJAQNGjRAUlIShg0bBgBIT09HZmYmwsLC1Nv98MMPcevWLfWwwcTERCgUCrRu3Vrd5sCBAxrbTkxMVG+jtsqDqUuXLuHo0aNo3LixTuuTfHFoGRERkTiGK85iV/7TYneDyOA4cQURkREEBQWhf//+mDx5Mk6fPo2ff/4ZUVFRGDVqlPpOfVlZWQgMDFRXQjk7O2PixImIjo7G0aNHkZKSgsjISISFhaFLly4AgH79+qF169YYO3YsfvvtNxw8eBDz5s3D9OnT1RVfU6dOxZ9//om33noLFy5cwKpVq/DNN99g5syZ6v4VFhYiNTUVqampAICMjAykpqYiMzMTwKNgavjw4fjll1+wdetWlJWVQalUQqlUoriYE1tKnWOWSuwuVIlD+ojqJycnB2PGjIFCoYCLiwsmTpyIwsLCatu//vrraNWqFezt7eHj44N///vfyMvL09r+77//RvPmzWFhYYHc3FwDHQUREZk7hlNEEmB/jl/OzMHWrVsRGBiIPn36YODAgejWrRtWr16tfr6kpATp6em4f/++etmyZcvw3HPPYdiwYejRowc8PT3x3XffqZ+3srLCvn37YGVlhbCwMLz88ssYN24cFixYoG7j7++P/fv3IzExEe3bt8eSJUuwdu1aREREqNv88ssv6NChAzp06AAAiI6ORocOHTB//nwAj4KzvXv34saNGwgODkbTpk3VjxMnThjsnBHJBUM2EsuYMWNw/vx5JCYmYt++fTh+/DimTJlSZfubN2/i5s2bWLx4Mc6dO4eNGzciISEBEydO1Np+4sSJeOqppwzVfSK945A+InnisD4S1YO2zRjMkNlwdXXFtm3bqnzez88PgiBoLLOzs0NcXBzi4uKqXM/X17fSsL3H9erVC7/++mu1zz++75r6RkTG4ZTxAAX+us0PR+YhLS0NCQkJOHPmDDp27AgAWLFiBQYOHIjFixerK3Mratu2Lb799lv1vwMCAvDhhx/i5ZdfRmlpKayt//l68OWXXyI3Nxfz58/Hf/7zH8MfEBFpxaF9ZA5YOUVVutvKRuwukMgKM5zF7gIRmTBWG0mXww1+RJSD5ORkuLi4qIMpAAgPD4elpSVOnTpV6+3k5eVBoVBoBFN//PEHFixYgM2bN8PSkn8PpF99PS+I3QUikhheaYjMlNMV/udPRKRPUg3bpDbnWJrSQ+wuiCY/P1/jUVRUVK/tKZVK9c0wyllbW8PV1RVKpbJW27hz5w4++OADjaGARUVFGD16ND777DP4+PjUq49ExsQhfUTyxW+nRERERFQvpTcdxO5CvZUpHVB60zCPMuWj8+Pt7Q1nZ2f1Y9GiRVr7MmfOHFhYWFT7uHCh/pUn+fn5GDRoEFq3bo3Y2Fj18rlz5yIoKAgvv/xyvfdB0lfkwxubyIGhgjdWsZFUcM4piXD0z+MQKiIikhynjAcG2a5Uq4yIDOn69etQKBTqf5ffVfVxs2bNwoQJE6rdVosWLeDp6Ylbt25pLC8tLUVOTg48PT2rXb+goAD9+/eHk5MTdu/ejQYNGqifO3LkCH7//Xfs2rULANRzDjZp0gTvvvsu3n///Wq3TabPr/ltsbtARCaG4RQRERERkREoFAqNcKoqbm5ucHNzq7FdWFgYcnNzkZKSgpCQEACPgiWVSoXQ0NAq18vPz0dERARsbW2xd+9e2NnZaTz/7bff4sGDf4LpM2fO4JVXXsF///tfBAQE1NgvIiIiXTGcIiIiItIT6ytZKA1oJnY3yEwEBQWhf//+mDx5MuLj41FSUoKoqCiMGjVKfae+rKws9OnTB5s3b0bnzp2Rn5+Pfv364f79+9iyZYt6/ivgUShmZWVVKYC6c+eOen8uLi5GPUYiIjIPDKdMTEGASnYTXT9o2wz25zi8g4iIiEhXW7duRVRUFPr06QNLS0sMGzYMX3zxhfr5kpISpKen4/79+wCAs2fPqu/k17JlS41tZWRkwM/Pz2h9p9rLC9A+BJT+wcnQieSN4RQRERERkUy5urpi27ZtVT7v5+ennjMKAHr16qXx79qoyzokngJfC7G7QFStIp9i2GbaiN0Nkhh5ldgQERGR7HEydN0ZamJ6IiKSDylWh3FyfNIXhlN60M8nXewuGMzdVky0iYiIiIiIiMhwGE6RJDxoy8ljiYhq4nBD98u2Y5bKAD0hIiKqm76eF8TuAhFJEMMpIiIiIiIiIiISDcMpIiIiIj3inFpERMYlxbmYiEg3DKeIiIiIiIiIiEg0Zh1OjW10QuwuaHD0zxO7C0RERERERCRRhqgS4zxgJAVmHU6ZqoIAeU5+y0nRiYikxSnjgd63ySFvRERERPQ4hlNUo7utbMTuAhERERERERGZKIZTRBJhf67+1QSN0ov10BMiIiIiIiIi42E4RURERERERESSVOBrIXYXyAgYThERERHpGefWIiIiIqo9hlMkKZwUnYiIiIiIassQd68jIuNjOEVERERERERERKJhOKUn/XzSxe6CQfGOfYbHqjEiIiIiIiIyRwynTFRBgErsLtQZQ5q6Y4hIREREREREcsNwSmIc/fPE7gIRERERERERmaC4uDj4+fnBzs4OoaGhOH36dJVtv/vuO3Ts2BEuLi5o2LAhgoOD8fXXX6ufLykpwdtvv4127dqhYcOG8PLywrhx43Dz5k2d+8VwioiIiIiIiIjIxO3YsQPR0dGIiYnB2bNn0b59e0RERODWrVta27u6uuLdd99FcnIy/ve//yEyMhKRkZE4ePAgAOD+/fs4e/Ys3nvvPZw9exbfffcd0tPT8fzzz+vcN+t6HRmRgTxo2wz253gbbiIiIiIiIiJ9WLp0KSZPnozIyEgAQHx8PPbv34/169djzpw5ldr36tVL499vvPEGNm3ahJ9++gkRERFwdnZGYmKiRpuVK1eic+fOyMzMhI+PT637xsopqjXOZ0REREREREQkP8XFxUhJSUF4eLh6maWlJcLDw5GcnFzj+oIgICkpCenp6ejRo0eV7fLy8mBhYQEXFxed+sfKKSIiIiIiIiIimcrPz9f4t62tLWxtbTWW3blzB2VlZfDw8NBY7uHhgQsXLlS57by8PDRr1gxFRUWwsrLCqlWr0LdvX61tHz58iLfffhujR4+GQqHQ6RgYTpmwggAVnK7ItziOQ/uIiIiIiIhIzmyu28DKzjCjkMoeqgAA3t7eGstjYmIQGxurl304OTkhNTUVhYWFSEpKQnR0NFq0aFFpyF9JSQlGjBgBQRDw5Zdf6rwfhlNERERERERERDJ1/fp1jUqlx6umAKBJkyawsrJCdna2xvLs7Gx4enpWuW1LS0u0bNkSABAcHIy0tDQsWrRII5wqD6auXbuGI0eO6Fw1BXDOKSIiIiIiIiIi2VIoFBoPbeGUjY0NQkJCkJSUpF6mUqmQlJSEsLCwWu9LpVKhqKhI/e/yYOrSpUs4fPgwGjduXKdjYDilR/180vWyHUf/PL1sxxCMPSn6g7bNjLo/IiIiIiIiIlMUHR2NNWvWYNOmTUhLS8O0adNw79499d37xo0bh7lz56rbL1q0CImJifjzzz+RlpaGJUuW4Ouvv8bLL78M4FEwNXz4cPzyyy/YunUrysrKoFQqoVQqUVxcrFPfzH5Y39hGJ/D13a5id4OIiIiIiIiIyGBGjhyJ27dvY/78+VAqlQgODkZCQoJ6kvTMzExYWv5Tw3Tv3j289tpruHHjBuzt7REYGIgtW7Zg5MiRAICsrCzs3bsXwKMhfxUdPXq00rxU1TH7cIqkjxOjExEREREREdVfVFQUoqKitD537NgxjX8vXLgQCxcurHJbfn5+EARBL/3isD4TVxCgErsLRERERERERERVYjhFOjP2vFMA554iIiIiIiIiMlUMp4iIiIiIiIiISDQMp0g2WD1FREREREQV7cp/WuwuEJEeMJySKEf/PLG7QCaO85ERERlOaQB/UCEiIiKqLYZTZsAQIYQY804Bpls9ZarHRUREREREVB9O1/RzNziSNoZTetbPJ13sLpg8BjnGwwo+IiIiIiIiMjSGU0RERKRVgb+92F0gIiIiIjPAcIpkidVTRETyxLmYiIiIpCVRGSh2F4gYTkmZ1IdUiTXvFBERERERkTni3QnJVDGcMhO8MxsREREREZkiBjZE8sdwCsDYRifE7gLVAYf2ERGRFHHoIhEREZFuGE5RvXBoX/0xZCMiQypsxks9ERFJB+c3IiJt+ImViIhIJu435xBtIiIiIjI9DKcMoJ9PuthdMBusOiIikh8Oe9Ndgb+92F0gIiIiMhiGU2bEUJOic2ifNPB1ICIiIiKna4LYXSADkeLE71dvuOm8jm0mv7dQZQynJM7RP0/sLhARERERERERGQzDKZI9OQ/tk3PfiYioMg5ZJGPLycnBmDFjoFAo4OLigokTJ6KwsLDadV599VUEBATA3t4ebm5uGDJkCC5cuKB+/rfffsPo0aPh7e0Ne3t7BAUF4fPPPzf0oVA1nK8Uid0FyZNiVRER1R7DKdILDikjIiIiMr4xY8bg/PnzSExMxL59+3D8+HFMmTKl2nVCQkKwYcMGpKWl4eDBgxAEAf369UNZWRkAICUlBe7u7tiyZQvOnz+Pd999F3PnzsXKlSuNcUhERGSGrMXuABlXQYAKTleYSRIRERHJXVpaGhISEnDmzBl07NgRALBixQoMHDgQixcvhpeXl9b1KoZXfn5+WLhwIdq3b4+rV68iICAAr7zyikb7Fi1aIDk5Gd999x2ioqIMd0BERGS2mFKQSeDwOCIiwzDUXeI4/M20WHvdF7sLspCfn6/xKCqq31Ct5ORkuLi4qIMpAAgPD4elpSVOnTpVq23cu3cPGzZsgL+/P7y9vatsl5eXB1dX13r1l0xHXSbBpvrhsEUydaycMpB+Puk4lNlKL9ty9M9DYYazXrZlSHdb2aBRerHY3ZANBmpERKZFqoFbYTP+Flkb9lmWsLI1zLkqK3q03cfDn5iYGMTGxtZ5u0qlEu7u7hrLrK2t4erqCqVSWe26q1atwltvvYV79+6hVatWSExMhI2N9mkaTpw4gR07dmD//v117itJl22mDYp8TOMz/K78pzFccVbsbshKojJQ7C4QAWDllNrYRifE7gLVE8MeIiIi/bjfXCV2F0zS9evXkZeXp37MnTtXa7s5c+bAwsKi2kfFCczrYsyYMfj111/x448/4sknn8SIESPw8OHDSu3OnTuHIUOGICYmBv369avXPomIiKrCyikzxHmniIhICkoDmsH6SpbY3ZA8Qw2tFEuQZ7bYXRCNQqGAQqGosd2sWbMwYcKEatu0aNECnp6euHXrlsby0tJS5OTkwNPTs9r1nZ2d4ezsjCeeeAJdunRBo0aNsHv3bowePVrd5o8//kCfPn0wZcoUzJs3r8Z+E9VWojIQfT3rF7CaEw7pI3PAhIL0inftI6paXW73/fDhQ0yfPh2NGzeGo6Mjhg0bhuxszS92mZmZGDRoEBwcHODu7o7Zs2ejtLRUo82xY8fw9NNPw9bWFi1btsTGjRs1nj9+/DgGDx4MLy8vWFhYYM+ePVr7k5aWhueffx7Ozs5o2LAhOnXqhMzMTJ3PBZGpkeqQPpInNzc3BAYGVvuwsbFBWFgYcnNzkZKSol73yJEjUKlUCA0NrfX+BEGAIAgac2CdP38evXv3xvjx4/Hhhx/q9fiIDIlBDpE8MZwikyKXoX1y6SfpV11u9z1z5kz88MMP2LlzJ3788UfcvHkTL774ovr5srIyDBo0CMXFxThx4gQ2bdqEjRs3Yv78+eo2GRkZGDRoEHr37o3U1FTMmDEDkyZNwsGDB9Vt7t27h/bt2yMuLq7Kvly5cgXdunVDYGAgjh07hv/973947733YGdnV4+zQkREdRUUFIT+/ftj8uTJOH36NH7++WdERUVh1KhR6jv1ZWVlITAwEKdPnwYA/Pnnn1i0aBFSUlKQmZmJEydO4F//+hfs7e0xcOBAAI+G8vXu3Rv9+vVDdHQ0lEollEolbt++LdqxEhGRaeOwPpmQy6ToACdGF4OuFWsFAZxLxNjqcrvvvLw8rFu3Dtu2bcOzzz4LANiwYQOCgoJw8uRJdOnSBYcOHcIff/yBw4cPw8PDA8HBwfjggw/w9ttvIzY2FjY2NoiPj4e/vz+WLFkC4NGXmZ9++gnLli1DREQEAGDAgAEYMGBAtcfw7rvvYuDAgfj000/VywICAvRyfsiwCptZwjFLmv/dc2gfUf1s3boVUVFR6NOnDywtLTFs2DB88cUX6udLSkqQnp6O+/cf3VHRzs4O//3vf7F8+XLcvXsXHh4e6NGjB06cOKGeXH3Xrl24ffs2tmzZgi1btqi35evri6tXrxr1+IiIyDywcsqA+vmki92FKjGcIDKuutzuOyUlBSUlJQgPD1cvCwwMhI+PD5KTk9XbbdeuHTw8PNRtIiIikJ+fj/Pnz6vbVNxGeZvybdSGSqXC/v378eSTTyIiIgLu7u4IDQ2tcvgfmRZTm/NI3zikj8Tk6uqKbdu2oaCgAHl5eVi/fj0cHR3Vz/v5+UEQBPTq1QsA4OXlhQMHDiA7OxvFxcW4fv06tm7dilat/rnLdGxsrHqoX8UHgymSC1Ma2iflY7l6w03sLpAJYThFJkfqQ+ak3j8C8vPzNR4V5+Coq7rc7lupVMLGxgYuLi4ayz08PNTrKJVKjWCq/Pny56prk5+fjwcPHtSq/7du3UJhYSE+/vhj9O/fH4cOHcILL7yAF198ET/++GOttkFERERE0pGoDBS7C0RqHNZHBsGhfWRIjn9awspW/9l6WdGjbXp7e2ssj4mJQWxsrNZ15syZg08++aTa7aalpemlf2JSqR5VWw4ZMgQzZ84EAAQHB+PEiROIj49Hz549xeyeWbnfXAWHG6b12xKH9hERERGZN4ZTZqwgQAWnK6b1BYdIH65fv65xq29bW9sq2xrydt+enp4oLi5Gbm6uRvVUdna2eh1PT0/1JLcVny9/rvx/H7/DX3Z2NhQKBeztazdcq0mTJrC2tkbr1q01lpfPX0Vkrgw9pI9DKolIqq7ecINf87pPkp+oDERfzwt67JGmXflPY7jirMG2bwxSHtJHpG9MJioY2+iE2F2olqN/nthd0Imuk3TrE4fOUX0oFAqNR3XhlCFv9x0SEoIGDRogKSlJvSw9PR2ZmZkICwsDAISFheH333/XCL4SExOhUCjUQVJYWJjGNsrblG+jNmxsbNCpUyekp2vOpXfx4kX4+vrWejtUmbXXfbG7QCassBk/6hERkXTYZor3HZGkjZ9YiIyIoZn5qsvtvp2dnTFx4kRER0fj6NGjSElJQWRkJMLCwtClSxcAQL9+/dC6dWuMHTsWv/32Gw4ePIh58+Zh+vTp6lBt6tSp+PPPP/HWW2/hwoULWLVqFb755hv18DwAKCwsRGpqKlJTUwEAGRkZSE1NRWZmprrN7NmzsWPHDqxZswaXL1/GypUr8cMPP+C1114zxikkE8dJxYmIiIjMF8MpMlkMgkhqtm7disDAQPTp0wcDBw5Et27dsHr1avXzj9/uGwCWLVuG5557DsOGDUOPHj3g6emJ7777Tv28lZUV9u3bBysrK4SFheHll1/GuHHjsGDBAnUbf39/7N+/H4mJiWjfvj2WLFmCtWvXIiIiQt3ml19+QYcOHdChQwcAQHR0NDp06ID58+er27zwwguIj4/Hp59+inbt2mHt2rX49ttv0a1bN4OcLyKpY6BGRESGwiF9jzhdE8TuAhkJ55wysH4+6TiU2armhiIx9LxTnBid6B/lt/uuSvntviuys7NDXFwc4uLiqlzP19cXBw4cqHbfvXr1wq+//lrt84/vW5tXXnkFr7zySo3tyPQU+NvDKaN2d3ckIiKSAlOYd8pQeKc+khpWTsmM3Oadon8YqpJLzLm9iMzJs00MN2krPcJKJCIicXAeICISG8MpMjhOjE5EJD5OjK1fphyk3W+uErsLRGQmWL2jHYf0kTniJ1VCQQA/hBIRkTSYcuijqwJ/e7G7QEQkewx6DOPqDTexu0Ampk7hVFxcHPz8/GBnZ4fQ0FD1naVqsn37dlhYWGDo0KF12S3JmLlXT0mhD7riEFISg6lcX4I8s8XuAhkQAzQieTGVa4suOIm0fDFMMw6bCzfE7gI9RudwaseOHYiOjkZMTAzOnj2L9u3bIyIiArdu3ap2vatXr+LNN99E9+7d69xZeoShge7kGA4RmRteX6TPWJU8Ug5/pNy3x3EoJxGvLYYml+oZuQQ+cuknkSHo/Kll6dKlmDx5MiIjI9G6dWvEx8fDwcEB69evr3KdsrIyjBkzBu+//z5atGjx/9q7+7io6rx//C9uHJBkwDsYSRC0VjAtCozwrhsJTOrKzd3VIjR/3mwFXVfi5aa7JV55ldal5bUum99uzNp0tXbXLjMXIxVbFbUlaVOBrrwJ7wbzMkFEuXHO7w93RkZmYGaYc87nnPN6Ph7zKM+cc+ZzzgzzmXnN+/M5XWqwFmXGVavdhE4ZYWifWgGVnI+rZkWaFl7XpC3sX6gtLYVARhcc06h2E4jcYt9Cdgx+ruFcXyQir8Kp5uZmlJeXIyMj49oOAgORkZGBsrIyt9u9+OKLiIqKwvTp031vqUJye+5Wuwm6JcJV5ZQOqFixReQZI/Qv/mSUCatFC6iUbA/nmyLqOvYt2qBkUCJyQCVy27Qi4nCT2k2gLgj2ZuWzZ8/iypUriI6OdloeHR2NqirXl9jeuXMn3nnnHVRUVHj8OE1NTWhquvbCqq+v96aZ5KMLg2wIP8whAP4iajBlhCo50h4l+hf2LVeHefU4yfcAX4gWlBFR5/jdhVz5U/0d+Jn5K7Wb4cQowVRIjfrFCiQuWZOICxcuIDc3F2+99Rb69Onj8XaLFy9GRESE4xYbGytjK7VJq/NOGaV6StRgikgvfOlf2Lf4h9IVPSKEQiK0gYjkx+8upAalgyl/VKppZa4x0havKqf69OmDoKAg1NY6X4WotrYWFoul3fqHDx/GsWPH8NBDDzmW2WxXf7ENDg5GdXU1Bg0a1G67+fPno6CgwPHv+vp6vsmTX10aeiO6HzipdjOI6J+U6F/Yt2hX66AbEXzYOO/ZHNJH5B/87qIdJdZE3G9xXc0mBxGrp4iMzqvKKZPJhJSUFGzdutWxzGazYevWrUhPT2+3fmJiIr755htUVFQ4bv/yL/+Ce++9FxUVFW7ftENCQmA2m51uWqeVyaOVGPIlQvUUIF91E6umiLynRP+ix77FSNSqXtJq1ZSvV+ozynxmZAz87uIdX4dcabWKRoShdCK0gUgUXlVOAUBBQQGmTp2K1NRU3HnnnVi+fDkuXryIadOmAQCmTJmCG2+8EYsXL0ZoaCiGDh3qtH1kZCQAtFtOpAdKBlOihHxE/mLk/iU4phGtp8LUbobHLiR0R/jRS2o3Q3ZaDaaI6Bo99S0Rh5tQNyhE7WboipoVVAymiJx5HU5NmjQJP/zwAxYsWACr1Yrk5GQUFxc7JhqsqalBYCAn1VZCj4Q6NByNULsZmubP4X2smLqGV70kX7B/oc4oObyPwRSRPrBvIRGpFUwpeWVEIm95HU4BQH5+PvLz813eV1pa2uG2q1ev9uUhSUFKXLXvx8Em9KxulvUxPGUPlboSUjGYIvIP9i/y0/oV+/Q+/5Te55tKstR2vhKRn7Fv0Qal552y4/xT4gr/XlK7CaQg/kzgAqs+jIkBExGRNshZ1dQ66EZWTRERGYySlUxaH86n1TnGSHwMpxSklUnRlSLinEmXht7ocUhlX5ehFhGpQe2Jq9Wu8JEjRGIoRURKM1WdULsJ9E9yh0Z/qr9D88EUkZwYTmlcj4Q6WfarxFX7ROYudGIgRUQkFn8ESnqrlvL1Sn1EpC9KDonSSzWNXAGSCKGU2vNN+Xo1SPK/oqIixMfHIzQ0FGlpadi3b5/bdd966y2MHj0aPXv2RM+ePZGRkdHh+k8++SQCAgKwfPlyr9vFTy9EHWgbRokUSPladWb00JGI9Kkr4ZJIoZTa1Wi+CI5pVLsJRKQjagcodv4KqVgtRaJZv349CgoKUFhYiK+++gq33XYbsrKycObMGZfrl5aW4tFHH8X27dtRVlaG2NhYZGZm4uTJ9vN/btiwAXv27EFMTIxPbWM4RW4pEWSIOLSPiIg8I1qY4k1IpbdqKSIif2B1i7OuBEsMpUhEr732GmbOnIlp06ZhyJAhWLlyJcLCwrBq1SqX669ZswZPP/00kpOTkZiYiLfffhs2mw1bt251Wu/kyZN45plnsGbNGnTr1s2ntvl0tT7yXWZcNT6rGezXffZIqEPD0Qi/7pOIiPRL61fs64wWQycRgj615zEjIhKRPWTy9Ip+IoZSolSkkXzq6+ud/h0SEoKQkBCnZc3NzSgvL8f8+fMdywIDA5GRkYGysjKPHqexsREtLS3o1auXY5nNZkNubi7mzp2LW265xedjYDhFqvtxsAk9q5vVbga1IddcZkREREREHSmxJuJ+S5XazWhHxNBJaXqZW0xp4cclBJnkmQPuSvPV/cbGxjotLywsxMKFC52WnT17FleuXEF0dLTT8ujoaFRVefY399xzzyEmJgYZGRmOZa+88gqCg4Pxr//6rz4cwTUMp6hDFwbZEH6Yoz+JiLyVZKlFpTW68xX/KTimEa2nwmRskTwuJHRH+NFLajeD/klPk6Hf10e8L6dE5N6xE30R3/8HtZtBZEjHjx+H2Wx2/Pv6qil/WLJkCdatW4fS0lKEhoYCAMrLy/Hf//3f+OqrrxAQENCl/evnE4yf5fbcrXYTvMJKF+PgPF1EZMdhWPogwpA+IiKRcBia//BcGoPZbHa6uQqn+vTpg6CgINTW1jotr62thcVi6XD/S5cuxZIlS/DZZ5/h1ltvdSz/29/+hjNnziAuLg7BwcEIDg7G999/jzlz5iA+Pt6rY2A4RZ3ixOhERPrjzwobhitERETkji8T7Yd/790wuIjDTV4/htGYTCakpKQ4TWZun9w8PT3d7XavvvoqFi1ahOLiYqSmpjrdl5ubi3/84x+oqKhw3GJiYjB37lxs2bLFq/ZxWJ8K5JgUnYiIiIiIiMjOn1VTnG9KHwoKCjB16lSkpqbizjvvxPLly3Hx4kVMmzYNADBlyhTceOONWLx4MYCr80ktWLAAa9euRXx8PKxWKwCgR48e6NGjB3r37o3evXs7PUa3bt1gsVgweLB3mQcrp3REzqF9SlRPERERGY0/q866Ug3HIaJE+uRt9QngW5WLnT8DDA5HI/K/SZMmYenSpViwYAGSk5NRUVGB4uJixyTpNTU1OH36tGP9N954A83NzfjZz36Gfv36OW5Lly71e9tYOUXC4FX7OteV4Y8MGIlITpwY3XiCYxrVbgIRERF5KT8/H/n5+S7vKy0tdfr3sWPHvN6/L9sArJwiIiIyLD1d2U2LOFcXEXmL8+qQp1h5RlrDT6UqyYyrlmW/Wh/ax4nRiYi8w+FYpBVJltrOVyIiaoMBixg43xQpgeFUB3J77la7CURERJrBSiD1sAqOiETBIEN9IoV6XZnDjIyFn2TIK5y3SD2sKiPSP87hYxwM8oiIPCNS0EJE8mE4pUNyDu1TAkMYIiLl+LvihqGL9nBoKBFdj9UuRKQ0hlMqkmveKbmxeoqIiMh3DPCIiLzD6inP+ftcqTFMM/x7SfHHJPUxnCIhsXrKv7wJFD2pvNNqsEpEymD4oizON0VEouG8U0TkLX6a6YRWJ0WXe2gfq6eUxbCOiDrCYVnaweCOiJSmlyoUVk91jueItIzhFBERkYfu61OldhNkIUflDUMYfePk/URE1BnOXUbeYDilMi0Pj5K7eorVQlfxPBBpV5KlVu0mqIoBlTM5zkdXg0VW3WnfuXPnkJOTA7PZjMjISEyfPh0NDQ0ebStJEh544AEEBATg448/drqvpqYG2dnZCAsLQ1RUFObOnYvW1lYZjoBEJWKwwMog9+Q4NxyeSUpiOKVjWr9qHxERkV4wqCO55OTk4ODBgygpKcGmTZvwxRdfYNasWR5tu3z5cgQEBLRbfuXKFWRnZ6O5uRm7d+/Ge++9h9WrV2PBggX+bj7pGIMN5TC0846p6oTaTSAXGE6R0Fg11HWcH4xIW9QaLiXXpNoMZYjkU1lZieLiYrz99ttIS0vDqFGjsGLFCqxbtw6nTp3qcNuKigosW7YMq1atanffZ599hkOHDuGDDz5AcnIyHnjgASxatAhFRUVobm6W63CIPMIghq4XcbhJ7SaQHzCc0jk9TIxu5IDKyMdORN4ReXiW0QMquY6fV+mjsrIyREZGIjU11bEsIyMDgYGB2Lt3r9vtGhsb8dhjj6GoqAgWi8XlfocNG4bo6GjHsqysLNTX1+PgwYP+PQgiHzCgukauc8HKN1IaP9UIQMvzThEREZF7Rg/myFl9fb3Trampa7/2W61WREVFOS0LDg5Gr169YLVa3W43e/ZsjBgxAg8//LDb/bYNpgA4/t3Rfomux4BDXiKHdL7OWaaXq0uS9xhOeSC35261m2B4RqwgMuIxE5G65KzEYUgjHiWr7bRwcYAbTtvQ46Q8txtOXz3XsbGxiIiIcNwWL17ssi3z5s1DQEBAh7eqKt+uHrpx40Zs27YNy5cv9/VUkZ95O/+NkkOYRJwU3U7kYIaIvBesdgNIfj0S6tBwNEK2/V8YZEP4YeacREREbckZyKk5pE+tedH04Pjx4zCbzY5/h4SEuFxvzpw5eOKJJzrc18CBA2GxWHDmzBmn5a2trTh37pzL4XoAsG3bNhw+fBiRkZFOyydOnIjRo0ejtLQUFosF+/btc7q/tvZqwOhuvyS28O8lXBjQfvJ7JRw70Rfx/X+QZd8l1kTcb/EtqNU6OcM5VryRGhhOCSIzrhqf1QxWuxlC+3GwCT2rjTEJJ6umiMgXjf1tCDvRtdCi4cZA9DgpT0XNhYTuCD96SZZ9i4aVYl1zXx99ftk0m81O4ZQ7ffv2Rd++nX85TE9Px/nz51FeXo6UlBQAV8Mnm82GtLQ0l9vMmzcPM2bMcFo2bNgwvP7663jooYcc+33ppZdw5swZx7DBkpISmM1mDBkypNN2EZG8WDVGesRyF4PQw8To5D1vnxe5XydERAxtiPwnKSkJ48aNw8yZM7Fv3z7s2rUL+fn5mDx5MmJiYgAAJ0+eRGJioqMSymKxYOjQoU43AIiLi0NCQgIAIDMzE0OGDEFubi6+/vprbNmyBc8//zzy8vLcVnsRqYVBjXhEHg5K4mI45SHOOyUGVhQRkRHofdiU3gMquY/PH0P6RL66I3lnzZo1SExMxNixYzF+/HiMGjUKb775puP+lpYWVFdXo7HR8/eVoKAgbNq0CUFBQUhPT8fjjz+OKVOm4MUXX5TjEEhw/gga5B4mZqSASu5j5ZA+UguH9ZHm6H14HwM4In1JstSi0hrd+YoCkXNon51eh/jpPXgj8fTq1Qtr1651e398fDwkqeOrX7m6f8CAAdi8eXOX20ekFCPMP6X3EI5X6jM2Vk4JJDOuWtb9c2gfEZH+aakiRm9Bjt6Oh4iIxKFEMMWqKVITwynSJL1WF/nzuBgWEhEpR6lgSs2r9AH6H3JKpGdqV6UoEXzotbJIS8el9HxTEYebFH08kg/DKYPR04TXeg2oiIhEoFQIoodqI60dg5aq64hIDFqa4FpLQY4n9HY8RO4wnPKCEpOiyz20T26s1vGdVsI2rb9GiUg8Wgt32lKy7WpXTRGROLRaLaLUsDG9BDpKHgeH9JHa+CnHgFg9RUQkvq4Mn/JXZYySYYgWAyottlkNSZZatZtARAakl4CKyCgYTpHfKV09pYeAyt/HwAo2ItIiLYU9SreVVVNERN7TckClxaqprgz/VHtONFIfP+kIiMOmvKflgEqUtuupoo6I/EfpUORCQnfhQyrR29eRrlTVcTJ0Iu3rSgDgr3mnlB4+psWASottJuqqYLUbQOrokVCHhqMRajfDr34cbELP6ma1m+EVUYIpItKfxv42hJ3Q7m9Q9gAo/OgllVtyjVqhFKumiIi6xh723G+pUrklHVMjlOJcUyQKftrxkhKTogPar55Sa1gZwx4iEhHn3PGdCFVKWqjmIiLSErUCkRJropBVSaK2yxtauqIjiYnhlIHpdRiXVgIqudrJ+aaI9EOUYVRqV+6oGQ6pHUr589z7a6J8IjIuvQQQIgVBarbFiFVTpqoTajeB3GA4RbJRMyQRPaASvX1E5N59fcQeEiAXtQMqQNmQitVSRETyUjsYUbtaSe3HF4mvc6FFHG7yc0tITep/0iS3lBjap9fqKUDcAEjUdhGR/ui1Ukau4Mi+X1FCKRECQTtRqviIqOt4VTRnSodEooRS/gwH9VJRR+rihOg+yO25G3/4cYTazdCEC4NsCD+s3odr0SZJFzWY0nNISUT+03BjIHqcFCfwuj5E8mXydFGCKLmpFVRyvjUi+UQcbkLdoBC1m9Elx070RXz/H9RuBgDn4XX+njhdhDCKSHTi/CRHJBNRAiEl2iHCfFNKXTRAi86dO4ecnByYzWZERkZi+vTpaGho6HCby5cvIy8vD71790aPHj0wceJE1NY6f9mrqalBdnY2wsLCEBUVhblz56K1tdVpndLSUtxxxx0ICQnBTTfdhNWrVzvdv3jxYgwfPhzh4eGIiorChAkTUF3tXL3pSVvI/1ix4rm2lU+e3kQlUtUUEVFbRqiSsVc3dSVU8sc+5KL2kEoiV1g5JbjMuGp8VjNY1sfokVCHhqMRsj6G2uzBkFpVVKIEZKSunJwcnD59GiUlJWhpacG0adMwa9YsrF271u02s2fPxqeffoqPPvoIERERyM/PxyOPPIJdu3YBAK5cuYLs7GxYLBbs3r0bp0+fxpQpU9CtWze8/PLLAICjR48iOzsbTz75JNasWYOtW7dixowZ6NevH7KysgAAO3bsQF5eHoYPH47W1lb8+te/RmZmJg4dOoQbbrjBo7aQmBr72xB2wn9Bh2jVU6Q/Rp3XjcioRKqeckXEcEkkXQ0rOcyU7BhOkezUHtrXlhrD/BhMEQBUVlaiuLgYX375JVJTUwEAK1aswPjx47F06VLExMS026aurg7vvPMO1q5di/vuuw8A8O677yIpKQl79uzBXXfdhc8++wyHDh3C559/jujoaCQnJ2PRokV47rnnsHDhQphMJqxcuRIJCQlYtmwZACApKQk7d+7E66+/7giniouLnR579erViIqKQnl5OcaMGeNRW8g4GFDJy99VU3qde4yI1BNSY0JTnP8+U4seUOkJq6ZIVGIkBqQ6I8059ONgkyKBkVKPQ/5XX1/vdGtq6vqVQMrKyhAZGekIpgAgIyMDgYGB2Lt3r8ttysvL0dLSgoyMDMeyxMRExMXFoayszLHfYcOGITo62rFOVlYW6uvrcfDgQcc6bfdhX8e+D1fq6q6+J/Tq1cvjtlDHOPcOeULE4XwcWkokP6Uvb89qFSISDSunfKTkpOhKDO2Tm0jVU3ZyVVGpFUgpMd+UEleQ9ETkd80IDvb/66m19errITY21ml5YWEhFi5c2KV9W61WREVFOS0LDg5Gr169YLVa3W5jMpkQGRnptDw6OtqxjdVqdQqm7Pfb7+tonfr6ely6dAnduzvPu2Oz2fDss89i5MiRGDp0qMdtIXH5e2gfwOopIiLyL1ZPyc/fVVNGmH+MlMNwihyMMPfU9fw5F5VWq6SMVDXnqePHj8NsNjv+HRLi/ko48+bNwyuvvNLh/iorK/3WNiXk5eXhwIED2Llzp9pNoX8KjmlE66kwtZtBMpOjaopD+ohILv4e2gcwoJKT3obzRRzu+sgGEgvDKY1g9ZS8rg+WvA2rtBpMkWtms9kpnOrInDlz8MQTT3S4zsCBA2GxWHDmzBmn5a2trTh37hwsFovL7SwWC5qbm3H+/HmniqXa2lrHNhaLBfv27XPazn4FvbbrXH9VvdraWpjN5nZVU/n5+di0aRO++OIL9O/f36u2kNhYPSU2EYfzEZH4Ig43oW6Q+x/RiETG4aXUFsMpUpTIAVVbbcOmntXNwodPSgzpI9f69u2Lvn07/yUqPT0d58+fR3l5OVJSUgAA27Ztg81mQ1pamsttUlJS0K1bN2zduhUTJ04EAFRXV6Ompgbp6emO/b700ks4c+aMY9hgSUkJzGYzhgwZ4lhn8+bNTvsuKSlx7AMAJEnCM888gw0bNqC0tBQJCQlet4WMiQGVvnVlvinOs0YktvDvJVwYEKB2M9ph9ZT/yVE1xSF95G/ipwQCy+25W+0m+B2HeLUnejBF2pCUlIRx48Zh5syZ2LdvH3bt2oX8/HxMnjzZcaW+kydPIjEx0VEJFRERgenTp6OgoADbt29HeXk5pk2bhvT0dMfV8TIzMzFkyBDk5ubi66+/xpYtW/D8888jLy/PMRzxySefxJEjR/CrX/0KVVVV+P3vf48PP/wQs2fPdrQvLy8PH3zwAdauXYvw8HBYrVZYrVZcunTJ47YQkW/kqprikD4ikptcAYXehqCpieeStILhlIaIMhl1V7HKx794PrVjzZo1SExMxNixYzF+/HiMGjUKb775puP+lpYWVFdXo7HxWqXC66+/jgcffBATJ07EmDFjYLFY8Je//MVxf1BQEDZt2oSgoCCkp6fj8ccfx5QpU/Diiy861klISMCnn36KkpIS3HbbbVi2bBnefvttZGVlOdZ54403UFdXh3vuuQf9+vVz3NavX+9xW6hzXakk8ccV0+QKKzgkzXc8d0REREQc1kcuGHFidKNipZyyevXqhbVr17q9Pz4+HpLkPPY+NDQURUVFKCoqcrvdgAED2g3bu94999yD/fv3u73/+sd1xZO2GMV9faqw7Wyi2s0QCof3iYVVU0SkFDkmRgc4vM8f5Kqa8kfFXFfmm+Jk6PrEn+tIFaz28Q+eRyLylpyhBauAvCP6+fJHtR4RiU30Cak5JM13PHekNWJ/KtIApeed0svQPiIi8p4WwgLRAxdRyHmeRKia4mToRMrRexUJQxbvyXnOOBE6yYWfIMklJYZ7seqHiEgdcocXDKg6xvNDRHojd2DBgMpzWjhXolfskTr46UiD9FQ9xYDKd109d94GkHp63RGpySgVJQxgXJP7vPgreNRClR6R3piqTqjyuFoJCrQQuqhN7nOk9aoptf7GyDP85EhucbJsIiLqCgZUzng+iEjPlAguGFC5Z5Rzo/dhrEbGT0l+oPS8U4C+qlhYPeU9njMi4/JXRYtS8xIxkLlKifMgwlxTRERyM0oI4w0lzom/wketVOqR8viJkYTAsIWISL+MHlBp7fi7GoAaZegqkUi6Wk3ir8BAqWFfDKiu4bkgbxUVFSE+Ph6hoaFIS0vDvn373K578OBBTJw4EfHx8QgICMDy5ctdrnfy5Ek8/vjj6N27N7p3745hw4bh73//u1ft0tanJXKiRPUUh/aJh0EeEfmLkpU2Wgto/EWp49ZL1dR9farUbgIRaQRDGeXOgdbnmqJr1q9fj4KCAhQWFuKrr77CbbfdhqysLJw5c8bl+o2NjRg4cCCWLFkCi8Xicp0ff/wRI0eORLdu3fDXv/4Vhw4dwrJly9CzZ0+v2mbMT4oyUGNon94wdFEOQ0cibfPnZNUMqORjtOMlIgKUDTKMHFAZ+djJd6+99hpmzpyJadOmYciQIVi5ciXCwsKwatUql+sPHz4c//Vf/4XJkycjJCTE5TqvvPIKYmNj8e677+LOO+9EQkICMjMzMWjQIK/axk9NGqe36ikGVB3j+SESR1cqPIw87MkogY2Sx+nPgJFX6SMirTFiSKPkMfszbOR8U+pqbm5GeXk5MjIyHMsCAwORkZGBsrIyn/e7ceNGpKam4uc//zmioqJw++2346233vJ6P8b4hEiawgCGiEhZSg8J03NA1XBjoK6Pj4j0y5/BgdLDwIwUUBnpWK/HK/W5V19f73Rramp/rs6ePYsrV64gOjraaXl0dDSsVqvPj33kyBG88cYbuPnmm7FlyxY89dRT+Nd//Ve89957Xu0n2OcWUDu5PXfjDz+OUPxxM+Oq8VnNYFkfo0dCHRqORsj6GG1dGGRD+GF+uG9LzdBOT1eHJNKL4JhGtJ4KU7sZPrMHOD1O6ucHCTVCKb3MNUVEXRdxuAl1g1wPuzGCYyf6Ir7/D2o3Q1ZKB1Oca8o/zEebEBwcIMu+W1uvhlCxsbFOywsLC7Fw4UJZHvN6NpsNqampePnllwEAt99+Ow4cOICVK1di6tSpHu+H4RQRERGhsb8NYSeUD1cabgzUfEDFSqlrjDxklYichdSY0BTXrOhj2sMbvYVUeqiW4pA+eR0/fhxms9nxb1fzQ/Xp0wdBQUGorXXuq2tra91Odu6Jfv36YciQIU7LkpKS8Oc//9mr/fDTlE7obe4pgMP72vLnuZDzeeSFAYjIF1oeCqdmu/1dNcX5pojI3wGCWpU3eghz7NQ6Fr1VTZmqTqjdBFmZzWanm6twymQyISUlBVu3bnUss9ls2Lp1K9LT031+7JEjR6K62jmP+PbbbzFgwACv9qPNT4IC45dz/2JAxXNARO75O0xQe4iYlgIqLQdqRERGcOxEX02HVFpvP4mpoKAAb731Ft577z1UVlbiqaeewsWLFzFt2jQAwJQpUzB//nzH+s3NzaioqEBFRQWam5tx8uRJVFRU4LvvvnOsM3v2bOzZswcvv/wyvvvuO6xduxZvvvkm8vLyvGobP1XpiB6rpwCGM0SkT6IOfxIhoBI59BGlfayaItInPVZ3qF2Bo8WAR+02+/s545A+cUyaNAlLly7FggULkJycjIqKChQXFzsmSa+pqcHp06cd6586dQq33347br/9dpw+fRpLly7F7bffjhkzZjjWGT58ODZs2IA//vGPGDp0KBYtWoTly5cjJyfHq7ZxzinSBKNOkM5gjoiMqm0ApPacVCKEUW2pHSASkb6Ffy/hwgB5Jm9WixbmolI7kLJTO0x0h1fq85/8/Hzk5+e7vK+0tNTp3/Hx8ZCkzsPFBx98EA8++GCX2iXWpy3SBDWqpwDjBTVyHK8vzx2v1EckNjkqXkQLP9SqVhKlSqot0Z6btrpaDXhfnyo/tYTIuET9Ai9K4GEfKidKEARw+B6RnVifuHRCzXmn9B4kGCWgMspxEpG4RAxB7GGRXKGR3PsXFYf0EZESRAmo7NQMhUQMyQB5niMO6SNPcVgf+aRHQh0ajkao8th6H+LHYIrIOJIstai0Rnd5P8ExjWg9FeaHFmmLqwDJ0yGAWgyfRAwMiUif5BraF1JjQlNcs9/32xWuAiJ/D/8TLYRyRbTwkIxHe5/MqFN6r54C9BvgyHlcag3HJCLt0mIYcn31k7ub1sj1XLBqSvvOnTuHnJwcmM1mREZGYvr06WhoaPBoW0mS8MADDyAgIAAff/yx031ffvklxo4di8jISPTs2RNZWVn4+uuvZTgCIvG0rWzytsKpK9uqRa5gyl9VU6IOVyX/0t6nM41Qc2ifUtQOOy4Msuk2pCIibTDCHDlaDKhIeaJefdIIcnJycPDgQZSUlGDTpk344osvMGvWLI+2Xb58OQIC2lfHNDQ0YNy4cYiLi8PevXuxc+dOhIeHIysrCy0tLf4+BPIzf32Rl2s4llYrdFyFTloMoohExXCKNE8vAZVejoOI1CFnBQwDKnXx/JM7lZWVKC4uxttvv420tDSMGjUKK1aswLp163Dq1KkOt62oqMCyZcuwatWqdvdVVVXh3LlzePHFFzF48GDccsstKCwsRG1tLb7//nu5DocMRKsBlR4Z4bkwVZ1QuwnkAYZTOqXU0D61q6fstB7syN1+X58nIwwRJVKbVipOGJCoQ87zziF9yquvr3e6NTV1rcKlrKwMkZGRSE1NdSzLyMhAYGAg9u7d63a7xsZGPPbYYygqKoLFYml3/+DBg9G7d2+88847aG5uxqVLl/DOO+8gKSkJ8fHxXWozaYuck1kbIRQRnZzPASdCJ29xQnQZ5fbcjT/8OELtZhiGPeDR0mTpWg/ViEgsck+M3tjfhrAT2nmP1ToGgsoKP3YJwcHyfJlqbb0MAIiNjXVaXlhYiIULF/q8X6vViqioKKdlwcHB6NWrF6xWq9vtZs+ejREjRuDhhx92eX94eDhKS0sxYcIELFq0CABw8803Y8uWLQgO5tcHuZmqTqA5sb/azSCd00o4yPmmjIOfMKnLRKmestNK4KNUO0V7foiIqHNyB1P+rJryR/WfEeZvA4Djx4+jrq7OcZs/f77L9ebNm4eAgIAOb1VVvp2zjRs3Ytu2bVi+fLnbdS5duoTp06dj5MiR2LNnD3bt2oWhQ4ciOzsbly5d8ulxSVn+/ELP6in9kfu8s2qKfMGfPmSmZvVUZlw1PqsZrMhj9UioQ8PRCEUeyxMiV1FpJTwjIm1i9ZT2sWJKv8xmM8xmc6frzZkzB0888USH6wwcOBAWiwVnzpxxWt7a2opz5865HK4HANu2bcPhw4cRGRnptHzixIkYPXo0SktLsXbtWhw7dgxlZWUIDLz697527Vr07NkT//M//4PJkyd3egxEngqpMaEprlntZhgGA0ESFcMp0jXRQioGU0TkSpKlFpXWaLWb4TEGVPJRIpjiXFPi69u3L/r27fyqX+np6Th//jzKy8uRkpIC4Gr4ZLPZkJaW5nKbefPmYcaMGU7Lhg0bhtdffx0PPfQQgKtzUgUGBjpdyc/+b5uNn2WMKPx7CRcGtL+yo78woFKGEsEUq6bIV/xkqXNKTmgt8vCxC4NsqgZDaj2+yM8JkV6IOBxJifCB1T3+p8VzqpUJ/fUqKSkJ48aNw8yZM7Fv3z7s2rUL+fn5mDx5MmJiYgAAJ0+eRGJiIvbt2wcAsFgsGDp0qNMNAOLi4pCQkAAAuP/++/Hjjz8iLy8PlZWVOHjwIKZNm4bg4GDce++96hwseU1rc/WwokdeWjy/WnsNU9cwnFJAbs/dajdBMaKHIUqHRGqHYl3hbbBppNc5EV2lxTBFVEqdS1ZN6c+aNWuQmJiIsWPHYvz48Rg1ahTefPNNx/0tLS2orq5GY6Pnz31iYiI++eQT/OMf/0B6ejpGjx6NU6dOobi4GP369ZPjMEgDlKiI0WKAogVKnVdWTVFXcFifASg595RWtA2M5BjyJ0IgJXpQSETO/D20T+65p+w4xK/rGPJRV/Tq1Qtr1651e398fDwkqeMvjK7uv//++3H//fd3uX1E3uIQP/8yeuBnqjqhdhPIQz59miwqKkJ8fDxCQ0ORlpbmKBN25a233sLo0aPRs2dP9OzZExkZGR2ur1dGqirRWihir27qapWTP/ZBZHTsX7Spsb+NAYuPlDxvrJoio2Lf4j9KVcYYPVDxFyXPo79fGxzSZzxeh1Pr169HQUEBCgsL8dVXX+G2225DVlZWuyuF2JWWluLRRx/F9u3bUVZWhtjYWGRmZuLkyZNdbjyJS2sBVVvXh1We3kSi5fNPxsX+xf+UDiMYUHlH6+fLX/NNiThvG+mHVvsWf1V7aPkLPgOqruH5I63xOpx67bXXMHPmTEybNg1DhgzBypUrERYWhlWrVrlcf82aNXj66aeRnJyMxMREvP3227DZbNi6dWuXG681alZPKTkxuh0DEiLyBvsXeSaXZkAlHjUqzVg1RUbFvsX/lJxXKKTGxJDFB0qfM841Rf7gVTjV3NyM8vJyZGRkXNtBYCAyMjJQVlbm0T4aGxvR0tKCXr16uV2nqakJ9fX1TjfSJgZUyuM5Jy1Son+Rs29h5YczDvNzT43zIkcwxav0kRbwu4t+MKDynB6CKS1X/JHvvAqnzp49iytXriA62nnC1ujoaFitVo/28dxzzyEmJsapk7je4sWLERER4bjFxsZ600xyQ43qKYBhiRap9Voh41KifzFy36JW1QwDqmsY2BEpj99drpLji74alTIMqDrGKjPSOkUvr7NkyRKsW7cOGzZsQGhoqNv15s+fj7q6Osft+PHjCrZSXkaaGJ2Up1YQyNc1qc2T/kUrfYtcFSkMqNSj5jngcD4i3/G7S8fUCqgYwLSn1jlh1RT5U7A3K/fp0wdBQUGorXX+4FxbWwuLxdLhtkuXLsWSJUvw+eef49Zbb+1w3ZCQEISEhHjTNPJQZlw1PqsZrPjj9kioQ8PRCMUfl4i0QYn+hX2LeuzhTNgJRX8TU53awZxcwRSH9JFWaP27i6nqBJoT+/t9v3oQUmNCU1yz2s1QnZpBnRbmmfLXhQVIGV59SjSZTEhJSXGaENA+QWB6errb7V599VUsWrQIxcXFSE1N9b21OmHUKhMO75MXzy9pGfsXZahdRWOUoW1GOU4i0bFvuUauahQ1AwqjV1EZ+dhJn7z+CbOgoABvvfUW3nvvPVRWVuKpp57CxYsXMW3aNADAlClTMH/+fMf6r7zyCl544QWsWrUK8fHxsFqtsFqtaGho8N9RkFfUnE+IAYr4ON8UqYX9yzVyVqaoHVAB+g1vRDourVRN8WICJDf2LfJTu4LGaCGVCMcr13POIX3G5tWwPgCYNGkSfvjhByxYsABWqxXJyckoLi52TDRYU1ODwMBrmdcbb7yB5uZm/OxnP3PaT2FhIRYuXNi11mtYbs/d+MOPI9Ruhio4xM//GPqRHmi9f7mvTxW2nU1U/HG1TC/D/UQJpOxECCCJRKH1voU8Zw9s9DjcT+0wqi21w0jSL6/DKQDIz89Hfn6+y/tKS0ud/n3s2DFfHoJkptbcU3YMqIjIFfYvygiOaUTrqTC1m+HQNtzRUlAlWihFRK6xb7kq4nAT6gbJM/di+PcSLgwIkGXf3tJTSCVSKCU3Vk2Rdj4B6pBR556yY7WPf/A8EumT3JNOi1pdYx8aJ2rwI3r75HxeORE6EXVEtIoa+/A3LQY8orZbtOeY9IXhlIGJMLcQg5Wu8ff5E+E1QUTKETWgshMlCBKlHZ0R/fkkIu/5+2pjcleniBpeaCGoEr2Noj637vBKfdrDcEplRq+eAhhQ+YrnjUj/lKhU0Uqg0TYgkjskUvKx/EXu55FVU0SkFyKFQCK1pSNyB1Mc0keAj3NOkX6oPfeUHeegIiIib3QUGnkyb5VWQidPaCVgvB6v1EekDjnnngLEmn+qM9eHQnLPUyV6COWK1iqmSLsYTglA7Sv3MaDSHjmqpjikj6jr5LhiX5KlFpXWaL/u83qiTZDeVXoKnjqj1WCKiPRNSwFVWx2FR54GV1oMoNxRIphi1RTZMZwioTCg6hyH8xGRHPQWUBmBUsEUh/QR6Y/c1VOAdgMqd/QUOhGJiHNOCULtuadEqprpkVDHAMYNEc+L2q9dIiNQKhxgFY52aP254pA+Is9peWJnDgnTLi0/d1r+mzEyhlPkIFJABYgZxKhJzvMh2nNPROrReuhhBEo+R6yaIqKu0nLIYVRKPWcc0kdtMZwioTGguorngYiUDAkYUImLzw0R+YuSwQADKm0I/17ic0WqYTglEBGGR4lYQWPkYX5KHLuIzzmRlsk5XIkBlbEp/ZywaopI/xhQkZ3Szw+rpuh6DKeoHVHDCqOFVEY6ViISU3BMI0MqQTCYIiI9YEAlJj0FU5xvSrsYTglGhOopQNyACjBGaKOVYxTl9UpkJGqEBgyo1MOAkIjkpnQFCwMqsfD5IFEwnCJN0msVldLHJXIISURiYUCiPLXOudwBKK/UR+Q9uatB1AioGIqoT43ngMP5yB2GUwISpRpFC8GFnkIqvRwHEclPrSFXrOJRjl6DKSKithhQqYfBFImG4RR1SAsBFaDtkEqttmvluSXSIiUqQ9QMERhSyYvnlojUoFZwwCoqZfF8k6gYTglKlOoprbEHPVoIqrTSTiIidxii+JfaoZ8SgSeH9BGROwxM5KfmOVYi/ORk6NrGcIo6pdUKG1HDHxHapdXnlIiciTAES+1ARQ94DonIE0p88VZ72BWreuTB80pawHBKYCJVT2k5zGhbTaVWKKT248tBpNenVpw7dw45OTkwm82IjIzE9OnT0dDQ0OE2ly9fRl5eHnr37o0ePXpg4sSJqK11DiRqamqQnZ2NsLAwREVFYe7cuWhtbXVap7S0FHfccQdCQkJw0003YfXq1U73L168GMOHD0d4eDiioqIwYcIEVFe7/ruXJAkPPPAAAgIC8PHHH3t9HoxCqQoREQIqgFVUvhLlvInyOiIi9akdUAGsovInEc6lCK8pEh/DKfKYlgOqtpQIq0QIxNzRy/OoRTk5OTh48CBKSkqwadMmfPHFF5g1a1aH28yePRuffPIJPvroI+zYsQOnTp3CI4884rj/ypUryM7ORnNzM3bv3o333nsPq1evxoIFCxzrHD16FNnZ2bj33ntRUVGBZ599FjNmzMCWLVsc6+zYsQN5eXnYs2cPSkpK0NLSgszMTFy8eLFdm5YvX46AgAA/nBHSG1YAeU6kc6VUMMUhfUTkDVb7dI0o50+pYIpD+rQvWO0GUMdye+7GH34coXYzHDLjqvFZzWC1m+FXHYVHDUcjurQ9kV1lZSWKi4vx5ZdfIjU1FQCwYsUKjB8/HkuXLkVMTEy7berq6vDOO+9g7dq1uO+++wAA7777LpKSkrBnzx7cdddd+Oyzz3Do0CF8/vnniI6ORnJyMhYtWoTnnnsOCxcuhMlkwsqVK5GQkIBly5YBAJKSkrBz5068/vrryMrKAgAUFxc7Pfbq1asRFRWF8vJyjBkzxrG8oqICy5Ytw9///nf069dPlnNF3kuy1KLSGq12MxzsoUvrqTCVWyIWUcIoItIuU9UJNCf2l/1xIg43oW5QiOyP4wl7wHJhAH8Y84QIgRSRL1g5RV4zUuXN9RVQrm5aYqTnrivq6+udbk1NXf/Fp6ysDJGRkY5gCgAyMjIQGBiIvXv3utymvLwcLS0tyMjIcCxLTExEXFwcysrKHPsdNmwYoqOvBRNZWVmor6/HwYMHHeu03Yd9Hfs+XKmru/ra7tWrl2NZY2MjHnvsMRQVFcFisXh66IZm9EoRkaqD1CTyeeBwPiJyR7ShWKJUAolKxPMj2muIxMbKKfKJHiuoSDu6HzqN4ECT3/fbamsGAMTGxjotLywsxMKFC7u0b6vViqioKKdlwcHB6NWrF6xWq9ttTCYTIiMjnZZHR0c7trFarU7BlP1++30drVNfX49Lly6he/fuTvfZbDY8++yzGDlyJIYOHepYPnv2bIwYMQIPP/ywh0dNShKteqoto1ZSiRpI2SkZTBk9qCUi/2El1TWihVFtKRlMcUifPrBySgNEnXiaVTikV8ePH0ddXZ3jNn/+fLfrzps3DwEBAR3eqqq09aUsLy8PBw4cwLp16xzLNm7ciG3btmH58uXqNYw6JXoVjL2CSPTQpqu0cIyiv1aIyD0lv4iLXPkiYqWQUox87KRfrJyiLmEFlXYwTPSc2WyG2Wz2aN05c+bgiSee6HCdgQMHwmKx4MyZM07LW1tbce7cObdD5CwWC5qbm3H+/Hmn6qna2lrHNhaLBfv27XPazn41v7brXH+Fv9raWpjN5nZVU/n5+Y7J2vv3vzanxbZt23D48OF2VVwTJ07E6NGjUVpa2uE5MLL7+lRh29lExR5P5AqqttqGN1qvqBI9iLoegyki8oZI80+50jak0Xs1lVYCKZFDTRIXwymNEG1i9LYYUInP38GUqNV8aujbty/69u3b6Xrp6ek4f/48ysvLkZKSAuBq4GOz2ZCWluZym5SUFHTr1g1bt27FxIkTAQDV1dWoqalBenq6Y78vvfQSzpw54xg2WFJSArPZjCFDhjjW2bx5s9O+S0pKHPsAAEmS8Mwzz2DDhg0oLS1FQkKC0/rz5s3DjBkznJYNGzYMr7/+Oh566KFOj5+UpZWAyu76cEcLYZXWAik1cUgfkfaJHlDZ6TGo0kogZad0MMUhffrBYX0aInIgwKoccfG5EUNSUhLGjRuHmTNnYt++fdi1axfy8/MxefJkx5X6Tp48icTEREclVEREBKZPn46CggJs374d5eXlmDZtGtLT03HXXXcBADIzMzFkyBDk5ubi66+/xpYtW/D8888jLy8PISFXP0Q++eSTOHLkCH71q1+hqqoKv//97/Hhhx9i9uzZjvbl5eXhgw8+wNq1axEeHg6r1Qqr1YpLly4BuFp9NXToUKcbAMTFxbULsqg9Nb6ca7k6pu3wPxFCoOvbI0KbfKXl1wURXaPGF3KtVcPYh75pbQicVtsNaO81YlRFRUWIj49HaGgo0tLS2o3CuN5HH32ExMREhIaGYtiwYe1+9G5oaEB+fj769++P7t27Y8iQIVi5cqXX7WI4RX7DEISoY2vWrEFiYiLGjh2L8ePHY9SoUXjzzTcd97e0tKC6uhqNjde++L7++ut48MEHMXHiRIwZMwYWiwV/+ctfHPcHBQVh06ZNCAoKQnp6Oh5//HFMmTIFL774omOdhIQEfPrppygpKcFtt92GZcuW4e2330ZWVpZjnTfeeAN1dXW455570K9fP8dt/fr1Mp8VkpNegghX4ZC/QyIlHkNtarweWDVFpC9aDh+uD31ECH5EbJOv1HhtsGrKe+vXr0dBQQEKCwvx1Vdf4bbbbkNWVla76Ufsdu/ejUcffRTTp0/H/v37MWHCBEyYMAEHDhxwrFNQUIDi4mJ88MEHqKysxLPPPov8/Hxs3LjRq7YFSJIk/F9AfX09IiIisPNADHqEM08TdXhfWxzmJwa5AsOuVPE1XLBh1NBTqKur83heJzv7e0FGv1/KdrW+z0//P5/aRtpjfz0t+fJuhPZQZpS7knNPtaWlIX4kD7WCSqXCqcsNrZg3fEeX+pZ7hv8GwcGhsrSvtfUySr98if2LQTg+r0TNkOXzSlvNif07X0kGWhji11X+Ghao5cDJU2qFlkqEU622Znx+5u0u9S9jRi2QtX/5YueLHrcvLS0Nw4cPx+9+9zsAV6/SHRsbi2eeeQbz5s1rt/6kSZNw8eJFbNq0ybHsrrvuQnJysqM6aujQoZg0aRJeeOEFxzopKSl44IEH8J//+Z8eHwuTHpIFq6jUJ2IwRUTq0EsFFflG78EUESlPyxVUnnJV1eTLTe/0HEzpTXNzM8rLy5GRkeFYFhgYiIyMDJSVlbncpqyszGl9AMjKynJaf8SIEdi4cSNOnjwJSZKwfft2fPvtt8jMzPSqfQynNEgr4QADKiIiZ2p+WWdAZTxJllo+70Q6p+YXdCMEVNQxvgbEUV9f73Rramr/3Jw9exZXrlxBdLRzRX10dDSsVqvL/Vqt1k7XX7FiBYYMGYL+/fvDZDJh3LhxKCoqwpgxY7w6Bl6tj2RlD6g4zE9ZDAaJyBV7UMFhfvqndijFqikiY9DKVfzI/9QMprRWNWX69pRsQ3wDbc0AgNjYWKflhYWFWLhwoSyPeb0VK1Zgz5492LhxIwYMGIAvvvgCeXl5iImJaVd11RGGUxqV23O3JuaessuMq2ZApQCGUkTiu69PlWpzT9klWWoZUOmU2qEUESnPVHVCtbmnAAZURsNqKTEdP37cac4p+1W72+rTpw+CgoJQW+v8WaG2thYWi8Xlfi0WS4frX7p0Cb/+9a+xYcMGZGdnAwBuvfVWVFRUYOnSpV6FUxzWp2FaGd5nlxlXzfBERjy3ROQNhhj6I8pzyqopZZ07dw45OTkwm82IjIzE9OnT0dDQ0OE299xzDwICApxuTz75ZLv1Vq9ejVtvvRWhoaGIiopCXl6eXIdBGhdxuImhhQGI8BxrrWpKKWaz2enmKpwymUxISUnB1q1bHctsNhu2bt2K9PR0l/tNT093Wh8ASkpKHOu3tLSgpaUFgYHO0VJQUBBsNptXx8DKKVIch/r5n1LBlNYCUSJRiVA9BXCYn56IEkyR8nJycnD69GmUlJSgpaUF06ZNw6xZs7B27doOt5s5cyZefPFFx7/DwsKc7n/ttdewbNky/Nd//RfS0tJw8eJFHDt2TI5DID9Qu3rKjlVU+iRCKEX+UVBQgKlTpyI1NRV33nknli9fjosXL2LatGkAgClTpuDGG2/E4sWLAQD/9m//hrvvvhvLli1DdnY21q1bh7///e948803AVwNxe6++27MnTsX3bt3x4ABA7Bjxw68//77eO2117xqG8MpjdPa8L62ONTPP1gxRURdxWF+2iZSMMWqKWVVVlaiuLgYX375JVJTUwFcnftj/PjxWLp0KWJiYtxuGxYW5nYYx48//ojnn38en3zyCcaOHetYfuutt/r3AEiX7EEGQyp9ECmYYtVU102aNAk//PADFixYAKvViuTkZBQXFzsmPa+pqXGqghoxYgTWrl2L559/Hr/+9a9x88034+OPP8bQoUMd66xbtw7z589HTk4Ozp07hwEDBuCll15yWZHbEYZTOqD1gApgFZUvGEoRaZso1VN2DKi0R6RQitRRVlaGyMhIRzAFABkZGQgMDMTevXvx05/+1O22a9aswQcffACLxYKHHnoIL7zwgqN6qqSkBDabDSdPnkRSUhIuXLiAESNGYNmyZe0m3SVxiFI9ZccqKu0TKZgi/8nPz0d+fr7L+0pLS9st+/nPf46f//znbvdnsVjw7rvvdrldnHNKJ7Q+3IrzUXlHjXOl9dcYEXUuyVLLwEMjRHyeWDXVOU8u9e0Nq9WKqKgop2XBwcHo1auX28uCA8Bjjz2GDz74ANu3b8f8+fPxhz/8AY8//rjj/iNHjsBms+Hll1/G8uXL8ac//Qnnzp3D/fffj+bm5i61mYyF4YZ2ifbcsWpK/1g5pSNarqCyaxu6sJqqPQZ4RPoiWvWUHauoxCViKKUXwUdPy3apb3h5qe958+bhlVde6XCXlZWVPjdn1qxZjv8fNmwY+vXrh7Fjx+Lw4cMYNGgQbDYbWlpa8Nvf/haZmZkAgD/+8Y+wWCzYvn07srKyfH5skpdo1VMAK6i0RrRQioyD4RQJi3NSOWMwRURK4mTpYhE9lGLVlGc8udQ3AMyZMwdPPPFEh/saOHAgLBYLzpw547S8tbUV586dczuflCtpaWkAgO+++w6DBg1Cv379AABDhgxxrNO3b1/06dMHNTU1Hu+XyI7zUIlP5FCKVVPGwHBKZ/RQPdUWK6nECKU4pI9IPqJWT9kxpFKX6KEUecd+ie/O9O3bF3379u10vfT0dJw/fx7l5eVISUkBAGzbtg02m80ROHmioqICAByh1MiRIwEA1dXV6N//ahXOuXPncPbsWQwYMMDj/ZI6RKyesmNIJR6RQykyFs45pUN6DRLs81KJENYowUjHSkTis89HxbBEGVo616yaUk9SUhLGjRuHmTNnYt++fdi1axfy8/MxefJkx5X6Tp48icTEROzbtw8AcPjwYSxatAjl5eU4duwYNm7ciClTpmDMmDGOq/H95Cc/wcMPP4x/+7d/w+7du3HgwAFMnToViYmJuPfee1U7XtKPiMNNDEVUppXngFVTxsFwSqf0GlDZ6TmoEu249P5aIhKB1r7cayk40RItBoBae+3q0Zo1a5CYmIixY8di/PjxGDVqFN58803H/S0tLaiurkZjYyMAwGQy4fPPP0dmZiYSExMxZ84cTJw4EZ988onTft9//32kpaUhOzsbd999N7p164bi4mJ069ZN0eMj32jlC71WAhI94TknUXFYn47pbYifO9cHOVoc/idSGEVE6hB9eJ8rHPLnH1oKo9piMCWGXr16Ye3atW7vj4+PhyRJjn/HxsZix44dne7XbDbjnXfewTvvvOOXdpLyRB7ed722YQmH/MlDi4GUVkJW8g+GUzpnlICqLS2EVVoJo1g1RaQsLQZUQPtwhWFV57QaSBGRtmgpoLLjvFT+o8VAyo7BlPEwnDIAIwZUbbkLgpQMrbQSRrXFYIpIHVoNqNpqG7wwqLpGT4EUq6aItEOLARXQPlhhWOUZLQdSdgymjInhlEEYPaByxZPAyJMAS4vBExGJTQ8BlZ2Rq6r0FEa1xWCKSHu0GlC1xaF/7ukhkLJjMGVcDKcMhAGV94waPLFqikh9egqo2tJzWKXXMKotBlNEJAKjV1XpKYxqi8GUsTGcMhh76MCQioiIROAu0BE5tDJCCOUKgykibdND9ZQ7eg+r9BpGtcVgihhOGRSrqMgdVk0RiUOv1VOe8CQAkiPAMmrw1BkGU0T6oOeAqq2OwhyRgysjhFBE7jCcMjBWURERic/IAVVnGCQpg8EUkb4YJaByx5MASI4Ai8GTe6yaIoDhFIFVVHQNq6aIxMSAitTCYIpIn4weUHWGQZJyGEyRXaDaDSAx5PbczWCCiEhgDAmIiMifGAqQ2vgapLYYTpETBlTGxeeeSHwMqEhJfL0R6R/DAVILX3t0PYZT1A5DCuPhc06kHQwMSG739ani64zIQBgSkNL4miNXOOcUucTJ0o2DwRSR9tiDA85DRf7EQIrIuDgHFSmBoRR1hJVT1CEGF/rG55dI21jhQv7C1xERmapOMDwg2fC1RZ1h5RR1qm2AwUoq/WAwRaQfvJof+YqhFBFdzx4isJKK/IGhFHmK4RR5hcP99IHBFJH+cKgfeYOhFBF1hkP9qCsYSpG3OKyPfJLbczcDDo3i80akbxzqR53h64OIPMWhfuQLvmbIF6ycoi7hkD/tYChFZCyspKLrMZQiIl9xqB95gqEUdQXDKfIbDvkTE0MpImNjSGVsDKSIyJ8YUpErDKXIHxhOkd+xmkocDKaIyO76kIJhlX4xkCIiuTGkIgZS5G8Mp0hW14cjDKuUwVCKiDrDiip9YSBFRGq4PqBgWKVvDKRITgynSFGsqpIPAyki8kXbUINBlfYwlCIikbCiSn8YSJFSGE6RahhUdQ3DKCLyNwZVYmMQRURa0TbQYFClTQylSGkMp0gIroIWBlbtMZAiIqVwjioxMJAiIq1jUCU+BlEkAoZTJKyOghgjBVcMpIhIBB2FJAyu/IdhFBHpGeeoEgcDKRINwynSJD1WWjGEIiKtYpWVdxhAERFd1VFAwuDKvxhGkegYTpFueBruqBFiMXgiIiPpLHwxQnjFAIqIqGtYZeU9BlCkZQynyHAYFBERqcvb4EbtMItBExGR+joLXowSXjGAIr1iOEVERERCYzhERESd8SW0UTvQYtBEdA3DKSIiIiIiIjIchkNE4ghUuwFERERERERERGRcDKeIiIiIiIiIiEg1DKeIiIiIiIiIiEg1DKeIiMjQMm/gZNtERERERGpiOEVERERERERERKphOEVERIY3vschtZtARERERGRYDKeIiIiIiIiIiEg1mgqnhsVWqt0EIiLSKVZPERERERGpQ1PhFBERERERERER6Yvmwqnb4o6r3QQiItIpVk8RERERESlPc+EUERERERERERHphybDKVZPERGRXFg9RURERESkLE2GU0REREREREREpA+aDadYPUVERHJh9RQRERERkXI0G04REREREREREZH2+RROFRUVIT4+HqGhoUhLS8O+ffs6XP+jjz5CYmIiQkNDMWzYMGzevNmnxl6P1VNEpCXnzp1DTk4OzGYzIiMjMX36dDQ0NHS4zeXLl5GXl4fevXujR48emDhxImpra53WqampQXZ2NsLCwhAVFYW5c+eitbXVaZ3S0lLccccdCAkJwU033YTVq1c73b948WIMHz4c4eHhiIqKwoQJE1BdXe20jtVqRW5uLiwWC2644Qbccccd+POf/+z7CXFBlP4FYPUUEWmDL30LAJSVleG+++7DDTfcALPZjDFjxuDSpUvt1mtqakJycjICAgJQUVEhwxHIT6S+hYhIbf5+T5QkCQsWLEC/fv3QvXt3ZGRk4H//93+9bpfX4dT69etRUFCAwsJCfPXVV7jtttuQlZWFM2fOuFx/9+7dePTRRzF9+nTs378fEyZMwIQJE3DgwAGvG0tEpGU5OTk4ePAgSkpKsGnTJnzxxReYNWtWh9vMnj0bn3zyCT766CPs2LEDp06dwiOPPOK4/8qVK8jOzkZzczN2796N9957D6tXr8aCBQsc6xw9ehTZ2dm49957UVFRgWeffRYzZszAli1bHOvs2LEDeXl52LNnD0pKStDS0oLMzExcvHjRsc6UKVNQXV2NjRs34ptvvsEjjzyCX/ziF9i/f79fzg/7FyIi7/nSt5SVlWHcuHHIzMzEvn378OWXXyI/Px+Bge2/GvzqV79CTEyMXM2XHfsWIqJr5HhPfPXVV/Hb3/4WK1euxN69e3HDDTcgKysLly9f9qptAZIkSd5skJaWhuHDh+N3v/sdAMBmsyE2NhbPPPMM5s2b1279SZMm4eLFi9i0aZNj2V133YXk5GSsXLnSo8esr69HREQE6urqYDab293/dU2sN4dARCpquGDDqKGn3P49d8T+XpDR75cIDjT5vW2ttmZ8fvr/+dS2zlRWVmLIkCH48ssvkZqaCgAoLi7G+PHjceLECZcf/Ovq6tC3b1+sXbsWP/vZzwAAVVVVSEpKQllZGe666y789a9/xYMPPohTp04hOjoaALBy5Uo899xz+OGHH2AymfDcc8/h008/depEJk+ejPPnz6O4uNhle3/44QdERUVhx44dGDNmDACgR48eeOONN5Cbm+tYr3fv3njllVcwY8aMLp8jpfsX++vpq0PR6BHu/reazQ1DfDgaIlLS5YZWzBu+o2t9S5//T5a+Bfhn/3J2ld/7F1/6FuDqe+X999+PRYsWdbj/v/71rygoKMCf//xn3HLLLdi/fz+Sk5P91n4lqPndJSNqhmyvKSJSRqutGZ+febtr/YuM7wXets/f74mSJCEmJgZz5szBv//7vwO4+h0mOjoaq1evxuTJkz0+lmCP1wTQ3NyM8vJyzJ8/37EsMDAQGRkZKCsrc7lNWVkZCgoKnJZlZWXh448/dvs4TU1NaGpqcvy7rq4OwNUn15WGCzZPD4GIVHax4erfq5e5uJNWqRmQ4c++VWoG0P69JiQkBCEhIV3ad1lZGSIjIx1fHgAgIyMDgYGB2Lt3L37605+226a8vBwtLS3IyMhwLEtMTERcXJwjnCorK8OwYcMcwRRw9T32qaeewsGDB3H77bejrKzMaR/2dZ599lm37bW/7/bq1cuxbMSIEVi/fj2ys7MRGRmJDz/8EJcvX8Y999zj7eloR4n+xV3f0tDQ8Yvp8sXWDu8nIvVdbrj6dypi3+LYN/zfv/jSt5w5cwZ79+5FTk4ORowYgcOHDyMxMREvvfQSRo0a5VivtrYWM2fOxMcff4ywsDCf26gmtb+7tNqau9B6IhKB/e9YD/2LHO+JR48ehdVqdfquERERgbS0NJSVlckXTp09exZXrlxx+hIEANHR0aiqqnK5jdVqdbm+1Wp1+ziLFy/Gf/zHf7RbHhvLCikivfi///s/REREeLWNyWSCxWJBqfVdmVp1tTro+veawsJCLFy4sEv7tVqtiIqKcloWHByMXr16uX0/tFqtMJlMiIyMdFre9j3U3Xus/b6O1qmvr8elS5fQvXt3p/tsNhueffZZjBw5EkOHDnUs//DDDzFp0iT07t0bwcHBCAsLw4YNG3DTTTd5eBbcU6J/cde3jLnzh05aV9vJ/UQkiq71LR/I1Kqr5OhffOlbjhw5AgBYuHAhli5diuTkZLz//vsYO3YsDhw4gJtvvhmSJOGJJ57Ak08+idTUVBw7dsznNqpJ7e8upWff96HVRCSirvUv8r4XeNq/yPGeaP+vt++brngVTill/vz5Tunc+fPnMWDAANTU1Hj9ghBBfX09YmNjcfz4cb8PFVIKj0EMejiGuro6xMXFOVXkeCo0NBRHjx5Fc7N8v0RKkoSAgACnZR39qj1v3jy88sorHe6zsrLSL21TSl5eHg4cOICdO3c6LX/hhRdw/vx5fP755+jTpw8+/vhj/OIXv8Df/vY3DBs2TKXWek5vfQugj/cEHoP6tN5+QPy+BfCuf5Gzb7HZrv58/8tf/hLTpk0DANx+++3YunUrVq1ahcWLF2PFihW4cOGC06/r5B77F/Fovf0Aj0EUeutfROZVONWnTx8EBQW1u1JUbW0tLBaLy20sFotX6wPuS5wjIiI0+6IGALPZrOn2AzwGUejhGFxNuuqJ0NBQhIaG+rk1vpszZw6eeOKJDtcZOHAgLBZLu4kGW1tbce7cuQ7fP5ubm3H+/Hmn6qm276EWi6XdFTbs77lt13H1Pmw2m9tVTeXn5zsm1O3fv79j+eHDh/G73/0OBw4cwC233AIAuO222/C3v/0NRUVFHs/D4Y4S/Yte+xZAH+8JPAb1ab39APsWoPO+pV+/fgCAIUOc59NLSkpCTU0NAGDbtm0oKytr956ZmpqKnJwcvPfee94cjmr43aXrtP6+oPX2AzwGUeihf5HjPdH+39raWkf/Yv+3t3MUenWGTSYTUlJSsHXrVscym82GrVu3Ij093eU26enpTusDQElJidv1iYi0pG/fvkhMTOzwZjKZkJ6ejvPnz6O8vNyx7bZt22Cz2ZCWluZy3ykpKejWrZvTe2h1dTVqamoc76Hp6en45ptvnL6clJSUwGw2O754ePI+LEkS8vPzsWHDBmzbtg0JCQlO6zc2NgJo3zEHBQU5foXvCvYvRETXyNm3xMfHIyYmBtXV1U7Lv/32WwwYMAAA8Nvf/hZff/01KioqUFFR4bhs+Pr16/HSSy/JdNT+x76FiOgaOd4TExISYLFYnNapr6/H3r17vX/flLy0bt06KSQkRFq9erV06NAhadasWVJkZKRktVolSZKk3Nxcad68eY71d+3aJQUHB0tLly6VKisrpcLCQqlbt27SN9984/Fj1tXVSQCkuro6b5srBK23X5J4DKLgMWjbuHHjpNtvv13au3evtHPnTunmm2+WHn30Ucf9J06ckAYPHizt3bvXsezJJ5+U4uLipG3btkl///vfpfT0dCk9Pd1xf2trqzR06FApMzNTqqiokIqLi6W+fftK8+fPd6xz5MgRKSwsTJo7d65UWVkpFRUVSUFBQVJxcbFjnaeeekqKiIiQSktLpdOnTztujY2NkiRJUnNzs3TTTTdJo0ePlvbu3St999130tKlS6WAgADp008/9cv5Ubp/0cNrkccgBq0fg9bbL0n6OAZf+dK3vP7665LZbJY++ugj6X//93+l559/XgoNDZW+++47l49x9OhRCYC0f/9+uQ/H7/jdxTdaPwatt1+SeAyi0MMxtCXHe+KSJUukyMhI6X/+53+kf/zjH9LDDz8sJSQkSJcuXfKqbV6HU5IkSStWrJDi4uIkk8kk3XnnndKePXsc9919993S1KlTndb/8MMPpZ/85CeSyWSSbrnlFq+/yFy+fFkqLCyULl++7EtzVaf19ksSj0EUPAZt+7//+z/p0UcflXr06CGZzWZp2rRp0oULFxz32z/8b9++3bHs0qVL0tNPPy317NlTCgsLk376059Kp0+fdtrvsWPHpAceeEDq3r271KdPH2nOnDlSS0uL0zrbt2+XkpOTJZPJJA0cOFB69913ne4H4PLWdr1vv/1WeuSRR6SoqCgpLCxMuvXWW6X333/fb+dHkpTtX/TwWuQxiEHrx6D19kuSPo7BV770LZIkSYsXL5b69+8vhYWFSenp6dLf/vY3t4+h5XBKkvjdxRdaPwatt1+SeAyi0MMxXM/f74k2m0164YUXpOjoaCkkJEQaO3asVF1d7XW7AiSpC9dEJCIiIiIiIiIi6gLfZvUiIiIiIiIiIiLyA4ZTRERERERERESkGoZTRERERERERESkGoZTRERERERERESkGmHCqaKiIsTHxyM0NBRpaWnYt29fh+t/9NFHSExMRGhoKIYNG4bNmzcr1FLXvGn/W2+9hdGjR6Nnz57o2bMnMjIyOj1eJXj7HNitW7cOAQEBmDBhgrwN9IC3x3D+/Hnk5eWhX79+CAkJwU9+8hNNvZYAYPny5Rg8eDC6d++O2NhYzJ49G5cvX1aotc6++OILPPTQQ4iJiUFAQAA+/vjjTrcpLS3FHXfcgZCQENx0001YvXq17O0k49B63wKwf2H/4h9a7lsA9i8kHvYv6vcv7FvU71sAbfcv7FsE48OVB/1u3bp1kslkklatWiUdPHhQmjlzphQZGSnV1ta6XH/Xrl1SUFCQ9Oqrr0qHDh2Snn/+ealbt27SN998o3DLr/K2/Y899phUVFQk7d+/X6qsrJSeeOIJKSIiQjpx4oTCLb/G22OwO3r0qHTjjTdKo0ePlh5++GFlGuuGt8fQ1NQkpaamSuPHj5d27twpHT16VCotLZUqKioUbvk13h7DmjVrpJCQEGnNmjXS0aNHpS1btkj9+vWTZs+erXDLr9q8ebP0m9/8RvrLX/4iAZA2bNjQ4fpHjhyRwsLCpIKCAunQoUPSihUrpKCgIKm4uFiZBpOuab1vkST2L+xf/EPrfYsksX8hsbB/Ub9/Yd+ift8iSdrvX9i3iEWIcOrOO++U8vLyHP++cuWKFBMTIy1evNjl+r/4xS+k7Oxsp2VpaWnSL3/5S1nb6Y637b9ea2urFB4eLr333ntyNbFTvhxDa2urNGLECOntt9+Wpk6dqvobvLfH8MYbb0gDBw6UmpublWpip7w9hry8POm+++5zWlZQUCCNHDlS1nZ6wpM3+F/96lfSLbfc4rRs0qRJUlZWlowtI6PQet8iSexf2L/4h576Fkli/0LqY/+ifv/CvkUMeupf2LeoT/Vhfc3NzSgvL0dGRoZjWWBgIDIyMlBWVuZym7KyMqf1ASArK8vt+nLypf3Xa2xsREtLC3r16iVXMzvk6zG8+OKLiIqKwvTp05VoZod8OYaNGzciPT0deXl5iI6OxtChQ/Hyyy/jypUrSjXbiS/HMGLECJSXlzvKZ48cOYLNmzdj/PjxirS5q0T6WyZ90XrfArB/Yf/iH0bsWwDx/p5JP9i/XKVm/8K+Rf2+BTBm/yLa37LeBKvdgLNnz+LKlSuIjo52Wh4dHY2qqiqX21itVpfrW61W2drpji/tv95zzz2HmJiYdi90pfhyDDt37sQ777yDiooKBVrYOV+O4ciRI9i2bRtycnKwefNmfPfdd3j66afR0tKCwsJCJZrtxJdjeOyxx3D27FmMGjUKkiShtbUVTz75JH79618r0eQuc/e3XF9fj0uXLqF79+4qtYy0Tut9C8D+hf2LfxixbwHYv5B82L9cpWb/wr5F/b4FMGb/wr5FXqpXThndkiVLsG7dOmzYsAGhoaFqN8cjFy5cQG5uLt566y306dNH7eb4zGazISoqCm+++SZSUlIwadIk/OY3v8HKlSvVbprHSktL8fLLL+P3v/89vvrqK/zlL3/Bp59+ikWLFqndNCJSGfsX9Wi9f2HfQkQd0Vr/wr5FHOxfqCOqV0716dMHQUFBqK2tdVpeW1sLi8XichuLxeLV+nLypf12S5cuxZIlS/D555/j1ltvlbOZHfL2GA4fPoxjx47hoYceciyz2WwAgODgYFRXV2PQoEHyNvo6vjwP/fr1Q7du3RAUFORYl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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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<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>
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