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diff --git a/content/blog/csca5632-w4/index.md b/content/blog/csca5632-w4/index.md new file mode 100644 index 0000000..3fa102e --- /dev/null +++ b/content/blog/csca5632-w4/index.md @@ -0,0 +1,62 @@ ++++ +title = "📰 Classifying BBC News Articles with Machine Learning" +description = "Using machine learning to automatically classify BBC news articles by topic—comparing unsupervised and supervised approaches to see which performs best." +date = 2025-10-31 +[taxonomies] +tags = ["machine_learning"] +[extra] +styles = ["notebooks.css", ] ++++ + +In this project, I explored how machine learning can help categorize BBC news +articles into topics like **business**, **politics**, **sport**, +**entertainment**, and **tech**. The idea was to see how well different models +could understand the content of an article and assign it to the right +category—without any human input. + +## Getting Started + +The dataset comes from a Kaggle competition and includes a mix of labeled and +unlabeled articles. Before diving into modeling, I spent some time cleaning the +data—removing duplicates, checking for balance across categories, and making +sure everything was in the right format. + +## Preprocessing the Text + +To prepare the articles for analysis, I used a technique called TF-IDF, which +helps highlight the most meaningful words in each article. I also tried a few +tweaks, like replacing numbers with a placeholder, to see if that would improve +performance. Turns out, it didn’t help much—some numbers (like years) actually +carry useful context. + +## Unsupervised Learning: Finding Patterns Without Labels + +I started with an unsupervised approach using Non-negative Matrix Factorization +(NMF). This method doesn’t rely on labeled data—it just looks for patterns in +the text. Surprisingly, it did quite well, reaching around **91% accuracy** +after some tuning. + +## Supervised Learning: Training with Labels + +Next, I tried supervised models, which learn from labeled examples. I used +Logistic Regression and LinearSVC, and both performed even better than NMF. +With enough training data, they reached up to **97% accuracy**. + +What stood out was how efficient LinearSVC was—it managed to get solid results +even with a smaller portion of the training data. + +## Final Thoughts + +This project was a great way to compare different approaches to text +classification. It showed that while unsupervised models can be useful, +supervised learning tends to be more accurate when labels are available. It +also highlighted how preprocessing choices can impact performance in subtle +ways. + +If you're curious about the details, the full notebook is embedded below 👇 + +<!-- markdownlint-disable MD033 --> +<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe> + +You can also view the notebook in [a separate page](notebook.html), or check it +on [GitHub](https://github.com/Farzat07/BBC-News-Classification-Kaggle-Mini-Project). diff --git a/content/blog/csca5632-w4/notebook.html b/content/blog/csca5632-w4/notebook.html new file mode 100644 index 0000000..097fd3d --- /dev/null +++ b/content/blog/csca5632-w4/notebook.html @@ -0,0 +1,8666 @@ +<!DOCTYPE html> + +<html lang="en"> +<head><meta charset="utf-8"/> +<meta content="width=device-width, initial-scale=1.0" name="viewport"/> +<title>cours2w4submission</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script> +<style type="text/css"> + pre { line-height: 125%; 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+|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Collapse { + display: flex; + flex-direction: column; + align-items: stretch; +} + +.jp-Collapse-header { + padding: 1px 12px; + background-color: var(--jp-layout-color1); + border-bottom: solid var(--jp-border-width) var(--jp-border-color2); + color: var(--jp-ui-font-color1); + cursor: pointer; + display: flex; + align-items: center; + font-size: var(--jp-ui-font-size0); + font-weight: 600; + text-transform: uppercase; + user-select: none; +} + +.jp-Collapser-icon { + height: 16px; +} + +.jp-Collapse-header-collapsed .jp-Collapser-icon { + transform: rotate(-90deg); + margin: auto 0; +} + +.jp-Collapser-title { + line-height: 25px; +} + +.jp-Collapse-contents { + padding: 0 12px; + background-color: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + overflow: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */ + +/** + * (DEPRECATED) Support for consuming icons as CSS background images + */ + +/* Icons urls */ + +:root { + --jp-icon-add-above: 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+ --jp-icon-add-below: 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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: 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+ --jp-icon-bug: url(data:image/svg+xml;base64,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); + --jp-icon-build: 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+ --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=); + --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K); + --jp-icon-offline-bolt: 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+ --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==); + --jp-icon-pdf: 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+ --jp-icon-python: 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+ --jp-icon-r-kernel: url(data:image/svg+xml;base64,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); + --jp-icon-react: 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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: 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+ --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==); + --jp-icon-toc: url(data:image/svg+xml;base64,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); + --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K); + --jp-icon-trusted: url(data:image/svg+xml;base64,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); + --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-users: url(data:image/svg+xml;base64,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); + --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-word: url(data:image/svg+xml;base64,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); + --jp-icon-yaml: url(data:image/svg+xml;base64,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); +} + +/* Icon CSS class declarations */ + +.jp-AddAboveIcon { + background-image: var(--jp-icon-add-above); +} + +.jp-AddBelowIcon { + background-image: var(--jp-icon-add-below); +} + +.jp-AddIcon { + background-image: var(--jp-icon-add); +} + +.jp-BellIcon { + background-image: var(--jp-icon-bell); +} + +.jp-BugDotIcon { + background-image: var(--jp-icon-bug-dot); +} + +.jp-BugIcon { + background-image: var(--jp-icon-bug); +} + +.jp-BuildIcon { + background-image: var(--jp-icon-build); +} + +.jp-CaretDownEmptyIcon { + background-image: var(--jp-icon-caret-down-empty); +} + +.jp-CaretDownEmptyThinIcon { + background-image: var(--jp-icon-caret-down-empty-thin); +} + +.jp-CaretDownIcon { + background-image: var(--jp-icon-caret-down); +} + +.jp-CaretLeftIcon { + background-image: var(--jp-icon-caret-left); +} + +.jp-CaretRightIcon { + background-image: var(--jp-icon-caret-right); +} + +.jp-CaretUpEmptyThinIcon { + background-image: var(--jp-icon-caret-up-empty-thin); +} + +.jp-CaretUpIcon { + background-image: var(--jp-icon-caret-up); +} + +.jp-CaseSensitiveIcon { + background-image: var(--jp-icon-case-sensitive); +} + +.jp-CheckIcon { + background-image: var(--jp-icon-check); +} + +.jp-CircleEmptyIcon { + background-image: var(--jp-icon-circle-empty); +} + +.jp-CircleIcon { + background-image: var(--jp-icon-circle); +} + +.jp-ClearIcon { + background-image: var(--jp-icon-clear); +} + +.jp-CloseIcon { + background-image: var(--jp-icon-close); +} + +.jp-CodeCheckIcon { + background-image: var(--jp-icon-code-check); +} + +.jp-CodeIcon { + background-image: var(--jp-icon-code); +} + +.jp-CollapseAllIcon { + background-image: var(--jp-icon-collapse-all); +} + +.jp-ConsoleIcon { + background-image: var(--jp-icon-console); +} + +.jp-CopyIcon { + background-image: var(--jp-icon-copy); +} + +.jp-CopyrightIcon { + background-image: var(--jp-icon-copyright); +} + +.jp-CutIcon { + background-image: var(--jp-icon-cut); +} + +.jp-DeleteIcon { + background-image: var(--jp-icon-delete); +} + +.jp-DownloadIcon { + background-image: var(--jp-icon-download); +} + +.jp-DuplicateIcon { + background-image: var(--jp-icon-duplicate); +} + +.jp-EditIcon { + background-image: var(--jp-icon-edit); +} + +.jp-EllipsesIcon { + background-image: var(--jp-icon-ellipses); +} + +.jp-ErrorIcon { + background-image: var(--jp-icon-error); +} + +.jp-ExpandAllIcon { + background-image: var(--jp-icon-expand-all); +} + +.jp-ExtensionIcon { + background-image: var(--jp-icon-extension); +} + +.jp-FastForwardIcon { + background-image: var(--jp-icon-fast-forward); +} + +.jp-FileIcon { + background-image: var(--jp-icon-file); +} + +.jp-FileUploadIcon { + background-image: var(--jp-icon-file-upload); +} + +.jp-FilterDotIcon { + background-image: var(--jp-icon-filter-dot); +} + +.jp-FilterIcon { + background-image: var(--jp-icon-filter); +} + +.jp-FilterListIcon { + background-image: var(--jp-icon-filter-list); +} + +.jp-FolderFavoriteIcon { + background-image: var(--jp-icon-folder-favorite); +} + +.jp-FolderIcon { + background-image: var(--jp-icon-folder); +} + +.jp-HomeIcon { + background-image: var(--jp-icon-home); +} + +.jp-Html5Icon { + background-image: var(--jp-icon-html5); +} + +.jp-ImageIcon { + background-image: var(--jp-icon-image); +} + +.jp-InfoIcon { + background-image: var(--jp-icon-info); +} + +.jp-InspectorIcon { + background-image: var(--jp-icon-inspector); +} + +.jp-JsonIcon { + background-image: var(--jp-icon-json); +} + +.jp-JuliaIcon { + background-image: var(--jp-icon-julia); +} + +.jp-JupyterFaviconIcon { + background-image: var(--jp-icon-jupyter-favicon); +} + +.jp-JupyterIcon { + background-image: var(--jp-icon-jupyter); +} + +.jp-JupyterlabWordmarkIcon { + background-image: var(--jp-icon-jupyterlab-wordmark); +} + +.jp-KernelIcon { + background-image: var(--jp-icon-kernel); +} + +.jp-KeyboardIcon { + background-image: var(--jp-icon-keyboard); +} + +.jp-LaunchIcon { + background-image: var(--jp-icon-launch); +} + +.jp-LauncherIcon { + background-image: var(--jp-icon-launcher); +} + +.jp-LineFormIcon { + background-image: var(--jp-icon-line-form); +} + +.jp-LinkIcon { + background-image: var(--jp-icon-link); +} + +.jp-ListIcon { + background-image: var(--jp-icon-list); +} + +.jp-MarkdownIcon { + background-image: var(--jp-icon-markdown); +} + +.jp-MoveDownIcon { + background-image: var(--jp-icon-move-down); +} + +.jp-MoveUpIcon { + background-image: var(--jp-icon-move-up); +} + +.jp-NewFolderIcon { + background-image: var(--jp-icon-new-folder); +} + +.jp-NotTrustedIcon { + background-image: var(--jp-icon-not-trusted); +} + +.jp-NotebookIcon { + background-image: var(--jp-icon-notebook); +} + +.jp-NumberingIcon { + background-image: var(--jp-icon-numbering); +} + +.jp-OfflineBoltIcon { + background-image: var(--jp-icon-offline-bolt); +} + +.jp-PaletteIcon { + background-image: var(--jp-icon-palette); +} + +.jp-PasteIcon { + background-image: var(--jp-icon-paste); +} + +.jp-PdfIcon { + background-image: var(--jp-icon-pdf); +} + +.jp-PythonIcon { + background-image: var(--jp-icon-python); +} + +.jp-RKernelIcon { + background-image: var(--jp-icon-r-kernel); +} + +.jp-ReactIcon { + background-image: var(--jp-icon-react); +} + +.jp-RedoIcon { + background-image: var(--jp-icon-redo); +} + +.jp-RefreshIcon { + background-image: var(--jp-icon-refresh); +} + +.jp-RegexIcon { + background-image: var(--jp-icon-regex); +} + +.jp-RunIcon { + background-image: var(--jp-icon-run); +} + +.jp-RunningIcon { + background-image: var(--jp-icon-running); +} + +.jp-SaveIcon { + background-image: var(--jp-icon-save); +} + +.jp-SearchIcon { + background-image: var(--jp-icon-search); +} + +.jp-SettingsIcon { + background-image: var(--jp-icon-settings); +} + +.jp-ShareIcon { + background-image: var(--jp-icon-share); +} + +.jp-SpreadsheetIcon { + background-image: var(--jp-icon-spreadsheet); +} + +.jp-StopIcon { + background-image: var(--jp-icon-stop); +} + +.jp-TabIcon { + background-image: var(--jp-icon-tab); +} + +.jp-TableRowsIcon { + background-image: var(--jp-icon-table-rows); +} + +.jp-TagIcon { + background-image: var(--jp-icon-tag); +} + +.jp-TerminalIcon { + background-image: var(--jp-icon-terminal); +} + +.jp-TextEditorIcon { + background-image: var(--jp-icon-text-editor); +} + +.jp-TocIcon { + background-image: var(--jp-icon-toc); +} + +.jp-TreeViewIcon { + background-image: var(--jp-icon-tree-view); +} + +.jp-TrustedIcon { + background-image: var(--jp-icon-trusted); +} + +.jp-UndoIcon { + background-image: var(--jp-icon-undo); +} + +.jp-UserIcon { + background-image: var(--jp-icon-user); +} + +.jp-UsersIcon { + background-image: var(--jp-icon-users); +} + +.jp-VegaIcon { + background-image: var(--jp-icon-vega); +} + +.jp-WordIcon { + background-image: var(--jp-icon-word); +} + +.jp-YamlIcon { + background-image: var(--jp-icon-yaml); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * (DEPRECATED) Support for consuming icons as CSS background images + */ + +.jp-Icon, +.jp-MaterialIcon { + background-position: center; + background-repeat: no-repeat; + background-size: 16px; + min-width: 16px; + min-height: 16px; +} + +.jp-Icon-cover { + background-position: center; + background-repeat: no-repeat; + background-size: cover; +} + +/** + * (DEPRECATED) Support for specific CSS icon sizes + */ + +.jp-Icon-16 { + background-size: 16px; + min-width: 16px; + min-height: 16px; +} + +.jp-Icon-18 { + background-size: 18px; + min-width: 18px; + min-height: 18px; +} + +.jp-Icon-20 { + background-size: 20px; + min-width: 20px; + min-height: 20px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.lm-TabBar .lm-TabBar-addButton { + align-items: center; + display: flex; + padding: 4px; + padding-bottom: 5px; + margin-right: 1px; + background-color: var(--jp-layout-color2); +} + +.lm-TabBar .lm-TabBar-addButton:hover { + background-color: var(--jp-layout-color1); +} + +.lm-DockPanel-tabBar .lm-TabBar-tab { + width: var(--jp-private-horizontal-tab-width); +} + +.lm-DockPanel-tabBar .lm-TabBar-content { + flex: unset; +} + +.lm-DockPanel-tabBar[data-orientation='horizontal'] { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * Support for icons as inline SVG HTMLElements + */ + +/* recolor the primary elements of an icon */ +.jp-icon0[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon1[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon2[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon3[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon4[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon0[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon1[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon2[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon3[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon4[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/* recolor the accent elements of an icon */ +.jp-icon-accent0[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-accent1[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-accent2[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-accent3[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-accent4[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-accent0[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-accent1[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-accent2[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-accent3[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-accent4[stroke] { + stroke: var(--jp-layout-color4); +} + +/* set the color of an icon to transparent */ +.jp-icon-none[fill] { + fill: none; +} + +.jp-icon-none[stroke] { + stroke: none; +} + +/* brand icon colors. Same for light and dark */ +.jp-icon-brand0[fill] { + fill: var(--jp-brand-color0); +} + +.jp-icon-brand1[fill] { + fill: var(--jp-brand-color1); +} + +.jp-icon-brand2[fill] { + fill: var(--jp-brand-color2); +} + +.jp-icon-brand3[fill] { + fill: var(--jp-brand-color3); +} + +.jp-icon-brand4[fill] { + fill: var(--jp-brand-color4); +} + +.jp-icon-brand0[stroke] { + stroke: var(--jp-brand-color0); +} + +.jp-icon-brand1[stroke] { + stroke: var(--jp-brand-color1); +} + +.jp-icon-brand2[stroke] { + stroke: var(--jp-brand-color2); +} + +.jp-icon-brand3[stroke] { + stroke: var(--jp-brand-color3); +} + +.jp-icon-brand4[stroke] { + stroke: var(--jp-brand-color4); +} + +/* warn icon colors. Same for light and dark */ +.jp-icon-warn0[fill] { + fill: var(--jp-warn-color0); +} + +.jp-icon-warn1[fill] { + fill: var(--jp-warn-color1); +} + +.jp-icon-warn2[fill] { + fill: var(--jp-warn-color2); +} + +.jp-icon-warn3[fill] { + fill: var(--jp-warn-color3); +} + +.jp-icon-warn0[stroke] { + stroke: var(--jp-warn-color0); +} + +.jp-icon-warn1[stroke] { + stroke: var(--jp-warn-color1); +} + +.jp-icon-warn2[stroke] { + stroke: var(--jp-warn-color2); +} + +.jp-icon-warn3[stroke] { + stroke: var(--jp-warn-color3); +} + +/* icon colors that contrast well with each other and most backgrounds */ +.jp-icon-contrast0[fill] { + fill: var(--jp-icon-contrast-color0); +} + +.jp-icon-contrast1[fill] { + fill: var(--jp-icon-contrast-color1); +} + +.jp-icon-contrast2[fill] { + fill: var(--jp-icon-contrast-color2); +} + +.jp-icon-contrast3[fill] { + fill: var(--jp-icon-contrast-color3); +} + +.jp-icon-contrast0[stroke] { + stroke: var(--jp-icon-contrast-color0); +} + +.jp-icon-contrast1[stroke] { + stroke: var(--jp-icon-contrast-color1); +} + +.jp-icon-contrast2[stroke] { + stroke: var(--jp-icon-contrast-color2); +} + +.jp-icon-contrast3[stroke] { + stroke: var(--jp-icon-contrast-color3); +} + +.jp-icon-dot[fill] { + fill: var(--jp-warn-color0); +} + +.jp-jupyter-icon-color[fill] { + fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0)); +} + +.jp-notebook-icon-color[fill] { + fill: var(--jp-notebook-icon-color, var(--jp-warn-color0)); +} + +.jp-json-icon-color[fill] { + fill: var(--jp-json-icon-color, var(--jp-warn-color1)); +} + +.jp-console-icon-color[fill] { + fill: var(--jp-console-icon-color, white); +} + +.jp-console-icon-background-color[fill] { + fill: var(--jp-console-icon-background-color, var(--jp-brand-color1)); +} + +.jp-terminal-icon-color[fill] { + fill: var(--jp-terminal-icon-color, var(--jp-layout-color2)); +} + +.jp-terminal-icon-background-color[fill] { + fill: var( + --jp-terminal-icon-background-color, + var(--jp-inverse-layout-color2) + ); +} + +.jp-text-editor-icon-color[fill] { + fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3)); +} + +.jp-inspector-icon-color[fill] { + fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3)); +} + +/* CSS for icons in selected filebrowser listing items */ +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] { + fill: #fff; +} + +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] { + fill: var(--jp-brand-color1); +} + +/* stylelint-disable selector-max-class, selector-max-compound-selectors */ + +/** +* TODO: come up with non css-hack solution for showing the busy icon on top +* of the close icon +* CSS for complex behavior of close icon of tabs in the main area tabbar +*/ +.lm-DockPanel-tabBar + .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty + > .lm-TabBar-tabCloseIcon + > :not(:hover) + > .jp-icon3[fill] { + fill: none; +} + +.lm-DockPanel-tabBar + .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty + > .lm-TabBar-tabCloseIcon + > :not(:hover) + > .jp-icon-busy[fill] { + fill: var(--jp-inverse-layout-color3); +} + +/* stylelint-enable selector-max-class, selector-max-compound-selectors */ + +/* CSS for icons in status bar */ +#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] { + fill: #fff; +} + +#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] { + fill: var(--jp-brand-color1); +} + +/* special handling for splash icon CSS. While the theme CSS reloads during + splash, the splash icon can loose theming. To prevent that, we set a + default for its color variable */ +:root { + --jp-warn-color0: var(--md-orange-700); +} + +/* not sure what to do with this one, used in filebrowser listing */ +.jp-DragIcon { + margin-right: 4px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * Support for alt colors for icons as inline SVG HTMLElements + */ + +/* alt recolor the primary elements of an icon */ +.jp-icon-alt .jp-icon0[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-alt .jp-icon1[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-alt .jp-icon2[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-alt .jp-icon3[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-alt .jp-icon4[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-alt .jp-icon0[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-alt .jp-icon1[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-alt .jp-icon2[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-alt .jp-icon3[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-alt .jp-icon4[stroke] { + stroke: var(--jp-layout-color4); +} + +/* alt recolor the accent elements of an icon */ +.jp-icon-alt .jp-icon-accent0[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-alt .jp-icon-accent1[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-alt .jp-icon-accent2[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-alt .jp-icon-accent3[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-alt .jp-icon-accent4[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-alt .jp-icon-accent0[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-alt .jp-icon-accent1[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-alt .jp-icon-accent2[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-alt .jp-icon-accent3[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-alt .jp-icon-accent4[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content { + display: none !important; +} + +/** + * Support for hover colors for icons as inline SVG HTMLElements + */ + +/** + * regular colors + */ + +/* recolor the primary elements of an icon */ +.jp-icon-hover :hover .jp-icon0-hover[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover :hover .jp-icon1-hover[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover :hover .jp-icon2-hover[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover :hover .jp-icon3-hover[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover :hover .jp-icon4-hover[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-hover :hover .jp-icon0-hover[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover :hover .jp-icon1-hover[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover :hover .jp-icon2-hover[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover :hover .jp-icon3-hover[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover :hover .jp-icon4-hover[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/* recolor the accent elements of an icon */ +.jp-icon-hover :hover .jp-icon-accent0-hover[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-hover :hover .jp-icon-accent1-hover[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-hover :hover .jp-icon-accent2-hover[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-hover :hover .jp-icon-accent3-hover[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-hover :hover .jp-icon-accent4-hover[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] { + stroke: var(--jp-layout-color4); +} + +/* set the color of an icon to transparent */ +.jp-icon-hover :hover .jp-icon-none-hover[fill] { + fill: none; +} + +.jp-icon-hover :hover .jp-icon-none-hover[stroke] { + stroke: none; +} + +/** + * inverse colors + */ + +/* inverse recolor the primary elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] { + stroke: var(--jp-layout-color4); +} + +/* inverse recolor the accent elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-IFrame { + width: 100%; + height: 100%; +} + +.jp-IFrame > iframe { + border: none; +} + +/* +When drag events occur, `lm-mod-override-cursor` is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-IFrame { + position: relative; +} + +body.lm-mod-override-cursor .jp-IFrame::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-HoverBox { + position: fixed; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-FormGroup-content fieldset { + border: none; + padding: 0; + min-width: 0; + width: 100%; +} + +/* stylelint-disable selector-max-type */ + +.jp-FormGroup-content fieldset .jp-inputFieldWrapper input, +.jp-FormGroup-content fieldset .jp-inputFieldWrapper select, +.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea { + font-size: var(--jp-content-font-size2); + border-color: var(--jp-input-border-color); + border-style: solid; + border-radius: var(--jp-border-radius); + border-width: 1px; + padding: 6px 8px; + background: none; + color: var(--jp-ui-font-color0); + height: inherit; +} + +.jp-FormGroup-content fieldset input[type='checkbox'] { + position: relative; + top: 2px; + margin-left: 0; +} + +.jp-FormGroup-content button.jp-mod-styled { + cursor: pointer; +} + +.jp-FormGroup-content .checkbox label { + cursor: pointer; + font-size: var(--jp-content-font-size1); +} + +.jp-FormGroup-content .jp-root > fieldset > legend { + display: none; +} + +.jp-FormGroup-content .jp-root > fieldset > p { + display: none; +} + +/** copy of `input.jp-mod-styled:focus` style */ +.jp-FormGroup-content fieldset input:focus, +.jp-FormGroup-content fieldset select:focus { + -moz-outline-radius: unset; + outline: var(--jp-border-width) solid var(--md-blue-500); + outline-offset: -1px; + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-FormGroup-content fieldset input:hover:not(:focus), +.jp-FormGroup-content fieldset select:hover:not(:focus) { + background-color: var(--jp-border-color2); +} + +/* stylelint-enable selector-max-type */ + +.jp-FormGroup-content .checkbox .field-description { + /* Disable default description field for checkbox: + because other widgets do not have description fields, + we add descriptions to each widget on the field level. + */ + display: none; +} + +.jp-FormGroup-content #root__description { + display: none; +} + +.jp-FormGroup-content .jp-modifiedIndicator { + width: 5px; + background-color: var(--jp-brand-color2); + margin-top: 0; + margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1); + flex-shrink: 0; +} + +.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator { + background-color: var(--jp-error-color0); + margin-right: 0.5em; +} + +/* RJSF ARRAY style */ + +.jp-arrayFieldWrapper legend { + font-size: var(--jp-content-font-size2); + color: var(--jp-ui-font-color0); + flex-basis: 100%; + padding: 4px 0; + font-weight: var(--jp-content-heading-font-weight); + border-bottom: 1px solid var(--jp-border-color2); +} + +.jp-arrayFieldWrapper .field-description { + padding: 4px 0; + white-space: pre-wrap; +} + +.jp-arrayFieldWrapper .array-item { + width: 100%; + border: 1px solid var(--jp-border-color2); + border-radius: 4px; + margin: 4px; +} + +.jp-ArrayOperations { + display: flex; + margin-left: 8px; +} + +.jp-ArrayOperationsButton { + margin: 2px; +} + +.jp-ArrayOperationsButton .jp-icon3[fill] { + fill: var(--jp-ui-font-color0); +} + +button.jp-ArrayOperationsButton.jp-mod-styled:disabled { + cursor: not-allowed; + opacity: 0.5; +} + +/* RJSF form validation error */ + +.jp-FormGroup-content .validationErrors { + color: var(--jp-error-color0); +} + +/* Hide panel level error as duplicated the field level error */ +.jp-FormGroup-content .panel.errors { + display: none; +} + +/* RJSF normal content (settings-editor) */ + +.jp-FormGroup-contentNormal { + display: flex; + align-items: center; + flex-wrap: wrap; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-contentItem { + margin-left: 7px; + color: var(--jp-ui-font-color0); +} + +.jp-FormGroup-contentNormal .jp-FormGroup-description { + flex-basis: 100%; + padding: 4px 7px; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-default { + flex-basis: 100%; + padding: 4px 7px; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel { + font-size: var(--jp-content-font-size1); + font-weight: normal; + min-width: 120px; +} + +.jp-FormGroup-contentNormal fieldset:not(:first-child) { + margin-left: 7px; +} + +.jp-FormGroup-contentNormal .field-array-of-string .array-item { + /* Display `jp-ArrayOperations` buttons side-by-side with content except + for small screens where flex-wrap will place them one below the other. + */ + display: flex; + align-items: center; + flex-wrap: wrap; +} + +.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group { + padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent); + margin-top: 2px; +} + +/* RJSF compact content (metadata-form) */ + +.jp-FormGroup-content.jp-FormGroup-contentCompact { + width: 100%; +} + +.jp-FormGroup-contentCompact .form-group { + display: flex; + padding: 0.5em 0.2em 0.5em 0; +} + +.jp-FormGroup-contentCompact + .jp-FormGroup-compactTitle + .jp-FormGroup-description { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color2); +} + +.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel { + padding-bottom: 0.3em; +} + +.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control { + width: 100%; + box-sizing: border-box; +} + +.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle { + padding-bottom: 7px; +} + +.jp-FormGroup-contentCompact + .jp-objectFieldWrapper + .jp-objectFieldWrapper + .form-group { + padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent); + margin-top: 2px; +} + +.jp-FormGroup-contentCompact ul.error-detail { + margin-block-start: 0.5em; + margin-block-end: 0.5em; + padding-inline-start: 1em; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-SidePanel { + display: flex; + flex-direction: column; + min-width: var(--jp-sidebar-min-width); + overflow-y: auto; + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + font-size: var(--jp-ui-font-size1); +} + +.jp-SidePanel-header { + flex: 0 0 auto; + display: flex; + border-bottom: var(--jp-border-width) solid var(--jp-border-color2); + font-size: var(--jp-ui-font-size0); + font-weight: 600; + letter-spacing: 1px; + margin: 0; + padding: 2px; + text-transform: uppercase; +} + +.jp-SidePanel-toolbar { + flex: 0 0 auto; +} + +.jp-SidePanel-content { + flex: 1 1 auto; +} + +.jp-SidePanel-toolbar, +.jp-AccordionPanel-toolbar { + height: var(--jp-private-toolbar-height); +} + +.jp-SidePanel-toolbar.jp-Toolbar-micro { + display: none; +} + +.lm-AccordionPanel .jp-AccordionPanel-title { + box-sizing: border-box; + line-height: 25px; + margin: 0; + display: flex; + align-items: center; + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + font-size: var(--jp-ui-font-size0); +} + +.jp-AccordionPanel-title { + cursor: pointer; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + text-transform: uppercase; +} + +.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title { + /* Title is rotated for horizontal accordion panel using CSS */ + display: block; + transform-origin: top left; + transform: rotate(-90deg) translate(-100%); +} + +.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel { + user-select: none; + text-overflow: ellipsis; + white-space: nowrap; + overflow: hidden; +} + +.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser { + transform: rotate(-90deg); + margin: auto 0; + height: 16px; +} + +.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser { + transform: rotate(0deg); +} + +.lm-AccordionPanel .jp-AccordionPanel-toolbar { + background: none; + box-shadow: none; + border: none; + margin-left: auto; +} + +.lm-AccordionPanel .lm-SplitPanel-handle:hover { + background: var(--jp-layout-color3); +} + +.jp-text-truncated { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Spinner { + position: absolute; + display: flex; + justify-content: center; + align-items: center; + z-index: 10; + left: 0; + top: 0; + width: 100%; + height: 100%; + background: var(--jp-layout-color0); + outline: none; +} + +.jp-SpinnerContent { + font-size: 10px; + margin: 50px auto; + text-indent: -9999em; + width: 3em; + height: 3em; + border-radius: 50%; + background: var(--jp-brand-color3); + background: linear-gradient( + to right, + #f37626 10%, + rgba(255, 255, 255, 0) 42% + ); + position: relative; + animation: load3 1s infinite linear, fadeIn 1s; +} + +.jp-SpinnerContent::before { + width: 50%; + height: 50%; + background: #f37626; + border-radius: 100% 0 0; + position: absolute; + top: 0; + left: 0; + content: ''; +} + +.jp-SpinnerContent::after { + background: var(--jp-layout-color0); + width: 75%; + height: 75%; + border-radius: 50%; + content: ''; + margin: auto; + position: absolute; + top: 0; + left: 0; + bottom: 0; + right: 0; +} + +@keyframes fadeIn { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} + +@keyframes load3 { + 0% { + transform: rotate(0deg); + } + + 100% { + transform: rotate(360deg); + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +button.jp-mod-styled { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + border: none; + box-sizing: border-box; + text-align: center; + line-height: 32px; + height: 32px; + padding: 0 12px; + letter-spacing: 0.8px; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +input.jp-mod-styled { + background: var(--jp-input-background); + height: 28px; + box-sizing: border-box; + border: var(--jp-border-width) solid var(--jp-border-color1); + padding-left: 7px; + padding-right: 7px; + font-size: var(--jp-ui-font-size2); + color: var(--jp-ui-font-color0); + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +input[type='checkbox'].jp-mod-styled { + appearance: checkbox; + -webkit-appearance: checkbox; + -moz-appearance: checkbox; + height: auto; +} + +input.jp-mod-styled:focus { + border: var(--jp-border-width) solid var(--md-blue-500); + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-select-wrapper { + display: flex; + position: relative; + flex-direction: column; + padding: 1px; + background-color: var(--jp-layout-color1); + box-sizing: border-box; + margin-bottom: 12px; +} + +.jp-select-wrapper:not(.multiple) { + height: 28px; +} + +.jp-select-wrapper.jp-mod-focused select.jp-mod-styled { + border: var(--jp-border-width) solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); + background-color: var(--jp-input-active-background); +} + +select.jp-mod-styled:hover { + cursor: pointer; + color: var(--jp-ui-font-color0); + background-color: var(--jp-input-hover-background); + box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5); +} + +select.jp-mod-styled { + flex: 1 1 auto; + width: 100%; + font-size: var(--jp-ui-font-size2); + background: var(--jp-input-background); + color: var(--jp-ui-font-color0); + padding: 0 25px 0 8px; + border: var(--jp-border-width) solid var(--jp-input-border-color); + border-radius: 0; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +select.jp-mod-styled:not([multiple]) { + height: 32px; +} + +select.jp-mod-styled[multiple] { + max-height: 200px; + overflow-y: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-switch { + display: flex; + align-items: center; + padding-left: 4px; + padding-right: 4px; + font-size: var(--jp-ui-font-size1); + background-color: transparent; + color: var(--jp-ui-font-color1); + border: none; + height: 20px; +} + +.jp-switch:hover { + background-color: var(--jp-layout-color2); +} + +.jp-switch-label { + margin-right: 5px; + font-family: var(--jp-ui-font-family); +} + +.jp-switch-track { + cursor: pointer; + background-color: var(--jp-switch-color, var(--jp-border-color1)); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 34px; + height: 16px; + width: 35px; + position: relative; +} + +.jp-switch-track::before { + content: ''; + position: absolute; + height: 10px; + width: 10px; + margin: 3px; + left: 0; + background-color: var(--jp-ui-inverse-font-color1); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 50%; +} + +.jp-switch[aria-checked='true'] .jp-switch-track { + background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0)); +} + +.jp-switch[aria-checked='true'] .jp-switch-track::before { + /* track width (35) - margins (3 + 3) - thumb width (10) */ + left: 19px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-toolbar-height: calc( + 28px + var(--jp-border-width) + ); /* leave 28px for content */ +} + +.jp-Toolbar { + color: var(--jp-ui-font-color1); + flex: 0 0 auto; + display: flex; + flex-direction: row; + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + background: var(--jp-toolbar-background); + min-height: var(--jp-toolbar-micro-height); + padding: 2px; + z-index: 8; + overflow-x: hidden; +} + +/* Toolbar items */ + +.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer { + flex-grow: 1; + flex-shrink: 1; +} + +.jp-Toolbar-item.jp-Toolbar-kernelStatus { + display: inline-block; + width: 32px; + background-repeat: no-repeat; + background-position: center; + background-size: 16px; +} + +.jp-Toolbar > .jp-Toolbar-item { + flex: 0 0 auto; + display: flex; + padding-left: 1px; + padding-right: 1px; + font-size: var(--jp-ui-font-size1); + line-height: var(--jp-private-toolbar-height); + height: 100%; +} + +/* Toolbar buttons */ + +/* This is the div we use to wrap the react component into a Widget */ +div.jp-ToolbarButton { + color: transparent; + border: none; + box-sizing: border-box; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; + padding: 0; + margin: 0; +} + +button.jp-ToolbarButtonComponent { + background: var(--jp-layout-color1); + border: none; + box-sizing: border-box; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; + padding: 0 6px; + margin: 0; + height: 24px; + border-radius: var(--jp-border-radius); + display: flex; + align-items: center; + text-align: center; + font-size: 14px; + min-width: unset; + min-height: unset; +} + +button.jp-ToolbarButtonComponent:disabled { + opacity: 0.4; +} + +button.jp-ToolbarButtonComponent > span { + padding: 0; + flex: 0 0 auto; +} + +button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label { + font-size: var(--jp-ui-font-size1); + line-height: 100%; + padding-left: 2px; + color: var(--jp-ui-font-color1); + font-family: var(--jp-ui-font-family); +} + +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar.jp-Toolbar-micro { + padding: 0; + min-height: 0; +} + +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar { + border: none; + box-shadow: none; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-WindowedPanel-outer { + position: relative; + overflow-y: auto; +} + +.jp-WindowedPanel-inner { + position: relative; +} + +.jp-WindowedPanel-window { + position: absolute; + left: 0; + right: 0; + overflow: visible; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* Sibling imports */ + +body { + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); +} + +/* Disable native link decoration styles everywhere outside of dialog boxes */ +a { + text-decoration: unset; + color: unset; +} + +a:hover { + text-decoration: unset; + color: unset; +} + +/* Accessibility for links inside dialog box text */ +.jp-Dialog-content a { + text-decoration: revert; + color: var(--jp-content-link-color); +} + +.jp-Dialog-content a:hover { + text-decoration: revert; +} + +/* Styles for ui-components */ +.jp-Button { + color: var(--jp-ui-font-color2); + border-radius: var(--jp-border-radius); + padding: 0 12px; + font-size: var(--jp-ui-font-size1); + + /* Copy from blueprint 3 */ + display: inline-flex; + flex-direction: row; + border: none; + cursor: pointer; + align-items: center; + justify-content: center; + text-align: left; + vertical-align: middle; + min-height: 30px; + min-width: 30px; +} + +.jp-Button:disabled { + cursor: not-allowed; +} + +.jp-Button:empty { + padding: 0 !important; +} + +.jp-Button.jp-mod-small { + min-height: 24px; + min-width: 24px; + font-size: 12px; + padding: 0 7px; +} + +/* Use our own theme for hover styles */ +.jp-Button.jp-mod-minimal:hover { + background-color: var(--jp-layout-color2); +} + +.jp-Button.jp-mod-minimal { + background: none; +} + +.jp-InputGroup { + display: block; + position: relative; +} + +.jp-InputGroup input { + box-sizing: border-box; + border: none; + border-radius: 0; + background-color: transparent; + color: var(--jp-ui-font-color0); + box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color); + padding-bottom: 0; + padding-top: 0; + padding-left: 10px; + padding-right: 28px; + position: relative; + width: 100%; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; + font-size: 14px; + font-weight: 400; + height: 30px; + line-height: 30px; + outline: none; + vertical-align: middle; +} + +.jp-InputGroup input:focus { + box-shadow: inset 0 0 0 var(--jp-border-width) + var(--jp-input-active-box-shadow-color), + inset 0 0 0 3px var(--jp-input-active-box-shadow-color); +} + +.jp-InputGroup input:disabled { + cursor: not-allowed; + resize: block; + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color2); +} + +.jp-InputGroup input:disabled ~ span { + cursor: not-allowed; + color: var(--jp-ui-font-color2); +} + +.jp-InputGroup input::placeholder, +input::placeholder { + color: var(--jp-ui-font-color2); +} + +.jp-InputGroupAction { + position: absolute; + bottom: 1px; + right: 0; + padding: 6px; +} + +.jp-HTMLSelect.jp-DefaultStyle select { + background-color: initial; + border: none; + border-radius: 0; + box-shadow: none; + color: var(--jp-ui-font-color0); + display: block; + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + height: 24px; + line-height: 14px; + padding: 0 25px 0 10px; + text-align: left; + -moz-appearance: none; + -webkit-appearance: none; +} + +.jp-HTMLSelect.jp-DefaultStyle select:disabled { + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color2); + cursor: not-allowed; + resize: block; +} + +.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span { + cursor: not-allowed; +} + +/* Use our own theme for hover and option styles */ +/* stylelint-disable-next-line selector-max-type */ +.jp-HTMLSelect.jp-DefaultStyle select:hover, +.jp-HTMLSelect.jp-DefaultStyle select > option { + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color0); +} + +select { + box-sizing: border-box; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-StatusBar-Widget { + display: flex; + align-items: center; + background: var(--jp-layout-color2); + min-height: var(--jp-statusbar-height); + justify-content: space-between; + padding: 0 10px; +} + +.jp-StatusBar-Left { + display: flex; + align-items: center; + flex-direction: row; +} + +.jp-StatusBar-Middle { + display: flex; + align-items: center; +} + +.jp-StatusBar-Right { + display: flex; + align-items: center; + flex-direction: row-reverse; +} + +.jp-StatusBar-Item { + max-height: var(--jp-statusbar-height); + margin: 0 2px; + height: var(--jp-statusbar-height); + white-space: nowrap; + text-overflow: ellipsis; + color: var(--jp-ui-font-color1); + padding: 0 6px; +} + +.jp-mod-highlighted:hover { + background-color: var(--jp-layout-color3); +} + +.jp-mod-clicked { + background-color: var(--jp-brand-color1); +} + +.jp-mod-clicked:hover { + background-color: var(--jp-brand-color0); +} + +.jp-mod-clicked .jp-StatusBar-TextItem { + color: var(--jp-ui-inverse-font-color1); +} + +.jp-StatusBar-HoverItem { + box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)'; +} + +.jp-StatusBar-TextItem { + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + line-height: 24px; + color: var(--jp-ui-font-color1); +} + +.jp-StatusBar-GroupItem { + display: flex; + align-items: center; + flex-direction: row; +} + +.jp-Statusbar-ProgressCircle svg { + display: block; + margin: 0 auto; + width: 16px; + height: 24px; + align-self: normal; +} + +.jp-Statusbar-ProgressCircle path { + fill: var(--jp-inverse-layout-color3); +} + +.jp-Statusbar-ProgressBar-progress-bar { + height: 10px; + width: 100px; + border: solid 0.25px var(--jp-brand-color2); + border-radius: 3px; + overflow: hidden; + align-self: center; +} + +.jp-Statusbar-ProgressBar-progress-bar > div { + background-color: var(--jp-brand-color2); + background-image: linear-gradient( + -45deg, + rgba(255, 255, 255, 0.2) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.2) 50%, + rgba(255, 255, 255, 0.2) 75%, + transparent 75%, + transparent + ); + background-size: 40px 40px; + float: left; + width: 0%; + height: 100%; + font-size: 12px; + line-height: 14px; + color: #fff; + text-align: center; + animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite; +} + +.jp-Statusbar-ProgressBar-progress-bar p { + color: var(--jp-ui-font-color1); + font-family: var(--jp-ui-font-family); + font-size: var(--jp-ui-font-size1); + line-height: 10px; + width: 100px; +} + +@keyframes jp-Statusbar-ExecutionTime-progress-bar { + 0% { + background-position: 0 0; + } + + 100% { + background-position: 40px 40px; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-commandpalette-search-height: 28px; +} + +/*----------------------------------------------------------------------------- +| Overall styles +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette { + padding-bottom: 0; + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); +} + +/*----------------------------------------------------------------------------- +| Modal variant +|----------------------------------------------------------------------------*/ + +.jp-ModalCommandPalette { + position: absolute; + z-index: 10000; + top: 38px; + left: 30%; + margin: 0; + padding: 4px; + width: 40%; + box-shadow: var(--jp-elevation-z4); + border-radius: 4px; + background: var(--jp-layout-color0); +} + +.jp-ModalCommandPalette .lm-CommandPalette { + max-height: 40vh; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item { + margin-left: 4px; + margin-right: 4px; +} + +.jp-ModalCommandPalette + .lm-CommandPalette + .lm-CommandPalette-item.lm-mod-disabled { + display: none; +} + +/*----------------------------------------------------------------------------- +| Search +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette-search { + padding: 4px; + background-color: var(--jp-layout-color1); + z-index: 2; +} + +.lm-CommandPalette-wrapper { + overflow: overlay; + padding: 0 9px; + background-color: var(--jp-input-active-background); + height: 30px; + box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color); +} + +.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper { + box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color), + inset 0 0 0 3px var(--jp-input-active-box-shadow-color); +} + +.jp-SearchIconGroup { + color: white; + background-color: var(--jp-brand-color1); + position: absolute; + top: 4px; + right: 4px; + padding: 5px 5px 1px; +} + +.jp-SearchIconGroup svg { + height: 20px; + width: 20px; +} + +.jp-SearchIconGroup .jp-icon3[fill] { + fill: var(--jp-layout-color0); +} + +.lm-CommandPalette-input { + background: transparent; + width: calc(100% - 18px); + float: left; + border: none; + outline: none; + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + line-height: var(--jp-private-commandpalette-search-height); +} + +.lm-CommandPalette-input::-webkit-input-placeholder, +.lm-CommandPalette-input::-moz-placeholder, +.lm-CommandPalette-input:-ms-input-placeholder { + color: var(--jp-ui-font-color2); + font-size: var(--jp-ui-font-size1); +} + +/*----------------------------------------------------------------------------- +| Results +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette-header:first-child { + margin-top: 0; +} + +.lm-CommandPalette-header { + border-bottom: solid var(--jp-border-width) var(--jp-border-color2); + color: var(--jp-ui-font-color1); + cursor: pointer; + display: flex; + font-size: var(--jp-ui-font-size0); + font-weight: 600; + letter-spacing: 1px; + margin-top: 8px; + padding: 8px 0 8px 12px; + text-transform: uppercase; +} + +.lm-CommandPalette-header.lm-mod-active { + background: var(--jp-layout-color2); +} + +.lm-CommandPalette-header > mark { + background-color: transparent; + font-weight: bold; + color: var(--jp-ui-font-color1); +} + +.lm-CommandPalette-item { + padding: 4px 12px 4px 4px; + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + font-weight: 400; + display: flex; +} + +.lm-CommandPalette-item.lm-mod-disabled { + color: var(--jp-ui-font-color2); +} + +.lm-CommandPalette-item.lm-mod-active { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark { + color: var(--jp-ui-inverse-font-color0); +} + +.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] { + fill: var(--jp-layout-color0); +} + +.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) { + background: var(--jp-layout-color2); +} + +.lm-CommandPalette-itemContent { + overflow: hidden; +} + +.lm-CommandPalette-itemLabel > mark { + color: var(--jp-ui-font-color0); + background-color: transparent; + font-weight: bold; +} + +.lm-CommandPalette-item.lm-mod-disabled mark { + color: var(--jp-ui-font-color2); +} + +.lm-CommandPalette-item .lm-CommandPalette-itemIcon { + margin: 0 4px 0 0; + position: relative; + width: 16px; + top: 2px; + flex: 0 0 auto; +} + +.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon { + opacity: 0.6; +} + +.lm-CommandPalette-item .lm-CommandPalette-itemShortcut { + flex: 0 0 auto; +} + +.lm-CommandPalette-itemCaption { + display: none; +} + +.lm-CommandPalette-content { + background-color: var(--jp-layout-color1); +} + +.lm-CommandPalette-content:empty::after { + content: 'No results'; + margin: auto; + margin-top: 20px; + width: 100px; + display: block; + font-size: var(--jp-ui-font-size2); + font-family: var(--jp-ui-font-family); + font-weight: lighter; +} + +.lm-CommandPalette-emptyMessage { + text-align: center; + margin-top: 24px; + line-height: 1.32; + padding: 0 8px; + color: var(--jp-content-font-color3); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Dialog { + position: absolute; + z-index: 10000; + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + top: 0; + left: 0; + margin: 0; + padding: 0; + width: 100%; + height: 100%; + background: var(--jp-dialog-background); +} + +.jp-Dialog-content { + display: flex; + flex-direction: column; + margin-left: auto; + margin-right: auto; + background: var(--jp-layout-color1); + padding: 24px 24px 12px; + min-width: 300px; + min-height: 150px; + max-width: 1000px; + max-height: 500px; + box-sizing: border-box; + box-shadow: var(--jp-elevation-z20); + word-wrap: break-word; + border-radius: var(--jp-border-radius); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color1); + resize: both; +} + +.jp-Dialog-content.jp-Dialog-content-small { + max-width: 500px; +} + +.jp-Dialog-button { + overflow: visible; +} + +button.jp-Dialog-button:focus { + outline: 1px solid var(--jp-brand-color1); + outline-offset: 4px; + -moz-outline-radius: 0; +} + +button.jp-Dialog-button:focus::-moz-focus-inner { + border: 0; +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus { + outline-offset: 4px; + -moz-outline-radius: 0; +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus { + outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1)); +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus { + outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1)); +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus { + outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600)); +} + +button.jp-Dialog-close-button { + padding: 0; + height: 100%; + min-width: unset; + min-height: unset; +} + +.jp-Dialog-header { + display: flex; + justify-content: space-between; + flex: 0 0 auto; + padding-bottom: 12px; + font-size: var(--jp-ui-font-size3); + font-weight: 400; + color: var(--jp-ui-font-color1); +} + +.jp-Dialog-body { + display: flex; + flex-direction: column; + flex: 1 1 auto; + font-size: var(--jp-ui-font-size1); + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + overflow: auto; +} + +.jp-Dialog-footer { + display: flex; + flex-direction: row; + justify-content: flex-end; + align-items: center; + flex: 0 0 auto; + margin-left: -12px; + margin-right: -12px; + padding: 12px; +} + +.jp-Dialog-checkbox { + padding-right: 5px; +} + +.jp-Dialog-checkbox > input:focus-visible { + outline: 1px solid var(--jp-input-active-border-color); + outline-offset: 1px; +} + +.jp-Dialog-spacer { + flex: 1 1 auto; +} + +.jp-Dialog-title { + overflow: hidden; + white-space: nowrap; + text-overflow: ellipsis; +} + +.jp-Dialog-body > .jp-select-wrapper { + width: 100%; +} + +.jp-Dialog-body > button { + padding: 0 16px; +} + +.jp-Dialog-body > label { + line-height: 1.4; + color: var(--jp-ui-font-color0); +} + +.jp-Dialog-button.jp-mod-styled:not(:last-child) { + margin-right: 12px; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-Input-Boolean-Dialog { + flex-direction: row-reverse; + align-items: end; + width: 100%; +} + +.jp-Input-Boolean-Dialog > label { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-MainAreaWidget > :focus { + outline: none; +} + +.jp-MainAreaWidget .jp-MainAreaWidget-error { + padding: 6px; +} + +.jp-MainAreaWidget .jp-MainAreaWidget-error > pre { + width: auto; + padding: 10px; + background: var(--jp-error-color3); + border: var(--jp-border-width) solid var(--jp-error-color1); + border-radius: var(--jp-border-radius); + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + white-space: pre-wrap; + word-wrap: break-word; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/** + * google-material-color v1.2.6 + * https://github.com/danlevan/google-material-color + */ +:root { + --md-red-50: #ffebee; + --md-red-100: #ffcdd2; + --md-red-200: #ef9a9a; + --md-red-300: #e57373; + --md-red-400: #ef5350; + --md-red-500: #f44336; + --md-red-600: #e53935; + --md-red-700: #d32f2f; + --md-red-800: #c62828; + --md-red-900: #b71c1c; + --md-red-A100: #ff8a80; + --md-red-A200: #ff5252; + --md-red-A400: #ff1744; + --md-red-A700: #d50000; + --md-pink-50: #fce4ec; + --md-pink-100: #f8bbd0; + --md-pink-200: #f48fb1; + --md-pink-300: #f06292; + --md-pink-400: #ec407a; + --md-pink-500: #e91e63; + --md-pink-600: #d81b60; + --md-pink-700: #c2185b; + --md-pink-800: #ad1457; + --md-pink-900: #880e4f; + --md-pink-A100: #ff80ab; + --md-pink-A200: #ff4081; + --md-pink-A400: #f50057; + --md-pink-A700: #c51162; + --md-purple-50: #f3e5f5; + --md-purple-100: #e1bee7; + --md-purple-200: #ce93d8; + --md-purple-300: #ba68c8; + --md-purple-400: #ab47bc; + --md-purple-500: #9c27b0; + --md-purple-600: #8e24aa; + --md-purple-700: #7b1fa2; + --md-purple-800: #6a1b9a; + --md-purple-900: #4a148c; + --md-purple-A100: #ea80fc; + --md-purple-A200: #e040fb; + --md-purple-A400: #d500f9; + --md-purple-A700: #a0f; + --md-deep-purple-50: #ede7f6; + --md-deep-purple-100: #d1c4e9; + --md-deep-purple-200: #b39ddb; + --md-deep-purple-300: #9575cd; + --md-deep-purple-400: #7e57c2; + --md-deep-purple-500: #673ab7; + --md-deep-purple-600: #5e35b1; + --md-deep-purple-700: #512da8; + --md-deep-purple-800: #4527a0; + --md-deep-purple-900: #311b92; + --md-deep-purple-A100: #b388ff; + --md-deep-purple-A200: #7c4dff; + --md-deep-purple-A400: #651fff; + --md-deep-purple-A700: #6200ea; + --md-indigo-50: #e8eaf6; + --md-indigo-100: #c5cae9; + --md-indigo-200: #9fa8da; + --md-indigo-300: #7986cb; + --md-indigo-400: #5c6bc0; + --md-indigo-500: #3f51b5; + --md-indigo-600: #3949ab; + --md-indigo-700: #303f9f; + --md-indigo-800: #283593; + --md-indigo-900: #1a237e; + --md-indigo-A100: #8c9eff; + --md-indigo-A200: #536dfe; + --md-indigo-A400: #3d5afe; + --md-indigo-A700: #304ffe; + --md-blue-50: #e3f2fd; + --md-blue-100: #bbdefb; + --md-blue-200: #90caf9; + --md-blue-300: #64b5f6; + --md-blue-400: #42a5f5; + --md-blue-500: #2196f3; + --md-blue-600: #1e88e5; + --md-blue-700: #1976d2; + --md-blue-800: #1565c0; + --md-blue-900: #0d47a1; + --md-blue-A100: #82b1ff; + --md-blue-A200: #448aff; + --md-blue-A400: #2979ff; + --md-blue-A700: #2962ff; + --md-light-blue-50: #e1f5fe; + --md-light-blue-100: #b3e5fc; + --md-light-blue-200: #81d4fa; + --md-light-blue-300: #4fc3f7; + --md-light-blue-400: #29b6f6; + --md-light-blue-500: #03a9f4; + --md-light-blue-600: #039be5; + --md-light-blue-700: #0288d1; + --md-light-blue-800: #0277bd; + --md-light-blue-900: #01579b; + --md-light-blue-A100: #80d8ff; + --md-light-blue-A200: #40c4ff; + --md-light-blue-A400: #00b0ff; + --md-light-blue-A700: #0091ea; + --md-cyan-50: #e0f7fa; + --md-cyan-100: #b2ebf2; + --md-cyan-200: #80deea; + --md-cyan-300: #4dd0e1; + --md-cyan-400: #26c6da; + --md-cyan-500: #00bcd4; + --md-cyan-600: #00acc1; + --md-cyan-700: #0097a7; + --md-cyan-800: #00838f; + --md-cyan-900: #006064; + --md-cyan-A100: #84ffff; + --md-cyan-A200: #18ffff; + --md-cyan-A400: #00e5ff; + --md-cyan-A700: #00b8d4; + --md-teal-50: #e0f2f1; + --md-teal-100: #b2dfdb; + --md-teal-200: #80cbc4; + --md-teal-300: #4db6ac; + --md-teal-400: #26a69a; + --md-teal-500: #009688; + --md-teal-600: #00897b; + --md-teal-700: #00796b; + --md-teal-800: #00695c; + --md-teal-900: #004d40; + --md-teal-A100: #a7ffeb; + --md-teal-A200: #64ffda; + --md-teal-A400: #1de9b6; + --md-teal-A700: #00bfa5; + --md-green-50: #e8f5e9; + --md-green-100: #c8e6c9; + --md-green-200: #a5d6a7; + --md-green-300: #81c784; + --md-green-400: #66bb6a; + --md-green-500: #4caf50; + --md-green-600: #43a047; + --md-green-700: #388e3c; + --md-green-800: #2e7d32; + --md-green-900: #1b5e20; + --md-green-A100: #b9f6ca; + --md-green-A200: #69f0ae; + --md-green-A400: #00e676; + --md-green-A700: #00c853; + --md-light-green-50: #f1f8e9; + --md-light-green-100: #dcedc8; + --md-light-green-200: #c5e1a5; + --md-light-green-300: #aed581; + --md-light-green-400: #9ccc65; + --md-light-green-500: #8bc34a; + --md-light-green-600: #7cb342; + --md-light-green-700: #689f38; + --md-light-green-800: #558b2f; + --md-light-green-900: #33691e; + --md-light-green-A100: #ccff90; + --md-light-green-A200: #b2ff59; + --md-light-green-A400: #76ff03; + --md-light-green-A700: #64dd17; + --md-lime-50: #f9fbe7; + --md-lime-100: #f0f4c3; + --md-lime-200: #e6ee9c; + --md-lime-300: #dce775; + --md-lime-400: #d4e157; + --md-lime-500: #cddc39; + --md-lime-600: #c0ca33; + --md-lime-700: #afb42b; + --md-lime-800: #9e9d24; + --md-lime-900: #827717; + --md-lime-A100: #f4ff81; + --md-lime-A200: #eeff41; + --md-lime-A400: #c6ff00; + --md-lime-A700: #aeea00; + --md-yellow-50: #fffde7; + --md-yellow-100: #fff9c4; + --md-yellow-200: #fff59d; + --md-yellow-300: #fff176; + --md-yellow-400: #ffee58; + --md-yellow-500: #ffeb3b; + --md-yellow-600: #fdd835; + --md-yellow-700: #fbc02d; + --md-yellow-800: #f9a825; + --md-yellow-900: #f57f17; + --md-yellow-A100: #ffff8d; + --md-yellow-A200: #ff0; + --md-yellow-A400: #ffea00; + --md-yellow-A700: #ffd600; + --md-amber-50: #fff8e1; + --md-amber-100: #ffecb3; + --md-amber-200: #ffe082; + --md-amber-300: #ffd54f; + --md-amber-400: #ffca28; + --md-amber-500: #ffc107; + --md-amber-600: #ffb300; + --md-amber-700: #ffa000; + --md-amber-800: #ff8f00; + --md-amber-900: #ff6f00; + --md-amber-A100: #ffe57f; + --md-amber-A200: #ffd740; + --md-amber-A400: #ffc400; + --md-amber-A700: #ffab00; + --md-orange-50: #fff3e0; + --md-orange-100: #ffe0b2; + --md-orange-200: #ffcc80; + --md-orange-300: #ffb74d; + --md-orange-400: #ffa726; + --md-orange-500: #ff9800; + --md-orange-600: #fb8c00; + --md-orange-700: #f57c00; + --md-orange-800: #ef6c00; + --md-orange-900: #e65100; + --md-orange-A100: #ffd180; + --md-orange-A200: #ffab40; + --md-orange-A400: #ff9100; + --md-orange-A700: #ff6d00; + --md-deep-orange-50: #fbe9e7; + --md-deep-orange-100: #ffccbc; + --md-deep-orange-200: #ffab91; + --md-deep-orange-300: #ff8a65; + --md-deep-orange-400: #ff7043; + --md-deep-orange-500: #ff5722; + --md-deep-orange-600: #f4511e; + --md-deep-orange-700: #e64a19; + --md-deep-orange-800: #d84315; + --md-deep-orange-900: #bf360c; + --md-deep-orange-A100: #ff9e80; + --md-deep-orange-A200: #ff6e40; + --md-deep-orange-A400: #ff3d00; + --md-deep-orange-A700: #dd2c00; + --md-brown-50: #efebe9; + --md-brown-100: #d7ccc8; + --md-brown-200: #bcaaa4; + --md-brown-300: #a1887f; + --md-brown-400: #8d6e63; + --md-brown-500: #795548; + --md-brown-600: #6d4c41; + --md-brown-700: #5d4037; + --md-brown-800: #4e342e; + --md-brown-900: #3e2723; + --md-grey-50: #fafafa; + --md-grey-100: #f5f5f5; + --md-grey-200: #eee; + --md-grey-300: #e0e0e0; + --md-grey-400: #bdbdbd; + --md-grey-500: #9e9e9e; + --md-grey-600: #757575; + --md-grey-700: #616161; + --md-grey-800: #424242; + --md-grey-900: #212121; + --md-blue-grey-50: #eceff1; + --md-blue-grey-100: #cfd8dc; + --md-blue-grey-200: #b0bec5; + --md-blue-grey-300: #90a4ae; + --md-blue-grey-400: #78909c; + --md-blue-grey-500: #607d8b; + --md-blue-grey-600: #546e7a; + --md-blue-grey-700: #455a64; + --md-blue-grey-800: #37474f; + --md-blue-grey-900: #263238; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| RenderedText +|----------------------------------------------------------------------------*/ + +:root { + /* This is the padding value to fill the gaps between lines containing spans with background color. */ + --jp-private-code-span-padding: calc( + (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2 + ); +} + +.jp-RenderedText { + text-align: left; + padding-left: var(--jp-code-padding); + line-height: var(--jp-code-line-height); + font-family: var(--jp-code-font-family); +} + +.jp-RenderedText pre, +.jp-RenderedJavaScript pre, +.jp-RenderedHTMLCommon pre { + color: var(--jp-content-font-color1); + font-size: var(--jp-code-font-size); + border: none; + margin: 0; + padding: 0; +} + +.jp-RenderedText pre a:link { + text-decoration: none; + color: var(--jp-content-link-color); +} + +.jp-RenderedText pre a:hover { + text-decoration: underline; + color: var(--jp-content-link-color); +} + +.jp-RenderedText pre a:visited { + text-decoration: none; + color: var(--jp-content-link-color); +} + +/* console foregrounds and backgrounds */ +.jp-RenderedText pre .ansi-black-fg { + color: #3e424d; +} + +.jp-RenderedText pre .ansi-red-fg { + color: #e75c58; +} + +.jp-RenderedText pre .ansi-green-fg { + color: #00a250; +} + +.jp-RenderedText pre .ansi-yellow-fg { + color: #ddb62b; +} + +.jp-RenderedText pre .ansi-blue-fg { + color: #208ffb; +} + +.jp-RenderedText pre .ansi-magenta-fg { + color: #d160c4; +} + +.jp-RenderedText pre .ansi-cyan-fg { + color: #60c6c8; +} + +.jp-RenderedText pre .ansi-white-fg { + color: #c5c1b4; +} + +.jp-RenderedText pre .ansi-black-bg { + background-color: #3e424d; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-red-bg { + background-color: #e75c58; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-green-bg { + background-color: #00a250; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-yellow-bg { + background-color: #ddb62b; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-blue-bg { + background-color: #208ffb; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-magenta-bg { + background-color: #d160c4; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-cyan-bg { + background-color: #60c6c8; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-white-bg { + background-color: #c5c1b4; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-black-intense-fg { + color: #282c36; +} + +.jp-RenderedText pre .ansi-red-intense-fg { + color: #b22b31; +} + +.jp-RenderedText pre .ansi-green-intense-fg { + color: #007427; +} + +.jp-RenderedText pre .ansi-yellow-intense-fg { + color: #b27d12; +} + +.jp-RenderedText pre .ansi-blue-intense-fg { + color: #0065ca; +} + +.jp-RenderedText pre .ansi-magenta-intense-fg { + color: #a03196; +} + +.jp-RenderedText pre .ansi-cyan-intense-fg { + color: #258f8f; +} + +.jp-RenderedText pre .ansi-white-intense-fg { + color: #a1a6b2; +} + +.jp-RenderedText pre .ansi-black-intense-bg { + background-color: #282c36; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-red-intense-bg { + background-color: #b22b31; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-green-intense-bg { + background-color: #007427; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-yellow-intense-bg { + background-color: #b27d12; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-blue-intense-bg { + background-color: #0065ca; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-magenta-intense-bg { + background-color: #a03196; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-cyan-intense-bg { + background-color: #258f8f; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-white-intense-bg { + background-color: #a1a6b2; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-default-inverse-fg { + color: var(--jp-ui-inverse-font-color0); +} + +.jp-RenderedText pre .ansi-default-inverse-bg { + background-color: var(--jp-inverse-layout-color0); + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-bold { + font-weight: bold; +} + +.jp-RenderedText pre .ansi-underline { + text-decoration: underline; +} + +.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] { + background: var(--jp-rendermime-error-background); + padding-top: var(--jp-code-padding); +} + +/*----------------------------------------------------------------------------- +| RenderedLatex +|----------------------------------------------------------------------------*/ + +.jp-RenderedLatex { + color: var(--jp-content-font-color1); + font-size: var(--jp-content-font-size1); + line-height: var(--jp-content-line-height); +} + +/* Left-justify outputs.*/ +.jp-OutputArea-output.jp-RenderedLatex { + padding: var(--jp-code-padding); + text-align: left; +} + +/*----------------------------------------------------------------------------- +| RenderedHTML +|----------------------------------------------------------------------------*/ + +.jp-RenderedHTMLCommon { + color: var(--jp-content-font-color1); + font-family: var(--jp-content-font-family); + font-size: var(--jp-content-font-size1); + line-height: var(--jp-content-line-height); + + /* Give a bit more R padding on Markdown text to keep line lengths reasonable */ + padding-right: 20px; +} + +.jp-RenderedHTMLCommon em { + font-style: italic; +} + +.jp-RenderedHTMLCommon strong { + font-weight: bold; +} + +.jp-RenderedHTMLCommon u { + text-decoration: underline; +} + +.jp-RenderedHTMLCommon a:link { + text-decoration: none; + color: var(--jp-content-link-color); +} + +.jp-RenderedHTMLCommon a:hover { + text-decoration: underline; + color: var(--jp-content-link-color); +} + +.jp-RenderedHTMLCommon a:visited { + text-decoration: none; + color: var(--jp-content-link-color); +} + +/* Headings */ + +.jp-RenderedHTMLCommon h1, +.jp-RenderedHTMLCommon h2, +.jp-RenderedHTMLCommon h3, +.jp-RenderedHTMLCommon h4, +.jp-RenderedHTMLCommon h5, +.jp-RenderedHTMLCommon h6 { + line-height: var(--jp-content-heading-line-height); + font-weight: var(--jp-content-heading-font-weight); + font-style: normal; + margin: var(--jp-content-heading-margin-top) 0 + var(--jp-content-heading-margin-bottom) 0; +} + +.jp-RenderedHTMLCommon h1:first-child, +.jp-RenderedHTMLCommon h2:first-child, +.jp-RenderedHTMLCommon h3:first-child, +.jp-RenderedHTMLCommon h4:first-child, +.jp-RenderedHTMLCommon h5:first-child, +.jp-RenderedHTMLCommon h6:first-child { + margin-top: calc(0.5 * var(--jp-content-heading-margin-top)); +} + +.jp-RenderedHTMLCommon h1:last-child, +.jp-RenderedHTMLCommon h2:last-child, +.jp-RenderedHTMLCommon h3:last-child, +.jp-RenderedHTMLCommon h4:last-child, +.jp-RenderedHTMLCommon h5:last-child, +.jp-RenderedHTMLCommon h6:last-child { + margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom)); +} + +.jp-RenderedHTMLCommon h1 { + font-size: var(--jp-content-font-size5); +} + +.jp-RenderedHTMLCommon h2 { + font-size: var(--jp-content-font-size4); +} + +.jp-RenderedHTMLCommon h3 { + font-size: var(--jp-content-font-size3); +} + +.jp-RenderedHTMLCommon h4 { + font-size: var(--jp-content-font-size2); +} + +.jp-RenderedHTMLCommon h5 { + font-size: var(--jp-content-font-size1); +} + +.jp-RenderedHTMLCommon h6 { + font-size: var(--jp-content-font-size0); +} + +/* Lists */ + +/* stylelint-disable selector-max-type, selector-max-compound-selectors */ + +.jp-RenderedHTMLCommon ul:not(.list-inline), +.jp-RenderedHTMLCommon ol:not(.list-inline) { + padding-left: 2em; +} + +.jp-RenderedHTMLCommon ul { + list-style: disc; +} + +.jp-RenderedHTMLCommon ul ul { + list-style: square; +} + +.jp-RenderedHTMLCommon ul ul ul { + list-style: circle; +} + +.jp-RenderedHTMLCommon ol { + list-style: decimal; +} + +.jp-RenderedHTMLCommon ol ol { + list-style: upper-alpha; +} + +.jp-RenderedHTMLCommon ol ol ol { + list-style: lower-alpha; +} + +.jp-RenderedHTMLCommon ol ol ol ol { + list-style: lower-roman; +} + +.jp-RenderedHTMLCommon ol ol ol ol ol { + list-style: decimal; +} + +.jp-RenderedHTMLCommon ol, +.jp-RenderedHTMLCommon ul { + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon ul ul, +.jp-RenderedHTMLCommon ul ol, +.jp-RenderedHTMLCommon ol ul, +.jp-RenderedHTMLCommon ol ol { + margin-bottom: 0; +} + +/* stylelint-enable selector-max-type, selector-max-compound-selectors */ + +.jp-RenderedHTMLCommon hr { + color: var(--jp-border-color2); + background-color: var(--jp-border-color1); + margin-top: 1em; + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon > pre { + margin: 1.5em 2em; +} + +.jp-RenderedHTMLCommon pre, +.jp-RenderedHTMLCommon code { + border: 0; + background-color: var(--jp-layout-color0); + color: var(--jp-content-font-color1); + font-family: var(--jp-code-font-family); + font-size: inherit; + line-height: var(--jp-code-line-height); + padding: 0; + white-space: pre-wrap; +} + +.jp-RenderedHTMLCommon :not(pre) > code { + background-color: var(--jp-layout-color2); + padding: 1px 5px; +} + +/* Tables */ + +.jp-RenderedHTMLCommon table { + border-collapse: collapse; + border-spacing: 0; + border: none; + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + table-layout: fixed; + margin-left: auto; + margin-bottom: 1em; + margin-right: auto; +} + +.jp-RenderedHTMLCommon thead { + border-bottom: var(--jp-border-width) solid var(--jp-border-color1); + vertical-align: bottom; +} + +.jp-RenderedHTMLCommon td, +.jp-RenderedHTMLCommon th, +.jp-RenderedHTMLCommon tr { + vertical-align: middle; + padding: 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} + +.jp-RenderedMarkdown.jp-RenderedHTMLCommon td, +.jp-RenderedMarkdown.jp-RenderedHTMLCommon th { + max-width: none; +} + +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr { + text-align: right; +} + +.jp-RenderedHTMLCommon th { + font-weight: bold; +} + +.jp-RenderedHTMLCommon tbody tr:nth-child(odd) { + background: var(--jp-layout-color0); +} + +.jp-RenderedHTMLCommon tbody tr:nth-child(even) { + background: var(--jp-rendermime-table-row-background); +} + +.jp-RenderedHTMLCommon tbody tr:hover { + background: var(--jp-rendermime-table-row-hover-background); +} + +.jp-RenderedHTMLCommon p { + text-align: left; + margin: 0; + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon img { + -moz-force-broken-image-icon: 1; +} + +/* Restrict to direct children as other images could be nested in other content. */ +.jp-RenderedHTMLCommon > img { + display: block; + margin-left: 0; + margin-right: 0; + margin-bottom: 1em; +} + +/* Change color behind transparent images if they need it... */ +[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background { + background-color: var(--jp-inverse-layout-color1); +} + +[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background { + background-color: var(--jp-inverse-layout-color1); +} + +.jp-RenderedHTMLCommon img, +.jp-RenderedImage img, +.jp-RenderedHTMLCommon svg, +.jp-RenderedSVG svg { + max-width: 100%; + height: auto; +} + +.jp-RenderedHTMLCommon img.jp-mod-unconfined, +.jp-RenderedImage img.jp-mod-unconfined, +.jp-RenderedHTMLCommon svg.jp-mod-unconfined, +.jp-RenderedSVG svg.jp-mod-unconfined { + max-width: none; +} + +.jp-RenderedHTMLCommon .alert { + padding: var(--jp-notebook-padding); + border: var(--jp-border-width) solid transparent; + border-radius: var(--jp-border-radius); + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon .alert-info { + color: var(--jp-info-color0); + background-color: var(--jp-info-color3); + border-color: var(--jp-info-color2); +} + +.jp-RenderedHTMLCommon .alert-info hr { + border-color: var(--jp-info-color3); +} + +.jp-RenderedHTMLCommon .alert-info > p:last-child, +.jp-RenderedHTMLCommon .alert-info > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-warning { + color: var(--jp-warn-color0); + background-color: var(--jp-warn-color3); + border-color: var(--jp-warn-color2); +} + +.jp-RenderedHTMLCommon .alert-warning hr { + border-color: var(--jp-warn-color3); +} + +.jp-RenderedHTMLCommon .alert-warning > p:last-child, +.jp-RenderedHTMLCommon .alert-warning > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-success { + color: var(--jp-success-color0); + background-color: var(--jp-success-color3); + border-color: var(--jp-success-color2); +} + +.jp-RenderedHTMLCommon .alert-success hr { + border-color: var(--jp-success-color3); +} + +.jp-RenderedHTMLCommon .alert-success > p:last-child, +.jp-RenderedHTMLCommon .alert-success > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-danger { + color: var(--jp-error-color0); + background-color: var(--jp-error-color3); + border-color: var(--jp-error-color2); +} + +.jp-RenderedHTMLCommon .alert-danger hr { + border-color: var(--jp-error-color3); +} + +.jp-RenderedHTMLCommon .alert-danger > p:last-child, +.jp-RenderedHTMLCommon .alert-danger > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon blockquote { + margin: 1em 2em; + padding: 0 1em; + border-left: 5px solid var(--jp-border-color2); +} + +a.jp-InternalAnchorLink { + visibility: hidden; + margin-left: 8px; + color: var(--md-blue-800); +} + +h1:hover .jp-InternalAnchorLink, +h2:hover .jp-InternalAnchorLink, +h3:hover .jp-InternalAnchorLink, +h4:hover .jp-InternalAnchorLink, +h5:hover .jp-InternalAnchorLink, +h6:hover .jp-InternalAnchorLink { + visibility: visible; +} + +.jp-RenderedHTMLCommon kbd { + background-color: var(--jp-rendermime-table-row-background); + border: 1px solid var(--jp-border-color0); + border-bottom-color: var(--jp-border-color2); + border-radius: 3px; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); + display: inline-block; + font-size: var(--jp-ui-font-size0); + line-height: 1em; + padding: 0.2em 0.5em; +} + +/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0. + * At the bottom of cells this is a bit too much as there is also spacing + * between cells. Going all the way to 0 gets too tight between markdown and + * code cells. + */ +.jp-RenderedHTMLCommon > *:last-child { + margin-bottom: 0.5em; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|----------------------------------------------------------------------------*/ + +.lm-cursor-backdrop { + position: fixed; + width: 200px; + height: 200px; + margin-top: -100px; + margin-left: -100px; + will-change: transform; + z-index: 100; +} + +.lm-mod-drag-image { + will-change: transform; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-lineFormSearch { + padding: 4px 12px; + background-color: var(--jp-layout-color2); + box-shadow: var(--jp-toolbar-box-shadow); + z-index: 2; + font-size: var(--jp-ui-font-size1); +} + +.jp-lineFormCaption { + font-size: var(--jp-ui-font-size0); + line-height: var(--jp-ui-font-size1); + margin-top: 4px; + color: var(--jp-ui-font-color0); +} + +.jp-baseLineForm { + border: none; + border-radius: 0; + position: absolute; + background-size: 16px; + background-repeat: no-repeat; + background-position: center; + outline: none; +} + +.jp-lineFormButtonContainer { + top: 4px; + right: 8px; + height: 24px; + padding: 0 12px; + width: 12px; +} + +.jp-lineFormButtonIcon { + top: 0; + right: 0; + background-color: var(--jp-brand-color1); + height: 100%; + width: 100%; + box-sizing: border-box; + padding: 4px 6px; +} + +.jp-lineFormButton { + top: 0; + right: 0; + background-color: transparent; + height: 100%; + width: 100%; + box-sizing: border-box; +} + +.jp-lineFormWrapper { + overflow: hidden; + padding: 0 8px; + border: 1px solid var(--jp-border-color0); + background-color: var(--jp-input-active-background); + height: 22px; +} + +.jp-lineFormWrapperFocusWithin { + border: var(--jp-border-width) solid var(--md-blue-500); + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-lineFormInput { + background: transparent; + width: 200px; + height: 100%; + border: none; + outline: none; + color: var(--jp-ui-font-color0); + line-height: 28px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-JSONEditor { + display: flex; + flex-direction: column; + width: 100%; +} + +.jp-JSONEditor-host { + flex: 1 1 auto; + border: var(--jp-border-width) solid var(--jp-input-border-color); + border-radius: 0; + background: var(--jp-layout-color0); + min-height: 50px; + padding: 1px; +} + +.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host { + border-color: red; + outline-color: red; +} + +.jp-JSONEditor-header { + display: flex; + flex: 1 0 auto; + padding: 0 0 0 12px; +} + +.jp-JSONEditor-header label { + flex: 0 0 auto; +} + +.jp-JSONEditor-commitButton { + height: 16px; + width: 16px; + background-size: 18px; + background-repeat: no-repeat; + background-position: center; +} + +.jp-JSONEditor-host.jp-mod-focused { + background-color: var(--jp-input-active-background); + border: 1px solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); +} + +.jp-Editor.jp-mod-dropTarget { + border: var(--jp-border-width) solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ +.jp-DocumentSearch-input { + border: none; + outline: none; + color: var(--jp-ui-font-color0); + font-size: var(--jp-ui-font-size1); + background-color: var(--jp-layout-color0); + font-family: var(--jp-ui-font-family); + padding: 2px 1px; + resize: none; +} + +.jp-DocumentSearch-overlay { + position: absolute; + background-color: var(--jp-toolbar-background); + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color); + top: 0; + right: 0; + z-index: 7; + min-width: 405px; + padding: 2px; + font-size: var(--jp-ui-font-size1); + + --jp-private-document-search-button-height: 20px; +} + +.jp-DocumentSearch-overlay button { + background-color: var(--jp-toolbar-background); + outline: 0; +} + +.jp-DocumentSearch-overlay button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-overlay button:active { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-overlay-row { + display: flex; + align-items: center; + margin-bottom: 2px; +} + +.jp-DocumentSearch-button-content { + display: inline-block; + cursor: pointer; + box-sizing: border-box; + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-button-content svg { + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-input-wrapper { + border: var(--jp-border-width) solid var(--jp-border-color0); + display: flex; + background-color: var(--jp-layout-color0); + margin: 2px; +} + +.jp-DocumentSearch-input-wrapper:focus-within { + border-color: var(--jp-cell-editor-active-border-color); +} + +.jp-DocumentSearch-toggle-wrapper, +.jp-DocumentSearch-button-wrapper { + all: initial; + overflow: hidden; + display: inline-block; + border: none; + box-sizing: border-box; +} + +.jp-DocumentSearch-toggle-wrapper { + width: 14px; + height: 14px; +} + +.jp-DocumentSearch-button-wrapper { + width: var(--jp-private-document-search-button-height); + height: var(--jp-private-document-search-button-height); +} + +.jp-DocumentSearch-toggle-wrapper:focus, +.jp-DocumentSearch-button-wrapper:focus { + outline: var(--jp-border-width) solid + var(--jp-cell-editor-active-border-color); + outline-offset: -1px; +} + +.jp-DocumentSearch-toggle-wrapper, +.jp-DocumentSearch-button-wrapper, +.jp-DocumentSearch-button-content:focus { + outline: none; +} + +.jp-DocumentSearch-toggle-placeholder { + width: 5px; +} + +.jp-DocumentSearch-input-button::before { + display: block; + padding-top: 100%; +} + +.jp-DocumentSearch-input-button-off { + opacity: var(--jp-search-toggle-off-opacity); +} + +.jp-DocumentSearch-input-button-off:hover { + opacity: var(--jp-search-toggle-hover-opacity); +} + +.jp-DocumentSearch-input-button-on { + opacity: var(--jp-search-toggle-on-opacity); +} + +.jp-DocumentSearch-index-counter { + padding-left: 10px; + padding-right: 10px; + user-select: none; + min-width: 35px; + display: inline-block; +} + +.jp-DocumentSearch-up-down-wrapper { + display: inline-block; + padding-right: 2px; + margin-left: auto; + white-space: nowrap; +} + +.jp-DocumentSearch-spacer { + margin-left: auto; +} + +.jp-DocumentSearch-up-down-wrapper button { + outline: 0; + border: none; + width: var(--jp-private-document-search-button-height); + height: var(--jp-private-document-search-button-height); + vertical-align: middle; + margin: 1px 5px 2px; +} + +.jp-DocumentSearch-up-down-button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-up-down-button:active { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-filter-button { + border-radius: var(--jp-border-radius); +} + +.jp-DocumentSearch-filter-button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-filter-button-enabled { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-filter-button-enabled:hover { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-search-options { + padding: 0 8px; + margin-left: 3px; + width: 100%; + display: grid; + justify-content: start; + grid-template-columns: 1fr 1fr; + align-items: center; + justify-items: stretch; +} + +.jp-DocumentSearch-search-filter-disabled { + color: var(--jp-ui-font-color2); +} + +.jp-DocumentSearch-search-filter { + display: flex; + align-items: center; + user-select: none; +} + +.jp-DocumentSearch-regex-error { + color: var(--jp-error-color0); +} + +.jp-DocumentSearch-replace-button-wrapper { + overflow: hidden; + display: inline-block; + box-sizing: border-box; + border: var(--jp-border-width) solid var(--jp-border-color0); + margin: auto 2px; + padding: 1px 4px; + height: calc(var(--jp-private-document-search-button-height) + 2px); +} + +.jp-DocumentSearch-replace-button-wrapper:focus { + border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color); +} + +.jp-DocumentSearch-replace-button { + display: inline-block; + text-align: center; + cursor: pointer; + box-sizing: border-box; + color: var(--jp-ui-font-color1); + + /* height - 2 * (padding of wrapper) */ + line-height: calc(var(--jp-private-document-search-button-height) - 2px); + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-replace-button:focus { + outline: none; +} + +.jp-DocumentSearch-replace-wrapper-class { + margin-left: 14px; + display: flex; +} + +.jp-DocumentSearch-replace-toggle { + border: none; + background-color: var(--jp-toolbar-background); + border-radius: var(--jp-border-radius); +} + +.jp-DocumentSearch-replace-toggle:hover { + background-color: var(--jp-layout-color2); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.cm-editor { + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + font-family: var(--jp-code-font-family); + border: 0; + border-radius: 0; + height: auto; + + /* Changed to auto to autogrow */ +} + +.cm-editor pre { + padding: 0 var(--jp-code-padding); +} + +.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog { + background-color: var(--jp-layout-color0); + color: var(--jp-content-font-color1); +} + +.jp-CodeMirrorEditor { + cursor: text; +} + +/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */ +@media screen and (min-width: 2138px) and (max-width: 4319px) { + .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor { + border-left: var(--jp-code-cursor-width1) solid + var(--jp-editor-cursor-color); + } +} + +/* When zoomed out less than 33% */ +@media screen and (min-width: 4320px) { + .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor { + border-left: var(--jp-code-cursor-width2) solid + var(--jp-editor-cursor-color); + } +} + +.cm-editor.jp-mod-readOnly .cm-cursor { + display: none; +} + +.jp-CollaboratorCursor { + border-left: 5px solid transparent; + border-right: 5px solid transparent; + border-top: none; + border-bottom: 3px solid; + background-clip: content-box; + margin-left: -5px; + margin-right: -5px; +} + +.cm-searching, +.cm-searching span { + /* `.cm-searching span`: we need to override syntax highlighting */ + background-color: var(--jp-search-unselected-match-background-color); + color: var(--jp-search-unselected-match-color); +} + +.cm-searching::selection, +.cm-searching span::selection { + background-color: var(--jp-search-unselected-match-background-color); + color: var(--jp-search-unselected-match-color); +} + +.jp-current-match > .cm-searching, +.jp-current-match > .cm-searching span, +.cm-searching > .jp-current-match, +.cm-searching > .jp-current-match span { + background-color: var(--jp-search-selected-match-background-color); + color: var(--jp-search-selected-match-color); +} + +.jp-current-match > .cm-searching::selection, +.cm-searching > .jp-current-match::selection, +.jp-current-match > .cm-searching span::selection { + background-color: var(--jp-search-selected-match-background-color); + color: var(--jp-search-selected-match-color); +} + +.cm-trailingspace { + background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=); + background-position: center left; + background-repeat: repeat-x; +} + +.jp-CollaboratorCursor-hover { + position: absolute; + z-index: 1; + transform: translateX(-50%); + color: white; + border-radius: 3px; + padding-left: 4px; + padding-right: 4px; + padding-top: 1px; + padding-bottom: 1px; + text-align: center; + font-size: var(--jp-ui-font-size1); + white-space: nowrap; +} + +.jp-CodeMirror-ruler { + border-left: 1px dashed var(--jp-border-color2); +} + +/* Styles for shared cursors (remote cursor locations and selected ranges) */ +.jp-CodeMirrorEditor .cm-ySelectionCaret { + position: relative; + border-left: 1px solid black; + margin-left: -1px; + margin-right: -1px; + box-sizing: border-box; +} + +.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo { + white-space: nowrap; + position: absolute; + top: -1.15em; + padding-bottom: 0.05em; + left: -1px; + font-size: 0.95em; + font-family: var(--jp-ui-font-family); + font-weight: bold; + line-height: normal; + user-select: none; + color: white; + padding-left: 2px; + padding-right: 2px; + z-index: 101; + transition: opacity 0.3s ease-in-out; +} + +.jp-CodeMirrorEditor .cm-ySelectionInfo { + transition-delay: 0.7s; + opacity: 0; +} + +.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo { + opacity: 1; + transition-delay: 0s; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-MimeDocument { + outline: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-filebrowser-button-height: 28px; + --jp-private-filebrowser-button-width: 48px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-FileBrowser .jp-SidePanel-content { + display: flex; + flex-direction: column; +} + +.jp-FileBrowser-toolbar.jp-Toolbar { + flex-wrap: wrap; + row-gap: 12px; + border-bottom: none; + height: auto; + margin: 8px 12px 0; + box-shadow: none; + padding: 0; + justify-content: flex-start; +} + +.jp-FileBrowser-Panel { + flex: 1 1 auto; + display: flex; + flex-direction: column; +} + +.jp-BreadCrumbs { + flex: 0 0 auto; + margin: 8px 12px; +} + +.jp-BreadCrumbs-item { + margin: 0 2px; + padding: 0 2px; + border-radius: var(--jp-border-radius); + cursor: pointer; +} + +.jp-BreadCrumbs-item:hover { + background-color: var(--jp-layout-color2); +} + +.jp-BreadCrumbs-item:first-child { + margin-left: 0; +} + +.jp-BreadCrumbs-item.jp-mod-dropTarget { + background-color: var(--jp-brand-color2); + opacity: 0.7; +} + +/*----------------------------------------------------------------------------- +| Buttons +|----------------------------------------------------------------------------*/ + +.jp-FileBrowser-toolbar > .jp-Toolbar-item { + flex: 0 0 auto; + padding-left: 0; + padding-right: 2px; + align-items: center; + height: unset; +} + +.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent { + width: 40px; +} + +/*----------------------------------------------------------------------------- +| Other styles +|----------------------------------------------------------------------------*/ + +.jp-FileDialog.jp-mod-conflict input { + color: var(--jp-error-color1); +} + +.jp-FileDialog .jp-new-name-title { + margin-top: 12px; +} + +.jp-LastModified-hidden { + display: none; +} + +.jp-FileSize-hidden { + display: none; +} + +.jp-FileBrowser .lm-AccordionPanel > h3:first-child { + display: none; +} + +/*----------------------------------------------------------------------------- +| DirListing +|----------------------------------------------------------------------------*/ + +.jp-DirListing { + flex: 1 1 auto; + display: flex; + flex-direction: column; + outline: 0; +} + +.jp-DirListing-header { + flex: 0 0 auto; + display: flex; + flex-direction: row; + align-items: center; + overflow: hidden; + border-top: var(--jp-border-width) solid var(--jp-border-color2); + border-bottom: var(--jp-border-width) solid var(--jp-border-color1); + box-shadow: var(--jp-toolbar-box-shadow); + z-index: 2; +} + +.jp-DirListing-headerItem { + padding: 4px 12px 2px; + font-weight: 500; +} + +.jp-DirListing-headerItem:hover { + background: var(--jp-layout-color2); +} + +.jp-DirListing-headerItem.jp-id-name { + flex: 1 0 84px; +} + +.jp-DirListing-headerItem.jp-id-modified { + flex: 0 0 112px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; +} + +.jp-DirListing-headerItem.jp-id-filesize { + flex: 0 0 75px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; +} + +.jp-id-narrow { + display: none; + flex: 0 0 5px; + padding: 4px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; + color: var(--jp-border-color2); +} + +.jp-DirListing-narrow .jp-id-narrow { + display: block; +} + +.jp-DirListing-narrow .jp-id-modified, +.jp-DirListing-narrow .jp-DirListing-itemModified { + display: none; +} + +.jp-DirListing-headerItem.jp-mod-selected { + font-weight: 600; +} + +/* increase specificity to override bundled default */ +.jp-DirListing-content { + flex: 1 1 auto; + margin: 0; + padding: 0; + list-style-type: none; + overflow: auto; + background-color: var(--jp-layout-color1); +} + +.jp-DirListing-content mark { + color: var(--jp-ui-font-color0); + background-color: transparent; + font-weight: bold; +} + +.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark { + color: var(--jp-ui-inverse-font-color0); +} + +/* Style the directory listing content when a user drops a file to upload */ +.jp-DirListing.jp-mod-native-drop .jp-DirListing-content { + outline: 5px dashed rgba(128, 128, 128, 0.5); + outline-offset: -10px; + cursor: copy; +} + +.jp-DirListing-item { + display: flex; + flex-direction: row; + align-items: center; + padding: 4px 12px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-DirListing-checkboxWrapper { + /* Increases hit area of checkbox. */ + padding: 4px; +} + +.jp-DirListing-header + .jp-DirListing-checkboxWrapper + + .jp-DirListing-headerItem { + padding-left: 4px; +} + +.jp-DirListing-content .jp-DirListing-checkboxWrapper { + position: relative; + left: -4px; + margin: -4px 0 -4px -8px; +} + +.jp-DirListing-checkboxWrapper.jp-mod-visible { + visibility: visible; +} + +/* For devices that support hovering, hide checkboxes until hovered, selected... +*/ +@media (hover: hover) { + .jp-DirListing-checkboxWrapper { + visibility: hidden; + } + + .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper, + .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper { + visibility: visible; + } +} + +.jp-DirListing-item[data-is-dot] { + opacity: 75%; +} + +.jp-DirListing-item.jp-mod-selected { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.jp-DirListing-item.jp-mod-dropTarget { + background: var(--jp-brand-color3); +} + +.jp-DirListing-item:hover:not(.jp-mod-selected) { + background: var(--jp-layout-color2); +} + +.jp-DirListing-itemIcon { + flex: 0 0 20px; + margin-right: 4px; +} + +.jp-DirListing-itemText { + flex: 1 0 64px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + user-select: none; +} + +.jp-DirListing-itemText:focus { + outline-width: 2px; + outline-color: var(--jp-inverse-layout-color1); + outline-style: solid; + outline-offset: 1px; +} + +.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus { + outline-color: var(--jp-layout-color1); +} + +.jp-DirListing-itemModified { + flex: 0 0 125px; + text-align: right; +} + +.jp-DirListing-itemFileSize { + flex: 0 0 90px; + text-align: right; +} + +.jp-DirListing-editor { + flex: 1 0 64px; + outline: none; + border: none; + color: var(--jp-ui-font-color1); + background-color: var(--jp-layout-color1); +} + +.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before { + color: var(--jp-success-color1); + content: '\25CF'; + font-size: 8px; + position: absolute; + left: -8px; +} + +.jp-DirListing-item.jp-mod-running.jp-mod-selected + .jp-DirListing-itemIcon::before { + color: var(--jp-ui-inverse-font-color1); +} + +.jp-DirListing-item.lm-mod-drag-image, +.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image { + font-size: var(--jp-ui-font-size1); + padding-left: 4px; + margin-left: 4px; + width: 160px; + background-color: var(--jp-ui-inverse-font-color2); + box-shadow: var(--jp-elevation-z2); + border-radius: 0; + color: var(--jp-ui-font-color1); + transform: translateX(-40%) translateY(-58%); +} + +.jp-Document { + min-width: 120px; + min-height: 120px; + outline: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Main OutputArea +| OutputArea has a list of Outputs +|----------------------------------------------------------------------------*/ + +.jp-OutputArea { + overflow-y: auto; +} + +.jp-OutputArea-child { + display: table; + table-layout: fixed; + width: 100%; + overflow: hidden; +} + +.jp-OutputPrompt { + width: var(--jp-cell-prompt-width); + color: var(--jp-cell-outprompt-font-color); + font-family: var(--jp-cell-prompt-font-family); + padding: var(--jp-code-padding); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; + opacity: var(--jp-cell-prompt-opacity); + + /* Right align prompt text, don't wrap to handle large prompt numbers */ + text-align: right; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + + /* Disable text selection */ + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-OutputArea-prompt { + display: table-cell; + vertical-align: top; +} + +.jp-OutputArea-output { + display: table-cell; + width: 100%; + height: auto; + overflow: auto; + user-select: text; + -moz-user-select: text; + -webkit-user-select: text; + -ms-user-select: text; +} + +.jp-OutputArea .jp-RenderedText { + padding-left: 1ch; +} + +/** + * Prompt overlay. + */ + +.jp-OutputArea-promptOverlay { + position: absolute; + top: 0; + width: var(--jp-cell-prompt-width); + height: 100%; + opacity: 0.5; +} + +.jp-OutputArea-promptOverlay:hover { + background: var(--jp-layout-color2); + box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0); + cursor: zoom-out; +} + +.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover { + cursor: zoom-in; +} + +/** + * Isolated output. + */ +.jp-OutputArea-output.jp-mod-isolated { + width: 100%; + display: block; +} + +/* +When drag events occur, `lm-mod-override-cursor` is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated { + position: relative; +} + +body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: transparent; +} + +/* pre */ + +.jp-OutputArea-output pre { + border: none; + margin: 0; + padding: 0; + overflow-x: auto; + overflow-y: auto; + word-break: break-all; + word-wrap: break-word; + white-space: pre-wrap; +} + +/* tables */ + +.jp-OutputArea-output.jp-RenderedHTMLCommon table { + margin-left: 0; + margin-right: 0; +} + +/* description lists */ + +.jp-OutputArea-output dl, +.jp-OutputArea-output dt, +.jp-OutputArea-output dd { + display: block; +} + +.jp-OutputArea-output dl { + width: 100%; + overflow: hidden; + padding: 0; + margin: 0; +} + +.jp-OutputArea-output dt { + font-weight: bold; + float: left; + width: 20%; + padding: 0; + margin: 0; +} + +.jp-OutputArea-output dd { + float: left; + width: 80%; + padding: 0; + margin: 0; +} + +.jp-TrimmedOutputs pre { + background: var(--jp-layout-color3); + font-size: calc(var(--jp-code-font-size) * 1.4); + text-align: center; + text-transform: uppercase; +} + +/* Hide the gutter in case of + * - nested output areas (e.g. in the case of output widgets) + * - mirrored output areas + */ +.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt { + display: none; +} + +/* Hide empty lines in the output area, for instance due to cleared widgets */ +.jp-OutputArea-prompt:empty { + padding: 0; + border: 0; +} + +/*----------------------------------------------------------------------------- +| executeResult is added to any Output-result for the display of the object +| returned by a cell +|----------------------------------------------------------------------------*/ + +.jp-OutputArea-output.jp-OutputArea-executeResult { + margin-left: 0; + width: 100%; +} + +/* Text output with the Out[] prompt needs a top padding to match the + * alignment of the Out[] prompt itself. + */ +.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output { + padding-top: var(--jp-code-padding); + border-top: var(--jp-border-width) solid transparent; +} + +/*----------------------------------------------------------------------------- +| The Stdin output +|----------------------------------------------------------------------------*/ + +.jp-Stdin-prompt { + color: var(--jp-content-font-color0); + padding-right: var(--jp-code-padding); + vertical-align: baseline; + flex: 0 0 auto; +} + +.jp-Stdin-input { + font-family: var(--jp-code-font-family); + font-size: inherit; + color: inherit; + background-color: inherit; + width: 42%; + min-width: 200px; + + /* make sure input baseline aligns with prompt */ + vertical-align: baseline; + + /* padding + margin = 0.5em between prompt and cursor */ + padding: 0 0.25em; + margin: 0 0.25em; + flex: 0 0 70%; +} + +.jp-Stdin-input::placeholder { + opacity: 0; +} + +.jp-Stdin-input:focus { + box-shadow: none; +} + +.jp-Stdin-input:focus::placeholder { + opacity: 1; +} + +/*----------------------------------------------------------------------------- +| Output Area View +|----------------------------------------------------------------------------*/ + +.jp-LinkedOutputView .jp-OutputArea { + height: 100%; + display: block; +} + +.jp-LinkedOutputView .jp-OutputArea-output:only-child { + height: 100%; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +@media print { + .jp-OutputArea-child { + break-inside: avoid-page; + } +} + +/*----------------------------------------------------------------------------- +| Mobile +|----------------------------------------------------------------------------*/ +@media only screen and (max-width: 760px) { + .jp-OutputPrompt { + display: table-row; + text-align: left; + } + + .jp-OutputArea-child .jp-OutputArea-output { + display: table-row; + margin-left: var(--jp-notebook-padding); + } +} + +/* Trimmed outputs warning */ +.jp-TrimmedOutputs > a { + margin: 10px; + text-decoration: none; + cursor: pointer; +} + +.jp-TrimmedOutputs > a:hover { + text-decoration: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Table of Contents +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-toc-active-width: 4px; +} + +.jp-TableOfContents { + display: flex; + flex-direction: column; + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + height: 100%; +} + +.jp-TableOfContents-placeholder { + text-align: center; +} + +.jp-TableOfContents-placeholderContent { + color: var(--jp-content-font-color2); + padding: 8px; +} + +.jp-TableOfContents-placeholderContent > h3 { + margin-bottom: var(--jp-content-heading-margin-bottom); +} + +.jp-TableOfContents .jp-SidePanel-content { + overflow-y: auto; +} + +.jp-TableOfContents-tree { + margin: 4px; +} + +.jp-TableOfContents ol { + list-style-type: none; +} + +/* stylelint-disable-next-line selector-max-type */ +.jp-TableOfContents li > ol { + /* Align left border with triangle icon center */ + padding-left: 11px; +} + +.jp-TableOfContents-content { + /* left margin for the active heading indicator */ + margin: 0 0 0 var(--jp-private-toc-active-width); + padding: 0; + background-color: var(--jp-layout-color1); +} + +.jp-tocItem { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-tocItem-heading { + display: flex; + cursor: pointer; +} + +.jp-tocItem-heading:hover { + background-color: var(--jp-layout-color2); +} + +.jp-tocItem-content { + display: block; + padding: 4px 0; + white-space: nowrap; + text-overflow: ellipsis; + overflow-x: hidden; +} + +.jp-tocItem-collapser { + height: 20px; + margin: 2px 2px 0; + padding: 0; + background: none; + border: none; + cursor: pointer; +} + +.jp-tocItem-collapser:hover { + background-color: var(--jp-layout-color3); +} + +/* Active heading indicator */ + +.jp-tocItem-heading::before { + content: ' '; + background: transparent; + width: var(--jp-private-toc-active-width); + height: 24px; + position: absolute; + left: 0; + border-radius: var(--jp-border-radius); +} + +.jp-tocItem-heading.jp-tocItem-active::before { + background-color: var(--jp-brand-color1); +} + +.jp-tocItem-heading:hover.jp-tocItem-active::before { + background: var(--jp-brand-color0); + opacity: 1; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Collapser { + flex: 0 0 var(--jp-cell-collapser-width); + padding: 0; + margin: 0; + border: none; + outline: none; + background: transparent; + border-radius: var(--jp-border-radius); + opacity: 1; +} + +.jp-Collapser-child { + display: block; + width: 100%; + box-sizing: border-box; + + /* height: 100% doesn't work because the height of its parent is computed from content */ + position: absolute; + top: 0; + bottom: 0; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +/* +Hiding collapsers in print mode. + +Note: input and output wrappers have "display: block" propery in print mode. +*/ + +@media print { + .jp-Collapser { + display: none; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Header/Footer +|----------------------------------------------------------------------------*/ + +/* Hidden by zero height by default */ +.jp-CellHeader, +.jp-CellFooter { + height: 0; + width: 100%; + padding: 0; + margin: 0; + border: none; + outline: none; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Input +|----------------------------------------------------------------------------*/ + +/* All input areas */ +.jp-InputArea { + display: table; + table-layout: fixed; + width: 100%; + overflow: hidden; +} + +.jp-InputArea-editor { + display: table-cell; + overflow: hidden; + vertical-align: top; + + /* This is the non-active, default styling */ + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + border-radius: 0; + background: var(--jp-cell-editor-background); +} + +.jp-InputPrompt { + display: table-cell; + vertical-align: top; + width: var(--jp-cell-prompt-width); + color: var(--jp-cell-inprompt-font-color); + font-family: var(--jp-cell-prompt-font-family); + padding: var(--jp-code-padding); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + opacity: var(--jp-cell-prompt-opacity); + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; + + /* Right align prompt text, don't wrap to handle large prompt numbers */ + text-align: right; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + + /* Disable text selection */ + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +/*----------------------------------------------------------------------------- +| Mobile +|----------------------------------------------------------------------------*/ +@media only screen and (max-width: 760px) { + .jp-InputArea-editor { + display: table-row; + margin-left: var(--jp-notebook-padding); + } + + .jp-InputPrompt { + display: table-row; + text-align: left; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Placeholder +|----------------------------------------------------------------------------*/ + +.jp-Placeholder { + display: table; + table-layout: fixed; + width: 100%; +} + +.jp-Placeholder-prompt { + display: table-cell; + box-sizing: border-box; +} + +.jp-Placeholder-content { + display: table-cell; + padding: 4px 6px; + border: 1px solid transparent; + border-radius: 0; + background: none; + box-sizing: border-box; + cursor: pointer; +} + +.jp-Placeholder-contentContainer { + display: flex; +} + +.jp-Placeholder-content:hover, +.jp-InputPlaceholder > .jp-Placeholder-content:hover { + border-color: var(--jp-layout-color3); +} + +.jp-Placeholder-content .jp-MoreHorizIcon { + width: 32px; + height: 16px; + border: 1px solid transparent; + border-radius: var(--jp-border-radius); +} + +.jp-Placeholder-content .jp-MoreHorizIcon:hover { + border: 1px solid var(--jp-border-color1); + box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25); + background-color: var(--jp-layout-color0); +} + +.jp-PlaceholderText { + white-space: nowrap; + overflow-x: hidden; + color: var(--jp-inverse-layout-color3); + font-family: var(--jp-code-font-family); +} + +.jp-InputPlaceholder > .jp-Placeholder-content { + border-color: var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Private CSS variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-cell-scrolling-output-offset: 5px; +} + +/*----------------------------------------------------------------------------- +| Cell +|----------------------------------------------------------------------------*/ + +.jp-Cell { + padding: var(--jp-cell-padding); + margin: 0; + border: none; + outline: none; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Common input/output +|----------------------------------------------------------------------------*/ + +.jp-Cell-inputWrapper, +.jp-Cell-outputWrapper { + display: flex; + flex-direction: row; + padding: 0; + margin: 0; + + /* Added to reveal the box-shadow on the input and output collapsers. */ + overflow: visible; +} + +/* Only input/output areas inside cells */ +.jp-Cell-inputArea, +.jp-Cell-outputArea { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Collapser +|----------------------------------------------------------------------------*/ + +/* Make the output collapser disappear when there is not output, but do so + * in a manner that leaves it in the layout and preserves its width. + */ +.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser { + border: none !important; + background: transparent !important; +} + +.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser { + min-height: var(--jp-cell-collapser-min-height); +} + +/*----------------------------------------------------------------------------- +| Output +|----------------------------------------------------------------------------*/ + +/* Put a space between input and output when there IS output */ +.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper { + margin-top: 5px; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea { + overflow-y: auto; + max-height: 24em; + margin-left: var(--jp-private-cell-scrolling-output-offset); + resize: vertical; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] { + max-height: unset; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after { + content: ' '; + box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%); + width: 100%; + height: 100%; + position: sticky; + bottom: 0; + top: 0; + margin-top: -50%; + float: left; + display: block; + pointer-events: none; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child { + padding-top: 6px; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt { + width: calc( + var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset) + ); +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay { + left: calc(-1 * var(--jp-private-cell-scrolling-output-offset)); +} + +/*----------------------------------------------------------------------------- +| CodeCell +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| MarkdownCell +|----------------------------------------------------------------------------*/ + +.jp-MarkdownOutput { + display: table-cell; + width: 100%; + margin-top: 0; + margin-bottom: 0; + padding-left: var(--jp-code-padding); +} + +.jp-MarkdownOutput.jp-RenderedHTMLCommon { + overflow: auto; +} + +/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */ +.jp-collapseHeadingButton { + display: flex; + min-height: var(--jp-cell-collapser-min-height); + font-size: var(--jp-code-font-size); + position: absolute; + background-color: transparent; + background-size: 25px; + background-repeat: no-repeat; + background-position-x: center; + background-position-y: top; + background-image: var(--jp-icon-caret-down); + right: 0; + top: 0; + bottom: 0; +} + +.jp-collapseHeadingButton.jp-mod-collapsed { + background-image: var(--jp-icon-caret-right); +} + +/* + set the container font size to match that of content + so that the nested collapse buttons have the right size +*/ +.jp-MarkdownCell .jp-InputPrompt { + font-size: var(--jp-content-font-size1); +} + +/* + Align collapseHeadingButton with cell top header + The font sizes are identical to the ones in packages/rendermime/style/base.css +*/ +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] { + font-size: var(--jp-content-font-size5); + background-position-y: calc(0.3 * var(--jp-content-font-size5)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] { + font-size: var(--jp-content-font-size4); + background-position-y: calc(0.3 * var(--jp-content-font-size4)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] { + font-size: var(--jp-content-font-size3); + background-position-y: calc(0.3 * var(--jp-content-font-size3)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] { + font-size: var(--jp-content-font-size2); + background-position-y: calc(0.3 * var(--jp-content-font-size2)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] { + font-size: var(--jp-content-font-size1); + background-position-y: top; +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] { + font-size: var(--jp-content-font-size0); + background-position-y: top; +} + +/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */ +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton { + display: none; +} + +.jp-Notebook.jp-mod-showHiddenCellsButton + :is(.jp-MarkdownCell:hover, .jp-mod-active) + .jp-collapseHeadingButton { + display: flex; +} + +/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which +is a consequence of the showHiddenCellsButton option in Notebook Settings)*/ +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton { + margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding)); + margin-top: var(--jp-code-padding); + border: 1px solid var(--jp-border-color2); + background-color: var(--jp-border-color3) !important; + color: var(--jp-content-font-color0) !important; + display: flex; +} + +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover { + background-color: var(--jp-border-color2) !important; +} + +.jp-showHiddenCellsButton { + display: none; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +/* +Using block instead of flex to allow the use of the break-inside CSS property for +cell outputs. +*/ + +@media print { + .jp-Cell-inputWrapper, + .jp-Cell-outputWrapper { + display: block; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-notebook-toolbar-padding: 2px 5px 2px 2px; +} + +/*----------------------------------------------------------------------------- + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-NotebookPanel-toolbar { + padding: var(--jp-notebook-toolbar-padding); + + /* disable paint containment from lumino 2.0 default strict CSS containment */ + contain: style size !important; +} + +.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused { + border: none; + box-shadow: none; +} + +.jp-Notebook-toolbarCellTypeDropdown select { + height: 24px; + font-size: var(--jp-ui-font-size1); + line-height: 14px; + border-radius: 0; + display: block; +} + +.jp-Notebook-toolbarCellTypeDropdown span { + top: 5px !important; +} + +.jp-Toolbar-responsive-popup { + position: absolute; + height: fit-content; + display: flex; + flex-direction: row; + flex-wrap: wrap; + justify-content: flex-end; + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + background: var(--jp-toolbar-background); + min-height: var(--jp-toolbar-micro-height); + padding: var(--jp-notebook-toolbar-padding); + z-index: 1; + right: 0; + top: 0; +} + +.jp-Toolbar > .jp-Toolbar-responsive-opener { + margin-left: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-Notebook-ExecutionIndicator { + position: relative; + display: inline-block; + height: 100%; + z-index: 9997; +} + +.jp-Notebook-ExecutionIndicator-tooltip { + visibility: hidden; + height: auto; + width: max-content; + width: -moz-max-content; + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color1); + text-align: justify; + border-radius: 6px; + padding: 0 5px; + position: fixed; + display: table; +} + +.jp-Notebook-ExecutionIndicator-tooltip.up { + transform: translateX(-50%) translateY(-100%) translateY(-32px); +} + +.jp-Notebook-ExecutionIndicator-tooltip.down { + transform: translateX(calc(-100% + 16px)) translateY(5px); +} + +.jp-Notebook-ExecutionIndicator-tooltip.hidden { + display: none; +} + +.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip { + visibility: visible; +} + +.jp-Notebook-ExecutionIndicator span { + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + color: var(--jp-ui-font-color1); + line-height: 24px; + display: block; +} + +.jp-Notebook-ExecutionIndicator-progress-bar { + display: flex; + justify-content: center; + height: 100%; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/* + * Execution indicator + */ +.jp-tocItem-content::after { + content: ''; + + /* Must be identical to form a circle */ + width: 12px; + height: 12px; + background: none; + border: none; + position: absolute; + right: 0; +} + +.jp-tocItem-content[data-running='0']::after { + border-radius: 50%; + border: var(--jp-border-width) solid var(--jp-inverse-layout-color3); + background: none; +} + +.jp-tocItem-content[data-running='1']::after { + border-radius: 50%; + border: var(--jp-border-width) solid var(--jp-inverse-layout-color3); + background-color: var(--jp-inverse-layout-color3); +} + +.jp-tocItem-content[data-running='0'], +.jp-tocItem-content[data-running='1'] { + margin-right: 12px; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-Notebook-footer { + height: 27px; + margin-left: calc( + var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) + + var(--jp-cell-padding) + ); + width: calc( + 100% - + ( + var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) + + var(--jp-cell-padding) + var(--jp-cell-padding) + ) + ); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + color: var(--jp-ui-font-color3); + margin-top: 6px; + background: none; + cursor: pointer; +} + +.jp-Notebook-footer:focus { + border-color: var(--jp-cell-editor-active-border-color); +} + +/* For devices that support hovering, hide footer until hover */ +@media (hover: hover) { + .jp-Notebook-footer { + opacity: 0; + } + + .jp-Notebook-footer:focus, + .jp-Notebook-footer:hover { + opacity: 1; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Imports +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| CSS variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-side-by-side-output-size: 1fr; + --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size); + --jp-private-notebook-dragImage-width: 304px; + --jp-private-notebook-dragImage-height: 36px; + --jp-private-notebook-selected-color: var(--md-blue-400); + --jp-private-notebook-active-color: var(--md-green-400); +} + +/*----------------------------------------------------------------------------- +| Notebook +|----------------------------------------------------------------------------*/ + +/* stylelint-disable selector-max-class */ + +.jp-NotebookPanel { + display: block; + height: 100%; +} + +.jp-NotebookPanel.jp-Document { + min-width: 240px; + min-height: 120px; +} + +.jp-Notebook { + padding: var(--jp-notebook-padding); + outline: none; + overflow: auto; + background: var(--jp-layout-color0); +} + +.jp-Notebook.jp-mod-scrollPastEnd::after { + display: block; + content: ''; + min-height: var(--jp-notebook-scroll-padding); +} + +.jp-MainAreaWidget-ContainStrict .jp-Notebook * { + contain: strict; +} + +.jp-Notebook .jp-Cell { + overflow: visible; +} + +.jp-Notebook .jp-Cell .jp-InputPrompt { + cursor: move; +} + +/*----------------------------------------------------------------------------- +| Notebook state related styling +| +| The notebook and cells each have states, here are the possibilities: +| +| - Notebook +| - Command +| - Edit +| - Cell +| - None +| - Active (only one can be active) +| - Selected (the cells actions are applied to) +| - Multiselected (when multiple selected, the cursor) +| - No outputs +|----------------------------------------------------------------------------*/ + +/* Command or edit modes */ + +.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt { + opacity: var(--jp-cell-prompt-not-active-opacity); + color: var(--jp-cell-prompt-not-active-font-color); +} + +.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt { + opacity: var(--jp-cell-prompt-not-active-opacity); + color: var(--jp-cell-prompt-not-active-font-color); +} + +/* cell is active */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser { + background: var(--jp-brand-color1); +} + +/* cell is dirty */ +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt { + color: var(--jp-warn-color1); +} + +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before { + color: var(--jp-warn-color1); + content: '•'; +} + +.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser { + background: var(--jp-warn-color1); +} + +/* collapser is hovered */ +.jp-Notebook .jp-Cell .jp-Collapser:hover { + box-shadow: var(--jp-elevation-z2); + background: var(--jp-brand-color1); + opacity: var(--jp-cell-collapser-not-active-hover-opacity); +} + +/* cell is active and collapser is hovered */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover { + background: var(--jp-brand-color0); + opacity: 1; +} + +/* Command mode */ + +.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected { + background: var(--jp-notebook-multiselected-color); +} + +.jp-Notebook.jp-mod-commandMode + .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) { + background: transparent; +} + +/* Edit mode */ + +.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor { + border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color); + box-shadow: var(--jp-input-box-shadow); + background-color: var(--jp-cell-editor-active-background); +} + +/*----------------------------------------------------------------------------- +| Notebook drag and drop +|----------------------------------------------------------------------------*/ + +.jp-Notebook-cell.jp-mod-dropSource { + opacity: 0.5; +} + +.jp-Notebook-cell.jp-mod-dropTarget, +.jp-Notebook.jp-mod-commandMode + .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget { + border-top-color: var(--jp-private-notebook-selected-color); + border-top-style: solid; + border-top-width: 2px; +} + +.jp-dragImage { + display: block; + flex-direction: row; + width: var(--jp-private-notebook-dragImage-width); + height: var(--jp-private-notebook-dragImage-height); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background); + overflow: visible; +} + +.jp-dragImage-singlePrompt { + box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12); +} + +.jp-dragImage .jp-dragImage-content { + flex: 1 1 auto; + z-index: 2; + font-size: var(--jp-code-font-size); + font-family: var(--jp-code-font-family); + line-height: var(--jp-code-line-height); + padding: var(--jp-code-padding); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background-color); + color: var(--jp-content-font-color3); + text-align: left; + margin: 4px 4px 4px 0; +} + +.jp-dragImage .jp-dragImage-prompt { + flex: 0 0 auto; + min-width: 36px; + color: var(--jp-cell-inprompt-font-color); + padding: var(--jp-code-padding); + padding-left: 12px; + font-family: var(--jp-cell-prompt-font-family); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + line-height: 1.9; + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; +} + +.jp-dragImage-multipleBack { + z-index: -1; + position: absolute; + height: 32px; + width: 300px; + top: 8px; + left: 8px; + background: var(--jp-layout-color2); + border: var(--jp-border-width) solid var(--jp-input-border-color); + box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12); +} + +/*----------------------------------------------------------------------------- +| Cell toolbar +|----------------------------------------------------------------------------*/ + +.jp-NotebookTools { + display: block; + min-width: var(--jp-sidebar-min-width); + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); + overflow: auto; +} + +.jp-ActiveCellTool { + padding: 12px 0; + display: flex; +} + +.jp-ActiveCellTool-Content { + flex: 1 1 auto; +} + +.jp-ActiveCellTool .jp-ActiveCellTool-CellContent { + background: var(--jp-cell-editor-background); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + border-radius: 0; + min-height: 29px; +} + +.jp-ActiveCellTool .jp-InputPrompt { + min-width: calc(var(--jp-cell-prompt-width) * 0.75); +} + +.jp-ActiveCellTool-CellContent > pre { + padding: 5px 4px; + margin: 0; + white-space: normal; +} + +.jp-MetadataEditorTool { + flex-direction: column; + padding: 12px 0; +} + +.jp-RankedPanel > :not(:first-child) { + margin-top: 12px; +} + +.jp-KeySelector select.jp-mod-styled { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + border: var(--jp-border-width) solid var(--jp-border-color1); +} + +.jp-KeySelector label, +.jp-MetadataEditorTool label, +.jp-NumberSetter label { + line-height: 1.4; +} + +.jp-NotebookTools .jp-select-wrapper { + margin-top: 4px; + margin-bottom: 0; +} + +.jp-NumberSetter input { + width: 100%; + margin-top: 4px; +} + +.jp-NotebookTools .jp-Collapse { + margin-top: 16px; +} + +/*----------------------------------------------------------------------------- +| Presentation Mode (.jp-mod-presentationMode) +|----------------------------------------------------------------------------*/ + +.jp-mod-presentationMode .jp-Notebook { + --jp-content-font-size1: var(--jp-content-presentation-font-size1); + --jp-code-font-size: var(--jp-code-presentation-font-size); +} + +.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt, +.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt { + flex: 0 0 110px; +} + +/*----------------------------------------------------------------------------- +| Side-by-side Mode (.jp-mod-sideBySide) +|----------------------------------------------------------------------------*/ +.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell { + margin-top: 3em; + margin-bottom: 3em; + margin-left: 5%; + margin-right: 5%; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell { + display: grid; + grid-template-columns: minmax(0, 1fr) min-content minmax( + 0, + var(--jp-side-by-side-output-size) + ); + grid-template-rows: auto minmax(0, 1fr) auto; + grid-template-areas: + 'header header header' + 'input handle output' + 'footer footer footer'; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell { + grid-template-columns: minmax(0, 1fr) min-content minmax( + 0, + var(--jp-side-by-side-resized-cell) + ); +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader { + grid-area: header; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper { + grid-area: input; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper { + /* overwrite the default margin (no vertical separation needed in side by side move */ + margin-top: 0; + grid-area: output; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter { + grid-area: footer; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle { + grid-area: handle; + user-select: none; + display: block; + height: 100%; + cursor: ew-resize; + padding: 0 var(--jp-cell-padding); +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after { + content: ''; + display: block; + background: var(--jp-border-color2); + height: 100%; + width: 5px; +} + +.jp-mod-sideBySide.jp-Notebook + .jp-CodeCell.jp-mod-resizedCell + .jp-CellResizeHandle::after { + background: var(--jp-border-color0); +} + +.jp-CellResizeHandle { + display: none; +} + +/*----------------------------------------------------------------------------- +| Placeholder +|----------------------------------------------------------------------------*/ + +.jp-Cell-Placeholder { + padding-left: 55px; +} + +.jp-Cell-Placeholder-wrapper { + background: #fff; + border: 1px solid; + border-color: #e5e6e9 #dfe0e4 #d0d1d5; + border-radius: 4px; + -webkit-border-radius: 4px; + margin: 10px 15px; +} + +.jp-Cell-Placeholder-wrapper-inner { + padding: 15px; + position: relative; +} + +.jp-Cell-Placeholder-wrapper-body { + background-repeat: repeat; + background-size: 50% auto; +} + +.jp-Cell-Placeholder-wrapper-body div { + background: #f6f7f8; + background-image: -webkit-linear-gradient( + left, + #f6f7f8 0%, + #edeef1 20%, + #f6f7f8 40%, + #f6f7f8 100% + ); + background-repeat: no-repeat; + background-size: 800px 104px; + height: 104px; + position: absolute; + right: 15px; + left: 15px; + top: 15px; +} + +div.jp-Cell-Placeholder-h1 { + top: 20px; + height: 20px; + left: 15px; + width: 150px; +} + +div.jp-Cell-Placeholder-h2 { + left: 15px; + top: 50px; + height: 10px; + width: 100px; +} + +div.jp-Cell-Placeholder-content-1, +div.jp-Cell-Placeholder-content-2, +div.jp-Cell-Placeholder-content-3 { + left: 15px; + right: 15px; + height: 10px; +} + +div.jp-Cell-Placeholder-content-1 { + top: 100px; +} + +div.jp-Cell-Placeholder-content-2 { + top: 120px; +} + +div.jp-Cell-Placeholder-content-3 { + top: 140px; +} + +</style> +<style type="text/css"> +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* +The following CSS variables define the main, public API for styling JupyterLab. +These variables should be used by all plugins wherever possible. In other +words, plugins should not define custom colors, sizes, etc unless absolutely +necessary. This enables users to change the visual theme of JupyterLab +by changing these variables. + +Many variables appear in an ordered sequence (0,1,2,3). These sequences +are designed to work well together, so for example, `--jp-border-color1` should +be used with `--jp-layout-color1`. The numbers have the following meanings: + +* 0: super-primary, reserved for special emphasis +* 1: primary, most important under normal situations +* 2: secondary, next most important under normal situations +* 3: tertiary, next most important under normal situations + +Throughout JupyterLab, we are mostly following principles from Google's +Material Design when selecting colors. We are not, however, following +all of MD as it is not optimized for dense, information rich UIs. +*/ + +:root { + /* Elevation + * + * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: + * + * https://github.com/material-components/material-components-web + * https://material-components-web.appspot.com/elevation.html + */ + + --jp-shadow-base-lightness: 0; + --jp-shadow-umbra-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.2 + ); + --jp-shadow-penumbra-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.14 + ); + --jp-shadow-ambient-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.12 + ); + --jp-elevation-z0: none; + --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color), + 0 1px 1px 0 var(--jp-shadow-penumbra-color), + 0 1px 3px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color), + 0 2px 2px 0 var(--jp-shadow-penumbra-color), + 0 1px 5px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color), + 0 4px 5px 0 var(--jp-shadow-penumbra-color), + 0 1px 10px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color), + 0 6px 10px 0 var(--jp-shadow-penumbra-color), + 0 1px 18px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color), + 0 8px 10px 1px var(--jp-shadow-penumbra-color), + 0 3px 14px 2px var(--jp-shadow-ambient-color); + --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color), + 0 12px 17px 2px var(--jp-shadow-penumbra-color), + 0 5px 22px 4px var(--jp-shadow-ambient-color); + --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color), + 0 16px 24px 2px var(--jp-shadow-penumbra-color), + 0 6px 30px 5px var(--jp-shadow-ambient-color); + --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color), + 0 20px 31px 3px var(--jp-shadow-penumbra-color), + 0 8px 38px 7px var(--jp-shadow-ambient-color); + --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color), + 0 24px 38px 3px var(--jp-shadow-penumbra-color), + 0 9px 46px 8px var(--jp-shadow-ambient-color); + + /* Borders + * + * The following variables, specify the visual styling of borders in JupyterLab. + */ + + --jp-border-width: 1px; + --jp-border-color0: var(--md-grey-400); + --jp-border-color1: var(--md-grey-400); + --jp-border-color2: var(--md-grey-300); + --jp-border-color3: var(--md-grey-200); + --jp-inverse-border-color: var(--md-grey-600); + --jp-border-radius: 2px; + + /* UI Fonts + * + * The UI font CSS variables are used for the typography all of the JupyterLab + * user interface elements that are not directly user generated content. + * + * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 + * is applied to a parent element. When children elements, such as headings, are sized + * in em all things will be computed relative to that body size. + */ + + --jp-ui-font-scale-factor: 1.2; + --jp-ui-font-size0: 0.83333em; + --jp-ui-font-size1: 13px; /* Base font size */ + --jp-ui-font-size2: 1.2em; + --jp-ui-font-size3: 1.44em; + --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI', + helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', + 'Segoe UI Symbol'; + + /* + * Use these font colors against the corresponding main layout colors. + * In a light theme, these go from dark to light. + */ + + /* Defaults use Material Design specification */ + --jp-ui-font-color0: rgba(0, 0, 0, 1); + --jp-ui-font-color1: rgba(0, 0, 0, 0.87); + --jp-ui-font-color2: rgba(0, 0, 0, 0.54); + --jp-ui-font-color3: rgba(0, 0, 0, 0.38); + + /* + * Use these against the brand/accent/warn/error colors. + * These will typically go from light to darker, in both a dark and light theme. + */ + + --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); + --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); + --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); + --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); + + /* Content Fonts + * + * Content font variables are used for typography of user generated content. + * + * The font sizing here is done assuming that the body font size of --jp-content-font-size1 + * is applied to a parent element. When children elements, such as headings, are sized + * in em all things will be computed relative to that body size. + */ + + --jp-content-line-height: 1.6; + --jp-content-font-scale-factor: 1.2; + --jp-content-font-size0: 0.83333em; + --jp-content-font-size1: 14px; /* Base font size */ + --jp-content-font-size2: 1.2em; + --jp-content-font-size3: 1.44em; + --jp-content-font-size4: 1.728em; + --jp-content-font-size5: 2.0736em; + + /* This gives a magnification of about 125% in presentation mode over normal. */ + --jp-content-presentation-font-size1: 17px; + --jp-content-heading-line-height: 1; + --jp-content-heading-margin-top: 1.2em; + --jp-content-heading-margin-bottom: 0.8em; + --jp-content-heading-font-weight: 500; + + /* Defaults use Material Design specification */ + --jp-content-font-color0: rgba(0, 0, 0, 1); + --jp-content-font-color1: rgba(0, 0, 0, 0.87); + --jp-content-font-color2: rgba(0, 0, 0, 0.54); + --jp-content-font-color3: rgba(0, 0, 0, 0.38); + --jp-content-link-color: var(--md-blue-900); + --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont, + 'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji', + 'Segoe UI Emoji', 'Segoe UI Symbol'; + + /* + * Code Fonts + * + * Code font variables are used for typography of code and other monospaces content. + */ + + --jp-code-font-size: 13px; + --jp-code-line-height: 1.3077; /* 17px for 13px base */ + --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ + --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace; + --jp-code-font-family: var(--jp-code-font-family-default); + + /* This gives a magnification of about 125% in presentation mode over normal. */ + --jp-code-presentation-font-size: 16px; + + /* may need to tweak cursor width if you change font size */ + --jp-code-cursor-width0: 1.4px; + --jp-code-cursor-width1: 2px; + --jp-code-cursor-width2: 4px; + + /* Layout + * + * The following are the main layout colors use in JupyterLab. In a light + * theme these would go from light to dark. + */ + + --jp-layout-color0: white; + --jp-layout-color1: white; + --jp-layout-color2: var(--md-grey-200); + --jp-layout-color3: var(--md-grey-400); + --jp-layout-color4: var(--md-grey-600); + + /* Inverse Layout + * + * The following are the inverse layout colors use in JupyterLab. In a light + * theme these would go from dark to light. + */ + + --jp-inverse-layout-color0: #111; + --jp-inverse-layout-color1: var(--md-grey-900); + --jp-inverse-layout-color2: var(--md-grey-800); + --jp-inverse-layout-color3: var(--md-grey-700); + --jp-inverse-layout-color4: var(--md-grey-600); + + /* Brand/accent */ + + --jp-brand-color0: var(--md-blue-900); + --jp-brand-color1: var(--md-blue-700); + --jp-brand-color2: var(--md-blue-300); + --jp-brand-color3: var(--md-blue-100); + --jp-brand-color4: var(--md-blue-50); + --jp-accent-color0: var(--md-green-900); + --jp-accent-color1: var(--md-green-700); + --jp-accent-color2: var(--md-green-300); + --jp-accent-color3: var(--md-green-100); + + /* State colors (warn, error, success, info) */ + + --jp-warn-color0: var(--md-orange-900); + --jp-warn-color1: var(--md-orange-700); + --jp-warn-color2: var(--md-orange-300); + --jp-warn-color3: var(--md-orange-100); + --jp-error-color0: var(--md-red-900); + --jp-error-color1: var(--md-red-700); + --jp-error-color2: var(--md-red-300); + --jp-error-color3: var(--md-red-100); + --jp-success-color0: var(--md-green-900); + --jp-success-color1: var(--md-green-700); + --jp-success-color2: var(--md-green-300); + --jp-success-color3: var(--md-green-100); + --jp-info-color0: var(--md-cyan-900); + --jp-info-color1: var(--md-cyan-700); + --jp-info-color2: var(--md-cyan-300); + --jp-info-color3: var(--md-cyan-100); + + /* Cell specific styles */ + + --jp-cell-padding: 5px; + --jp-cell-collapser-width: 8px; + --jp-cell-collapser-min-height: 20px; + --jp-cell-collapser-not-active-hover-opacity: 0.6; + --jp-cell-editor-background: var(--md-grey-100); + --jp-cell-editor-border-color: var(--md-grey-300); + --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); + --jp-cell-editor-active-background: var(--jp-layout-color0); + --jp-cell-editor-active-border-color: var(--jp-brand-color1); + --jp-cell-prompt-width: 64px; + --jp-cell-prompt-font-family: var(--jp-code-font-family-default); + --jp-cell-prompt-letter-spacing: 0; + --jp-cell-prompt-opacity: 1; + --jp-cell-prompt-not-active-opacity: 0.5; + --jp-cell-prompt-not-active-font-color: var(--md-grey-700); + + /* A custom blend of MD grey and blue 600 + * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ + --jp-cell-inprompt-font-color: #307fc1; + + /* A custom blend of MD grey and orange 600 + * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ + --jp-cell-outprompt-font-color: #bf5b3d; + + /* Notebook specific styles */ + + --jp-notebook-padding: 10px; + --jp-notebook-select-background: var(--jp-layout-color1); + --jp-notebook-multiselected-color: var(--md-blue-50); + + /* The scroll padding is calculated to fill enough space at the bottom of the + notebook to show one single-line cell (with appropriate padding) at the top + when the notebook is scrolled all the way to the bottom. We also subtract one + pixel so that no scrollbar appears if we have just one single-line cell in the + notebook. This padding is to enable a 'scroll past end' feature in a notebook. + */ + --jp-notebook-scroll-padding: calc( + 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - + var(--jp-code-padding) - var(--jp-cell-padding) - 1px + ); + + /* Rendermime styles */ + + --jp-rendermime-error-background: #fdd; + --jp-rendermime-table-row-background: var(--md-grey-100); + --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); + + /* Dialog specific styles */ + + --jp-dialog-background: rgba(0, 0, 0, 0.25); + + /* Console specific styles */ + + --jp-console-padding: 10px; + + /* Toolbar specific styles */ + + --jp-toolbar-border-color: var(--jp-border-color1); + --jp-toolbar-micro-height: 8px; + --jp-toolbar-background: var(--jp-layout-color1); + --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24); + --jp-toolbar-header-margin: 4px 4px 0 4px; + --jp-toolbar-active-background: var(--md-grey-300); + + /* Statusbar specific styles */ + + --jp-statusbar-height: 24px; + + /* Input field styles */ + + --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); + --jp-input-active-background: var(--jp-layout-color1); + --jp-input-hover-background: var(--jp-layout-color1); + --jp-input-background: var(--md-grey-100); + --jp-input-border-color: var(--jp-inverse-border-color); + --jp-input-active-border-color: var(--jp-brand-color1); + --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); + + /* General editor styles */ + + --jp-editor-selected-background: #d9d9d9; + --jp-editor-selected-focused-background: #d7d4f0; + --jp-editor-cursor-color: var(--jp-ui-font-color0); + + /* Code mirror specific styles */ + + --jp-mirror-editor-keyword-color: #008000; + --jp-mirror-editor-atom-color: #88f; + --jp-mirror-editor-number-color: #080; + --jp-mirror-editor-def-color: #00f; + --jp-mirror-editor-variable-color: var(--md-grey-900); + --jp-mirror-editor-variable-2-color: rgb(0, 54, 109); + --jp-mirror-editor-variable-3-color: #085; + --jp-mirror-editor-punctuation-color: #05a; + --jp-mirror-editor-property-color: #05a; + --jp-mirror-editor-operator-color: #a2f; + --jp-mirror-editor-comment-color: #408080; + --jp-mirror-editor-string-color: #ba2121; + --jp-mirror-editor-string-2-color: #708; + --jp-mirror-editor-meta-color: #a2f; + --jp-mirror-editor-qualifier-color: #555; + --jp-mirror-editor-builtin-color: #008000; + --jp-mirror-editor-bracket-color: #997; + --jp-mirror-editor-tag-color: #170; + --jp-mirror-editor-attribute-color: #00c; + --jp-mirror-editor-header-color: blue; + --jp-mirror-editor-quote-color: #090; + --jp-mirror-editor-link-color: #00c; + --jp-mirror-editor-error-color: #f00; + --jp-mirror-editor-hr-color: #999; + + /* + RTC user specific colors. + These colors are used for the cursor, username in the editor, + and the icon of the user. + */ + + --jp-collaborator-color1: #ffad8e; + --jp-collaborator-color2: #dac83d; + --jp-collaborator-color3: #72dd76; + --jp-collaborator-color4: #00e4d0; + --jp-collaborator-color5: #45d4ff; + --jp-collaborator-color6: #e2b1ff; + --jp-collaborator-color7: #ff9de6; + + /* Vega extension styles */ + + --jp-vega-background: white; + + /* Sidebar-related styles */ + + --jp-sidebar-min-width: 250px; + + /* Search-related styles */ + + --jp-search-toggle-off-opacity: 0.5; + --jp-search-toggle-hover-opacity: 0.8; + --jp-search-toggle-on-opacity: 1; + --jp-search-selected-match-background-color: rgb(245, 200, 0); + --jp-search-selected-match-color: black; + --jp-search-unselected-match-background-color: var( + --jp-inverse-layout-color0 + ); + --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); + + /* Icon colors that work well with light or dark backgrounds */ + --jp-icon-contrast-color0: var(--md-purple-600); + --jp-icon-contrast-color1: var(--md-green-600); + --jp-icon-contrast-color2: var(--md-pink-600); + --jp-icon-contrast-color3: var(--md-blue-600); + + /* Button colors */ + --jp-accept-color-normal: var(--md-blue-700); + --jp-accept-color-hover: var(--md-blue-800); + --jp-accept-color-active: var(--md-blue-900); + --jp-warn-color-normal: var(--md-red-700); + --jp-warn-color-hover: var(--md-red-800); + --jp-warn-color-active: var(--md-red-900); + --jp-reject-color-normal: var(--md-grey-600); + --jp-reject-color-hover: var(--md-grey-700); + --jp-reject-color-active: var(--md-grey-800); + + /* File or activity icons and switch semantic variables */ + --jp-jupyter-icon-color: #f37626; + --jp-notebook-icon-color: #f37626; + --jp-json-icon-color: var(--md-orange-700); + --jp-console-icon-background-color: var(--md-blue-700); + --jp-console-icon-color: white; + --jp-terminal-icon-background-color: var(--md-grey-800); + --jp-terminal-icon-color: var(--md-grey-200); + --jp-text-editor-icon-color: var(--md-grey-700); + --jp-inspector-icon-color: var(--md-grey-700); + --jp-switch-color: var(--md-grey-400); + --jp-switch-true-position-color: var(--md-orange-900); +} +</style> +<style type="text/css"> +/* Force rendering true colors when outputing to pdf */ +* { + -webkit-print-color-adjust: exact; +} + +/* Misc */ +a.anchor-link { + display: none; +} + +/* Input area styling */ +.jp-InputArea { + overflow: hidden; +} + +.jp-InputArea-editor { + overflow: hidden; +} + +.cm-editor.cm-s-jupyter .highlight pre { +/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */ + padding: var(--jp-code-padding) 4px; + margin: 0; + + font-family: inherit; + font-size: inherit; + line-height: inherit; + color: inherit; + +} + +.jp-OutputArea-output pre { + line-height: inherit; + font-family: inherit; +} + +.jp-RenderedText pre { + color: var(--jp-content-font-color1); + font-size: var(--jp-code-font-size); +} + +/* Hiding the collapser by default */ +.jp-Collapser { + display: none; +} + +@page { + margin: 0.5in; /* Margin for each printed piece of paper */ +} + +@media print { + .jp-Cell-inputWrapper, + .jp-Cell-outputWrapper { + display: block; + } +} +</style> +<!-- Load mathjax --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script> +<!-- MathJax configuration --> +<script type="text/x-mathjax-config"> + init_mathjax = function() { + if (window.MathJax) { + // MathJax loaded + MathJax.Hub.Config({ + TeX: { + equationNumbers: { + autoNumber: "AMS", + useLabelIds: true + } + }, + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + displayMath: [ ['$$','$$'], ["\\[","\\]"] ], + processEscapes: true, + processEnvironments: true + }, + displayAlign: 'center', + messageStyle: 'none', + CommonHTML: { + linebreaks: { + automatic: true + } + } + }); + + MathJax.Hub.Queue(["Typeset", MathJax.Hub]); + } + } + init_mathjax(); + </script> +<!-- End of mathjax configuration --><script type="module"> + document.addEventListener("DOMContentLoaded", async () => { + const diagrams = document.querySelectorAll(".jp-Mermaid > pre.mermaid"); + // do not load mermaidjs if not needed + if (!diagrams.length) { + return; + } + const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default; + const parser = new DOMParser(); + + mermaid.initialize({ + maxTextSize: 100000, + maxEdges: 100000, + startOnLoad: false, + fontFamily: window + .getComputedStyle(document.body) + .getPropertyValue("--jp-ui-font-family"), + theme: document.querySelector("body[data-jp-theme-light='true']") + ? "default" + : "dark", + }); + + let _nextMermaidId = 0; + + function makeMermaidImage(svg) { + const img = document.createElement("img"); + const doc = parser.parseFromString(svg, "image/svg+xml"); + const svgEl = doc.querySelector("svg"); + const { maxWidth } = svgEl?.style || {}; + const firstTitle = doc.querySelector("title"); + const firstDesc = doc.querySelector("desc"); + + img.setAttribute("src", `data:image/svg+xml,${encodeURIComponent(svg)}`); + if (maxWidth) { + img.width = parseInt(maxWidth); + } + if (firstTitle) { + img.setAttribute("alt", firstTitle.textContent); + } + if (firstDesc) { + const caption = document.createElement("figcaption"); + caption.className = "sr-only"; + caption.textContent = firstDesc.textContent; + return [img, caption]; + } + return [img]; + } + + async function makeMermaidError(text) { + let errorMessage = ""; + try { + await mermaid.parse(text); + } catch (err) { + errorMessage = `${err}`; + } + + const result = document.createElement("details"); 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attributes. + */ + function replaceVoidElement(match, tag, rest) { + rest = rest.trim(); + if (!rest.endsWith('/')) { + rest = `${rest} /`; + } + return `<${tag} ${rest}>`; + } + + void Promise.all([...diagrams].map(renderOneMarmaid)); + }); +</script> +<style> + .jp-Mermaid:not(.jp-RenderedMermaid) { + display: none; + } + + .jp-RenderedMermaid { + overflow: auto; + display: flex; + } + + .jp-RenderedMermaid.jp-mod-warning { + width: auto; + padding: 0.5em; + margin-top: 0.5em; + border: var(--jp-border-width) solid var(--jp-warn-color2); + 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; + } + + .jp-RenderedMermaid figure { + margin: 0; + overflow: auto; + max-width: 100%; + } + + .jp-RenderedMermaid img { + max-width: 100%; + } + + .jp-RenderedMermaid-Details > pre { + margin-top: 1em; + } + + .jp-RenderedMermaid-Summary { + color: var(--jp-warn-color2); + } + + .jp-RenderedMermaid:not(.jp-mod-warning) pre { + display: none; + } + + .jp-RenderedMermaid-Summary > pre { + display: inline-block; + white-space: normal; + } +</style> +<!-- End of mermaid configuration --></head> +<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light"> +<main> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h1 id="BBC-News-Classification-Kaggle-Mini-Project">BBC News Classification Kaggle Mini-Project<a class="anchor-link" href="#BBC-News-Classification-Kaggle-Mini-Project">¶</a></h1><p>The objectives of this mini-project are:</p> +<ul> +<li>Perform an exploratory data analysis (EDA) procedure.</li> +<li>Build and train an unsupervised learning model using Non-negative Matrix Factorisation (NMF).</li> +<li>Build and train a supervised learning model.</li> +<li>Compare the two models.</li> +</ul> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Project-setup">Project setup<a class="anchor-link" href="#Project-setup">¶</a></h2><p>Import the required modules and load the datasets.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># This Python 3 environment comes with many helpful analytics libraries installed</span> +<span class="c1"># It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python</span> +<span class="c1"># For example, here's several helpful packages to load</span> + +<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span> <span class="c1"># linear algebra</span> +<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span> <span class="c1"># data processing, CSV file I/O (e.g. pd.read_csv)</span> + +<span class="c1"># Input data files are available in the read-only "../input/" directory</span> +<span class="c1"># For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory</span> + +<span class="kn">import</span><span class="w"> </span><span class="nn">os</span> +<span class="k">for</span> <span class="n">dirname</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">filenames</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="s1">'/kaggle/input'</span><span class="p">):</span> + <span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">filenames</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="n">filename</span><span class="p">))</span> + +<span class="c1"># You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" </span> +<span class="c1"># You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>/kaggle/input/learn-ai-bbc/BBC News Train.csv +/kaggle/input/learn-ai-bbc/BBC News Sample Solution.csv +/kaggle/input/learn-ai-bbc/BBC News Test.csv +</pre> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">permutations</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">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">NMF</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">TfidfVectorizer</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">confusion_matrix</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearSVC</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">wordcloud</span><span class="w"> </span><span class="kn">import</span> <span class="n">WordCloud</span> +</pre></div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"/kaggle/input/learn-ai-bbc/BBC News Train.csv"</span><span class="p">)</span> +<span class="n">data_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"/kaggle/input/learn-ai-bbc/BBC News Test.csv"</span><span class="p">)</span> +<span class="n">sample_solution</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"/kaggle/input/learn-ai-bbc/BBC News Sample Solution.csv"</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Inspection">Inspection<a class="anchor-link" href="#Inspection">¶</a></h3> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h4 id="Training-data">Training data<a class="anchor-link" href="#Training-data">¶</a></h4> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">info</span><span class="p">())</span> +<span class="n">data_train</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre><class 'pandas.core.frame.DataFrame'> +RangeIndex: 1490 entries, 0 to 1489 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 ArticleId 1490 non-null int64 + 1 Text 1490 non-null object + 2 Category 1490 non-null object +dtypes: int64(1), object(2) +memory usage: 35.1+ KB +None +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div> +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0"> +<div> +<style scoped=""> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> +<thead> +<tr style="text-align: right;"> +<th></th> +<th>ArticleId</th> +<th>Text</th> +<th>Category</th> +</tr> +</thead> +<tbody> +<tr> +<th>0</th> +<td>1833</td> +<td>worldcom ex-boss launches defence lawyers defe...</td> +<td>business</td> +</tr> +<tr> +<th>1</th> +<td>154</td> +<td>german business confidence slides german busin...</td> +<td>business</td> +</tr> +<tr> +<th>2</th> +<td>1101</td> +<td>bbc poll indicates economic gloom citizens in ...</td> +<td>business</td> +</tr> +<tr> +<th>3</th> +<td>1976</td> +<td>lifestyle governs mobile choice faster bett...</td> +<td>tech</td> +</tr> +<tr> +<th>4</th> +<td>917</td> +<td>enron bosses in $168m payout eighteen former e...</td> +<td>business</td> +</tr> +</tbody> +</table> +</div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>Using the data tab in the competition, we can see that all rows are valid, but only 1440 out 1490 values are unique in the Text column.</p> +<p>In addition, the Category column has a dtype of object without being categorised.</p> +<p>The next cell fixes these two issues.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_train</span> <span class="o">=</span> <span class="n">data_train</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Text"</span><span class="p">])</span> +<span class="n">data_train</span><span class="o">.</span><span class="n">Category</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>Next we check the ratios of the categories using a pie chart. From the chart below, it seems that the categories are well-balanced.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div> +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"> +<pre><Axes: ></pre> +</div> +</div> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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 +"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h4 id="Test-data-and-provided-sample-solution">Test data and provided sample solution<a class="anchor-link" href="#Test-data-and-provided-sample-solution">¶</a></h4><p>These have the same structure as the training data, except that one drops the Category column, while the solution drops the Text column.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">data_test</span><span class="o">.</span><span class="n">info</span><span class="p">())</span> +<span class="n">data_test</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre><class 'pandas.core.frame.DataFrame'> +RangeIndex: 735 entries, 0 to 734 +Data columns (total 2 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 ArticleId 735 non-null int64 + 1 Text 735 non-null object +dtypes: int64(1), object(1) +memory usage: 11.6+ KB +None +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[13]:</div> +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0"> +<div> +<style scoped=""> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> +<thead> +<tr style="text-align: right;"> +<th></th> +<th>ArticleId</th> +<th>Text</th> +</tr> +</thead> +<tbody> +<tr> +<th>0</th> +<td>1018</td> +<td>qpr keeper day heads for preston queens park r...</td> +</tr> +<tr> +<th>1</th> +<td>1319</td> +<td>software watching while you work software that...</td> +</tr> +<tr> +<th>2</th> +<td>1138</td> +<td>d arcy injury adds to ireland woe gordon d arc...</td> +</tr> +<tr> +<th>3</th> +<td>459</td> +<td>india s reliance family feud heats up the ongo...</td> +</tr> +<tr> +<th>4</th> +<td>1020</td> +<td>boro suffer morrison injury blow middlesbrough...</td> +</tr> +</tbody> +</table> +</div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">sample_solution</span><span class="o">.</span><span class="n">info</span><span class="p">())</span> +<span class="n">sample_solution</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre><class 'pandas.core.frame.DataFrame'> +RangeIndex: 735 entries, 0 to 734 +Data columns (total 2 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 ArticleId 735 non-null int64 + 1 Category 735 non-null object +dtypes: int64(1), object(1) +memory usage: 11.6+ KB +None +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[14]:</div> +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0"> +<div> +<style scoped=""> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> +<thead> +<tr style="text-align: right;"> +<th></th> +<th>ArticleId</th> +<th>Category</th> +</tr> +</thead> +<tbody> +<tr> +<th>0</th> +<td>1018</td> +<td>sport</td> +</tr> +<tr> +<th>1</th> +<td>1319</td> +<td>tech</td> +</tr> +<tr> +<th>2</th> +<td>1138</td> +<td>business</td> +</tr> +<tr> +<th>3</th> +<td>459</td> +<td>entertainment</td> +</tr> +<tr> +<th>4</th> +<td>1020</td> +<td>politics</td> +</tr> +</tbody> +</table> +</div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Preprocessing">Preprocessing<a class="anchor-link" href="#Preprocessing">¶</a></h2><p>Tf-idf converts a count matrix into a metric more suitable for our usecase by reducing the impact of highly-occuring words, making sure that words that occur less frequently overall, and are thus more likely to contribute to defining the topic of the article, are properly accounted for.</p> +<p>Before applying tf-idf, there are preprocessing tasks under consideration:</p> +<ol> +<li>Convert to lower case.</li> +<li>Remove punctuation.</li> +<li>Remove stopwords.</li> +<li>Stemming/Lemmatisation.</li> +<li>Stripping extra white space.</li> +</ol> +<p>These are designed to remove the unnecessary features (such as meaningless words or words that appear frequently regardless of topic). Words which are essentially the same, such as <code>Business</code> and <code>business</code> should be treated as one word if possible to make the data more grouped and thus more useful.</p> +<p>Fortunately, all of these except of lemmatisation can be handled by TfidVectorizer easily, greatly simplifying the implementation. Note that when <code>analyzer == 'word'</code>, punctuation along with extra white space are automatically removed. Setting to lower case and removing stopwords can also be achieved by setting their respective parameters.</p> +<h3 id="An-idea-to-increase-performance">An idea to increase performance<a class="anchor-link" href="#An-idea-to-increase-performance">¶</a></h3><p>As for lemmatisation, it seems to be potentially useful according to <a href="https://stats.stackexchange.com/a/430776">this stackexchange answer</a>. However, <a href="https://mimno.infosci.cornell.edu/papers/schofield_tacl_2016.pdf">this paper</a> suggests that the resulting complexity is not worth it, so I decided not to implement it afterall.</p> +<p>Instead, I will try replacing the numbers in the text with NUM using regex. This was much easier to implement, and would give some insight into whether the type of modifications done by stemming/lemmatisation actually help.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">vectoriser</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s2">"english"</span><span class="p">)</span> <span class="c1"># lowercase is True by default and analyzer==word.</span> +<span class="n">combined_text</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data_train</span><span class="o">.</span><span class="n">Text</span><span class="p">,</span> <span class="n">data_test</span><span class="o">.</span><span class="n">Text</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> +<span class="n">vectorised</span> <span class="o">=</span> <span class="n">vectoriser</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">combined_text</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">vectorised</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>(2175, 29126) +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>Now we will try to visualise the most common words. Notice that because we used the version with the numbers, some of them appear here. Between the numbers, it seems 000, along with specific years, are frequently occurring.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">word_frequencies</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">vectorised</span><span class="o">.</span><span class="n">toarray</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="n">vectoriser</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">())</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> +<span class="n">wordcloud</span> <span class="o">=</span> <span class="n">WordCloud</span><span class="p">(</span><span class="n">background_color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span><span class="o">.</span><span class="n">generate_from_frequencies</span><span class="p">(</span><span class="n">word_frequencies</span><span class="o">.</span><span class="n">to_dict</span><span class="p">())</span> +<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">wordcloud</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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FloVLA9F01W8ISPIslkzTANzyGgaNi+hyrL9EVSJIwgtvBIGkFMWUWWZHZHs6hLA7DrN2h4OUr2yE37rytRgmqWoJq96X6+8HD9OlV3irw9zGLjNGV7nJo7R8PL4/pVkGQUyUCTAuhKlIjeQ0IfIKYPEtbaCChpTHXrPtFlCCFoeDmq7jRF+yol+ypFZ4SSPYrtlfFEHV94yCgosompJgkoaYJqCxGti6Sxm7DWQUDNoEom0jYm5MlSiY/sHeIT+/cRMrY2SeyJtVK0a6iyQlS79kHlGuex/CLihgF9PYS1DoJqC6ps3nLfjSCEjycs6u4itl+k4eWpu/NU3Glqzgw1bwHbKy4RLAtPWLi+BQgkSUaWNBQMVLlJ+nQlgiHHMJUUATVLSGvBUBLochhNDmPIMTQljMz2o9O3ctxm9w2pBm2BOIORFt5cvLJqknZ8l9cXrzAUbbttMtBM9XN4M3cVn9UTmITEfYluYlsI1BMICnaNE/lR3li8sooIyEjE9CCPZYZ4uvUAu6KtpIzwhm0N0UbNtekPZ0jqzbFmopZb+d0TgqJT46tTJ3k4PUBnMImhaBu2tx4kSSKiBbgv3o3te8w3SnSGkjyQ7GdXpJW2QJyYHryl1UEIwaJVoSuUZMGqcKYwsYYQzFklrlTm6A1ndpwM5Owqbyxe4duzZ6l7Doas0mLG2B1t58mW3Tya2UVENTd8jkIIfATj1UXOlqYo2DWiskm92MBuOBimhqzIeJ5Po2YTCBuoqkKjZuH7AjOgN8fAmo1uaMiKhO8JLMvBDDRdZ47jUc5XuXx2kv597belpNMafBLPtyhYZ3H8EjV3cs0+t/rWln8XQhAwdTLpCOo66b8AimQQUjtImceQJRPusvPktsnAEy3N6NLlB71sIn7UvJZrLkkSGTPMU21D67bRGYrTEYzhCB9NagZntAdjPJDuWbPfEdHVtDogcUBqX/X739j1yMp/N7xFxivPcXzxP9y0/2nzPgaiH2Mg+vEN9/GFh+OXKViXuVT8E8Zrz60/cQnwhY1DmZo3R8EeZpxvEVAypM2DdIffR0focRTJgKX7tBk0TU0+rl9npvYal0pfoGhfxfHL6+7v4eH5NrZdosTI0lYJXY7SF/kheiJPE9V7UNn66jWs64R0HU1V1qxAb4blFKm0uTZt7HT+vzBbfxPHX2v+vBF74z/NYPSHiehbNy0K4ePTJHUVZ4rJ6nPM1U9SckZoeLlbNwAI4eELB5calr/+PhIyuhIjonWS0HfTEjhCwhjCUOLIkoYsaUh3IZf9VpCQiKgmT2Z381bu6qpVjCcEL85d5JNdx4hpt56obgYfQcVp8ObiZfwbgk4VSeKx7BDhLay4PeFzpTLHZ0dfw/LdVb8ZisbRZC9/b/czRDRzUzn4QVXnWKqfrmAKU9H5neFnV02yvhCcKU5yPDdKQNFp32Ze/GOZIXZH2/CF4FCye2Ws3CwkSSJtRng8uxtT0fmnJz9Pwa6tyZgYLs8yFGllV2TrMRg3w2hlgT+++gqLdtNs3hFM8L7W/Xyq5+GbEq7r+68gLWk6NDOJKqU6w++MszhdpLU7haorVEt1psdydA9mCceDTI8u4Lk+bT1prJrF7GSBRDqMbmrYDZvF2TId/Rk8z6dSrJGbLZGfL3NjfPNWIUs6sqKjKVEU6fasF5IkkYjfnJwZaor28NO0h5++rXNtFzsaabfVl/tGaLf4cGWuxSDs1EBqeyXK9sRN9ylYwwyX/oyx6rewveKWz1H35hmvPstU7WU6Qo9zNP33MZQ40iZvvy8cis5Vzub/kLHKN7Z8/iYEtl/kQvF/MFr+Gp3h9zIU/1Hi+uCWWvnA0CAz5QpfPneB9+8aIKzf3ahYx69g+UUibJ0MVN0ZZutvMV55lunaqwg2mM1vEwIfy8tjeXkWGu9wqfR5VDlIVOsmG7ifnvDTJI293G3mvx5iWpAPtt3Hb178Fg3PXplWBIJL5RkulWYIqQYJffsBnwuNMqeLE8xbq/3liiST1EM8nB7AlDe/2h4uzfLawmXOFFd/twFFZ1eklX968BOE1a1bjrJmlE/3PcqrC5c4W5xcQzT+bOIt2gLxbZOBjmBiRwR2NEnhkfQgj2V28erCZRas1YuCy+W5VdaNnYLlu1hLMQ+dwSS/uOeDPJweRL8Nd5hru+Tny+w52ktLZ4I3nj3HyIVpsu0JLp+dxLFcHnhqLz2727DqDl/+7y/z4U8/ytvfOc/48CyJTJS9R3tp607x4pdPEokHefzDh/jaZ19d9XlJsCq+6R7WYsfIwO1OzptdXe40bL9EZR3zDzRXkguNdzix+FsU7Es4/too6K3AExaT1Rdx/Sr7Ez9L0tiNIt98MnX8GqPlr3O1/BVy1vnbOv8yGn6R0crXyVuXuD/9d8mYhzZ0Gfz2K2/w34+fXPnbcl0830dC4te+89KqZ/LegT5+9YPv35E+bgTHr2J761tE1oMQPo5f5VzhM0zVXqHqzOCJ+h0jAhvB9esUrGFsr0RU6yNh7EZ6F6iBy5JETA+sTCwVd3Va3gtzF0jqIRLJ7ZOBiVqOVxeG12yPqCZPtexH2+JkciI/yluLV9dsHwhn+WTXMUJLRGA744UmK/xI9wOMnV/EuoG8XCzNcKk8w2CkhZbA1nPEd2r8kpYC057I7uFyeW4NGVi0yyuR/3cCMhJ/b88zHIx1oku3F4MEzYnaDGrN1XM2SrlQw7FdBg90UcxVGD49wcSVebKdSWKpMG9++yyO5RGKBJAkMIM6SBKBiElurkThuXPYlrs6Uh0JSVKZrT3PROWrVJ0xfGFjqll6oz9ONvAw6m3EdXm+RdkeZr7xOouNt6k5U/i4zdgxrYeO0AdoDb533XH29OKvkWscp+EurGx7ouMPCGzgup6rvcJI6XNkAg+TNA8xUfkqBes0DW8eGYP28DN0Rz5GQN2aZegHPgfP8avU3HmE8LnedO8Lh7q7yNnCZyjaw0tE4HazFQSeaLDQOMuV8l/iiCrtwYdYb4XYdA14XCl9kYnq8xTsYTyxvfzptWhOkEW7aW04ms4QVLPrEpOHujuJGNdMuLLEksbA2la7E3deRMP1azj+2ojs9WB7FcrOGFdKf8FM/W1q7gyesO5wDzeCwMdF4BMz+t4VRACWTbcyj2eGOF2YWEMGTuTHOJbqZ7/fuS3xHU/4zDQKnCmsJdxhzeSRzCCytHmXSd6qcrk8t0Y5UZcVOoNJjqX6tu3SkCQJGZkjyR4iqkneqq6KlneFx1S9KRu8HTKw0+gPZwhray0gdc/B8p2lALSdXUCpksyeWAd7om1EtMBtE4Fg2GTPkV7CsSCyLNPemyaWDOG5HtFECMtKYtVtZFkiGAkQTQTxPYEkLT0vWSIca5KC/r3tWHUb4Qt69rQhK9f6JoSH5S0yUfkKhpImoGZx/RoVZ4TLxT9ElyNE9SF0ZXvPVZJkGt4iResiupIkrPUgENSdWar2GFfcPyKq7yKgtiJLq61grcH3ENWbgYqLjePkGscRbBw/5YkGdXeWnHWKgn0eRTKJGfuIIXC8CpocRmLr3+odIQMjkzlczyNo6qQTIXRt66dpWE01p1rDJhw0iITMDQMvbgeesLG9Eo5fRZObfi8hfCyvzFTtFWZqry9NIDuXtmj7RWZqb2DIUWJaDyGtbZ29BDnrPBPV58lZ5zc9AW4FrqgyW3uDqdrLdIWfIiCl13zc+1uzDGWu5Vhfnz5zIxT5zpvhXFHflIWm4eYo2MNM1V5jtPJN7A3iK+4mZFR0OUZE62YrLgLf96mVG7i2ixkyMAI7qwwoIXEw0U3SCLNoVVYF5E3V80zUcixaVVoC0Zu0sj7ydpWpeoHZRmHVdkNWSRlhdsfatjRdTdbzTNcLlNzVK9+4FqItEKftNidpiWZ6XEIPMdsoUb9BF2C2XmS6XuAwPes3cBeRNiIY60gQu76H43t4vr+GwHm+T8N2UWSJct0iYGiEzc3Ha6iywrFkHwm9qTR7u9BNjdbuayml0USISDyIWJJijkoSwl+KTZIlRDK0Mgjd+A2kWmLNuBfR3Pd6+HjYXglVCpE2j2GoKTy/Tt46xaXC77NQfxNdjm+fDKA044T0AUJaJ0G1Gc9Wtq8wX3+Nqeo3qNgj6EpiDRlImoeJiT2U7SvYXoFc4/gtz+fjUrIvEdOHiBmHCGmdKJKJ7RVQ5RDKNgKs7wgZ+PrL5yhXGvR0pHjfQ0MkY1s/Tb5UY3hsgeHROXb3t3BoqAM1uL20nptD4AmLqjtDXB8AJDxhUXbGOFf4zA6uxlej6k4xWz9OSG1nMPYJrrdKCOHjigYXi58nb53fMFBwJ+Bhc6H4P4gbgxhydCm48Ro0RUFTrn30uXqdgKoS0Fa/0FXbxvEExh22Nbl+/ZbEyPXrzDdOMVb5FqOVb8JddglsBE0JE9baMZX4lo7zPZ/Z8UWKixUy7Uk6B2+e+bJVSJJEXzhDTyjNbL3I4nW58LbvcqUyx8Xy9LbIwHB5ltHKAra/eqWT0EMMhLO0mFsbfC+Upsk7a8lgZ6hZ8OZ2i/Ys6560BuJM1HJryUCjyPQNxOa7haBqbGit8Xx/Kftq9e+u5zOdL2FqKsNTi3Rl4oRaN08udVnliewQ2k3qICzn1juuh6o008y3Gih5/f7XT+ySJN2UR2/4u/AROOyK/02CWhuypCEQK2b2uforRI0hosauTfdz9XllkuYhkuahVduj+gCmmmGq+nVKzmXi5v41x8qSiiyF0eUYqrQ5d5wQLjV3iiOZf05Y60WRbz89847YKr/8/Bk+/40TvPDWMOXK9nxXC/kqb5we5Xf+5GX+5OsnKZTvnA/MFw4lZwSBaKbvNM5ypfRFKs7NAwtvFznrLBeL/3NNJLvjV5isPs9o+Wt3ZUVbcSYZrzxLzjq/VmL4BvyDv/gKX7+41v/7O6+9yW+8/Oqd6uIKbL+KdZN7IoTgcukvOZX7bUYrX+fdQgQAgmor6cB9K38LITb1j6IoyIpCuVDj1JLa2VaO3yyeyA4xsE4E+pnCRDP1cIvh2UIIjudG19QhABiItPBoeusD73B5lqK9dizoCCRoDcS33N5GSJsRgutkOCxYZRYa330r07KoV1OLYB03I+s/+7rtcHZ0lu+cvkqpZuELsSbDY8Nz0pQP3h/vvKlOiC8EpVqDl86PMpErYru3Thm+05AkFUNJrxABaFrEJEklHThKw53B9gpbfsevQQDrfLtSEFNJIUs6jre59OnNQJZUwnofIa17R4gAvItjBgKGRjRsIgSMT+ewnTv3QvnCpmhdpSvUzOOfrb/JeOXZO3a+ZQh8Gl6O4eIX2J/8G0io+MKl4kxyOvdf7mqQ21jlWYJqlpS5f9NZDtejbjs0XPfWO94mHL+yYUaH6ze4UvoiZ/L/FWsbWR93GiG1hbRxrRrgm8+eRfiCYNRkYbpAJB4ino4weWWO0YvT2A2Hp3/sIdr6MoSjJtFEiErhWvqlY7mMXZrhzWfP4DoeDz1zkHRrnKmRed741hlkWeKRHzpER3+WYPjWZsOH07s4XZjk7fwI7nUr+el6gfOlaSZreTqCiU2t8oRophOeLUyu8e9rssJAJMuxVP8GR2+MS6UZivbaFNSkEb6tjIcbEVT0dSe8huessRZ8L8FQVXpbmlonAkE0uHlVxZBq0BNK3jLgU5IkFEVGluHVi2O878AgmW24incSEjKaFORGs0FTWTCKKyw80UDgbWv8AyjbIyw03mS+/jo1ZwLHr+ALGx8Lf8djlWQMOclOZiS9a8mAqsqYerN7pWoDz79zE6OPS8WdQCCYrb/V9NGLW+e87wQcUWW8+hxD8U+hy2GK9lWmai9TdWfuyvmXYXlFSvYIBWuYpLln1W8Vy+biwgK5ap3FWo0zM3OEtGvBhvl6nclSibbIag0B35vG92bxRRFF6UFWurcldHQ9XL+GvY6boOHlWWic5lLpT7G84k0DcL4bkNEJKGmi+rXiP/n5EtVSHTOoUys3yM0UafvgffTsacMIakxfXeDcW1dJtcWRZGmNH3T04jRjF2fQTY10e4LXv/EOhx7fTbVYp161eOzDh2npTKKbm0vbC6sG/eEMQ5FWzhavBfz5CBatMq8vXuaTwWObvuaThTEW7fIadcDBcAs9ofSW1PzEUtW7RbuyblGc52bPcbY4ua2UwvUwUplfqWZ4PSzfpeE5+MK/bZfESpueS9VtMNcoMV0vUHTqNDybhueskA9HNGMAPOHjCh/H93B9j3cK4+tXZtwAywXGJhaLtCWiWwre1GSFqBZctf9iucrVuTzFWoNitcGezgwdyRi6qhAPBpjIFZuqra7LYrnGG5cnqVs2R/o7aEtE8HzBmfFZ7u9rw9A0Lk7NU23YDLVn+M7ZK3i+oDeboC0eQVcVXrs0TsNxyURDtCUixIIm3z59GVmSGGrP0JGMEg+tt1IWG7h8BZ6oLel/6NsO7J2rv8Zc7SXy1hni+hBp8wiKFECWVOruDOfzv7WtdjdCU75/Zw3771oyIF1XEKJuubdhvrk1mqvxaTy/wULj1JJq4erzSSiocoCAksZQ4k3futT03Th+jYozhe2X1hx363M7lJ1xKs4kEa2Lkn2VmfqbeGLj1YcuxzCUGLocRZWbL5wvbBy/huUVqXsL+Dc5fj0Imvcgb11cQwY84VOxLIYXF6naDmOFIoZ67dWp2TYt4TB7sqtLEvveLJ43ihBVED6y0g5o3A6b9YSN69fxhbsirez4VYrWFcYqz1K0L9+6kZtAljQUyUSWlKYwEAoCHyFcfDx84S5JhW6NnAbUpjqhrlwjTJ7nU69auI6L6/i4jku5UEVRZHRdI5GNMnl5Dm8DM2thvkwpVyEYMUlmo+Rni2i6SiwdoXOgBcdysOo2ZsgA9dbBXqqs0B1KszfWvooMABTsGm/mrvLDXUeRxebS5E7mx9ZNcdsdbaMzmNywiNJGsHyXumevqSEAMFpdYLS6sM5ROwtP+LhLk/J2yYAnfOquzVQ9z1yjRM6ukrerLFplZuslSm6dhudgeU3i0fAdPN9bUnFt/tMkA/4aRcdbntv3yVfr1CyHYq1BxnZWgvVuBUVS1lQbrTRsxhcKK9aAkblmEbqBlhSGpl7nwmj+2/N8FkpVrszmMDSViKlTaVhcncuTjYaZLVZYLNdQFYXJxRKGpuC4HtWGTW8mwTtjM+xpz6yInpXqDc6Mz7K3M7uSYbAeBB6OX8Hxy+hyFElSllwpPjVnCk2OoEqBbU+whcYZ8tYZPFEnHXiIqL4LVQ4DPgXr7C2PfzfgXUsGPM9fcQ14vnfbalI3gy9cqs40ZWecxcaZNatyVQpgKHHCWgcZ89CKmhw0V6o1d5bJ6kvkrKas7lYnYk/YLDROIUsKBfsyi40z6+4no6IrMZLGHhLGLqJaDwE1iyoHsL0SNbepejhXP07ZGd9y8GPVnWbROke/+Bhw7cPSFYVsOMyVxTyKLOP7Po53zSUQNU2Odraz57qsAwBflBF+DSQZ35sEPJpkYPsQeEvywHU0OYwQPmV7nOnaq4yWv76lthTJaK4IUJEldcmvGMWQE6iyuVJ/wBcurl/HFY2V1EbHrzVTBcU1guCzsZskovcQ1tpWrTwMQ1uqciahagq+5zFyfgq74eC5HqnWBL7v47k+Vt2hUbOwGw52w0E3NSRZQlaaPmPfFxx6fDctXalmRUMJnvvTN/A8nz1HetGNzd33nlCag/Eu/nz8bdzrVvQlp87J/BhVxyKsmTelc8ur+OP50TXlihVJZn+8k64tlioWQM217uiiYLPwaa7Ot/om+8LH8pp1BsZqi7w4d4G3cyOM13LrlnW+E3A9n1y5jqbILJaqtMbDiGbJjVtClWTMG6Spbdejbrvs68yyuyPDZ186iRBNMnA9JCBo6NzX04qpqUzmimRjYTKREN3pOG8MTzDYmsJ2PTzP5+ToNLGgSVsywvnJeRqOQ0cqSq5S470H+gmbBnXbYTJXRFdVDva00haPEtzgPRfCx/WrlOyLRPVdKFIAgY/t5SnZF4noA+hLY/qKpUWIFcLQ1NcVS9+7t1xab2WMrLnTeH6dsN5NJvBwk2wIl7o7Q9WZYN308evO0/zLW1lkCOEuLXjkVee5k3hXkYHrP/RcscbYdDOwLmQayHcwbU3gUffmeWnmn1D35m9Y9Ul0hZ6iJ/IM2cARVHl90+Zg7JOMVb7NxeJnmaufZGuBa4Kp2ivU3Dly1oV1c+EVySCid/NA+pdJGLs37IcQPg2vwFsL/5aJ6gtbIiY1d4780vmvzyoIaBp7shn2ZDNcXFjkA4MDvG/Xrf29itKD8Mt4zhkUbRfN1+32n6MvHOpeDk0OY/slhktfYLTyLcRNJuPVaBaUypr3kzYPEtP7iOhdhLVuVGlj07UkSSsDQtWdpmiNUHEnKNoj5KxzFKxLG1oMkvrQUkrhNcQzkeYEb7sks1FGL86gyHLTZTBbpFpqICsy89N5rpyZ4OLxUcr5Kl1Drdz3yC6GDnUjhODlL58k25mkb39HU7d/vszF46O4ztZJdMaMMBRppS+c4VL5GiluFi+q8urCJZ5o2XPTegWe8JmpF7hQnKbqrX6XW80Ye2JtW87TXy5j/t2nAsuhYlvYf+khlJwGry9c5uvT7/Dt2XNbMu/vFAK6yp7ODN88cYmudAJdUzc9tkqStFJo7EboajPrqBmQeO0bWL7Ckfk8Z8ZnKVQb9Gbjzd+EIGBo9Lek+NxLp5gvVXl0dw+dyRgvnLtKLG6gqyqPDvUQMnVkSSITDa/EOJiaSm8myV9/7xH++MWT7G5Ps7ezhf6WtURTkhRUJcTJhX9Jd/iHCWmdOF6F6dpz1L1Z9oZ/gai+a6XTAh9XVPD8OpY3jyuqCCGouVMIPBTJRJUDSDTHi6DaSsm+QME6z0z1OwS1NmruFAv1N1lsvLVkJbjhPi+dxxM1XL9Ow5vHEU3XVM2davZZCqDIQRTuRCbdaryryACA5wtOXpjgqy+d5bk3LiEBA90ZDP3Od7XiTnH9Zy4hczj9i/SEP4CpJG8p5NAZegJTiTNa+SaXip/f0rlnam8wV38bfx1fd1BpoTX4IIdTv4CuxG7h15IwlTgPZf8J/sz/j/nGSSx/c8F0Ag/bL7NonSVrHma9ifsnD99HNrzZQC0fVduHZjyB51xat73twBM2DXeBqNbNycX/xHTttU2lXwbUDCljP+3BR+kIPYYmh5ZcDTJ2w2XsYo5LZyZp62quanRTY2h/B2+/MszBI71NpTOaLqOw2kFIbVtyIfgIPCyvwFz9BPP1k01yYF9eil2QSBq7iWqrc9MPPTbEwUeaA5AkSTz8QR9JkvBcD98XK7XRFVWhb28HT33yAYQAVVdQFBlNV7n/yT3c98guPM9HN5rWAt/zVtrVDQ1li/ocSSPMRzoO8evnZ24oXuTxxckTHEsN3JQM1D2Hr0+fXhMrIAHPtB0kqd9ax34NBFTeJZaB7eBCaZr/ePEbnClMUnEbtyQCEk23TUg1iGkBwmoAU1ExFBVD1jCVZmXDNxavMF0vbLofvmhaXX/uQw+jyNKOLLLqtsNfvHkORTpPf0uS1liES9MLfP3EJcYWC8QCJpGAgef7TCwWsRy3KYi0NKnLksSB7hYuTi8gSxI9mTiqMsB/f+E4rfEIXek4XakYifDqWIBKw2ZsvsDLF0ep2w43CyvT5Rhd4Y8T0fqYqHyVkfLn8YSFKac5lP6ntATfgyY3XXg+DmOlP+NS8fdwvBICH184CDxenv75ZtChHCJq7OaR1v8IQHfkE5hqlonKlzid+7cI4RLSOskGHmdv4u8wUfkyN45/Ao+p6rc4n/8NLHdx5TwAr838EtLSoiWotfOejj+67ed0K2x7hvU8n9ffGeG5N4ap1lez/+U0wOGxeX79M98hGNi8Qa1SsymW68znKzQsB0mWePi+HkIblGHdWVz7QDU5TEvgKF2hpzCVxKZqYMuSSlzfhRUokmucY9Fa39y//pldPLF2ZavJETrD76Ev8uEVInAzk1Fz9QoqAQZjn6Th57Eap9nsWsYTDYrWlSUysBZfOneBhWqNkK7RGo3Ql4jTnUjQFgkTMY1VKwffmwZJQ5Uz+O4l0PcBTbP47aBpGVhkqvYys/W3qHs38xVLaHKY3sgztASOEdE6MZUkhpJYdS8btQanXr9KKGIyO5nH8wTxZAghBHPTBVzPW2ptOY9ZukbKpOYqR1F12oIPkzL3YXkFqs40841T1Nw5QlrbGmuOoio30MvmX6qmrBFOWXdCl5oThqLIK6beZVU2RVVW+rpVE2NUC/BQehBDfnZJya4JX/icLkwwbxUJqjrmBtX7LM/h5YVLa3z7siTxaHaIqBbYcWW8J7O7GQi3rPFp3wn0hNKb1uN3fJeJWp5fP/81zpemKDuNVaqGwEoZ4L5whv3xTtqXShpHNBNDVlElZaksvLxSn0WWZBzfY2pJhGmzcD2P6XyZbCKMoaoEDG3LsRs3wtBUntzbRzoaJBo0CegasiTxyYf203BcUpEgqixjex4HulsJ6BoSEAteC/ZUFYV9nS1kYiEMTaUjFePTT9yPqigEDI2grqEpMp98aD/GUmZCQNfozsQJmhqu5xMLmusKKKXMI0u5+CaqFCCqD+GJGgIfRTIIqh2ocmglXkBGpSX4BGG9F7HOmAxLlgb52qJIV+JkA48S1QebOijCR5EDGEoSVQ6vpABq14kaSSikzSMcTv/KTSy4zXL01yNpHuJg+v9Yim/aOYvBtsmAAArlBqcuTrKQr+JfR8tsu3kDC+U6x89NsJV3rW45K6ZNXVPobE1wdH83nu1x5eIM+VwVCVb08WVZIpYIMrB7PRW/7UFCwVSSdIc/QEht2VJQia6Eien9dIQe2xIZ2AgZ8z5aAkeJGwObrjAo0TRnp8x9xPQ+ys4Elpff1LGeb1N2xtmolnZ/KknUMJoVKiWJmXKF8WIJWZLoTyZ4avD6apUBfFHGdc/taJqk41fJNc7R8HLU3LkVNn0jVClAUG2lPfQInaEniesDaEpk/bxsAb4nEL7ACGjYlkejbjN+dZ5qqY7v3WIlJ0lIaM1AQVL4wiWm9xNUW2h4OYJqC9Jmn98thFXWPbd049/bn2x1WaUtEKc/kmW4PLMiGCSAglPjQmmGuBbCXMfU3/Ac5q0yVyvzq8zFqiTTEojRE0phbKEo0QqkZrzBRvelP5zl4fTgulUxbxcCgfArSFJzwggq+qbS8YQQlJ0G35o5w8nCGJbnrKHkrWaMvnCGgXCWvkiW/nCWtBEhpgUIqHrzmjdou+47BJWtTwayJDG5UERTFVoTETKxbVhqlqApCvGgSXcmTms8giJfe/cigdV9E0KQiYZWiRA1HJfpfIm643Cwu41UOIgiywR1mcG2NJ7vr9q/N3Ot0JOqyIRNnaChLUXXr//em2oaU01f93dmzT7XQ5JkglobwXWVYdeHLKlrznM9DGVtgSpJkjDVDL4dRVZkNFVZRfp9X1Bv2JQKdSoRi1CwKQ6lywkUNY5tO+ykVNC2yYAkQTIeoi3THBBqdQfLcWlY1154IQSO5yFtIctLVRRUVcbQVNKJEI8fGWBPfwtzU0VGhue4fHGaWtXGMDUURcJxPDp7UvQPtS716/ZXHNpShbmO0ONsZxUbVLO0hx7nTP4PbkvBUJZ0OkNPkjB2o0hbW/FIkoShxInpgxTtq5smAz4OFWeSjSwJH9u3m7rjkK83GMnluTi/yPHJKSZLZY51tq8iA7LShfBG8NxhZCXLTlgFAGyvyEz9DSo3CZJUpQARrZu24EPsS/w0mhy56buhGyqd/RnqlQZtXSmshkOlVGdqdBEzoG/ZnCpLKoYSoyV4dEvHvRsgSxIBRePB9AATtRy2vzq47a3Fq/SFM2TN6Jp7WnLqXKnMkbdXqwQaisaBeCdRLbCh3/lWCKj6hhaFsGrSGojRFUotCb40AJdm8FXzvWtuWyYiHk3zi44ES2IwAknSluqUuLDkQhJC4LhTaGoWSQpsmtRZvst0vcBfjL+9LhGIaibHUv081bKX+5M9xHdQJ2EjKLJMNGgwsVjE9wWGpt4WGQiZGm3JKAG9GXtwK6ulcsPvtuMyuVhElWW6UrE1BOJWVov12vxew9xCmYCpEY0ECFxnARdCUKlYXB1ZIJuN0tuTRlWawcKO41Io1jFNfVPBn5vBtsmAIss8dLCHBw50M7tQYmQyx9krM7x1ZpzTl6ZwXA9DU0lEg6ibSGuCpepVpkpLKsqevhYO7+nkyL5mqdq2zgRtnQkOHunhua+f5kOfOEIgqPH2q1eoVHZWMjiq99EWfARtm1WsVNkkpLaRMvcyX39nC4FtqxHX+8kG7ie8BYZ6IxLGLor2ZRYa72xqf084S7ETrFvo5OT0DBfmFri8kGN4cRHb9RjKpHm0t5uDbdfU64QQSHISVU6gagdwnfPsFIt1RJWCfekme0i0BI/RG36GnsjTbIaAhCImDz45hPBFM6d/qTLcRjro3+9QJJln2g7ytalTayLdX56/xKOZXeyPdax6P4QQzNaLvL6wNr0zpBi8v2XfhivdW0FGIqGHNlyR1z2bunfNQuQ47+B5U0iSgaJkEWg4znEUpQfw8P0CQjRQ1UEkZHx/ESEazaBXUcfzp5DlBIqcQOBTrX6GUPDTaNouJGlzwY8TtRwvzl9kor62nLCMxEfaD/PXBp7YsiTzMoTYaihjUx2wbruUaxbVhk1PNrHp1ML1kImGyUS3TyaiQZPH9/Zt+/h3M5bjW5Yt3evdYiHgzNkpMqkwvb1pDENb2a+ZLdSc/E+cHKOzPYEiS3i+T73hUCjWaMlGV9q/8Txbfaa3HZUnAa3pKNlUhAcOdPNXP3KMn/5Hf8DUfJEDu9r5e59+D91tW0sjkqTm6mS91ZjjeCzOl5ieyBMIakyN53Ac97Ze6BsR0/tpCz58W20okk5X6P3kGhdwN/A73QwSMv2Rj227cMYyoloPYa1z0/v7wqbiTi4pca0lcf/i699GkuD+jnZ+7uEHONbZjq6oS37M5j5CCDznDJ57BSGag67nXkKN/SqwM9KZN8Ng9JMMxj5BXB/c8rE3CvvssGv7ewYSEkORVvZE22h4NvnrVP8W7Qpni1P0hjPsjl4jqgLBZD3HS/MXV7WlywoZM8Jj2d3It3FDo6pJWDXJSRXcG+IR5q0yi1YZaFoIPW8WVe1BUZrvfqX6X1CVAVx3BLCQ5QSh4I9RrvwekiSj68dQ5Cz1xhdR5AyauhdV7UCSwggaqOpeNG0ISdq8G2K8usgr82uluzVZoTOQ5OeH3r8l4aUbUXUauFsUYwubOo/v70UIePHMVUKmftPiY/ewfZw4NcbUTJFisUahWOODHzjA7FyJWDRAa0ucWNTkT//8LYSAhYUyx98ZQwKefv9+ujqSKIqMaWgkkyFmZptB4J4vuDqywFvHR2jJRtk12IKExPkL07z2xmUcx+PAvk4O7u8gEtnaWHv7ZGBpAlYkCSEJFEWmLROlUK4jyxK6quxoJkAiFeZ9H76PKxdmcF2PZCZCW8fmJFI3A02OEFSzBDeoJb1ZyJJC0tjd9PNvIwBaQqElcHRJQnP7MJQoppJAkcxNuyyE72J5JUwlgXRD4OSvPP0U06Uy+Xqd45PTfOviZXRVoS0aYV9Llkd7u5umO7UPaUl0A3Q87yq3qzFwK8ioZAKH2BX7ESJa56ZjLJZx4zt0N60Bru/zK29/iYlaAdv3UCSJqGbyG4/8+LZK8pbsBp+58ibnCtP87T1PsC/euqXjmzEQTYni+UZ5FRkAuFCaoi+cXkUGxqs5RioLa4SGWs04R5K924sVuK4/AB3BBItWhaKzuj8z9SJzjesVAwVgIi251xSlqzmpK534/gKeN0fD+g6yHEKSNDxvGt+bR5biSwuLAKA1XQJCQYgirjeNqihImygmIxDk7SpXKnNrfgurJo9lhwgoG7s9NoOy21iTsXEr1G2HCxPzzBcr2I638pzvNqbKJc7MzXFmfo7RQp7FRh3b89BkmYCqETF0emIJPjSwi45olJC+OTfpXLXCqdlZ/uidk4R0nb9+6H72pDOEN3n89fjHz36DsWJhQ5n1ZCDAP37ivXRGY+t+o8VSHV1V2LOrlWQyxLefP08oaCJ3SaSSzQXs1HSBTDpCS0uUdCpCIh7k28+d58f/ygOEw+aymsFKm4oskUmF2b2rlStX5xG+YGxykXyhSkd7koP7OwgFDQKBrZPMHc3XW/5gW1JRRqfWmsZ2AooiEwoZjI8s0DOQIRQ2MU1txwbuoJrFVJIo8u1FJUsohLX2pWjPClthBDIqgSVCspkshpu2JWlochBDjlHzNkkGENheCUOOrVkyHGhtIRYwubKYp2RZ5Op1SpbFxFIQ4aO9zVx6SQ6BP4fwa0iSj6Lu5g7VxWqeDwVDSdIX+QhRrWtNBO5OwBeCqmMxXS+xK5rZ2TLCEuyOZ4nqJsOlBUYqi4xUcty8aPTGcIXPSHmRd/LTVJxr2T5CCEZHF2ltjWEYKvW6zfClWQ4c7FrXEndfoou3clc5X5patRofqy5yuTKH5bkYSvMdHVlSAbwxi6A1EOO+RPe2SM2N6A6luFqZX0MGpusFZhulpjIgEpq2C1lOI0nNiHVdP9x0A8hpHFfCF1UUOYsit4Ck0Ez6FkuWBIEsJ5aIhASoGPoDyHJ0DTneCLbnUnNtqu5azRBT0dgVad1yNb8bMdso0fDWD57dCK7ns1iqYbsesVCAYrXO8PQiA22plSC8OwkhBJdyi3x1+BJn5mcZKxaZr1Up2zae76NIErqiENA0zgUXeKyre9OFlACmymVeHB/h1clxTFXlUEsrcdNkMJm69cE34HIux9n5OSrO+pH+LaEQDXdJHXed++a5PqoqE4+HaGtNsLBQQW1R8Dwfz/NXAgR9IQgGDdKpMOl0hLmFEp6//jVLkoRuqETCJs6SKF+50sB1fGKxAG2t8S1f5zLuSPJ+ayZKJGQ2IyN3+OWybZeZqQKNhkOmJYbvC0rF+op/5nZf5pDWirlO5OeWIUmYShJdCS+pEm7+o5UlnajegyIbO6I/rUgmppKg5s1u+hjLLxKmfc32yVKJXK2GQJAIBBhIJVio1mm4a69PCAvfXwBcZLpA3voHuVnocpikMURX+H3IksGdMHzanstkrcjLc1fYFb15RPJWISPxqb6jVF2Lb09f4muT5zhbuDP1KSYncsiShBnQKJVqXLw4w4GDnax3z/pCGbqCSaJakNx1pY3nrTLj1Rw5u0JboLmavlqZY3SdokRtgQR7o2vfpe1gIJzlRG6UG+uJLlhlpusFyk6DuB5E0/au+l3X9tG0Usn4ogbCR9cPLl2zxDUpobXfmySpmOZ7rtv31liWEV5PT0CTFNoCsU23tRFGqwvrko2bQQiwXI9IwCRoauTKNfKVOp2pKKZ+Zy13Qggc3+PZq1f4H2dOMVupIEkSQVUjZQaaZFSAJwSe7zNTKaOrypYCTudrVc7Oz9FwXRquy5V8jgPZlm2RgaFUU82zbFnX+lStUHccvE0SFNvxKFcaLOYqBAIagaCOZbssLFSW7gkgmu7vStVCUWSCAR1ZAtf1qDccSuU61ZpFrWYhSVCvN7fV6jbVpW2SBFbDYWGxgmmoBAL6lvVF7ggZ6OtIcbElTiigo26xQ7dCuVTn+GtXeOjxISQJ5qYLGKbK7gMdW7749RBS29dNA9kqmul9zfbqbg57C2RAlU2SxhA7NaEpkoGhJmALKsm2V8RfxwT5q1//No7v0ZOIM5hO8XBPF3uyWSKGjqbckDWvDiHLKVznNLXS/0kk8Vtwm26PjRDV+9id+Em0HSrnuR5mGmXeWBjlC6On+Bu7bi+m5EZIkoSpqJiKSlwPENggh3+7WCbLvi/ItkR57bVh5ubKGIbK408MbUiidUVlKNrGgXgnz8+dX9nuCZ95q8SJ3Cit7TF84TNcmV1TH6AtEKc3nKY9uAMEG7gv3s13Zs+v2V73bMZri5zIj/KebLO2xuprkljRcVB7UJQ2Vk/8N5voN08CltHwHWx/ffOyLMmEtcC2v+7lZ3mmMLHGQrKZYx3XpdoQjM7laUs2RX1m8hV6W3bmGW0ETwhy9Tq/9ebrVOymmmRMN7i/rZ3Hu7qJGAau71O1bRbrNXL1Ol3RGAFt89+CtKTDsAxVlredbfCrT30A3/exPI+ybVFoNPg3L73AiZlp8o3NyUdPTOY4f3EaSZI4cqiHaNTk3IVpXn/zKi3ZKAFTQ1FlxscXuXBxGiEER+/vRddVSqU6V0fmeePtq8zMFjl3YZrWlhjzi2XeOj7KxGSeM2cn2TXQQqnU4KVXLnFlZJ5dAy0cPNBJ9G7HDKyH9z6wiyePDSLBjssIS1LTVTAzlccMaMzNFIhEgztCBABCaivmkkb1TiCgpracFqhIJglj144Js6iyscVrEjh+dV1tgN//yb9yLcIebupz9JxTS8JDKuHEb4J0ZybqiNZNS+AILeaRO9L+Mq6WF+7Yav1O4Prn0mg45HIVLl2cQYimhU0IgW27zM4UGRpq29CIdyjRRd6urCIDAPONEi/NX+RD7ffxTnGC6VphTXnfh1KDHIhtPoD1VugLZxiKtnKpPMtUfXW67HB5ls+Pvc5jmaF1yw8vQyKwFBNw5xCQdQx5/eFV4GN59rYD93wE47Ucry4Mr0nhvBV0TaWvJcXYfJ72ZJRCtU7dmueZI0Pb6MnWULYsnh8doeY0r70vnuD9fQP88mNPXOeiEKsktLfqWtqdSvPDQ/u4uLBINhzm6f5B9qS3b8VragGomKpKJhgiYZpbslTs3tVKf1+GdCqCqipIEuzb047vi6W6Is05TYgmUfN9sbKfrqscO9LH0ft7V/oiSdDXm+GBI32rtmUyUR481o/v+yvHbxV3hAzIsnRbUcM3QyIV5gMfPcRbr16mkKsysLuNnv7sjvm6TCWBJm8/VeZGGEpiG2RAI6x2sjhTIhAy0AMa2nX1wMcvzTA1soDrenQNttA12HLT65fRVqllbQauX2e9+gqqLFOo15kulRleaMaF7G/N0hIJrwnykaQgstqHrHTc0cG3JXCE1uCDO+JSuRGO7/HbF17idH6asUqO+UaFhufyw9/6nZV9ukJxnmob4kd7D69sKzsNRio5vjB6igvFWRzfI6qZDEYzfLTrAPvirTviQy/ZDYbLC/zxlTcZreQwFZW+cJo98RYMRV35CiVJQlUVQqGmD72vL0t3d/P5Bm6hEJrUw/SFswyEs1y+LiCuaNc5W5zC8V3eyY9TuGGVqkoKB+OdDERuLxj3ekiSxNFkH/NWmT8bf2vVbxWnwcXSNH888go/0fMwurL+8HY3AkMDqk5A0ZGR11QWtH2P0eoiB+NdbJUOCCGoug3+6+XvbKtWg6mpDHWk6W1JMJ0vMdieJhE20bU7L/duex4jxcJKnweSSY62t98wuUq3ZRBtCYf50OAujrS1oyoyLaEwAXX713b9u7Liit7ksYlECF1TME0NbUlafLm6Y9OIKi1tu9b+snF1+bzNf90Y2Lz+tuZCWVl1/FbwrqtNcEsIAIl993XhOB66rq7c6J2ArjTLAu9Ye3IEWdqayVeWNAw5xXPfOoPwBXuO9DB4oAvfF0xdnePLn3mJ6dEFPF+Q7UjwiZ99L2096RUt+xshScqWZSs9Ya+rAz9eKPLq6DjDi7kVI+s7M7M80NXBgdYW2qLXUq+EWLYuuAhR21QU9lahyWFi+sAa3X8A23Ooeg1q7urASVVWaTE3ZxKVkOgMNn3ivvBpeC6SZPFA+lrRobQZpi0QXfm7ZDc4kZvgq5PnGK/kaQtEUSSZhudwpbzAfxt+jZ/b/RhdofiGkr6bgeW5XCzN8dmrxxkuzdMbThFQNFzh8ebCGFO14opvU1VlwmGTru51irgg3XQlocoKaSPCkWTvKjLgCI+SU2Oilud8aZqys/o+94UztAXihFXzxiZvC/3hLAfjXbw6P8xMo7iy3UdQtOt8Y/odOgIJjqR6ievB27KwCSFwhMdoZYGIFiCmB25al2EZiiQT1kzSRoQ5q7jqt5prcTI/yoc7DiGto+Vxs77MNUq8vniZNxev4vhbyySo1C3ylTr5Sh3P9xmZzbO7M3PH3QPL8IRP2boW4xAzTDLBnR0TdEVBDwRIBO58CvOt0NmRaKopBoxVk/NGE/XtktTbPf6ukQHPvxZBuZ1iI4auIkkSluUwM5Xn6MODSBJcHZ4lv1gh2xZnWZt9u5BQ0OTQzuo9S8FNRyA3ISNLOpoU5fVvnsEwNVItsSYZ8HxOvHiRN759DlVT0HSV8Uuz9O/rJN0W35gMIG/ZOtEMeFz7nN6enOKd6RnKls1gujmxnJqeRZYkVFm5gQzYCFHBdydBNlGUPrhFsaetIqy2E9baMdXVA1reLjPbyDFZX1gxyV47JrBpMiBLEg9merg/1UlA1Wl4LrP1Ej898MDKPpqsENauvTOjlRyvzF3luZlLvL9tiPe37cZUNGbrJY7nJvnS+Bn2xdv4SOc+zC3U7bgR840KJ3KTPDt9gfe27uKjXQcwFZXJWoHXF8a4UllEW7KWqKqCqioEgzrVqkW53MB1vU0nK8T1IA+k+vnT8TdXZQvYvsdwZZbh8izVG0jXoUQ3KSOMKu/sM0+bEXZH2ziS7ONr06dW9ccRHmeKk3xl6iQCwa5oKwk9RGSJkNxqfFie/KuuRd21qbgWebvCifwohxO97JJbNkUGZEkirgXpDqXWkIG6Z3MyP8ZkLU9bII4mK7ckBJbnsmCVOJUf56tT72ypHsEyHM+nXLeYzjUzfxbLVep2fMvtbAdNMi2wPHdl/NcVBfM2Vu3vdmQz0Vvv9C7CHX0SvhD4no/nC6oNi3LFom45zUFoixjoyqBrCpVyncsXpjlwuAdJgvGRBSqlBoN72jacDDcLTQ6iSPqW89NvBllSt7QyUSUDXQ4jozB6YZqnPnGUVGusOUjZLl/+zEu096X58KcfI9Ua589+9zme/dPXefRDBwmE1icxkqRs2Trh460bCf2lsxd4vK+HD+0ZWqleOFks8V9ef4uXR0Z5arDv2nnlBMKr4LoXkCR5Sf1tZyeGTOB+AmqWG2e0N3PneWnhHS6Ux5Ym/mu/txhJ3pM9vKn2ZUmiPdgUfkobIQKKhi6r9ISTG04sL85e5q3FcdoCMf7xoQ9hyMrKvsfS3VwszfFHl9/gWLqLjBneNoE9mZvkrYVxAorGLx/8ABkzhCzJeL7PsXQPL8ysVQIEuHRxhuPHRykUmmZ9WZb4xb/3DIqycT9iWoAjyV5iWoCCXVsptuMKj9OFCcarORr+tSBZCXg0s4vEHZLY7Qtl+Kt9j/L64jCLVnXNu/rs7FnOl6Z4INXP+1r3czjZjb70LV4zs147Sojmf/tCsGCVOVOY4GJ5mgulaU4vVRr8h/s+QucWAiGzZpR9sXbezF1Ztd32PUYqC3xu9HV+ovchWswYiiQ3+7bcn6XeLU+iU/U8X5w4zgtzFxiuNLOClvdf3vdWiIVMgoZGKhIkGw8znS8TMnY+g8Bf6rPnN98SIQQ+gqrjUHOuvSOe72N73qpt10NXFJSbpF96vo/j+zdNPdRkGUWWd8QltxNwPA9PNJ+rKsuosrySqbB8HbIkoSnKqhHN8X286wSmZFlaIpE75/a6o2Rgdr7ESyeu8JfPnebq5AKee2O9rs3j9//VT6O5guOvX+HV5y8yPZFHkiQyLVF27WlH1ZTbvinGJqsTbgWKrG/Jl63KQUwlgRBQXKyQyEaJxII0qhaXz0wycmGan/qlD7Hn/l6EEBx7715+659+Hs/dWImsGWG7tUm4Wa1r7dOq2DYhXScTupYV0B6NoEjSGnGO5lDlgqghqwe5EzoD2cBhgsra4iAnC8PEtBC/cfR/J67dGANyZweGS+V5Kq7FEy2DGDesikOawfvbhvi1088yUS3QHUoQ07dn0pyqFSk6dQYiGbLmtWuUJYmwqnMw0caF4mrRGyHgpZcu8ZGPHqazM7kSeHurT0dCIqgafKj9Pv5s/C1qS4GClufwZ+NvYfnXAgdlSSJrxjiS6CWi7ayLYBmmojEQyfKvDv84/9/jnyNvryUEU/UCfz7xNl+cOE5A0TmY6CJtRIhoAUxFQyCouRZ1zyFvV5lvlJlpFCjadTY3vd4c/eEsT7cd5E/G3qDqrU4B9BH88cjLHM9d5YFUP/cne+kPZ4hpQVzhUXNtFq0KVyrzvL44zEvzl6i59qpeDUZaSOoh8naVi+VbB7bO5svM5MuEDJ1sPExbYueLOgGMFgq8NjnBd0avMFutkqvVKFoNqraNe93E/bmzp/mfZ0+vO24HVI1fe+aHeKK7Z8NsghfHRvkPr7/CydmNr/2XH32CD/QP0J/YmgruncJ/O3mcL126wGK9xk/sv4+fP/Ygf37+HF+9fIl35mZxPY8jbe38vYceoS+ewFRV8o06//H1V/nm1ctUbZtkMMiR1jZ+8cFH6IzGdmw0u2Nk4FuvXuC5Ny7x1plxKjUL5yaT1WYgEHR2p4hEA3T3pukfal0JxtCWXAi3C12OrCvBezuQJR1pC5OgIulL5TQhEDaWIkx9Svkqr37jNO29GXp2txFJhCgXaqiaQq1q4W8gUtHEtbSqzWKjKoOpYJCFao3hhRy7Ms3c3VPTzQC5mLl64Pf8GXw/D5KJ51xA1fbQJAQ7lzIZ1/vRlbXmuGbNd4OYFka+A4GF60EIgSt8LM9FoqkgeON7qUgScb2ZVlZxLBqeQ2ybEs1V18LyXFKB4BqfpCxJRDRz3Uk+mQwxO1vEdf0lwS7o6Lj5ileSJDRJ4YPt9/HVqXdWyIBY6sf1b19A0Xlfy94NA/h2ApIkoaFwMN7FP9r/Uf7H6KucKUxgrZPO5yGoehbH86MoS2WAl+/X8qrVFz6u8JdWs7dLA5rQZIX2QJyfHXwPv3nxm2sklAEuV+aYrOX56tQpNFlZeVd9IfCEj+O7NDyHundNs0BGoj0Y5x/s/TB5u8oL8xc2RQaK1QbnxueYXCwx1JFGAvZ2t7Crff1Ke9tFrlHjnbkZXhgbbbqHl6Lk11sKLlsOboQvbu1OVmQJQ1VXrDrrodn+1q/hTmHZ0lNzHC7nc/zlhXP857ffYKJUwvZcBPDy+Bj5Rp1fOPYQyUCAP3znBN+8fJmqY+MDVcchV6sxVizynz76w8QMc0csH3fka706scibp8c4eX6SfKlpioyFTWKRAKGAvi154qCho+kqZkBHN1QSqaZ5dWGuRK1qEQjefh1zRTJXTdy+L3j1+BV29bUQiwTQ13FD2LZLvlRnsVBhV092TTCjtMVKfbKkL+XKS7R1pxk5P0UgZKBqKqdevsjhx4YIx5qplJ7rUS3V0Q31Fiu7rYdQbfR5vXegjwvzC/zBW8dpiYRBwESxSGskwuGO1QWVZCmOYAHfn0OSV5vqbx8SQTWLKgfXJXDdoVbKTpXTxcvsifSiyztDGG/aI6mZRbOc1mb5a82fvhA0lirY6YqCehsuKU1umlHtdQLJhGj6mW98ipIEiUSYQr5GpdxopiHJEu3tiVtaB2RJoieUpieUwvKdFcGbG98VQ9Z4JLMLVZLv6D1vajNoHE724AiPrmCK1xcvr+tPF7Bltb4bIS+Z8rfSv5Bm8GR2D+8UxnkrN7Km6JPjezi+R9ndnDpoQNHoCCb5VM/DDEVbKTg1rlbnN3VsNh7mQE8rkYDBriUykIrsvO5HJhjiwY7ONVH8Fdvm+Mw0l3JNYao9qTR7M1kS5lrrka4o9MYTN03jG0ik+JlDR/jgwC7qjkPDdRkvFTk9N7tyjncrGq7LcG6RfKNOwgzQGY1iez4XFudZqNW4sLDAt65eIahpvDk1yWAyRUs4zGixwFS5RLHR4NzCPGfn5ziYbSFq3L4F7o6QgTOXp7k4Osd8voIiS/S0p9jdlyWTCBMNNeWDt4rIkj/csV3mZ69pkC/MlajX7B0JIFRlc5VJ3xeCb7x4nkg4QMDU1iUDluMyPVfktZMjdLUl1pIBaWsrYVlSUSQDSZbY/2A/F06MUi010HSVwmKFY0/txVwiPlbdZm4qTywVRr7JRyNd9/+bhhDrEoKHe7ooWRbHJ6fJ1xsIIdAUhT3ZNPtbV6eQSXICSU4hieqStOvOWQUkZEJqOzLrT/K6rLJgFfn27HGKdg1D0VYG8oCiczA+sOVzLsvH3ii3ez0USSauB1Almdl6eUUed7mPru8zUS1gKCpRzcS8jdVzRDMxFY28XcPyXPSl2ITlILgFq7Lu6iqVDjM5kaPRaE6OTVfBrSMJJUkiqgXYF+tg3iqtq36nywopI8yeaNtds8ikjQiPZ4ZI6WECisbx/CjT9QJV17rps7oVZJpkI6YHyRpRWgNbz/7QZZW+cIYPtt+HLwQXyzMsWpUNBYk27IskkdTD9ITSHEn28sG2gwRVA11RyRpRgopBzbu5GmEyEiQcMEhGgnSk1paf3ilkgiEe6uhk7w35/XPVKgWrwXBuEQEMJFN8cGCQ3vhaq5QsSbSGIzctYdwWidAaDuMDNdum5ji8NT1F1XHe9WTA9jwmyyUs1+WjQ3vojsWoOQ7JQIC/vHieimPz6uQ4IU1HkxU+MrSbrmiMs/NzvDY5zisT41RsmzNzc/TFE+9eMvD2mXHmcxUkCcIhk09/7BhPPbh7KSPg9tpuNBxGr8zjOB6yJJFfqFAu1fF9/7aFh1R5tWVACMGzr1zksWMDdLTEILg2QM9xPOYWy/zlN0/xyWcOEQnd3kORJRVZNpBliff/6IMMnxrn9GvDKKrC7sM9HH3v3pVAyXrVYvLyHL1LMRN3A+3RCH/1/kN8ZM9uxgoFJGBXJkVQ19eyeElGUTpRlA5c9zTXJF9vfxCSkAnrHRtmaoxUZ3grf5GyU+W5ueOrfusMZvmPR//+ls+pywqarNDwXRqeu3K9y0E8y+V5+yNprlQWOVeYIW/VVtwFvhDk7RpvLIzRHoyRNSOENGPVhL1sNhVL98pb0iu9Xi9gGS2BCDE9wPDiPFP14koKo+N75K06I5XcuuI7iXiQ6ak8nuevCJ1sxZT6cHqAC6VpJmr5Nb/FtRC7o22kjDvjj94IES3Ag+kB7kt08drCZf5k7A0ulWcoOQ084eOLZoDWehRXYjm9smnZkZeeZVg1aA8kuD/ZyzNtB+mPZNG2kRkhSRLPtB2kLRDn2ZmzfGf2PLONAq7v4+E3BWdu6NWykp6MhCLLRFSTx7NDvDe7l8eyQytEK6DoZANRBqNZrpavWQhMRV/3M9NVhc70zSuhqrJCQNFXMjCWEVYNzE0UmwpoGgFNozW8+h0IaToR/doYGjMMumNxhlLbd1NIUtMuGDEMIoZBJhQiaty+lfhOw/V9Sg2Lj+zazU8cOEg62KxzcKStna9cuoArBFfyOXrjcT46tIe/cbgpqPZQZydx0+SNyQncpToPVfv2LF7LuDNugslFSpUG2WSEx+7v50OP79uxtmVJQlEknvvaO0SiAYYvTGOYGopy+ybJG90Em+qPLGMYKuWqta2UyTXtoaLQfJm7d7Xwz//gf6VaquO5PolsZNU1plvjPPMTj5DtSBCK3p282rJlY6gKmXCQTPjmJkbfHcFbKhkryXHAo/nK7QAZkCRCSsuGq8+f6fshPt3zzLq/bXfF2h6M0RmM8+3pi/z7M9/mwWwPMhK6rJINRFbqFXyocx+u8PnPF17ib7/yOT7Vez8hzWC0kuO1+RHOFKb5tQc+SUfw2qDsCUHVtag6NgtWlapr4/mCsUqeiG4QUHRMWV0l+fxIppeS0+DF2cv8b6/+CX9t4AFCmsHVyiIvzV5ZV4ZVCPjKV07S25uhuzuF7wsmJrZWVOzB9CAvzF3gfGmKyg3WgY5Qkqda925w5J2HIWs8md3Dk9k9zDaKXC7PcaY4yYXSFOPVRXJ2lZpn43jNin0BRSOg6sS0ICkjTFsgRn8oy4FEF93BFDE9uGPOrQOxTvbHOviZ/sd5Y/Eqry0Mc2HJUlB2GtQ9GwkIqjoxPUTGiDQtAYkeHsnuIqoGVgjn9Xg0vYtH07t2qJfwSHqQR9KD/MrBT+xYm/ewFoos8czAICGtOd4bikJ7JMJQOsPFxQVc36c9EuWHBq6pQ4Y1nZZQmPZIlLFSkelKmYa3NSvTRrgjZKAZMOgRCwfY09+6o+aodDbCJ37iYc69M4HVcDj26CCt7TtTwliRtC0XVipVGpy+MEU8GtiRII7lNMDl61E1hWgi1FxPr6hSNf8diQc5+NAAsiKjqHfHJPuPvvw13j84wIf27LplWVFZaQHJoFkRDpoljHfqXZAx1dSGAZ+mrFMTDeYbBc6VRgHoC7fRYiaJqdsjTntiTfJRdS2+PTPMlyfPYsgqe2It/HDPwRUykDZCfKRzP92hBJ8bOc5vnn8RV3iEVYPucJJ/c+yHeU/rIAG1ucoq2HW+NnWO/3TuxZUAxIbn4vgef+2FP0SmGXT4WEs//+i+p1f6E9ZMHs/2888O/xB/cPl1fv3sdwioGr3hJA9nenmydZDPXn17zXWEQgZ9fRnqdZvLl+fI56t4nocsb66K3jW7xWoYskpXMMnRZN96h90Uv/HP/4xzx0cp5WsgQTBo8M//88/Q0nnzKHAhBM9/+SSvPnuOrr4MP/Zz70VbiknKGFESeoi9wXaGy5P8q3/yGX76Z5/k0KODtHY2TdPrWQTUJQtQM+ZhOxE360OSJBBNK8bj2SEeSPXjimupZsvWgRWrgCSjSjK6rKIr6ip305p2N4lqucE7b4/wlT99i+nxHO/54EHe/9FDtF4XQHo3S3f/IEOSJLpjsZX6PU1pd4lMMMjlnIQHRHSDvkR81TG6oiy5BYrUHAffv73g/GVsmQxULZuG46DIMiO5AntaMpg3SFlqqtLMg9QUYuGdTS3yfEG1YnHu1Dj7D3eDgFrVIpG6fQlhWdIYHllgfDTHyEQOXzQ1Er76nbO8fWZ8TVUvz/fJF2tcnVjk8N7OVZLB28WNaYCSJCEpEsIXuK6HXXcwAjqyIiMrMvoOF4K6FXK1Oq7voyu3NpfKcguSvDSYi+WKcDs30OhyeMP2Zhs5zpRGOJ6/SFBpmibPlkboCbVyLLmH3lDrls+nKU19gR/vO8Kj2X5s313KDgjSG7k2aSmyTMoMcSTVRUQzWbSq+MJHV1RiWoD+SIqgqq8MukFV52iqm7+//6l1wzZ9YeF550hqZ8lX64SM+1GkKA3nIrI3yu5QlZ/fPcRC7QJBrZ+U2UnGDFC3X6Ev/BT9kWsV2yQJHniwn47OJLOzRcJLJcBvFnNyIy6WppltFLFuCMjrDWXoD2cJqVsX7Xrs6QMM7utgenyREy8PM3F1Hs/b3CBXr9oUFyvEU+FVWTWKLKMggwphDETOJ+waZNQwrYH4lvu4E1ge8E1Fb5ry7zJ0U6V3sIWP/OgD/OFvPYvVcDZ9n+9hZyEBcWO1tacZdKqvTPpBTV2TldPUIWge43jeulka28GWZ69io8FcuQJITBSKDKSTGOrqHP9wyEBTlWaq1Q6/aI26zdXhWTxfoOsq5VId23Zp60zeZgChhIyC7wsqNYvpuSKFcg2EYHQqx3y+sqYC4/LAEwkZPH5sYFtZEuv14/qVSClXZWGmwOJskUqhhlW3OfjILlItUYSA+ak8kXiQWDKMfBeIQTwQQJVlHM9fU6XwRkhy+I5l9EtIKLK54aptuDLJlcokAkFboOmTHKvNMl1f4J3i5W2RAQmIaAYHEm0cSLTddF9NVkiZIVLmrUV3DEVlVzSzYVlk1ytQql+l4VTwRQUhfIQk8EUdWRSIqyU6Qj24iVY0JYomm0iSQqGW477MrqWSztfQ1honn6+wMF/CdT3S6WYA6ma/nZP5MWbqxTWpcgORFvrD2XVN2bfCgQf62HWwk4mr88xNFZi4urkIeYCWziQHHugn0xZH2uHCaN9v0DSV1o4ErR0J/vKzr901i+I9rIVEswjSjW/ssuCQqihNi9CN3+V185wvxFoT3TaxdTJQbzCyWKDuOERNcyXH8/rutqQiTM0VqTccZhbK1wo87ID5adkqcPD+nqZa2FwJw9B2JIBQkhQGe1voTIXZ1Zvh7KUZzl6aoastQTYVwTBusIAoCrFIgN7OFI8d7d+R65OWwpmEELi2x4UTo7z2jdOcemWYuckcVt3mH/z6pzn02BBWzeaFLx6nd08bx967DyNw51cafckEdcfl8mKOnkRs1TWrsrylcqO3C+UmqoqnCk31vZ/p/TAZMw5Azi7xnbkTvLZ4lo+1P3Y3urgjEMJC4KMqKcLGo2hqGwgfQ9uFLIexnKtU7ZNEzPdguVdxmEBTWlHkKOtZTnK5Km++eZV8oYppqCSTIZarxd3sHRZLue+vLlxm9rqaANDMotgTa9t2USJVU1A1hUgscMvCSddDkiTuf3SQ+x8d3NZ5v5fgeT6O7WJb7tJCpFnhTtNVDFNrVqG0mr8vr/YlCcyAjqYrW7L+CCHwPJ961V4xQ8uKjL50ruv7Y1ku4rr+6IaKfgeUDb/fIC/pgWz0zSmyjHIXye2WyUAiGGBwSWzGFwJDXctcHjrYw9RckdGpHC++PcynPnT/jpUylqSlEsbTBQIBnbmZIpFYAFXdiWj65s1vSUdpSUc5vK+LP//mO3z8/Qe5b08HyfidkVZdCwkh4NQrF/lPv/J56lWLtu40+x44ytf++JWVvTzXp7hQ4ct/+DL7Hxi4K2SgJRLiKxcu8gdvHmdvS4agfi2+4UBrlp+6/9Ad70MTEjIbx3hYvk1AMUkb14L0ElqEoGLQuKHM7q0ghNg20bsxqHRb1cSULPHgh6jb51mofIaI+QSen8fzC0ioqEoKPA9TG6Jun8JyLqEqraQiP4V03ScuRHPCf/bZM3zkI4dp70iwuFjhc599jUce3XXLgl8+guHKHOdLUxRvyJfvC6UZirTSdhfN7+sF7G71/m6mjZsFBu+kf/3686zX7tx0gbdfucxX/uRNSoUaruuz974uHvvAPt734fsAePGbZ/nWl04wNbaI6/rohsqn/uaTPPTkbuLJzY1fQgishsP0RJ7f/XdfY2Yyj+8LOntTPPH0fp7++P1IksTMZI43XrjEN/7iBKVCDc/zOXCkhyee3s8TT+/fmZvyfQoJbjnRL2e53C1smQx4vqBQbzCaa6YV9abiGDf4yp96aIiRqRy5YpULV+f493/4HD/7Iw8TCZs3zRvdDFKZKB/9sQd48dlz5BYr7D3YSd9gy221uYwbNQFkSeLogS6i4cBtWx22Ct/z+fxvPUvnYAtPfux+HnjffjzH4+uffXVln2DEpGd3Gy9/9dS26j1sB0FV57GeplVGvSHgLBO6W2RpGRs/k4gWou5anC+NsjfWC8Dl6hSLdomEvvm0NyEE84tlkvHQlglnbq7Iv/y532NmbJHWnjTv++QxPvrXHt9SGwC2N0bdPkXDvgRLBaQkScX1C/h+GdfPsXwvDK0XT5Tw/QqKFGP5fXZdj0bDoVCooWkqk1MF6g2Hes1G09RNvd+27/LHI69QW0df4Ic6DtO+Bd3+28W546N89re/zYWT4yvb3vORQ/z8P/n4ptsQQlDMVfmdf/1F5qaK9A618NP/2zNEYoGV99qxXBbni/zJf3mes2+NUi03MAIaHb0ZPv7Tj7L/WO+OxArVHIfRYp6SbbEvnSWkaWuyXk68foWxK3N86JNHeeKZ/biOR71mY163CBg60MHugx3oRvOZzkwW+Lf/5E/o29WyaTKAgKsXZ/nMb3+bT/3sE3T2pPE8n5NvXOGLn3uDvl2t9A218OZLw8xOFfjIjx3jsffvw3U8alWbYHjnCr3dw93Dlt/ismXheB6PDzRLxq5nFg4YOk8/sodQwOArL57lhbeGKVcb7O7L0tkSJ50IEw424wo2i1Q8hKYqqKpMPBnmoceH8DyfcMQkFLkz+ueyLPFjHz5CNhUhaN7tYB/B2PAsz/z4Q3TvaiMSD2LVV69oVU0hFAtQKdWWzHR3Ho/2deN46xOPW2UX3E0cTQzxTuEy/2PsWTJm0zqQt8ukjRjvyRxe2e/y1TlK5QZIkEw037GZuRKGoTY1IyR46dVLPHxsgJZslOAWrC++JyjlqxQWK4SigTXPb7NQ5TRB/TCGOoDAR1NaEMLFVHchcJAkA5CWpK9VNDmLrAZXCWjZtkc+X+X8+WkCAZ35uRLFpdVcNGresuBJ3bUZqc7z+sJlrOtSmSQgpJo8kOonqd9+EO9m0dad4uOffozZp3K8+uw5Lpwco1bZnIofNE3c5WKNP/z1rzM7kWfvkR4eemrvKiVT23a5cn6Kv/jDl5kcmWffkV4CIYN61WJmMs8f/odv8DN//4P07WkjfJupvYVGna9dHealiVHaI1F+bM8BHu3oXmV1be9KMT9d5M2XLyEQ7L2vi3RLjNB1k28pX+XcqXHKxTpIEo2azeJ8GdtuuhY2Y6Gt1WzmZ4sMn5viz//4NULhpjs4v1ihWrGYmy7SM5ilsydNbr7MGy9dwvN89h3qJt0SJbhBwbR7eHdjS2RgplRhplQmX6tTd5rqUOtZ0E5emGAhX6Vat3Bdj9nFMi8ev8zEbIG2dJRkPEQooDcjIjdpZvv4UwdJxUP4nk+9apHPVfD9pl9LkiT05M5E8q/6W5IY6rvmA10WaHE9H1WRN52KtR0IAXbdJhQNYAS0dc8jhMBzvbsSOLgM3/dJBgLEAqsJmON5lC2bS/OLmJraLF50m1ag20FfqA3Lcyg41RW3QEwL0x9qZ2+0Z2W/6bmmRn8kbDI2kUP4gkrVQtdV6jGblkyUfKHWdBV8l65FkUPIUgBNEUsL/aYsrhAtNK0ETVJtu5N4fglFjmJoQ6vakGUJXVeJRQPEbpi4si3Rm36Hru8z1yjxwtyFNbECqqRwMN5JRzCxZXW+20E0EeLgg30M1jqYmcgzfnl2U8fJsoTnCXLzJd74zgUuvjPBgWN9HHtiiKGDnStpiQAL0wXOnxjj5KuXeeQD+3j4/fsIxwLUyg2Gz07xuf/8bd5+6RLxdOS2yUDFsXlrdoo3ZiYZaNR5f0//mn06upM0ajaW5ZBfrHLqzRHaOhN09mXo7Enh2N7SxNwMrtZ0ZUm23F+JMdhMNo/ruDTqDo2GQ6Y1tvJqhKMmA7tbSaRCyJJEV1+aRsPGdT3yi1VOvnGV9u4UXX1pOrpTNz/JPbzrsKUZdCxXYKFSo9SweGN0Ak2RaYmE0W9Y4X/uq8cZHp+nUKpTqTVNitWazZnhac4MT2+ro4/e30cqHqLRcBm9MsdrL17Ec32yrTH6d7cRSzQFcO7E5Gw7Lg3Lod5waFgOtYZDWzZGKKAjAXXLQVcV9B0qmATNsTkcC1IuVKmVG2vSf4QvaFg2C1MFkpnoXSMEL14dpS+VZFc6CUhEDQNNkVeKF52dnSMRNHnfYD8x07xlxsGdQlyP8EBqD/tjfYzX55CQaDWTBJXVRT0qFYuWbJTerhTfeO4c5WqDbDrafN51lWwmSioZIREPYXwXg6LWq3x5/TYhBJZ7Bc8vo6sdmNrqycQ0NUwzRkvLzdXnboTre+TtKu8Ux/mz8bdWnx8IqgY/1H6IoKLf1TKxsiwh6yqqphAMGSibfM9kRaZcrDE6PMOf//6L9O5u5Yd+4iHau1NrVDyvXpjhzJsjCM/nx/7We0m3xFBUGSEE/fvaeeErp3j12bPc92A/nX3pFSloq+Gg6ddcL0IIXMdDWUq5FkLgewJZubaYqLsuVwtN8af+eIKkuVa3JJEKc+SRAQ4e6+X026N864snOfXWCIcf7KejK0mt2uCrf/oWP/tLz/DA40OEIiazUwU+//svbvneqopMKGzyUz/3HsIRE1mWm8WGPH8lAyGZifBgaojDD/Rx5sQ43/iL45x6a4QHnxi6Rwa+B7ElMvBATwf96WY0eUcsykguj7lUNer6SfDclRlmF8s73lmAYr7Ky89d4K//wvvQdYXnv3GGkUuzHDjcvfKh7dSEvBzQc/7yLM+9dolX3r7C+HQeIeCf/t0f4uiBLhq2yzdfOs/egVbu39+1Q+mFTVJz4OFB3nj2HIGwSbotfk1lUDTrEkyNLPCdvzjO/U/uuWvRu3957gKW4xIzDTRV5e899jBD2TR/+s5Zjk9O0xIJ8+3LV5gqlvnY/j0MpL57pUMVSSGiBdmn9a5sy1klCk6Z/nAHQHMFtURmJUmipzNJve6QiIfobE8gSxLFco2ZuSKqKhPZYd2MnYIkSUTMJza9/2bVMucaJT4/9jrfmD7NzA1WgYBq0BvO8KGO+5r5/N8DcB2PF756iuMvXaK1O8Uv/asfbep2rGM+nxpdYHYyR+dAlmxbHKRr901VFXYd6OBbf/Z2M3jO9VfIxOsvXWL/oW6S6WtukyuXZujqzRAI6riOR26hQrYttjJeub5Hrt4s6jaQSJEIrFX3nJ8t4no+LW1xHnh8F22dCX73//k641fn8YWgXrMRQChiEgjqVMsNvv2lk3jXidKs99yFWL09HAmQaYuRTIf58v98g4996iGCYYNapcHCXIn2riSarjI3XQAJsi3N/rR2JPjNf/MlJsfe3XUB7mF9bGnmWqjUePHyKBfm5ulKxFio1Pj0g4dJBlebyJ55dA+lLfjvNoNYuHkOTVdIpEJcuTiDYaiUinUsy2FmKo9pasSTYRRlp8gAvH16jP/3vz1HuWqRSYX54JP7+Op3zq7s43k++VKdP/nqCXb3t+wYGZAVmZ/6pQ/yH/7h/+DPf+87fPG/vUBLVwrhC77wu89hWy5WzSYcC/Cpv/M0wTsUN3EjSg2L/S1ZHu7pQgjB//2dF/h3H/sw+XqdbDjEP/nAeyg1LP73v/gKD3Z3flfJwHo4VxrllcXT/IM9PwnAk4/sairQyRKf+PBhJKkZJLssey1JEn/tU4+sCGl9v6Dk1DlbnORSeYaUESGkGuiyulRV0WbBqvBOYYy3F0cpONU1lRElJPZFO/g7Qx/4niECAF/4/RdxHRfHdpu+8JvE2jRqNgszJYr5Kj967J/d8Gszjc91PKyGg2U5K2RgdrJAqVAjmQ6TbomRTIW5cnGWTEsMq+Fw9dIsLz93no/+2AO0dybRl1KWlxcxSTOwpuIfwNkT47zy3HkuX5hGUWQMQ+OBx4c4+uggiiLT0h7n6Y/fz+f/20v84W8+SywR4v6HB2hpj6MvjUtz0wX+4DefZXaqwMilOa5enOWtl4dJpMP8zN/9AJ09TQvJwJ42fvEff5zP/t7z/IOf/T0c28UwNTp70vz9f/bDAJx6c4Q3XrzIyKU5ZEXCMDQeee8ejv4ApHl+P2JLM1c0YLC3NUM2EqQ7GcfzBRFjbbDIx586uONiQ4lokymrqkI8EWL08hySLOHYLpIE506Nk8nGiESDOxb57wvBl547TTwW5P2P7eHowW4UReZrz18jA6ah0Z6N8erbV3ZcySvVGuOnfulDnH79MsOnxpmbzBPPRPA9n9auJD2727j/id3E0+G7NlG1RcIcbm/lib4e6o7Ln505t6J5IctNEQ0tpDTrwt/FQuJ1z6LoVGk1m+RjrpGn5q0lpFONeQpOZeXv6zMEllPrlOt0MYQQKwTv+0mmtebZnCqM8+XJE+iyinJduWFP+Di+S9lpUHTq61b+O5rs5f1t+xiIZL+n7svA3jYG9rUjyzJf/dzr/M/ffZ6P/tWHSWYia65DM1Qi8QCReJCnP3l0Q3f70IHOlcm2CcHg7jZ832d2Kk9PfwbHcRFCEI6YZNtipDIRMi1RVK05VmmyQioQpOoUqbsO7joSs4ce6KN7INsMlJQkFFkikQ4Ti4dW+v5DP3KUx963F8/1UDWVeDLEgfu76eptujHiyTA/8tOPYjdcrIaDJEuomoKmKbS0x1GWBORMU6O7P8NP/dx7adSaWgOyIhMI6CtxFUcfGWRgTxv1qrXSn2QmQiy+82WR14MQAl8ILM+l7rhYnststULJupbtkqvXmK6UCWoahqoSUFX0pXT4W721vhDYnoftuTRcj4brULbtFbEt1xeMl4oENY2AqqErCrqiYKxD5L4XsKVeG6pKLGBiex6lhtUcLFlrlu9svXMpRpIEqiqTykSQZYl5ScJxXDItMaKx4I5OikIIzg7P8NChXu7b08G+wTYalsP1o4KmKcQjAQrlelMNaocgSRK6obH7cA/heJDO/ixzEzkadRvd0Ehmo7T3Zujf34GiKXdtQF7WFbBcF9tzKdYbvD05xWy5QsjQ8UVzVe2L9Qog3znkrBKnipf5obaHATiev8h4fQ5DXh39f7U6RdWtr9fECq6/l99LE91W4Amfol1jvLa1IkUAg5EWHssOcSzZR1h7d7pNNkJnf4aDD/YTDBmMX57j9efO0bMry/6jvWTa4qv2jSVCJLNRirkqR5/c3Zzw13kdEunwKiU/VVOIJ0NUKw1qVQur4VDMVSnkagRDBoGgjiRLuK6/EoAdUFUG4ynGSkVmqxUq9trMk1Q2Siob3fDaJEmiozu1xl9/fd0Bw9QY2H1z9UxoWiYDQZ3BPRvvm2mNkWndWgzKTuHl8TFGC3nqrovteVieu1IW+Gr+WjXNEzPTFC2LdCDYnKgVBU1ViOoGndEYD3d2rWrXF4K64/CF82exfQ/H83F8D8fzsDyPq4U8daeZTVN3Hb5y6SJvTU8125UVNEVGVxT64gkOtbYR24HSwncLW6YwjudRqNfJVZv+ra5E7O4yIanJZNu6kmiqgm27eJ7g0LGtF0fZDHKFGu3ZGMl4cN2Aa1mSUFUZx/HuyEpYNzX69rTTt6d9x9veDpLBAHOVCm+MT674Il8ZGSNXrxPQVMYLxeY9UeS7GlBWcetcqV4LTn27cIkLpTG6gqsV8Raswqr87eJihVq1gWO5zcG0P3tLWWvf85m4Mgc0n08obBJJ3J7GghACx3LJzZdwrOZgoygy6fY4mqa+K2R2VUkmoYd4X+t+Hs/s3rba4PVY/maE31zlXYt8b/r3XcdDkptKbcsyrGJJgtUXArGUUbSc6ePYbjPLR5bXfY7JTJRsW5xkNspHfuphfu0ffo6Xv3EaTVcJRwMErkuLa+tK0dGTZmp0Edf1SGYiaHqTePuej710Lk1XV6n7JVJhNL2pDBgIGlTKDXxfUK00sBousiyj6wrVcoNgUEdRZCK6wQNtnTw/McKVQp75ehXH875rAbjvdnzx0nm+OnyJQuPm7ug3piZ5Y2py1TYJ6IxGeW9v/7pkoGhZ/J8vfoeGe/NqgDXH4U/Pn12zXZEkPrxriK5o7PubDOTrDRzP5+P3NcsS3+0hKhDU6R1s4b//znOAxP7D3Rw43H3nzmdo1C0H23bXnextxyVXqBKPBrck93krrDnXLSSob68uw+bxk/ffx1+cOc8fHT+JJMn8o/c9yVi+QH8qyVSpzP/nS19Dl2V6kwnigbv3IeyOdrM7eu09SOsxulqO8uneD67a77m54zw/f2Ll77/4r8/z8ldPMXJhGlVT+PyZf41u3jwYs1Zp8L++7/8CYOhwN+/9+BE++bee2nbfxVLVupEL0/zH/+N/MnJhCuELki1RfuV3/he6drV8V+VdJWiWaTZj/O2h9/Oe7B4C6s5pSviuT61mNX30s8WVyXNuqoBuapgBHTOor8jgCgGu7VKrWtSrFqV8tSl4U7GYHs8RChkEwyZ6QNswfigQMjhwrI+f+jvv549+41v4nkBVZR58qll+WZIk9tzfjW07nHhlmF/75c/y9I8cI9sRR1EUKsUal89NEY4FeM9HDtM9kF057r0fPAhAKgPdfc16E3/9F96/cm4hBJ/8yUdWacynAkE+vmsPnzlznOOzUxyba2dfKkNnJPZ9ZZ2SJQlFlvFvM9Db9Xw839/2gsP1my6A9eALgeN5t7WYsT2P9ZzG0pIEsSLLG5ZSX65YqUgy8joz7LWqltdI8k5gy2RAVxQK9Qa/+/KbBHWVjx/cS9S8e4O+bbnMTOUJBg3MgL4ygCx/dDsJWZI4dl83z78+jKYqxGNBQoFrK4eG5TAxW+Crz5/lkfv7MPQdZvEChk9PMDO+SGmxgmO7GzKCZ37yYYJ3IdJ9VzrNLz3x6IpLRJVlHuzuBJoVDR/r7abYaPBEX893VYTo8cxBFGnt84ioQdqXChe9W2A1HEbPT/Grf+v3KC6W0U2NPYd7+Vu/8sP07G67I5NBWDU5EO/iA1aF6UaBuXqRuudg+U3SG1A1YlqQgXALQ9FW9sU6OJLsJajq6w5Q24XwBV/53Ov86X99gcXZ4pJ2RtM68Ku/8N+QJAnD1OnoS/PvPvsLAFw8Nc4LXznFF/+oKc3tuc2JYX66wJsvXGgWlIoF+Yf/7ic5+ODafP1lSLLEBz55lHKxzivfOMMf/+azhGNB9h1p6lAYpsbhhwf51d/5G/zJ7z3P1z7/BoXFCr7nE4qYdPVn+NCPP0RkOxoDN9xCVZZpDYX5f5/+GL/ywjf5wsWzXMrn+Nv3P8ih7M6Wgf9uoS0S4V889QH+2XveBzS199VtLqD+5fue5p8/9f5tF+lZnpRvhCJJtEcinPr5X9xew8vtyBKqvHb8+ZnDR/jp+w4vkSHW9CGgqvxf73+Gf/nUB5rS++sQhse6unmoo3PFJbtT1qMtk4GIoZMKBclVawQ1fVPayUIISpUG4zMF5vNliuU6dcvB8wWH93TQ1ZpYyRa4FWRJIhDQ+eRffQRFlpmfLd6xD0WSJT75zGF++49e4KvPn+XVEyO0pCMIIfjaC2f55kvnqdQsPE/ww0/fR2AHVQp9X/D53/ombzx7lkqx3sz9vUmmwpM/fOSukAFZkpA3ePkSAZND7a24nk9Iv7t55zeiN9i2LmPeHe2mM7jzxHG7KOYqXDo5xh/9+69RXCwTigR44qOHec/Hj9Dem2mWvL0jZMDgkcwg+2Id2L6L47v4QqyUQ5UlCVWSMRWdgKITVHVCqnFLpcKtQpIlHnl6P0MHO3Gc9VdqsiytstZ0D2b56F99hEefOQCAEA6+qCKhIMtNqWlFkekaaD5nTVfoHWrlX//Bz5HtiBOJXdMkUVSFpz52P0cfH8IXgtbOa9kvktR0AWTa4vzoz76HD/3Yg7hLfZQVGTOgkUhHtvzdrXf/PCGoODamqvKxwT18Y+Qyb0xPMF4qsi+doTeWIKRpqJusLrk7meaBts4t9etOQ5aaZXnZgcmrWUJ9510o0lJg4Z0quKbeggBJm7hHzQJGO5/Bs2UyYHseiiRxuLON6WJTS2Cj3H7X86nWLY6fm+DC1TnmFksUKw1qDRvbacpjxiMBktHQKjIwlyszPV9iMV9FUSQeu78fRWl+BMvRr1cuzjK4p22VYthOQwL6u1J84LE9vHNhisnZAlfGFggHdRbyVcJBg0wyzN7BNno6kmtKHN8OhC94/Vtn0A2NvUd7yXYkUG6igW7ehSJFt4KmKO8aH2dAXV8SVZMUAsp3QS5VYg05KS5WeOfVYV766imGT08QCBk8/pHDPPLB+xg40HlHC0+pskJCD5HQ73Y9idWQJOmWgXE3Ihg2CYZN2rpTSy6WOq6/iCRpaMra0tSyLBMMmysr/huRaomSaln//NJSTEDbHRbRmamU+aNzp6jYNgu1KvO1KgWrQb5RZ75e5dziPIaiosibK13zof6hNWTA931qDYeRyRxjMzlKlQa26+F7/k0X2Jqq8OjhPvo7V1vUhIC6ZbNYqDI5V2QhX6FaXxrbhUCVZcJBg2Q8RFs6SmsqSvgmUsUvn7jC5FyRUEDnkUN9yJLEm2fHWCxU8X2IRwMc2dtJOh7CcX2mF0pcGJllfrGCrit0tyXobkvSnrl5UGO52mB6ocT0fJGFfJVawwHA0BXCQYNsMsLu3hZCG+hP3Am8MTzOxamFbR+/uz3DscHbI39bnkkbrosnfHqScaaL5Q1fItf1yJdqXBiZ4wvfPMmJ8xPY6zD/hXwVy1kdqJEv1njr9BhvnR1H1xT272ojHgmiKhK+L6hVLY6/foVQ2MD3BeZS+U7Y4VWLJBEKGjzz5F56OpKcvjjF1YlFMokwgYBOR0uc3f1Zjh3sxthB9cEmBIvTRR7/6GEe//Bhdt/f8z1lKiw1GuiqinaHWOx2kbfLzDbyHErc3VxoWZZXYkqEEFh1m4snR3npKyd55eunkRWJvUd6eeZTD9Ez1IoZvD3C4vuCsmWxWK1hux5hUycTDu14sG/zu/Px/AWE8JEkHVkOIWHg+XNL21QkyUSWArjeAiCQJRNZDvH/J++/4+vK7vNe+Lt2Pb0f9A6w9zK9ajSjLlmSLcstcYscy75O4uTeXMc3seO0m+rkTd44b2zFieXYsiWrWL2MNNKMps+wdxIg0TtwetltvX/sA5AgARIgQWpkP/poSJ6zyzq7rPVbv/X8ngd0PJlHShspnUb6NoIQAVxvASldhNBRlAieV0JKu6G86F9LVcnguDO43qJv2az6zozXnlsRQRTl1r4JrvSouxYlp0Ld80vIpPSQSBQUNEXFUBqZEjWApqy8llJ6SFle97UTIrL8Tk+Ui/zekVdX3W62ERxsBDvSK8mdruuxUChzeXyeF49e5sjZUWbni1QtB8d1cd0be3JFEeiaSjwSoK0pfkMwMJcrMTqV4+LwDCcvTjA0NsdCvkKlZuG4Hqaukk5E6G5LsbOvhT1b2tjR10wkZK7alz3/5iCvnLhCNhmmqzVFvljhL751jOHxBVxP0t7kizQ9cXiAqbkir58a5psvnePSyCyhoMHBHR08dmiAWDhAZJX3R0rJYqHChSuznLg4zunBKS6PzZMrVBECQkGDbCrMQGeWuuWwrbeJWDiAscYkzPYcxqszON7tmcQJAWkjQVQPc2RonK++ee7abxHCn1BXLD9YCepXiaqu5y2Pm/FQgPfft/PeBwOJQID5coWvnj5POhxe1Y9ZSslCocILbw7yic++RK7gl3L5KU9/G+8mYh/ZVJRK3ebI2VFURXDs7BgP7O0hGg5QLFQ5+uoQDzy6FSF8EQ0zoLFtd/tdcxYMBQwO7OrkwK7OW2+8SRBCsOehfupVi2Kugl13UNRVppcNqNr60of3AlJKXrwyQm86SXsstqoWxd0453pwoTjKi3On7nkwoBsamqYukwUvnRzjf//HrzN0ZhxVVene2srf+bc/QSIT3RQ77rrj8MrlEf74jeNcnJnjsf4ePv7oA/RnN1sESuJ5RfLlT+F5JXStk6D5AIbWT778KVyvjKY2Y+rbMfWt5Et/hMTB1HcQMA+hq51Uai9gOxO43jxCGATNBzH1beTLn8bz8mhqC8HAQ1Rqr+C446giihC+fXUy8jGK1a+jKhFMfUejVM9Zce6AvotQ4KG1f4GUSCQlp8KFwgjfnzvGYGmUnF2k6tZxPIeQGiBrJukOt7Ij1suh1A4yZtJ3iVh+7+rY1vUD+lqeACqG+eTy5wJx2+vnq+F6e9xCucbXXzzL5549zuRswV/T1lRCAQNVUSiUqjiut8wFUlWFSGPC092aJLOKfftffucE3371ApfH55cJzKIxiClCYDkeEzN5xqZzvHz8Mh3NCX75xx/lycNbYJWS9CVUajYXrkzzp195g5nFEq7r4UkoXK7yu5/8Dlu6s3z+2RO8dGyImYUSCFgsVPj2qxcoluuEAjqPH1r5fkvpe8p89YUzfPqbR5lbKCEBZblCBQqlKvlilQtXZvnmy+f41Y8+zqMH++hoTqza1pxd5J+c/D0WrcJtURc0ofLzvR/g6eYH6EjH2d97tWJMNEiGM/kSJ674IlO9zSmiQdN326zUuTA5i/Tgke3d7Ou5MSO24fZsdId8rcZCuUpQ16k5qzPsHcfj018/wjdePLscCHS3JtnR30Jbk5/C+cPPvXLDfktIxoK0N8dpy8aYnCvy+qkR9mxpIxoO+KQKVfEVB4M6M1O5TRUaeqtAURV++tffzX/5jT/n//N/fQoEJLPRNVnl//gPfpFkdv2p1ruNf/7t7/LTB/bx7m1b7kkwUHZrTNduXTM/UZ2jYG9slrUZMAI6uqFh1WwunRzlX378f1JYKBNPhdn9QD8f/2c/SiKzfmvlWyGgazyzYwtv3zbAb3zxG0Tu0j2Qsk6h8lkUEcMw+nHcWRaLnyAafDea2kYs9Cia2ozrLZIrfZJY+MOoaguV+ksUy58nFfs1bHcCQ+vB0J9GyjqLpf9BVW0jEnwGTevAccbJFf8nQihEgj+C683hOGN4Xg3QiAafoVp/E9fLIWWNav2lFee+Fd265tb53uwRvjP9OmcLl5GN/12LglOm4JQZKo/zvdk3iQyH+LHOp3lP66MEVH85x/MKVIr/sbGHf06JhWtfQNW6ABXPy4N00M0HMMzHWcpw7Mk28a2f+LlNuy8xYyWP4VNffZPn37zE1JzvyPn3fuZJHjvQTzwSxENSrlr8yZdf54vfPUmxXKezOcFH33WQ9z+xe3l59nr0d2W5PLHAzEKRAzs6Obyrk87mJIlIEE1TWSyUefXUMM+9doGZhRLj0zn+wx99h0M7O4mETNQ1goHRqRz/+8tv0NuR5v/6+adZKFR47eQwX/v+GYrlOv/qD75JvlRja28Tv/43nyIS1PkPf/QcYzM5Lo/P89xrF28IBnLFKt9+5Tz/v09/H8f1aM3G2dXXwhOHB2hrimE5LjPzJU4PTvHsK+dYyFf4r596noVCmXc+vIOBrrvLM3r3ge2868C2FZ99/+wVXj4/jJSSf/033k1A15Y5eh6SuuXwm3/ydRCC+hp8m41gw8FAxbLJV2sYjVnOanj5+GXOXZ5mMV8lYGh8+Jl9vPORncQiAXRNZW6xdNNgQAhBMhaioyXJ5GyBy+Pzyz82mY7w9Pv28eYrg+QWyvRva6W77+6ooEkpmVsoEwzomKa2wnL5ytg849M5HMejuz1Fd3tqU9vguR5/8M//krHBGRLZKM0dKSI3EVXajNnkevCp4yfIV28tNV2yLF4bHSNXq5FuyFX//OGDd02T4lxhmN8f/CIx/ebqZ4tWCUO592V6hqljWTYXjo/w3//p5ygslGnuSPH4+w/w1IcPE2voFGye0ZXfbVxrhnN34OHJEpqSRYgAhr4FQ+/B9UqoIoYQxrKropQVhBJGEX5gIrGXj6KocRQl2sgYev62IogiAoCCxEIQRREGnjBB6CCXxKMU/MHXn4W7snzDuVfDUkbgM6PP8ubiWUYr03irFoRdsw8SV0qKdpnPj30HQ9G5P7WLpkAKRUkRTf7n5W0d6xi29Sqh8C+h6XsABc+bxXUGse0TK45rqBrtkc0T8Fki70opyRWrnB6cZHwmTzQc4InDAzx5eAvRcGA5g6BFAvzkuw9xeXyB05cmmc+V+d4bl/jgU3vXfH72b2unqyXJjz9zgGwqQjCgY+gNXoMQdLYk6O/K0pKO8tzrFzl2bpxCqcbpi1Ps295OeA1OjOO41Oo2f/sjj9LeFMP1JEFT59LoLBeHZxkcnePpB7fxxKEBDu3sQBGC9zy+k89/+wTT80VGGv4x4Kfiq3Wb4YkF/vRrb+K4Hvft6uKZh7Zz3+4uIiETXVORwECny96tbTy8v5d//F++TLlq8f03hzANnb7OzF0lRPv9+srjnxqZYjpX4u17BwiZxgryriIlqmnw1J4Bnj1xkUQ4yEPbVufErBcb7pmVhuyklBKP1Ss7Tl+aZGahhGlo9HakefrB7fR1pJcHLMu+uZgDQDhokIqFkMDsQgmnUROq6xpNrQl27u3Etl3SmSiJ1N0hQUkpefHNQTwp2bW1lW29zXieZGxykS986zgT03k8T9KUifLR9x6ivTm+aYOylJLLZ8bZdqCbbQd66OxvRjfXvl13k2x2LZ4fusJsqexLe+oaTkPsRV4nhGC7LiO5HGXbIqjpCAF/4+B+7laOwJUejnR5Irv/pttdKI4yWd246t6dwnNdBk+Nc+nkGCMXp/FcX941lgrT1JZEXedzI6WkZjt84eRZUiHf2W50MYcrJfd3d9CbThELrO8qzxRLDM0vcnpyGsfzUIVCdyrBlmyanrSvWjeymOPizDyKELxt69UyvdOTM8yUSoR12Nm8i7p1Cg8LTcmiqVlULUOl9gISB01tQYgApr6Hav1lhAjjeQV0reea1iyxw/1O0dT3ULdPYtkXkdgY2lZcb3r5ez/U8XC9HDXrJJZzCU1msbUuDK1nxblVJYOhX3uuxrUEzhUuczJ/ifHqDHXvRtW/teAhWbAKvDB7hLZAhrgewVQNNK1/eRvXPo/0yujmoyhKs5+O9toQwqRe/eqK4y0z7e8CZhaKFEo1bMclZYbY2ddKPBJYoYuiCkEmGaG3PcXY1CITs3lGpxb98jVWD1Jj4QDhoIGUrOnJEgoa7N/ewaXROY6d84XKxmdz7BxYO62taQrJeJDu1iRGQ121KRWlqyXJxeFZ6pZDT1uKga4s4UbavLc9TSig47gulZpF3bIbbRJMzxc4NTjJ9FwBTVN47FA/B3d00Ho90dDUCQUNAqbO/m3tvH5qhKn5AlfG55lbLNGU2rzM3XqQK1cp1erEQ4EbAhH/fkiiQZN8pUahfOdeQBsOBuLBAG3xGI7nNdY1bnxIhsbmKJSqxCIBdg20sqN/4+sZpqEtM08LpdoywUVRfEOMrTvbN3zMjcKT8NKRIQxDI5uKNIIBjzdOjvDK0SvomoKua1wZn2drTxPZVGQTZ+iCVHOM3h3t7DjUQ8+2NrSbyA5rm61xsAYs12WqVEIALdEITZHIci3stYGhEIKArhMzTcLG5pekXY+IFmRHrJv3tz960+2enznG9+dO3HSbm2FpzX+jKCyUKSyUWZgtLPNmquU685N5pscW6NrqvyO3ukYSn8T7F0dPsr05S1M0wshijlLdolSr8+5dGiE9va7KlmLdYmQhx8mJaVzPw/Uko4t58tXacjBQqlu8OjyK7bg80NNBUNeRwNGxCQbnFtjTluVgx15s5zICn9gnRBBD66PCCzjuLCDQ1FaC5kFK1a8Bi6hqC4bWt/ydqkQbRMMghjZA0DxEufYcUs6jKAmCxiHq9mkUJYKKB6qFIsJIWUPKss8hQEF6VQx9YMW5hVg9OJJIXls4zUR1hqpbX3WbW+FM/jJDqXFag1lag9ez7ct43hyCazNRAqSL505w20XyG8RioXJ1MqWptGVjrLV0kklGCAUNHNejWK7huN4NFvVLUFVlXcuzHc1J0vHGhE36Kfub+biYukY2GV0OBAACprY8GCuKHxykrvFASMZC/gxfguO6VOv2coAyOVvg9KVJXE+SjoXYu7XtxkBg6TcpCuGgwf5t7Rw7P06pUmcu5y9x3OtgAAR122EmX8JyXHT1Ki9MNoSRpnJFLMfdFOGhDQcDfZkUfZmbk5Cm54pUqjZt2Ti7B26tg70adFVdvpl1e3Vuwt2GlJKhkTmefnQ7mVTEvwGOyxeePU5na5IfeXov2XSET3/lTb7xwlkev3+A0CbN0BVV8CO/8CRvPHcGVVOwLZdEOuL7EKyyfXNX+p4sFfzCfQf57MkznJudpSuR4JcfvJ/ORBxDVVekJh/8r/+d927byru3b70nzoXNgeQtswIAIS1I1kzecru1IKXPidkohi9M+YFs0KCtJ8vY0AwL03le+sYJrLrNx/7JB2+a+bkes6UKO1sED/V28auPP8jw/CIf+9QX6EgmaIpESIVvrduRDgV5oKeTJ7f0EgsEGM8X+OPXjvL1sxf40f27ANjR0sT3Ll7m4uwcQ3ML7GptxnIcLs3OU6xbPNLbi6ZFSEY/hpQO/uDrP4ep2K9e95kkGf0Y4OH3Xv5AEgt9YLlNqhInFfPFhRKRv9HYFkAlFHhw1d8Rj/zkDZ/deO6V8E1uPF6aO07pFl4VN4OHx7nCFVoC6RuCAYQBQsGqfw8j8AxIFccZxLaPIpQo90q/VXpcTZvjD+JrnVpRrpK8/Wfdl0ReK0Zd6pc9z88Qeo0M4bLMdOOYmqosH6Nm2Tf1cdF1lWR05XKfpqpEGoJvwaUZvHE1yDJ1bXkJVUqWK9eklMwslBgcmUMI6G5Loamq//0a6+yO65JOhJePV65ajE7lOLDj3hHIAZriYc6MSb5+9AIPb+smE4ssT75dz2OuUOZrR86jqQqZ2J1nx+/KAq5fWuL6hhC3KYTjSYnXyAbomrLmw3i3kStWySQjxMIBqjWbC5dnGBqZ5xd+7CF2bW1FSskD+3v5j3/4nU11avRcjz/4Z59jYaaI67q3FHf6X6/8Nk3td3/Qfbiri73NLRyfmuIP3zjCR/7kz/i1hx7k7Vv66Ene/iB7p0gbcdKpW6+5Hkpu5WBy622fx3VcqsWNp+QMU2fvQwM885EHePjde/hvv/VZXvr6CaZG5vn+V49jhgx+/jfevyH77d1tLezvaEUVgr5Mim3NGRbKFcZyuXUFA7lajTdGxvniibPLWiEji3la49FldTOAw90dKIrg8yfOsKu1mZcvj6Aqgi3ZFE3Razuh1YLR1T7bCNn3TojBawfHjnSZqs4zVZtf1ZVxIxguTzBaubGsSzceBFQqhX9OKf8b/ofCRNW2Eon9Fnf229aPjpbE8sSqZjlcGJ7h4I4OVutUL4/Ns1ioomsqqXiI0C2E1DwpKVfrHDkzxrFzY4xO51gsVChV6n66vu5g2S6O6yJl45S3mNepikIouJLXIwSomt/eUNC4IfMlrllzl1ytVpP4mZGxmZxvSX9mlL/xm5/k1oGYXA6gHMejWLm9zNGd4B37tiKE4Pe/+Sof/DefpK8pTTxsIoFCpc7lmXlUReHvvOcRnti1ttLmenFXgoFw0Cdl2I5L6TYvYrlaZz7vs76TP8BqgVDAd+JzPY98scqLbw7S2ZqgtzNDLBKgUK6haSqVqrWp2QtFVfjYb30Y13FZz2FjyVvXUW8GhBCETYND7W10JxJ85dwFPn/6DK+OjvFITxcf2LH9nnoSXNuu9W537ZaKpiCWBmAJ1YqFZmhrDsr1qsX06MY5B6mWGDsO9XD4qR2omspP/t13YtUdjn3/AgvTeb716Vfp39nOfU/tJBJfnwXskoXrknnP1efk1teiVLf49vlBvnfxCr/2xEO0J2KU6hb/85UjXFm46vomgB3NWQrVGt88e4lSvc53L12mORrlQEfbiuu+2j1Y+dlGI/o7mwHc7JmwPJvRytS63q1bYa6eY97K+w6u17RZUdIY5pPo6Z24ziQgEUoERU2jKFnuVWagJRNjW08zi4UKi4UqX/jOCfYMtDHQnSVo6r4ug+vx3GsXeP3UMLOLJZqSEZ68bwuw9nUcHJ3l1RPDfPvVC0zPF6hbDrbrpyEURSEU0IlFgxiaQr5UYyHvm9vd6pILIdBuocC33ve9VrepW86KUnb/nq//xkspcW9TS+BO0JaK8SP37WR3VwtfP3qe8YUCxWodASQjQXZ37uA9h7bT35ImFrrzPveuBAOZZJip+QKlisXl8fkN7+95krnFMsMTCwgBnS1JjB+AR7QA2prjDI3MEQ4a6JrKkVOjHNrdRTRsoqoKjuNRqtTR9SXy0yadWwh23dd3Q4nTWthIivlOoQhBUNdpi0V5eksfAV3l1NQMr46MMlko8r4d21b1Y38roOrWqbkWScNf/zNMHb0hKiKRLM4UCEXMNYPPcrHGpVOjGz6vpqmYQYNQJICUklRTjLd96DBCCF755kmKi2W+/mcvk8hE6d/dsVxdcDOMLuYZWcgxkEkxX64s20gnQ7fOCliOQ6Fap2xZdKcSZCNhXrkySmEVF7iQodMUjdCTSvDKlTEuzsyzq6WZvuuWf2yvwmztNCOl51c9pwDaww+TDezGVO/e+quUkjfnfg9X1jDVOHGjl97o21ds40qPglNmM9btbelQdy1s18FQr85ohdCAAIgIQokAHggd6dVw5TiqenU2N1sp89zI0B23ZQkDyTQHm/1gTddUnjg8QLVu8+LRIabmivzxl1+jozlJJGQghKBYrnPu8hRzuTLJaJDdA608fnh1LQ4pJWPTOb7x4jleOzXMlUY/fWhHJ50tSZLxEJGgiaGraJqKIuD5Ny/x7CsX1tV2X7Ngc66D13DDXMK2niYObO8gvAFRr0Q0yJbuey9hrmsqqWiIgKET0DVy5aovNCQEpqaSCAcYaM0QNPRNEXa7KyNIV2uK4YkFCuUa54amWciXSUTXLou7HlNzBYYnFpiYySMQbO1pwryHg90ShBDs3d7O2UtTlBsDfq5Q4YEDvQQbWul1y2F6rkAytv7ft95zp5pjVBvObPWqdVPSTTx9bzID10JVFAbSaVKhEDEzwKujoxydmERXFZ/U8hbEfD3PeHWOB9K+62Y0EVpW+5MSBk+NkWlNrKrnUK/ZzE4scvr1y3fUhiW/gV339VHOV8jNFTjywgVOvTLI6zvPoJsaW/d23dQ9URGC6WKJ05PTLJQrTBZKRE2D1liUZCiI5bpcmp2nZjsslCtULJszUzMU63X6G5yfWMAkahqcnpohYhicn5mjVLcwr5uVKUKQCYc43NXOdy4MISVkIiHSkZUZDNezyNUvM1j4Cp508Li+akgQ0FJERDeWo2FbTiOgdoknwmj65nFeLha+hOUViGhttIcfvCEY8KRHzV1/9cDNIPGDC8uzVwQDnpfHc8dwrFN4VFfEHUIoqKEelpYy5qoV/uLc6U1pD8C7+rZwsPmqiM2erW3kG6I6R86O8vybg8TCAYIBHUUIKjX/WrRl42zvbeb+3d1s61nbovrYuTFeOXGZiyOzBAydQzs7G/X4GTKJCOGQubzM5Lgeo1M5YH3BALBp8ypFuWpIJAS0Nyd4x8M7aEqtv79UVWXNaom7DVVRiAbNFYJEdwt35Rfu2drK2aEpJmYnOHF+nDdPj/Dgvl4Cph/B3BCLy6VUjMS2Hb73xkWOnB2lWKlj6Cr37e4mvIkmQOuFogje/cROzg/NcOzMGJqmsHOglQf296A3Zo7VqsXoxCL93Vl0bXMtjB3bZezSNMMXppgem6dSql1H2W8oVakKH/mVp9edXt5spIJBPrR7J4fa2/jmhUv8wetvIGmk8+4VSeoak52bYbA0wYtzJ5eDgZbONMmsP0uVnsf3vniE/j0dGAFt2Q/DT6O6TI0scO7NKxx94fymtNkM6Bx4fBuhaIAr56eYHV/k6596GUVVSKSjtPf5s5HVUqJ64z06PTnDJ187his9fvb+A+xqbSJiGixUqnzytaNM5AqM5fwKhplikWQwxK+/7WF60kn2tLUwkS/w77/9AiHd4PGBHlqi0VUrhNLhIA/1dvG7z73Ij+7bTWssdkO5kyJUAmqChNFL3StiuUU86eBi4cmrmgL5xQqlGZvF+RKGqVEu1jj8yBYiseA9U9GUgCNvXeK8XnjSw5YrA2DXPk+t8hdY9e+gaF2NaoslaARCP7X8r6JV57XJsTtuhyr8wW9PtnnF59GQySMH+oiGA5y7Mk29EYhZts9Ej0UC9HVkeGR/Hwe2d9DRklx1dr60FPqNl84yMZtHURSyqQh/76ffRltTfNUJket6N1WcvZsIGDpmQ/vAk5J63SEeCZBORH5gPLT1YmkZ0HE9HNe7oXz7WuiainkT75r14K4EA48f3sKZwWnGp/PM5Ur809/7Gu95bBcP7+9la0/TDUxSKSWVms3F4Rk+/fWjvHlmhEKpRiigs3uglYM7O1ftoO4FejrS/Pt/9CFKlTqO45FOhlc8RJl0hPc+tZuWTHRVPezbhZSSF758jE/+uy8zPbbgr6PpKnbdQQj/mVBVhUgsxO4H+nFvg+G+2ehKxPn5+w7ytv5enr00xIG21nvGH9iIAmHRriz/e8fhXi6cGOGN756hXKjxxnfPUlgss+ehAbbu7SKZjVLKVzn75mWOvnCe8cuzhGMBtJpKtXTnpKJwNMjOw338g9/9aX77536faqXOtz79KjNjC/zav/4okfjqKf+66/JoXxcf3Ltz1e+TwQD/7/vfcdNz39fdzn3da5foXjswK0LB1DQsx+Xp7f10Jm8ka+pKhN7oM/RGn2l8Ilm0BrlY+BLncn+xvN3QuUnCRjOpTJTRy7M4jrep5Nv1QBECU9088SlNUQmqKycsjjOI45wlkfkKinp3OQICiJomO1JZOmLxFVkBANtx+c6rF/jzrx+hVKnz9376SZ55eDuJ6Mbtl23bZXB0jlLFojkVZf+2dtqb1ybv5ooVyrV7T8ADPxuQiAVpzcYZm85x/so01frmBYF3E66UXJ5e4NkTl3jl/DBzxcqa2eEP3LeTX3n32pLb68FdCQZURfCBt+0hHg3wyS++Rqlc59lXzvPCkUF0bWWZyme/dYwvf+8Uniep1e1lR8NQQGdbbzN/92+8bVnR6l5j6ZyaphCPBhps2KspJ4B4JMD+HR2oithUkqPnSr7+py8RTYZ55iMPcP8zuwmGDD7+9P/Lb33iYwTCAS4cH+a7X3iT9/3cYwQjPwAnvmsgAIRABbqTCX76wF4MVd1UvfWb4XR+iP984bMkbuHCV7ArhNWrHaBhatz/9l1ICX/6H7+OVbcZOjPO6OA039JfRWmYYzmWb6KzZW8n7/+5x/j8J77HxeMjd9xuIQSBoMH2A9384m++n0//12eZnylw8tVB/ttv/QW//h9+Ck1bW19io5/fDiQwWSjy1TPnOdzVTlcyTsi4cSBdeU5fj18VJiort003x0jH0qSborR0pLh0bgLjHqdhVRSiWpiryoW3D02omIqBqawMBoTQEUoMRc0AN+/D9jW18OLP/NItzyXxycwly2K0WODEzBRHpic4Nj2Jqaj84r5D7Ew3kQ6uzBIePz/O0XNjTC8U6WlL8a7HdhIJGht+TiRLpd6N364pBAM3P86ZwanGMsG9hxCCtmycnX0tjE3nWMhXOHlxnHBQX1Nr4K2Cl85e4ZvHL/K900N0pONsaUn7k+JVLnVr8s45OHflDRRC0JyO8vD+PhRF8KXnTjE6tUitbt+w7WKDYepJb/kBa8vGObSrk0cP9tPenLgbTdwQbuYprygKpnE3BjzJxOVZHn7PPnY/2E/Xlhak56FqKtFkmNaeDEZAY2E6z6vfOkX/7o41fQvuJYQQPxArY4FCRAvyE11P33S70/nLXC5NXt1PCJo7Ujz4zC40TeGN755l4sospVyFcrGGoggiiSC927vYur+LHYd6GdjdQUtnisFTd57WBb8sygjo3PfULkYuzXDk+XPMjC5w8pVBnv3Mazz+vgOEYxufwW0Gzk7N8vrIGOen5yhbFj9+cA/RgLkOadZG0LyKd3M0FqSYqzAxMn91piM3N4C5FTRFozmQXlep262QNuMkjdgN7VfUNjRtO1bt2xjmI0iCCLF6X2GqGu3RW3uLLEko265HSyRKXyLJvuYW3pic4FNnjvPZ82dI7w+RCa0MigulGqVKHct2KVctpucKqE0JAqa2IfKZAExDxzS0BtfAZnah6Fe1sPIeSilZyFd4+fhlBkdv3573TtHZkuTgzk6+89pFHNfl269eIGDqhIPmTUvfpfTL5KWUm5r1XS8uTs4xtVjkYF87P/rQHsKmvuY7kn2r6gyAryDY2ZIgHNxGtWZz8uIE84tliuUalZpf7uG63rLxkKkbRMMmTeko2/t8Asvebe0/MOLGDxpSQq1i0dKZItUcxwzo1CoWRkCnXrPRdI1sW5K+ne18+r89y0c+/na4R4GuJyUV22a2VGauXKZi29gNxzMhQFMUQrpOPBAgGQqSDgZvWiq0GYjqIXbFe3ksu++W2+as0op/B8Mm7b1NhGNBwrEg45dnKC5WqNdshCKIJUJ0b21ly74u2nuzaLrK3gd9pnWmNUFb7+pEKzOgc/jJHSzMFEg1x2nvW5uQJYSgpSvNg8/sJhIPMjY4g6KIG7zmBaCrCk9u6aUtfveNqVzpYbseuqowkElxf3cHpnpn72StamHVteV15FrF9iWt5dpOdpsNTahkzSQB1aTi1NZdtbMamgNpmgKra2xIWaJe/QLSWwAldA1vQGAE3r1mcLAWlpwBTc1ftkkHQ7RFoqSDIZ4bHuTF8WHub+0gHQzRE7/apkjIJBTw9e0LpRrffvUC7U1xAqa+Yp1fVXyyXDhokI6Hac3GG+z+pW0EuqbQnI5SLNeo1izGZnKcHZqiqyWJaWgN0R+HhXyFI2dGOTM4Ra5QQVUV5A+AO5CKh9jW08TO/hZOXhjn7NA0yVgIAfR1ZohHgmiaAtInO9Zth1KlTq5QxXFdMokIO29DRfdOMVsoY7su92/p5PGdvXf9fHd1pDV0jZZMjF/88ENcHJ7h+PkJBkfnGJ/OsZAvU6nZGJpKKGiQjAUZ6Mry6MF++ruya5pYSOkBDshrS6A0hPKDIc/dLQgBuqn6nWTjBRICIrEQxcUy9YqFEdBIZmNMDc/ftNJgM+F6HoV6ncsLi3z/yghHxseZLJQoWZYvUQ2EDZ2WaJStmTT72lo43NHuE9NukmG5UzSZCR7O7L7ldhEtREswfcPnmq6SaUnwrp9c37rbO3/yId55i22jyTC//Ds/uq7jLeHQE9s59MT2Nb8XQhAxTf7Ze2+eAdks7G5tZndr86033ACmxhZpycTZtrsDz5M8/41TePeYM6ApKgk9Smsgw0hlCsu7MWu5HggEPaE2OoI3XiPpzeFYR3C9GSzrxev200kF3sFmCA9FDJOBRIonu3r55KljvDw+QksksiIY6O1I09GcIGDolKp1/tdfXm+17CNgaKQSYbpbkxza2cl7H9tNLBpY9icQwp+oHNjWwUK+wvhMjpHJRT711Tf5wJN7SCfCSCmZz5c5dm6czz97DF3XiEUCWLZLoXTnGvobhWlodDQn+PF3HWB4Yp5Spc5zr13g5MUJHtjTw47+ZsIBA8+TVGsW84UKl8fnOXVxkuZ0lMcP9f9AgoG646CrKj1N90bM7Z5Mu4UQbO1pZmvPJnQqsoRrH8etfdHX2QQUbTt65GN3fuy3FAQtnWlmJxbJL/gzWSEEbT1ZLhwfIdueJNuaYG4qh34Psyez5TKfeP1Nvnz2AoVajd3NTexuaSZi6Giq6mcNLJuRXI6vnL/AZ0+doTeV5D+9/z10JeJr2pbeKdJmnLR569TIgeQW9idWr5++FZZnjytKxG78PauJT61rO8Gq1RfrFbNab6C1Vvs2+zxrYcuudqaHanz1L94AoLUjeVPfjWuxbmGvdRxLEYJnmh/kM6PfYs7Kre+41yFtxtmf3MbW6I2OcUbgXRjmkzd5HjbvvTVVjftaO/iTM8c5NTfDvqarg5dsmIn5Wdcm3ji9tk5GzXKYmMkzMZPntZMjvHJimH/68XeTiq8kTn/03QeZzZUolGsUSjWefeU8z75yY5VNJGjw8Y8+RM1yeOnYZV4/Nbxpv3kjiIYDPHXfVpCST3zuZcamckzNFfjL507wl8+tvV8qFryrboU3QyocYr5QYWqxeE+yZj90OXjpLeLWX8CI/EMQJn7i9IfuZ9wSQhEcfmoXl06OMD+VB3xVwgffsZvP/f53OP7iBYJhk4nhOXYe7r1nokN/+PoRzs3M8WhPF7943yHaolFfzxyxXOWwVBKzWK3ywuVhPnfqDL/7wov8m/e8k9A9IhSuhZxVJG+X6Q7fTqQvmaoc5cXpf4Ura+xN/hwd4YeJGisZ+aPl7zNS/h7j5ZcB2BJ7PwOx9xEzVkrWTlePMVT8BmPl76MpYX6k+49RhH5DQFCwR5iuHmO2doqcdYWau4AETCVCWGsmHdhOX+QdhPVWlJtY9kKjcseZ5cWZf8Vi/SIJo5cnW/8lhhLD9krM1k4xVT3KTPUkFXcO8NCVMEE1Q9Looy/2LuJGN6q4fX7K1bTzVV379SaPJyuvMVl9g/n6Rcr2FK6sElBTJM0ttAT30xZ6gICaQhX6daV8q7QDwdub7+Ns8TInchdYsAob+h0BxeCX+z/C9mjPslnXSuggJNKbwLJeBumhqh2o2gBC2dy6cUUI4mYAgSBXq1KwfN0AKSXffvUCf/a1NxmZXCBg6nzgyT3s7GsmGglcJT03DH5yxRrnL09zZmiKwdE5jp0b47uvX+Lh/b20NfnBthCCRDTExz/6GI8fHuD5Ny5x9OwYc7kyiiIIBnQyiQi7B1r58NP7aM/GuTQ2x/hs7gcWDPjthscPDbCjr4VTFyc5dn6MM0PTTMzkqdQsFCEImBrJaIju9hR7t7Szf1s73W13X+Z9NTy+qxeJ5M9fPEFPU5K93a3od9F/5q6PIFJKLMelUKxh2Q6u568tb3SJrjUbb/AHVIQI4rlTCCUOQkWIAPCDIVjdLSiK4KF37mHvQwM0dfgPo6IqHH5qp892vzRNpVSjc6CZ9//c4wTD96aE78zMLNlwmMd7e+hNJTHVtWd0QV1nf1sr06USXzxzDvctoEp4pTzFifwgfzP8rtvYW6AIFU0xKVuT5O1hmry9N2xVtMfJW1eoub6s73z9At1e5Ybtys4UJXsSyysT1LIrggCJxJMOQ4VvMFp+gZI9Sc3NYXtlXOmXadXEAmV7hrw1zEz1JP2xd9MSPEBIu7lamsTFcgvU3EWqboKam8fySpzPf4H52llK9hQ1N9c4j0QRGmUxTdmZoiP8iJ8nvoNJyujlOaKBZh58YjuelJw9PornuGvOfqSUuLLO+fznGC2/SNmexvIKOF4ND5eaW6DqzLNYv8RU9Rg7Ej+OJgKrGhRdj4Bq8t62R0noUd5YOM1YdeaW+xiKTtZM8P62J9ge6yasra6PIL15HPs0tcqnEEoaENjWEYQIEgh9BE3fy2aVG7pSslCr+tUGDRMm1/VYLFT4i28eZWhsnp62FI8c6OPxQwPEI4FGlco17ZUSy3bZs6WNo2dH+cMvvEKhVOPc5Sl29DUvBwPg90/JWIh9W9tpy8R5x8M7sCwHljlgGolokI7mBIau0duW5gNP7uHgdl+lMLZKf/Wht+/liYbqYfY6efV0Iszb7ttKd1uagKGx9TpFwKZUhI9/9FHypRpBUye5CuFWCIGh++6Hh3fp9HdmeLpUo1q3cV0XEKiqwNA0wiGDRDRIIhJcFpi715jJlxmfLzBfLPOfv/IizYko4YC+Kjn7cH8H79h/+54rcBeDgYV8mdmFElNzBRYKFYqlGpbj4rredTrq68OPvfMAWSMCQkcoKaR7BSkjgIJQUqjKjevAP+zo6G9CUZVl33EhoLkjxUPv3EtH/zS1cp1US5wdh3vR75GFcaFWoyeZoCUaIXALiWhDVUkGA7RGo+Tvcp2x7TnUXIuo7nNHSk4V27uxnniiOreimmAjEAg0ESSqtZG3rlB2ZrCvG+QlkoozQ81ZRBEannQp2uM4snLDYFdx5rC8IqrQiekdwFWRJturMF87x+XSs8xUTwASQ4mSMPswlAggcbwadTdP2ZmhYI81sgoqTYHdhPX1Lcl50qbsTLNQP89Y+UUst4SuBEkYvaiKjutZ2F4FyytRd/OYavSO05WK6i9J2LaL9BrX5CbHtL0SC/ULXCl+m4X6RUD4MsOBbnQlipQutlei5uaZKL9KUE3hyVsrYC79joFIJ67nEdJMzhWuMFWbp2CXsTwbV7p4UqIrGhEtREKP0hxI0R/p4KHMXuJ6BGUNEqDrjmJbryJEGE3fBih47hSeN4tV/w6avud2Lt8a18jl7PwsrpSYqoqh+t4wVyYWOHdlhrrl0NOW4tGDfbeU1m1KRalU66RiIQqlGrOLZaqNSjApJVXX5nvT52kJxmgPJdl6nVJh1bGYrRc5ujBCyNXIaFFikQCRsEFba5xz+UkG56axPAcFQUQPsDXWTH9Xhm2KunweV3qMlBaYrRcp2jVqno1skXiawTR5ZF2SNiO+X0rQ5GDDVbDs1Hl5bpC2YIKUGWbRqjBRWaTo1FBRaA0l6AwlGUhcvQ5Fu8ZENcdMrcC8XWbK8zBslUgpQNaO0hlOEdLurfBdrlylXLeIhQJM5UoslKoETQ1NvdG5tj195+zxu+NaWLU4fWmS104O89rJYUYmF2+90y3w1INbySYjjdrdLNIdRTZcDRWtCzbxxXqrwHMlmi6W2b5Lndd9T+3kvqd2Xreth2O7COFnEO7W+lJQ16k7LrlqjZrjLEvXXn8+KSW251GsW+SqVSL63Y2uy06Vseosu+O+3vtIeYp5q3BD+nakMkPZvX3LWk0JENU7GuecwpEVlmrq/dm8TcWZxfJKGEoMx6tStqew3BKedFak1yvODHU3jyIMYkbX8ueedCjZk5zPf57JymsoQieqd5A2t9EWuq9xfo+KM8+idYmJyuss1C8yVv5+o2repVt727pS+a5XZ752lnP5zyJQSJkDpMytxI1udCVM3S1QdqYoWuNU3DniRjfiDruNts40M5cdjr06iGHqJDORZbXH6yGlR8mZ4lLhq8zVz/olpHobGXMH7eGHiBkduJ5F0R5nrnaGyeqbDYEj0SAb3xqGorMnMUB/pIPLiXFemz/FlcokBbtMza3jeC5hPUhnsJkt0S62RrvZEu3Ek9ZNFTZd5zKOfZxI/N+gqG0IoeB5BRzrOOXC7xCK/BpLcsS3Y3Im8Qm9dddlqlTkxbFhXM8jGYoQNUzqtsP5y9M4jouiCJozUQY6b62xryj+DDoRDQEL2I57jQugpOTU+E9nv8WhVDfvaNtFU8vKqpYFq8wrs4P861Nf5T/d91NE9SC6olJzbQaLs/zZlVcZLM1StusoQqEpEOWDnQd4rHkrcT20rE1ieS7PT5/nZG6M8UqOvFXBkR4xPcD9mX4eaRrggUwfurJyIrRYr/B757/D483b2BZrYag0y6uzQ0xUc6gIHmvewjvb9pAJRJev+6XiDC/MXODE4iiztSIVxyasGTQHYhxMd/MjnQfueTBg6hrNySjN69AQaI7fuRz9XQkGnn3lHF/49gnODk1v/sFFAKF1Id1BaPiVI2+PCfxWhpSSN757hv2PbF21xnyp41x6mIu5CsXFMpqukm1PoqjKiu02Cwfa23htdIz542WipsH9nR0oq5DPPCm5NDfPNy9e4ktnzvFIb/ddFSAaqUzzhfEXloOBz409z7HcxWVDoiWUnCox7fZrcnUlTML0y3yK1jiWW1phzVqwxqi5OTQl4MvyugUW6hcoOZNU3XkiSsvytfLT8YsE1CRJs395YCnZk0xWXme49B0AMuYOdiZ/go7wo6hi5SvbJZ9ga/xDvDD128xWTzFa9lnrSaOPpHlromTFneX4wv8goCa5P/v3aQruIaAmbtjOX9X3g57NkJhWVIVkJsKBBwe4cmF6TV+PuldgvnaeweLXAQhqGfamfpbuyJPoytX72MJBuiNPMl8/z3OTv4Htldc891oDb1A12RnrY2fs1nawrrS4XHyRvujjK0SVVtTZYyNlHUVtv+b7KIqawZMry1uXBvaNoOzYTJWKHJme4GuDFzgyPQHAllSazlgc15UsFipICYahoevauvxT/OUCh9lcEfClio2Gb4QiFLJmlPvSPVwqznB8cZQnmret6I9GyvO8MjdE2oxwKN1NSDWoew7nC9P8oyN/gaYofKjzEO2hBGXH4qXZi/zTE3/Jb+15Pw9lB2gNJQAwFJWTuXFiepC97Z20h3xW/Z9deY2vjB9norpISyDOQGz1kt2Ti6N8b/oc/dEm3t2+h9ZgnNHKAjEtSMK42qdansO/O/01LM9hZ7yNn+x9EENRGSsvcjY/iSs9otq91xn4wH07+cB9qyuM3g3clWDgG98/x8jkIooQREIm731iFwNdWSIh38lKuQ2lvo5m/0GQ7ixO5TMo+l4UJYx0p9kM57G3GlzH4z/9gz/ld/7ob5NpTVIt1RCqoK03i3YNicR1PN747hn+4Hc+T26uRCBs0rO9lf/7//uzROKhTdff/sX7DhLSNb52/iJ/+3NfJGIatEYjhHQDTVWRUlK2LCaLRcqWRcQw2dPSzN9//BHMu+g8uSvey9bo1dl11kzwntaH+ImulQY1L8yd4OW5U7d9Hl0JLw+yjqxSdReouzkCmv98ztfOYbkFTCVGytxC3S2xWL9EwRqhbE4R0X3iYtWdp+Yu4sgaqhIgZWxlaf14pnaS4dJ3ARCo7Ev/AhlzBwo3LgUJFIJqkgey/4BvjP0adS/HYn2Ii4UvcX/219f1mzzp8WDTP6QpsAddudkMY3MepnMnR2lKdNG3tYXvfOU4+YUybd1pguEbO9z52jmmq0dYese3x3+UbGAPmrixlFhXIiTNAbbEP8C53GdX+CFci7pXpGhPM1e/SMroxZF1am4BV1pEtCwhLc1M7RwBJYqpxnClTdGeJtYgaFpemaI9RdmZZ65+iZqbRxUGcaOdmH6VmKqIEEJEsK2X0I0HARXPHcWxT6GqbVx7PY9OT/KzX/mLGxt7M0jw8D1dnIbFrqlqvKN3gPtbO9CkQjYVRQio1iwmZ/IMTyzQ037zJdVTFyf53huXmJjxict7t7SRTqx8Lt7fsZ/fu/AdLhSmOJkbZ2/Sz5ZZnsOV0hxn8hO8q203huK/82dyE3x1/AQL9TL/65FfpCucxlQ1JJKnWrczVJzl86NHiRmh5WBAFQr/Yv+HEEL4C2iNzmxLtJl/efLLzNVKnM6PrRoMSCk5tjjKb+55Hw9k+kibYRQED9CHQCxXNUlgrl4iZ1XYm+zkmdbdPJT1g0Ev07AIR2Ao91ZE7QeBu9I754oV6pZDW1Octz2wlfc/uZtQwEBTFT8yvY0RKrDMlhcgAqj6Dlz7tC/mgdLIEqib5335FkCtavPH/+GrKIpCrVxHKIK23iY+/Etvo7U7TSBkYlsO3/rzV8m2Jzn4xA6sms25o1f43l++yTs++iDmGnoNt4tkIMAHd+1kb2sLp6dmODc3R7FWx3JdbNvvfIO6zsH2NroTcfrSKbZns6SDwbtqWaQKFfUaYs2+5AC60AhpK4lKST1Ck5m4g/MYhLUmNBHEkbUGCW9hORjI2ZexvBIhrYmY3o2tlhFCoeRMUnGuqrAV7QkcWUMRGqYSWSb9udKmZE+St64AgpQ5QERrRVNCq2Z5hBAgVaJ6O3Gzh8X6RWruInO1c3jSRXDzJSNF6ES0VjLmjjXPAauXPN4usi1x0rEY6WyU+x/bxkvfOb3mjLXsTFOwl5QeBU2BPYS09JrXQhMBmgMHuMAX8Fg9GPCkgystNBFkvj6IIjQqzjx2g5ipK0E0YVJxc1TcRQwlRMLoJKa3MFe/iCMt2kL7GSx+D5DYXgWpyBuWZVR9B0bgSSql/4KifM5PYnpFhNAIhH+WazUGXOlLDN8uBBDVDf7W/vs43NJBMhAET3JgewdBU6dUtThydhTH9Xj7A1vZ2t3kOwsqAtf1KFfqTMzmOXVpkuPnx7k4MgsIOprjHNzRSeYaS20hBDvirXSFUoxWFnhp9tJyMHCxOMNQaRaB4Inm7cvaItO1ApdLc7SFEnQ11t8V4S+thVSTrbEW3pi/wkK9jOO5aI3BN7hKar4lGCeimxTtGkV7bS5ScyBGdzhFxozcsJRw7XVLmWH6IlkGizN8Zvh1Jqs57sv00hlKYtyhwNadolK3mM6XGJyap1K3b/D1WUJfc4q93a13dK678ktDQQNdUwmHDHrbU3S2bJ5oglDCqMY+hNqGcM4hRQhEjLtpAvKDxPjQLNm2BLFUBARcPjfBsRfPo+k76RxoxnM9Lp4Y5bH372ffw1uplusUFkuceOkST334PsxNLrLQVJXORJx0KEh7LMaWbJpctUbdcXA9X4HQ1DRSwSBtsSgt0QiZ8J1LZW4UfeG2VeuDW4MZDia33fZxFaGgiSAhLUPRnqDmLi5XDQAUrBFsr4KuhIgZHdheBYFC2Z5ZsV3JnsCVFroIE9DSaIo/K7acInU3h+WVECjE9C40JXjTkkEhBComUb2NojVG1ZtvZCzyjZT/2u+GKnRiRgeGGr3pOayGKlsytnbAsF5km+PEg2HMoE5zW4JtuzsIBFfnN9TdPFVnHgBdhAhpGVSxduWMIjRiRic3E/OxvDIVZwGQWF4Zv0pEwxQ6VTeH7gTwpIsj68s8j5CawFSjgMD16jheFX9eLhEoeNKm6uYIa1dn3arSAsb9uO440isDEkVtQ1Hb0I37ufa+BFRthUjQeqAIgd5Q+0wFggwk0ryzd4C2SBRDVfGEpK0pzsP7+3j99DBzi2XeODNC3XY4dWmSoKkjGiqX1brD3GKJwbE5Jmby1Cyb9qY47350J21NcQLXaZnEjCADsWbm6iVOLI5ScSyCqs75/BQz1SJNgSj90asVMmWnzly9SM21+fzIkRXvpis9pqp5CnaNimthSxe1cf+GS/NMVwvk7Aplp47turh4jFcWG86Tqy+tCCFImWHCmrlmIOBvCAFV5x1tuzi6MMJUrcDLs5cYKc8zEG2iP9pEeyhJyrz3fdhsvsT5iVleuTDC2Hwey/HwPK+xWOdnNXRNJRUJEjT0t2YwMNCVZT5XxvMk5apvPKTfxGxlQxBRFP0QUtZAxFBUE6FkYR1lRD+MaOlKc/9Tu9h6oAsQfOPPXubI987T2pWhc6AZKSW5uSI929vo29mObTnMT+X46h+/eNecDH2VQYMtmTRbMm/NKo6EEcXybObreeQ1YrMB1WBbrOum+94cAiEUYkYHZWeampuj6i40NOOXKgdq6GqYiN6G69URQqPizlJ1FhqzdUHRHseVFqYaI6JdTS3X3MVGhYL/yof1FhSxvtc0pDX5QYUrcb06FWcWU43fNEz2MwMtVKo2Ujqoip+9k4DjuOiaiqoqlKsWo9M5krEQ5Wodt0HeVVWBpqlYDUtcXVNvWQsdT4UJNzI2QsDugz1rbutXMRQAQUCNoyrmzQMjFIJqI3OwxuqhIy1sWUUXQQJqHMerE9azGEqIBesK9UZFhaGE8as4wmiKX8NvqlFsr0rBnsRQImjC8I/RWGpY0RYlgiq2E47+Oo59EZAoShpFbUKIlUsiiUCAt/f03/S6XQ9NCIKa3pAeTnC4pZ2Api33s4oiCAcNPvT2vVTrFpdGZilW6nz/yCCO4y3PMpd0H3RVRddVTEOjNRtj79Z2PvKOA0RC5qp99854G8OleV6cvcRUNU9HKMn5wiRFp8a2WAvJawZQR7pUXYuFeoU/vvzyqs9kygwRUDRcz0MqPhHxhZmLXCxMM1MrUHJqVB0bV3pM1/LLHIK1ENHMNfQfrmIpWHlv+176IlmOLAzz2vxlnp08w2tzQ9yf6ePBbD+H0z0ENtHlcj24ODnPt45f4hvHzpOOhggaOhMLRUCSiYaxXJea5bCvt3XNjMFGcFeCgY+88wCVqsWbZ0b50nOn2Lu1jZ72DNoduPotC5VIB+lO4ZQ/gRb6CRCRhvjQUrz0VwdCwHt+5hH2PbKVVFMM6UlaOtP88499gtx8aZkIZVsOhqmjqAqhSIDW7gyzkzm8u1TXv3Re2fjPapIxovEDlhXN7/HyzVhlhnPFEc7mr1BxaytamDHj/MrAh2772AKVhNHPdPU4VWeOqjOHxKPizFF15v00sJIkpGWR0kNXglSdearuPJZXxFRiFKxhXK9GxGhZrk4AcLwKrrQa5wFDidxSPGcJphJBaaSqJR6WV+JWfBqBgipCvHziCtW6QyISJBYJ4rgek7N52psTpOMhanWHKxOL7N3SxkvHr7CQr6AIQSIWpLMlwdnL02STEbpaknRtYibQlRaurCEQ6Ou5FkKgKyFu1hdkzH7S5lWS4LVLIO2h/cDVZ/r65ZGmwDayga0rvvPLGMUNJYb+eyIQIoRu7Lvms6t/Lr0XXbEE/+ThJ2/+2zYIP0CFfdva6W5PceriJK+fGvZdBKdzVGvW8uwyZOq0NcXp78yye6CFHX0t9HVkbnr8PckORisLvDBzgW9Nnuadbbu5WJjBVDUezq4krxqKRtIIkzYi/N2dz6w5SHcEkwRUHctz+KNLL/L50SPcn+nl6dad7E60kzYjmKrOPz76WSar+U26UqAqKruTHexOdvDTfQ9xvjDF/x56iW9OnOZsftInMSY7bn2gTcSbQ2OcHZvmQG87v/XjT9MUD/MPP/lVTF3jH37wCSYWCvyrz36Hp3b3s/8OswJwl4KBnrY0f+enn+D4+XH+8rmTfOy3P0V3W4rWbJxkLEgkZDZmD+tfifzwM/vIJCNIbx639nXU4I8AAs8daYgO7cIv0/mrExCEogECQeMqYVD4fgW1Sh277iA9ieOsrKdWVN8Bz3VuXWd9u7A9j/F8gecvX+Gl4RFGc3kK9Tq266EAEdOgMx5nV0szh9pbua+zg7Bxb8tyvjj+featAo9k9pAwVpKfguqdMYOFUEib21DQqThzlJ0ZpHSYq53GwyGudxDVfYU5gULSGMDxfH5BzhqiObCfRWsIR9YIamkSxo1StrcDKSXXCnis7+0SIAXTC0Xe+dBORqYWOXfFN3LZu7WN1kwMTVOZmi0sP2u246JpCooQFMs1nnv9Ih3NSWbmixiauqnBAGycHuzTze6d0uXa57JAWiCurWjxGtVPNRB311msUK/z5YvnGUil2ZnJMrCliXRHlF9NPt54Vq5uu+Q7IIRAWaeHiKlo9EWy3J/p42vjJ1CEoOTW2RFv5YHMyoqM9lCS7fFWnp+6QF8kS9IIrarPoDSe2Zpr8/3ZC/RHsnyw8wAPZ7f47QIuFqepuNYdmUvdDAqC7bFW/p897+dfnPgSl4oznM1N3PNgYKFYIWwafOShPbQ0CJya4l8h15Nsb8/yow/t4aXzw5TrNj/12P47Ot9dCQY86XH28jSvnBjm4vAslu0yMrnI1FwBTVVRlY0b1rz9oW1krlGlkt4ciCDSm25wBv5qLRMIIejob+K1b59GSsn2Q70gJV//1MuUClVe+dZJ8gsljAaxslKo4lgOAijnq4Siq6ui3SnmKxW+dOYczw1eZqJYJBMK0ZVIoKsKqlCQSCzXI1+t8uylQZ6/fIWtmQy//thDtESj90znuzWYIaQFSBpROkJZ1GueD/UOmcEChbjRgyI06l6hodZnk7eGkdIlorcS1psRDe2BhNlLzhqi7hYoWKNkzJ1UnTlcaWOqCSL61dIzTQldM7sHyysiWV+Gx5ZlPJzlNvqld+soJQOqdZsTFyZwPY+gqYOEoKmjKArFco2JuTyj04uMTC3ierIxYPiBUVs2juO4ZJLhG5Tj7hSK0FGFgSNr2LJy6wFAgivrt7xmtwqUbvb99d+t9Z7Z9Zeo179DOPJ3EEqmkdms4ziXqJU/QSTxu9wt3bdCvcbl/CLnF+bYmc1Ssi0my0UWq1V2ZLOcnZtlrlIhZOgYqspMuUzZsogHAqQCQVRFYaFapT+ZpDkcWdXmWAhBeyjJI9kBvjlxyhciCsQYiDVhXpdS3xJt5vHmbbw6O8S/Pf11HmvaQmswjqFolJw6V0pzBFWDA6ku+qJZFKHQHkxyqTjD6dwEUT1IWDOYrOT52sRJJip5Esadm9N50uN0bpxXZodImiFagnFiehDH8zi2OMJgaZaoHqA/dmt9hs2GIgSqovgiQ41nLGBouK6kUKmRigTpyiT56pvnmM2XbnG0W+OuPInHz0/w4tEhjp8fJ1/yBV7qloNtu6iqgqpufEBYSnkLJYpiHES640hZQyhpFLWTv0oZAfC9CR54ejdHnj/Hc194kzNvXAbg9OtD7HlgAKEIhk6PUa/abDvQzaWTo8QzUXRT49zRYZo7UuuqKd4oXh4e5ejEJHXX5cm+XvpTSeKBAIaq+mvNEizXJV+rMVEsMji3wMW5Ob59cYiP7tuDcRe1ta9FRAsyWZ3jlbnTtARTjQ7cvx4xPcS7Wh+47WMLlMb6fLAhEVyh5uYo2uNIPEJaE0H1aorVJwEGsLwiRXuMmpvHkXVUYWAqMUz16gwxoCauWRrwfQQ8eaOS4mqoOLM4Xt1vodAJqql1ZgfA0FTi0SCRoOFLyiqKzzYXAl1TySYjHNjeQShgsKUzs+xfj/BJpXXLIR4JEI9uLmNVFyEMNYrjVKk3yv+k9Na0/pV41N0crDOAupvwvFlc+1xjGbMBoQEutn2c9eQ8llL9uVqVuuvier7tu6nqJAKBNZ1ANUUlpOuEdJ3mcISQruN6HhXHRgJTpRK6qhDSdBAwXS5hOS5110FtDEKTpSI9icRNWxnTA/RHm0iZYUZK8xzo6qY3kr0h6I/pAbbHWvhQ10FOLI7x0uwldOEPchK/NHJPomM52NMUhadadlCwq5zJTzBVyxNQdBCC1mCcvFVZrji4U0gJV8rznM1PYqgauqKiIsjZVTpDKXbEW+kO33tuVND0K/AWyldVTqNBk4VSleHZRXqakjiuS91xcTbB9fOuBAMvvHmJ108OMzGbR1NVWtIRQkEdQ9cw9FsTjFZD0GxEmiKMou/DcS6CrKLoe1C0G4k3itAJaCmS5vqY4wJBUMssz8rWA096y2IsCqsTJA0lQszoRrK+tH3U6MJU4yiK4MF37OHy2QmGz09y8cQIUkI8HeFtHzpEbq7E0NlxZscXeeIDB3n1W6cQ6jmEEFw8McL2A92oy0Ih/sCw/muhENSyqxLXnhu8TK5W42B7K7943yFSwbUzEMV6nZeGR/jz46f4yrnzfHjPToxVMjgRvYO6W8CR61cG1EQATQmuOdg5nkPeLpOzS8xfZ0CTNRN3FgwIn0hmqgl/hu/VqbizfjAg/WAgdA2rPGZ0ookgFWeWgjVOxZlF4hFQE5hqDF25OoCaapyAmkBXwlheiYI9guPVbj4ASomH3ShXrKIKHVONNcodbx0MCOFruw90ZohHAjfcz1DAoLs1RXer75Fx/ex/SV78qgHR5sFUYwTUFBVnBssrUndyBNUU2hoVBVI6FO2JdSn6+QOthUAFlJteX5B4soxA9VVQV/QTEikdPFlrlHKGGoOcB7gIce310hHC9AnQN4HlulQdm1ytRr5eY7xYoGg3luIERIwAHdEoUcMkapjEDJPgNSqfIV2nKRSmORymKRzBa5iHlW2LqmNTtOpsz2RojUSpOg6elJia1vA1kCjSH6Bt1+VmQYup6qTNMJ3hFKcWx+mPZOkKp5avW92zMRQNTVFpCcb5iZ77MRSNU7lxJms5bM8lZgTpDKXoiWSI6/67oAmVt7VsZ6ZW4EJhiuGSX1HSFU7zgY59NAVi5K0KacO3THakiypUFCHQFIXWYJy2UAJTudUQJ8gEorQG41wqTjNcniNnlWkLJmkNJbkv3cPeZAfZQOwWx9l8pKNBxhd0xuavciOysQgLpSpHLk/Q05TiwsQsSHlDtcft4K4EA8fOjjG7WMLQNVozMX7pxx/hwI5OIiHzjkiEAEgL6U0i3TGQNlLE8JQUqrpEoPA7o5DWRG/03fRG331n57sJLK+O5fkpyZh24zqpEILm4CGag4du6/hdW1r41X/5EcaHZpkanceuO+x/dCvJbBT1uoBqfjrPq986zfxUjubOFO/92UcxGwYbYb2FXv099Mbec1vtuBbn5+Y42NbK2/v7SYdunqaLmia7m5tZ3FrlX37nedw1OuiDmb97x+26HiOVGZoDKf6vHT+FqawM8DZruIrpHQ3nvBple5qcNQxCIaK1rDALShh96EoIyyuRt0coOZMgJRGtjaC60hFNERpRvZ2kOcB09ShztfOUnSkCWgKd8KqyzxKXkj1Brj6E7ZUJa82+SNE6qxCEEDywp2dN1vh69r9bqz9hvZmY0cFC/RwgmamdxFTjRPS2Va+FKy2mayfWHXzbzjiamkQRYeBmvBaHWv0NVCWJprWiqVe9H6SUOO4MlnMRRUQIGPsBrRFk6EhZRohwI6hwkLIOrD3pcD2PqXKRI1OTfOnSWV4aH6HmODcMyaaqsiOd5aH2Lp7pHWBvtsUX01km7orlGfpspcxoocBwLs/l3GIjoNUaa9D+AHqtQqiqKEQMkzNzs7RGomvOwpcyFwJBbyRNbyRL2owsB2OXy1N0h5oIqmZDCyHAz/Y/ggAKTgXLs8kYfmZsKSuwxIyPG0F+aeuTrAxG/N+2I361v/eFg/IkjSimopMwgvzytkfZHutEIPCWyw/FqsdqCcb4lW1PUXZqjFdnOZ0f5sMdj95z0vP16G9JM1coc3l6fvmz7e1ZLs8s8JkXT3BqeJKLk/Nsa8vSlrrzYOWuBAOO55sR9XVk+Oi7D/LE4S2b1llIbw63+mWM6P8DwsCtfQ3PPo1q7Ode8wYK9iIL1gy2tNgdu++unCMcC7J1Xxdb9vkmHGs9oD//jz7Aj338aTzXIxwLLnMJNhuaELieh+WuL3Xteh5120FXlE0bhNeDnnAL54oj/OcLn6E9kFnBE4jrYd7b9vAdnyNp9rNQv0jNzTNXO43l5UmaWzHVRGMg8GEqMcJaE4viElVnjrnaaSSSuNFFULuRsd0U3IftVZiuHgM8js1/4qoc8XWvrMSj6i7yysy/W5bgTRh9DMTet+7fIYBEdO0syw8SaXMHFXuOK8VnATib+7SfPdGS6NepELqyTsEe41zuL3DXJVHuUa49S9A4iK71oqk3SwWrIFTq9lkcb55I8GowULdPULcv4noFwoG3sRRuKmo7qtZDKf+bBMMfQwgd2zqCbb2GYT7MWmHpn589yecvnOHozKRfZrdGi+quy/GZKU7MTvNnZ07w0Z17+buHH142EIubJj+zez+KELRFonx4+06k9PkeOzNNy2cPajof3bl7RXuWbMjVW7y3JafOUGmWN+Yv83/ufDctQX9QcqXHolVksDROk5lgpp7jUnECy7PpCjehC42zhREWrSJPNu/H8VwGi+PE9DAxI4TjuUzVFmkLpik7NUpOFVd6dIQyxLQQV8rTeEiaA0lUofCViVd5KLOTzmAWT3pcKo2zLdrBSHWGyeoCFbdORyjDRGWemmcR18OkjChB1eBE7jJd4SY6Q03E9PBb5j24f6CLQ30dKybQ9w10EjR0NFXh9YtjHO7v4KOP7mNnx+qSzBvBXRkx7tvVTa1m+4OG7Wzu2rXQQSTwnCE/3eYVQNbwnCm/qkBJrsu6FGCwdAZHOoTUMAV7kYAaIqrFWbTnmKlNIPHYFt1PVEswWRtmonoFgG3R/aSMJmhUsHvSY7Y+waI1R3Ogg4SxeetLvqPbrQlPqqoQTYRAyrtqVLSntYXB+QU+d+oMYcNgd0uzvxovVnYkEriyuMj3hq7wlXPneaCr45Y1v5uJuBGhLZBG4jvOXdvRreUyt+Fz6F2YapSF+iWmqkcASBr9GGpk+Xos/RnR2wlpWfLWCFOVN5F4RPU2gtqNXukhLU1TcB9bYu/jYuFLzNcvcHLhj5iovEZL8CARza9UqLnzLFpDTFWOMFc7i4dDc/AAneFHSZp9Nxz3VtjMZ8afGXq4sr5CCdD16njSxpPuTfUClhBQ46QCW+kMP85o+XkqziynF/+UmepxWkP3E9VbfWMnZ4qF+gWmK0cb5ZwRnFVso1dpKZX6y1B/EUWJEzQOoKtdlGrfwHHnUJUkocAj6Go3qpLGFXMszS6llNjuMJXaS9jOMIoSx3VnQfN1LDR9F0IYVMufolT4bZAeipJA1bcSCH6U6ycvjufx+Qtn+KNTRxkp5HA8D01R2JpK0x1NEDV9AR1PSopWncv5RUbyOYq2Rd6q85lzp0iYAd4/sJ32aMwXo7rmni6V+y7//Rpc379IWM4qXP9uV12bS8UZqo7FxeI0L8xcoD2Y5PHmLcvaAqpQSBgRbM9FIik7NSzP5uHMLr4/d4r7UttoD2YIqgbNZoIvjL9EQg+Ts0vk7TIhzWQg0kZLMMXxxUF0ReVAfICX587QH2nDVHXydpmR8jSPZffQHkzTFkiTDSQoO9XGeWGsMkdUC9IVauL1hfPU3DqPZHczXy8yVpmjI5QlpocYKc8Q1UIE1Xtb9XQzGLoKcqXNtBCCLW0ZPvb0/fzUYwcwNZVEJEhA/wEsE8xUiyzWqxiqSlQ3GSnlaAvFaApeZYo/cd+Ab585Ps/rp0bobc/Q1ZYkHDBW6OrfbpOFksRzhxqzL1+O0nPOIpQMirL+yoKCvUjZLRFUglTcEorQKGl56m4VR9qYSoDzxeNkzBbKThFXuhiKybniMQ4nHwfA9WwK9gK2V6cl0IWprq2Odreh3ukSzDrw9oE+6o7D0PwC//ONI3QlE8RNs0EgVEBC3XUo1GpMlUpMFUvoisr7d25Hv0X7pJS4rsfElTmiiRChSGB5qeOGbT1JMV8hEDLR9Ks2z0voC7etKTtsKJsjHhLRW9GVkO9M6PimXDGj84YZK0BYayKgplhkiJI9BUhCWjOmcmN5mSJ0wloTvdF3UHZmmKmeIG8NU3cLFO0JTMWffdlehao71xA6qpMN7KEn8hTNoQNoyr17Dv0BYp7p6nHKzhSetHGljedZ1Lwci/VLS1syVX2TuptvKB5qKOgNXf8ukmY/kUZJ5tVroRHVWumLvpOCNULBHqVgj2J7ZUrOFKYaQ0oPyytSdeaxvDJ9sXcxUz1G3hpZV/tVJYYi4gihUq2/jhEeQFNb8aSFJyvUrTPowe4GT+Dqc+YPtlFUJYnUHDS1FV1rW95GiAiqNoAZfC+eOwLSQyhxVLUdVVspfGW7LvPVCl+4eIbhQo6AqrEjneWBtk62pjKkAkFMTfMrdqSk5josVCuMFvKcnZ/l6PQEk+USXxu6wPZ0lpjpcwluF2sFhh6SolPjS2PHKFhVyk4dKeHHug+TNWPojQDPQ1JyqszXC8zV81ScGqpQiOkh6q6NrmgoQmA3xIhMRceTkqBmognFNwfSQwRVE7VxzIgWpObZTNUWoUE69KRHQDXwpKTmWVScGkW7ylw9z7yVx5UuZbeOsIrLZYsRLUjOKlNyqkzVFvCkR92zKToVqm6dBavAvFUgaUTv6QTmeiiNieC1EAKChk7Q2HwBpA0HA3O1MkPFeUKaQUQ3GC3lMFSVuBkk2Cgn6WzxPa4X8mVOXZwkHT/HzoFWkrEQpqGhKArKEhV5nejrSBMwdYQwEGoWzznT8IcPgRIFWW/U766/9tSRNhWniK3UcaWDI20st4qmGIS1KDEtyaXSKRxpYSgmYS1KRItzqXQKr8FWtqVNzl4gby+wM3aIgHrn5S5vZdzX0c5itYrlupyfnePE1DRRw0BXVVTF76gs16VkWWiKQnMkwgNdHTzWsz7XQs/1mBiZJ5av0tyZWjMY8DzJxPA8zR0pwrEAhrHy2B2hu18KFNKyy8Y+dmMWGte7VrjpLSGsNTeyABJbllGEQVBNYaxhDGSoEZqD+6g6s4DvZWC5RWaqxxopcIEiVFRhoIsQMbOLnshTtIcfIqrfuQDJRlF15xkuPcdM9QSetHBkbVk86VrM1k4zWzvdaL+GJkxUEaAj/BC6ErohGACfVNkWvp+F+nnGK69QdeapuTnKlRkkHorQ0JUwATVBJrCTLbH3UncXlgO0W0FXOzH0ATxZpVx7AU+WUdUMOi6OM43tXFlzX1VNo2ntKF4EXe1E1zqXv/MzlDFfeljuQSgJ/C53yQHymuvnOJybn+PViTEUIdiRyfJUdz8/smU72eCNXJElLNaqHJueJKBpfGXwPCdmpjg9O0NHNHZHwcDNIKUkb1Up2lWiepA90Szv69iHoV5VP1wiD4a1IK70MFWDtOkHvhkzji5UgqpJWA1gS4eecAs1t07CiGAoOkW7sswzSBhhXOmhIMiacSRgCh1T0VGEz3mI6/47V/dsHOkS0YPUXJukEcXyHOquRVswTcmtoQmVsBZY1h8JqwFUQ0EXGp70iGgBaq7lZ7feGisGq2IpAzs8u4ipaXfMG9hwMOBKieU5uLbHxcIMB9IdaEJhsV4hGPJv9msnrzA1V0BRBKoq+POvHwWOoqoKAUMjGPArCzaSrv23/+BH6O1IAwZCbUfWvwfSRmhbUbRtqMbBxpbrv3ua0FGF75wlhIKKiouLJnzNdlVoHEg8wuXKeQBUoaELnYOJRzGVIBWKeNKl5lZRhMCRTiNAeQs/QXeIsGHwwV07eaKvl1NT07w6MsZ4vkDJsrBdFyEE2UiY7kSC3c1NbGvK0Je6MRW+RDByHW/5mglFoKq+/O3Q+Uk0XSWVjeK5EqH4JDXP88VSXNfl9JtXUDQFRUmiRAXKkhHWPYKpxglpGQJaEtv163wTRk9DxnYlInobYa1lmQUf1lsw1TiqsnZaUhEafbF3kQ3uYbLyJtPVYyzUL1Bx5hFCYChRInoLaXM7fdF3Eje6V6Tel66xJy0QyjVGOgJVGH5FhjDXTTS8GTxpY3lFbK/UOIOyJuN/5X4OnixheaU11/mFUDBEiIOZv022vIuJyuss1M5TcqZwvBpBLUXa3E5r6DDtoYcIqEliRhfz9fOAcssKIaFEESKMkDbgUK49j+vNoypJhNAbVQE++Q9ckC5SurdcjpTSQ8oyjn0a172Cab4doSSR0gLqCJFY3nahVuFrQxdwPI/OaJwPb93FB7Zsx7yFUU4yEOSxzh56E0mOTU9xJb/I61NjdMZi9CdSePiue9cv5W0USwOPKhRagnH+7aGP3HR7TVFpCaT4ye633fDdE017AdgR62JHrAshBC2BFJ70/LZe187t18iHP9m0UslRCN8+/V2t9y3/G6AvcjUgvl7tESCqh+iLtK6oOln6fnei96a/7a0Ey3H5/W+9Rlc6zsff9dAdHWvDvUA6EKLsxJmoFOiNpDmxMMGhTCd95lUi1O9/5iWm5go37Ou6HuWqRbm6cXcuy/EJaz6B8EsYsX8C6Li1b+A55xsEwo2ldKJanLpbxfLqxPQU0/VxdkT3MFuf5Er5LHE9Q3uwh62RPVwun+dy+RwJPU1bsIcM/sMW0qJsie4iqWd5feE5tsf20x784XmYbhfJQIDHerp5tGdt9bxbdT2eK/nz//4ctUqdTEuC/h1t7DrcQ7opxvy0//wUFsucfG2I/l3tZFsSnHh1iHKxygNP7QDgpW+cxKo7ZNsSHH5sG539d06k2QgOpX+FQ+lfueaT1X91VG/nQPpjHEh/7JbbXo+I1saWWBtbbkoKXKPEUpZ4cfL/Jmb0cTD7fwJ+luJdHb+34XaseWYhyJi7eKbtP97RcdbTjo7Qo3SEHrnp/kII9iZ/nr3Jn7vNVug47iSuO4uiBAET271MsfJlLGcYTc2gKFFCgZuTUF3nPPXaN6lXPoXEQdO2owK2fZxq+X8QT/0xS11woV7ntUnfnfF9/dvYmWlat22uKgTpYIif3LmXf//aC5ybn2Uov0DRqXAyP0RPqJWsGce4A239mdoiHpLW4N2rt79d4t5SQPDXEZWaxfRikVjwzrNAGw4GpJTYnkvJrlNxLN7VsYNMIIx+TQr4oX295IrrrxlfD6Kh62YZ3iyIEFKWfMnP23iQusJbaA/1Nhi2KlvkbgzFpCnQzpboXnwfaxNVaET1JDtiB/3PVBNDGKSMZuK6T1JThUrGbMG4h2u1PyjcjIi0XtRrNudPjJJujrHrUA/JtC+YdD0c22VhtkhH1fbTk4tlCgtX1ba27++muSNJfqHM8187zk//H8/cZotuD2vVpt+43VXRo42fY2m/29tfXpOWvtNjrYU7+X3rPEPjPFf/fuftUYiHP4oQAQQ6qhIlE/8NFBEiZD4IQmnwkgSKEiYR+VuADaiNUkQfQeMw4DUEha7CcS7i2MeIJf+A/OIv+e1SkihKM64zzLVLBbbnMVf1q0E6Y3Hi5vrLPEWjtn4gmUIRCvl6jYpto6KgoHA8d4kH07tI3UEwcCp/GVu6mxoMXP/7NpK52Mi+t/vdDwPKdZ+ovxnYcDBQciwcz+O+rJ+6yQYiN6Sy3vXYTix7faVn60WioWwmlDiK8SCu9SYgQRjrUiAsOXkECmEt2iA8lTHVAIZyY0SlY6AIlenaGB3BPv9lQyOgBBuWpX4qSxMK2jWXUL9JyncJUkrKTpWC4xNYyk6VklOh7FSpexaWZ2O5DrZn4+LhSd9dzE/3+Wk6pfF/TajoikZAMTAUnYBqEFBNQmqAiBYiooeIaiFMxdg0tS7wWc9ly2amVKJq2zRFIiQCgVUZrWXLYixf4GvnLvDxh+7HbJQ9ea5HMVchEg0ST4aJxIPL12fl9QLXlXiuh+dJHNtXslxCOBogkY5Qr9oUc5sbgC61x5EuBbtMyalQcqqUGveu5tapuY175vluao50l+uaBeKG+2UoOqaqE1BNAopBUA0Q1gJEtRAxPUJECzXKuX64O6kfBvgEwMQ1n6hoalPjO9/s6NrBQlNvXO4CUJQ1eEKyjvSqqNoAV4MZDSE0blRIlDiNTt1Q1Q0T1/xJiooAnOWyQLWhxLmAh4ftORTsCmcLw0gk/ZF2POlRdCqU7Colt0p3qJkmM0lQM6k6Nd5YvEBIDTBenSOsBSnYFU7kBpFIukJNGIpOwS5TaPRjbYEMzYHkDX4gtwvbc8jbRXJ2iaJdJm+XKdhFqq6F7Tk40sVp/KkKBVWoaEJFUzSCqkFUC/t9oRYirodpDqbRhLZpsugKCjfqF2wc3z5xiWOXJza8X7FaZ3yhwPYfRGmh5TrUPYeQpjc6uhsv6t6tN5KANg0ijGrsw6k9B9RRtH48pYuqUySsRqm4RVzp+EV/UmIoJrpikLMWGg9ImLpXY6I2TMZoIaCGGmzSCqYSBASutCm7RSZrI7QHe5c7ZiHEhjvpmlun7NSouDWqjT/n63kW7YL/Etll8o0Hveb5g0vdtZaJMK70GkqH/nqaTxrzfQA0RSOgNAYW1SSkmoTUIBE9SEyPkNAjJPQoYc1n5QZUg7AWbPzdXCbgbAR1x+HKYo7B+QUGFxYo1y064jG6kgk643HaY1EM7epjVbMdRhZzfOr4SX7x/kPLwYCi+A6LMxOLTI0tUqtamAED3dSYny6QXyhTyFUIRQKEoyYTw/MgBOVibUWpTTFXYWpsgeJixS+tvAP4pCeLslPzB3qvTsWpU3QqLFoF8naJgl0iZxcp2GUqTpWqW6fm1v375bnY0sFtBAMKAlVR/RmaUNAVDVMxCGp+wBbSAoS1IDEtTFyPkDJiJI0YIS1IUPUDhagWJqQFGiYtt9uBCWyvQsEaoebOIaWLqSYJahlMNdEIeMqU7HEcr9JQO9SI6p0YahzbK2J5RVyvjuXlMZUEcNVRMGb0YShRhFDwpIPjVSnaI7gNlT1diRBQUwS1e6/vfrtYb8bn5tAQwkA2Kp5A4nk5PHcGIVaSvfzBXKPqOFQdB8fbmNGYlJKybSORvoBQo28OXFMqV7ArjFVnma3n0IXGldIktnSouHUs18HFxXadZab/RHWeieo8aSNG3bMRjmC0MrO8/wgzAJSdGnXPxpUuddcncd9uMOBKl5prUbBLFBoKorO1BeasPItWgUWrwIKVXy5VdKSD5dnYntsIAtTloDusBUjoUWJ6hLgeJWXE6Aq3ENcjhLUQES1ITA+jC+22MgRLwf5m4MjQOH/52hni4cC6iNZLcFyXfOXmapbrxYaDAVPVsFyXZ8cvYKgqP9K1B924l2I/CogwWvCDyzewYucYq15ie/QAY9Uhqm4ZVzogIWlkSBnNLFqzBNQQGaOFBWuaE7lX2Bk7RExPUnXLTNVGaQl0AApVt0TRyVGyb+Q93Aq+CpqHK10c6TJRneNSaYQr5QmGy5MMl6coOLdnKuFrGjg4S0GoC+tpYUAxSBpR2oJN9Eba6Qq10BbM0mymCagGmlAbs1dxy4Bnqljk0ydO8o0LlyjU6gjhO2j1JBM81tvDR/ftpiMeX9ZM96Sk7rjMVyorZv1GQGPrng5e/tYpZidyNLUlae5IkmmJc+boMLOTOXRDwzB1OnqzfO3PX6O5I4ldd2hqTwIC3dAYPDuBdcwmmgix8+D63f+WyXV4ywGXKz3GKzMMlccZr84wXp1lpDzJVG3+thzSXOTKFN5S/15fdXMAAqpB1kzSFszSFWphd3yAvkg7AdVEE6ovuboKyepmEEKh7IwzXPwqk5WXsb0i2cA+OiNP0xLySUe5+gUG85+jaI/iSgtdibAl/uO0hB5isX6eudoJKs40i7VzJAPbUDCoufOUnTH2pn+NpuBhFAS2VyZvXeJ87k+pONMIKYkaPbSEHqQ7+p6GXO/62r7ESHe8zc0ybioEBBRz1cybUKIoShbXPgu4SFnEsY9hWS+h6Vu5NpupqwqZYIh8vcZYMU++XqdjnWRkf+nWYyi3gCcl6UCQyCqVBGPVWS4Vx3kks4uMmeBPhp/F8myaA0m6I83siHbzySvfIBOIowmVU/nLHEptpTWQojxV5XJ5kuO5S7yt6QAZM85XJl5hsjpPcyBJb6SFbbFuvjT+IqPVWbbFulZp6drtX8qqlZwy49VZji9e4FT+EoOlMeqeva73z5YO9jViaPNWnhFWVpSoKHRH2ugNt7El0sXexBaaAin05T5w/QOxgDviYaxou+sSMDQO97cTu35J/CYoVGq8eO7KprRhw8HAlliWLbEsHpJzuZlbsl03HTKHa72Bal5dG7Y9i0VrDoCcNUfZKZIxW9ke38+r89+mI9SPKlSk9FCFRovZSUugk+ZAO3l7gYulU2SMFiZqwzieQ3doC92hLby5+PyGm5e3ywxXJnhj4Qwvzh1ntrbA2hpi9wY1z2KyNs9kbZ43F88CflQbUHW6Qq0cTG5nR6yPrnALmTVq85fwR28e49jEJNsyGX7m4D6aIxHOTM/wzYuX+PLZczx7cZB/9953squ5CV1dO0gUQhCOBviV3/4grushhFiuBPibf+8dN2y/ZU8H6nViSu//GX8g8zz/+m60ksCWDucLw1wqjXCxOMqZwhCz9cUNHWOzUXMtRivTjFameXX+FJ8ZfZaoHqY/0sHuWD/3pXbRH9mYlaqULp5nE1QzPNPxv6i587w89Y+ZqrxCVO/BUCO8Mv1b7Ez+IrvTHyeopinZY3x34td4sPl3sL1yg/Vv8/aOT/DN0Z+hLfw43cF3Ynk5xkrfJhPYiyJUZmtHuZj7NPvS/wcJcxuurDFc/CpD+S8QM3pJmTtZL7eg7FT5H5e/wDenXrmNK3lvYCo6/3DHz/Fges8N3+nG/QgRppj/LTx3kvz830RRkmj6ASKJ3+ZaPZSIYXKwpZXB3AJfG7rI1lSGbanMTd+hJXhSkqvX+PS5U9iux/Z0lp544obtHM+foAQ0E1UofsZRSjShktAjGIrms0ukf0zLcwgqBgHFQFM0POlnhgOq4e+Ph4uHIhQSDSlg2DiZz0NyqjDIc9Ovc7owyHR1/q71mS4eQ6UxhkpjfHv6NTShcTi5g7c138e2WA/ZW/R/10IgCKmBTVgk8NGVTfDxdz5Ec2L9WZXJxQLDs5vTZ21oJB8qzjFazjFfK2N7Hufz0/zqjsduGRBIKalZDqVKnVrdxnGWSnXWj7amBKahIb2yLz9sPs2y7KeioismJ/OvUnIKKCho15B5FqwZZurjKEIlaWbIGK2UnSIL1ixCCNJGE7as0x7oJWfPs2jNsWjN4njrkTT1pTcHi2N8a/oVLhSHmavnqHsWddf+gQcCa0EiqbkWg6UxRivTfHXyRUJqgLQZZ3d8gMOpXfSEWwmoxopMwbnZObZk0rx9Sz8PdXWhKoKeZIJHerp5c3yCPz9+kr/3pa/wT97+Ng60rV3vfu2gviSWtNYsSEq5Qqzq+u2Wsmq3mkW50mOuvshgaZwTuQscz12g2NBHdzwHe4Op2XuFkl3hdG6QC4Vhvjb5Iv2RTp5sOsTOeB9ZM7muY8SMXjoifpmXqSYJ6c0IoVFxp6m402hKiFRgJyGtGYFKWG8jE9xHrn4JIRQ0JUBU70AVBoYaJ6J3EDW6KNpge2V/Fu/kKVsTLNRO8/rMv0ARuu9IJ6uowqRkT5A0d2xsseOt+fqsC0JE0I2DxNN/jOeO+kRlJYaiZBqaA1eRDoR4X/92PnvuNOPFPJ88dZTxUoGf2rGP9E3MwHK1Gq9NjvHpcycZzucQAp7o7GFLOsmV8hQvzJ5grDJHwgjTHEjSH2nj0yPPEVRN2oMZrDX6uIQRZk+ij8+PfZ/WYIpFq0h7KM2eeB+fGfkuAdUgY8bpD7dRX2c/eUPbrSKDpTG+NfUKJ/OXqLl1bOnc0z7TkQ5Hcuc4UxiiPdTEnvgATzXfT0ew6Zb9iRCCsNa4N3dYzZCJhqnb7nJWYL0ZoZBprGovfTvYUDCQMf2IJWWECKg6cSNA4CaBQK5Q4cLwLEfPjjE1l6dmObgNIthG8et/8220NyfwAwAPp/InCCUGKJgiQW94OyDJmq0IFIJqCFVobI3uJaol2BbdjxCCiOZrIeyMHyKqxRFCIa6lcKRDWIuSMDJ40kVKj9Zg901T5iWnykR1hiML5zhdGGKsMk3eLt72y3GvIcEn4LguuFC0K+TsIjmryPniMM2BFJ3BZrrDbfRHOohqIcqWRUDTSAWvSmDqqoqpadzX2U5A0/jjI0f54yPHKdTq9KZuPVit56W73e9d6ZG3i5zJDzFYGmOqNs9cPcd8Pce8lceRb80A4FpI5HIKtOLWsAoOBbvEyfwltkV72BMfoOUWLG9VMTHUeGMZSEVpvPpSXnWlU4S+rDkg0FCFgSdtn/SGgiJM/PmQr1mgCANQGnX4vk+CxEVTQuxI/cKKd0cVBjGj768VMdLnHRgoSjNCRAGJ73hocH12JKRrbE9neayzh9cmxxjMLVC5aHNpcZ6d6SZawlFCuo7WEPYq2zaT5QJX8jmGFhe4uDiPKz3e0TvA7mwzbeE4Lg6PZvZQces0BZKE1QAeXkPVTxDXI8tk13hjjf/Jpv2kjRimYtAZyvK2pv0ENRO3ofSXMqIoQkEVgqgWQhEKtueSNCIoCB5I77ipwqe/JODy5uJZTuUHuVgcZbQyRc4u3qW7cGssEYDt8gQFu8RYZYb707t4IL2HsBZckxewlBnYjCqat+3pp1q3MTeo0Bs0dB7a2k3TBrIJa2FDwUDddbBcF7dhdhHVzTVZmRMzec4OTfHK8Su8cXqEmYXibQUBS/jYR/yaXiGCKFo/0p1CSt96VFdipM2rbMprO5zmgJ9SDWlXL5ZE0hbsXt5WqrHlKoGIFlten1qt41oS35isznKlPMG54hXeWDjNSGV9amdvZXh4VN06Y9UZxqozhNQA7cEsveF2pqpzbIv1oKv++lapvlIrQlUUmsJhHu7pYrZc5jMnTvPS8CjTpTLGOlKdmwmJpGRXyNkl5uqLjFSmOJm7xKXSKDmriC3fwmvQ60DRKXOuWGaqNs9UdY6iXeZQagftwSZUsfqavD+YX/+6+wO7roTxpEvdzeN4VVRh4HgVas4CmcB+fNXEEis7veu5JRJV6GhKCEONETf6iOndCKHiSgvPs9EaBN2/LvDceTxvDlXrQIgQrnMBz5sB6aHqW1CU1uV7pSkq6WCI9w1sY7FWYyjnD/BX8otcmJ+jLRrzg4GGHHHZsRkvFnxrY6uOpihsSaZ5b/82umMJooY/w7yeyCeRJPXoiqqoa7E1enUJKipC7E40CNSi0Vci2RnrXnP/nnDLmtfDlR5Vt8aZ/BDPzx7lbOEy07X5Nbe/16i6dcars0zVFqi4NVShsjXaRdZMYq7iWaAIQbCxTHCn2NJ6o2HZeqCrKg9s7dwUeeINBQODxTmGCvPk7RoRzSBv19iZaCGg6iseCst2ePHoIN966TwnL/rlEooi0DW/o7od5uZy0KFEUYwH4Fo/cBHa0Izj+m2vJ82tdSzfN96jYJf57sybvDJ/givlibfsUsCdouLWuFga5WJplO/MvM6PtD9JcyxEvl7n/Owcj/Z2LxMFwb+OIV3nJ/btoe64fPHsOc7MzNCdTNz1ti6tU1qeTc2zuFQc5VR+kCOLZ7lUGr3r5/9BIGcXOZo7z9nCZebtPB9oe5ykEUNn/exoRRiE9Q40ESBXv4AmAgTUJGVnioozSUzvpu7ll5UFbwZdjRDSmgmqWcZK36Y39gEUdCyviOOViRv9jWzCXw84zjms+vMEwz+DomjUql/Arr8MuATDP40Z/HGuDY5UIfjwtt3MV6s8e2WQc/Oz1F2HcwtznFuYW/UcmqIQ0nUywTAf2bGbd/Vtuemy7bUD+62wGpl4I/tfC1e6lJ0ag6Ux/nDoL5mqzb9lg3JXuhzLnWeiOsu72x7m/tRu2oLZGzIeAkFY25zMwO1gyTp8T/fmyI9vKBh4sKmHgKoxVyvzZOsWvjhyalX25ZEzo3z39UvLgQBASzpGW1OcdCJMNLxxtaRYpFGH7s3hVD6HdC/jC4DoKPoulPDPczdvylK9+Wx9kd89/78ZLI1ivZVZzncBrcEsH9iZ4jPHzvLdocs82d/L1syN6WkhBD93+AAd8RhfOnuOr5y7cNfbJvEj++/PHuWrky8wXp2l6t6Etv9XCDXP4gtjz3GhMMxPdr+L3fF+9HW+2gKBroS5v/m3OLv4RwzmP4srq+hKjL3pXyUZ2MF87RTrEvpBIRs8gKmmOLPwCS4XvoQrbXQlQsrcweGmf3SHv/SHC647gm29Rijy93Hs43juKEbgKTR9O+XCv8UM/ig3GB8Bv3zgfp7u6efViVG+fOk8r0+NL2sQXAtTVdmVaeaxjm7e2beFnZkba81Xk+K915BSMlmd4/WFM/zR5S+9ZYOA6zFTX+BPrnyNN+fP8sGOt/Fges+K66gIhagWvmujzkbv3WrSyhvBbcgRR8hbNT41+CZBzWA1Avfzb1xiYiYPQCig87d+7GGeemAbQVNHUW4vMxBsqNMJpRk9/Av4dVoubv1FpFwSm5HcrYAgb5c4Uxjif13+ElO1edwfgrXmzcJSBPxQei+BrEkmGKViO/SlVxdhWcLjvd20xaLsbG7i1dGxDdXPbhRVt8ZQaYw/GPrcsgDQXzecLw7zyctf4tHsAd7X9pgvNiXCPNLyb27Q0T+Q/fuA71EAEDf6OZz9TSRLHAKBpgRR0MkGD5AJXGXLB8//LQJbuwlHs4T0FpqDh5fNmTQRImlu5YHm37nmWAoKKuo6vAr+akFBoCGETr36VXT9MJpxEISKlOWb7tkTT9IRjfHe/u2U7DojhRxFy8JyXRQEcdOkN5EkpBuYqoqxSjagWrc5fnGCQ9s60LS7Z2t+KxzLnee5mTd4ee7ED00gsARHupwrXuG/X/oslmfxUGbfcoZAIIgbDbvyu5AcPj8xi+tJdnU233Lbmu1QqtZZLNfobUqiKhsPCDYcDDie63MHPIdq3cZdpRZ2ZHKRYrlGIhpk10ArT963hUwijKIo3Pnz6IEsI7F8wxBv3ncsxGOj3gTrxXw9z0vzx/n+7DGm/5oFAgBhLcju+ACRBplmayaz7LV+MxiaRk8yQcQ0ub+zY4UY0fWYm1yksFimZ3vbbS0lmYpfn99spv5K8DduB650Ga3O8Mr8SRQEH+p8ChAYqi9w4zguudkiIxcmSTbFUFSlodxYJpmNEYkFKSyUqJTrpJriuE6F/EKJYNgkEDbxXMncxBVmLldp75ANUyQV9Zr1VD+1rGGo0R/MRXgLQeATLK36d7GtY+jRJ1HVTjx3Am7Rh2iKgqYomKpG1DCIGYGGZa8fqOmqQkQ3lrVBVsNiscIffOElvtmSZEdPC1u6svS0ppbVXO82pJQcXTzP1yZf5HzxChV3c8Rx7jUc6bJg5/nc2HPois7OWB8JI4qyVE1wlyag3z01RKVu0Z1NkCv7YmuJUJCgoa8oox6aXuCVCyO8dO4KNduhvznNjz28h+5s4qZ97vXYcDAQ1gxaQjECqsaFwhxIbnDqyxWr1G2HVDzM9r5mWrM3erbfNmQdzx1HyjxIDxAI5e6YZ0jps7iP5c7z+vxpLhSHfyjY55uNiBZkb2LrMqu2NlchN19kVkIyEyWaDLMwk6eUq6CoCq3dGUr5qj+IhAxaQ2HcyRKiGRZmClSKNeo1C6tm09SeQtNVrpybZOTSNFJCqilGMBKguFimsFDGdT3iqTC25RBNhonEgiAEIxcm6RxoRtM1NEUlpkfYl9jKRG0Oa5P0un/YUHPrjJQnUVHYk9hCX6QDtfFuLklAL84WqJTqCEVQr1pMj87TvbWVbFuS/GKJwkKZcqGKpqlIIBAysao202PzhGNBbMvGdf96Xt+NQFFb0PTtONYbqFonqtoGQkfKKqrayno9FlQhiJkbX1pVhCAcNBkcn2c+X+HyxDxdLUm6W5N0NCXJJsK+e+xdcPp0PJcFq8ALs0c5XRgi/wOsFtgMuNLjUmmUF2aOYioGW6JdmIpOWA3etWWCsfk8lybn6MokmC1WEEBzPEJvc4qtbRlCph+EnxqZ4ujQOKNzeZoTEf7/7P13lF3ped6J/r6dT86VcwEFoJBDNzqyu0k2c6ZFSrYp2ZJlL2murbts2TNjy74z9vKMfZfuHc91GEsOSpaoIU2JudkkO2cA3cixUDmnUyeHHe8f51QBBVQBldAkZT9cYFfVOWens/f3vd/7Pu/zvHl9hL6WJD5VoS0Z3fD+Nh0MxHQ/QVXHdB0WqqU1uwmqpo3reAT8Gh3N904lbxqeiedM4jrDCBFEyC1Icje3C3jsyG7qeZ/pygLPT79V5wj8bLQM7iQkJCJqiMORPgDMisXNS+MMXZlE1RR2H+qgoTXG9XOjzIwtoukqju0yM7ZAc2eShrY4iibz1vMXae1pYHxghqmRBQq5MqVcmb4jnSSaIgxfm+byqUFUTaHvUAfJliiDlyYYuDiOrqt097eQWSjQ2puitSuFqqmcfvFKbfv1FkdNUngsdYSX59/Fcu0tqQb+RUDRqTBYnODHs+/wFV8Kv6wjCQnP9SjmSvgCBgvTmXqbr0t2sUB6Lofh12p2H0IwNTxPNBmi92AbqZYY+aUi85NLHH1yD2PXp3dM2/2++Bk2klGUPQgDKpXvYvh/HkluqhOfLVTjCR408Swa8vE3PvsIr7x3k/M3Jnn9/BDuWY/2pihPHO7laF8ryWgAn6FiaCqKvDOlBNdzKTrlWnlg/gzWX6Bx89WF9wipflRJYXeog6Dif6D36ODMIv/u+bdx3drCN6CrnOzr4OcfP0xfa03e+72hSZYKZZ7Y18UH9nfz2996lTM3J0iE/A82GBgrLHEjO89sJU9Y1ddM6foMFVmWUGWZkH/71oqrIAyE3I5XeQ7Z/wuAUucM7DBfwKulh3735p8xWJyg4m7edvkvAuJamO5AC+2BWt3q+rlRzKpN/4kejj65B4Bv/O6LdO9t4cgTe1BVhf/4z79Ja1eKcCKIbTkISTA9uoDreuSWSiiqTP+Jbnr3t/Ef/umf84W/9UE6+5owKxaf+CuPI8mCfKZINl1AliW+VHciHLk+xcTNOWbH0rR0JdF92mojGUlhf7iHTn8zNwvjP7NpyZ1A2anyvanXeSRxiL5QB37FwKxYDFwYJxCuGW6ZVQtFlXniE0e48NZNqmUTI6DXlB7l2nOt6xqyLKOoCr6gwRvfP08hW94xp7R7QtS032UknLuMfX76IclxJPlRVP3RO/6evOtvd2Irlrx3jsOGpnJoVwuHdrVQNW0GJxc4e32CV88O8q+/9gqGqrKnq5ET+9r5wNFe+jpS2yaheZ5H3i5xNTfM79z8r3+hAoFl/Gj2bfJ2iYgWJKI+OAIhgK4pdDfE+MpTx1BkiZcuDTKZzvHVN87x//pSbVxcyBUJGhpP7+/hxK42vvjIAX584SaT6c3J6W86GOgKxekIxmqqe7mFNT23m5Jh5tMFylWL6YXN6/vfC567iFN5Htn3GUDgOmMIYQD9ULcb3Qnk7RLvLF7kcnbwgZBegoqflB6lxZeiyUjSaMQJqwECsg+fbGDINSfCWmperLiOma5VdzusOejlrAIzlUXmKmnmqktkzBzFHZwEd4XaORHvX/m9UjRRVBlf8FaQZ1UsZFlCN2rEGsdyQBJ4rodju0iyi2O5KyQb3acRSQTRdKUW8S6zZu/Ytz9oYMVuXfuOXU0MX53m2tlRZscXefbLj6D77m5V+2jzoxQnygwWJnboKtQgCwm/7COlx2j1pWjyJYioIUJKgJBac0XTJQ1VUmr9/ghsz6HqmhSsMnm7RNbKM11ZYKQ4xXBxirxVfGBkRw+PPx79Pr/a+wX6Qh0Eo34+9UtP1sp69Z5xgUCSBX1HlwW26l9SvW1Jqq8WNSPCM58/AXWdDXmT4ihbQUA2+NWez/OLXZ+i4lTJWUWKdrlm+uVUKDlVClaJgl2i5FQo1Y3Aik6JvFVzBK24VarOxrTtf5qwVClzM5PmjYlRzs/NMJ7LUrBMbNdBQiKgaXRGouyOJjjU0MiJ5lZaguF1t6epMns6G9jVluTzTx9iejHH6ctjnL0+wffeuMyf/OBddrUnefbkHk70d9DZGEOWNz+WWq7N6cXLfG38Rw9sAaUKhTZ/I+3+Rtr8jTQZCRqNOD5ZR5NUFKHUzOfqhm/Lz13azDJanGGoOMliNbPlxYLp2ry3dI20meVzrc/UeRwPBntaUvzGJ5+gv70BEPQ2JfnR+Rt8/71rK++xHRdFltHrWgO9TQm+dfoK+fLmzm9TwcAbs0PkzFq7lovHQHaOFn/krr7Wg7tbmJrLks4WOXtlnM998BBC7GR7i4vnLoGo4LmzIGpKhDsFx3NIm1mem35jxwIBn6yT0qPsj/SyO9hJQo8QUHwYkoYua+j1m3jZhlMSAklIKxNkzZHeWzHUsT0Hp641vix9bLoWZadK1iowW1lgojTPdGWe+eoSS2Z+0wOiJtW8C/pCt0xHuvY1c/71AUauTjE1PE9zZ5K9x7qZGVtk6Mokuk/jwMle4g1hhi5PcuP8GOFoAFVXVmXTanbzt/4QivrRfSrP/+lb9D/UQzBck/kUt9UzJVkilgoRTQTJZ8uE44E1650HI7t5e/ESU9toL1SETFDx0+ZvpNPfRLMvRUKPEFaCNatoSUOXa9/ZslOaIikrJkJiZWr1cDwPx3BW7FarrlmbyOwy85U0Y6VZruaGuJYb2fFJa6Q4zbXsMCHFT4svhaKuLeu8PLmv184kBIh1PvugIIRArwfFftkgrAZx6k6eTt0q2vYcbM/FcZ0V0ynHc7BdZ+W9dt0Jr+xUKDvV+vNS+w6GChMM5Md+KjJ/Xt16+HuD13ljYoxr6XkWyyVyZpWKbeO4NTtzIQSZaoV0pcSNxQVemxihJRjiw927+EJfP4Z8t87EMvfA86BaMbkyNMvZ65MMjM9TKJm0NESQJYlvvXKJdy6NcqSvjU890U80tL4U8lo4k77CmfQV5qvpHb02hqTRYCQ4Ed/HvnA3UTWEXzFW3Fd1SVsxW5MQeLBi/167R+yV8bFkV8haBeYqaW7kRzmfGSBrFXA3kX0q2RWGCpP88ehzDyz7oSkyfl0j5NNR6sJtfl1BliSKFQvX8/BcbyUYWf6WfJqK63o4mxT521QwoAiJgKqhChkXj5juX7N2eGRvK1eHZkhnSwyMznHq4ihH9raia1uzirwdQgogaQfxnDk8r4IQEYRccxvcKWStAiPFKUaK09veVkD20Rlopt3fSIe/iZ5gG+3+JkKKD0XaeZMn23UoO1XSZpa5aprFapa0mSVt5lisZpirLpE2s5Tsyn3JkM1GkhZfkoh2ixkeTYZo7U2h6gr+sIEvoNPQGkeSBZmFPKqm0tyZxPCrOK5HpVjFHzI4Ft6LrEi0dCfBg0DYjxBw5PE+AhEfmqGy+1AHuUwR3VDRfSrNXUliDatXO0Y9q+ALGiiKvOb9FNNC9ARa6w6EG8sOSAj8io9mI0lCjxDTwsS1MA1GnCYjQVKLElaDGPL6qpvrYT1tMNdzyfmLdAaaafM10OJrYLAwznxliaJTXudTm0PZqXAtP0KTL0mLL7Utaee1XnMcl6nxNC3t8RWPiTthWw6lUpXZqQy9e5o3TVgTQtSCrS3wgpatzE3XpuJWMR0L07WxXIuyY6JJCuOl2W0HA7ZbwXQzmE4WQ06gyuGVts2NwvU8Xh4b5ruD17kwN8NMsSb0JKA27qrarQndtSlUq+SqVaaLeUazGcq2TUw3ONnSTsK32s47nSsxm84zNZ9lZDrN9ZE5yqZFb1uSZDRAd0sCSQgGxucZm8lw9voEqWiAjz66d0PH7nkeC2aGs5nrDBTGdkyDRZdU2vyNdNUl0feGuukKNLNUqKI4EslAYEvbrThVsmaBFl+KVn/DiizyopndEDfMxaXkVBguTm5p/xtByKcznytyZXyWZDiALAmGZtOMLWQoVk1evTKEX9PIl6voqkLVrl1z03aQJWnTngWbmo36Ig3IkoQh1VZDSSOAbw0Lx329TRzqa2UuXWB0Ks2fv3CeoF8nFQ9i6AqqIiNvss1QkR0ENh4KknYSp/JDPDeDpO5DUvbiedVacIBWW8Z4NZ10IXTAxfMsQCCEjOfZgItAAaEhxOpzmC4vcDk7RHUbA4QA/LKPvlAnH0gdZX+kl1b/3aIg24Ftu5hVC5//Vu1ckWRCkp+Q6qczUFOmcjyHkl1bBV3JDTFYGGeuukTOKlJ2qpSd6prtknvCnTQbSZTbetQ1XaX/RA/7jns4jrviJBhvDNdWlV5tBQ+Qaq2RR2/Xlujdv9px76nPHl/5OdkcxbadlUk+FF39oJeLVSzTJhj20dC2NjF1eVW+J9zJTGWB4eLkuqttWUgYko4uawRkgyYjwZHYXnaHOmjxJYlqoQeqpb/s9hbVQuwOdnA41scLs6e4kBlgrDhNzr53L/pGMZAfozvQwrHYXtQdCkAty8G2HMqlKlcvjBNLBFeeZ0WRkSRBtWIhqzKu7ZJbKnH+9DDduxuR1igtPigsS+YasoYha6siM9dzma7M78g1sdw8OXOQojVOWOslIvYgyeqG7x/HdclUK/zBpbOcnp6g6jj4FZWwrhNUNRoDQQJazW7c8zzyVpW5YoGiZVG0TAqmydtT4wgg6QvUggdJolCqUihVuT42z5Whaa4MzzI6s0Qs5OdEfxvH9rSzt6uBhlgt4J9N5/nOa5c4fWWM59++tqFgYDmbdCU7xOXsIDM7JDFsyDqd/iYeSx7mYLiPVr0R1/NwHcHw7BKSJNBVBVWWQNTS5QKBLAnsehZFkWr3oud5mLaDrshoqoIh6xg+nQYjzvH4Ps4tXedU+jJXckNMlxd+KvhGLfEw4wsZfnzhJrGQH1WWeP3qCAPTC/h1lW+9c5mQz6BUNXFcHzNLeQoVk8nFLLoib1qieFNPQUz3M13KMW8X6ArFqdgWLne3FkpC4tNPH6ClIcLvffNtXj1zk0s3pzi+r4M9XQ20N8eIR/zoqrLhEn9Lah5FmgHPRFb6QDmAY13Ccyt4zjTCzWKZ7yArfQih43lFXGcBVTuE62ZwnUlARpIbcOxxoIQktSIr3chK58p+PM9jtDjNu+mrm7k0d0GTND7QcIxf7PoUQWXtDMp62Ch5KLNU5PLlCT7w1L0fWFnIhFQ/h2N9HI7VugKWzBwjxWneXbrC2wsXWTSzdzHwj8X20e5fW/BCCHGXk+CdK8f1VorrQQiBqq5/S14+PcjceJpwPMjeY1333NbeUDezlTSvz59bc5UtEMS1MMfj/RyM7KIv1EmLr8bOXbn+3q2uks1mtDarHiaEoMlI8Fc6P87J+AHeXrzIn028uK2AdBnTlQWGilNMledXAsTtYm46w9RYmtnpJZYWi4wNzTM7lQGgsSVCKOzj0tkxGpojtHYkCEf9f6FtCWy3QMmaRBFB8tYIfrUNlSAbPelMtcILI0O8Nj6CByR8Pg43NPPlvQd4sr0LQ1FXjSGe51G2bc7NTfPy2DDfH7zORD7HW1PjHBkdwlAUOv0RvvP6Zb776iWmFnL4DJXulgR//dMn+dgje/EbKtIdq8fGeIgnj/TiOC7fee3yxs/fc/jGxIvMVNaWTd4sFCFzJNrHr/Z+gaQWZT5X4pWrw1SqFp2pGGMLS5SqFjNLOdqTUWQhmFrKo6sKsYCPqaUcFdOiOR4mqGuYjsPN6UV2tyTZ15JaactbXjwci+/jQHQXI8UpvjP5Ki/NnfmJ80ye2NeFaTv87g/f4cVLN4Fa6eCRvk5+4xNP8OOLA5y+Oc7R7lYsx+Gbpy4hSYJvvH2RkKGTCm8ua7LpkHimnGOuUqAjGONsepKOYBxNk1fd8v/b7z7PwNg8C0sFlnIlANKZEi+8c52XTt24Tct+4/v99//kYXpa6qt/oVMt/1ckpQPXncfzCshKD0JqwLYuISvd9QxAHvBjm6+j6o8DVczqK+BZ+AK/gmm+iuNMrQoG0maOqfICs9WtR7cNepxHkwf5692fXbWq3ijMqs34+CLXrk7R2hbHMh0KhQqWadPekQAhWFzMs7hQoFrZ2mQRVUMcjgY5GOnlK52fZKQ4xZXcMBeyA1zMDNDmb6Ir0EJY3b4b1k7h2JN7a5PsBm4cTVLoCjTzZOooP5h5E6iVApqMBP3hHh5JHuJIrA9FKLVa42138PnMNZJ6jIQeZao8R0+gfdPHWnFNSk6ZhBbd9Gd7gq00GHH6Qp38y2u/R9Uxtz0szZQXOL90fceCgamxNJbl8MzHD/Hdr53m1Os3SKRCKKrM8M05du9tJhoPMDWeRpYlevrWN7D5iwBNjhHSepktvUlI66m7Qm58gFsol/j2zat4QIM/yK8de5jP7t5HRFu7LCWEwKconGxu43BDE5/etZe/9YNvMV3I8frkKC2hEOGExr//xhs81N/JX/7YCQ70NtPWEEGSJOR7lGpURULXlA0H82Wnyitz7zJRmt0Rx9aUHuPJ1FG+0vUJFKEigIppkStV+blHDvJf375AvlKltzHJ8Z5WvvPeFTwXntnfS7ZU4dTNcWzX5Rc/cIyXrwxyeXwWx3VpjYW5NjlPdyq2wfk3agABAABJREFUEgysOm+hsCvYzq/v+jn2hDr5L6PfJ2+Xtn0+W0VzLMxfeuQgT+/v4dzINOWqSV9LivZkhGjAx1MHenDrpoGnBsb42psX+cd/8jwhn8FvfvYDnNjVdv+d3IZNBwNNvjCW6/D98SskjSCqJN11y18dmmFyLottO6tsnl3X27Kpj+c6CKHheRUc+zqy3I7nOUhSAiFUHOs6CA3wEJ4HQkYIA8e+gpD8OM4A4CJJEVw3B0JhmZZ3O0ZLU9siv4QUP/3hbj7Z/OSW04+lUpWz744QjviYmc5QrVr4/TrHH+rh1DuDOLZLT28DDQ0RLp4f29I+VkhuQiAj0xloJqXHOB7bS8bKYzo2CS38E9U0vxPSJjINtZV2kseSh3hr8QKHo30ciuymzd9YJwIGMCR9xXSl6ppMl+aZqywwW1lcyXRMlGbpCbQzXpoma+Zx8YhqIWQhM1WeJagECCsBFKEwWByj1deELCRmKwuMlaZ5NHkUy7VIm1kUoRBWg1iuxZKZJaqFSWhRotpqXoQkJAKKj73hLv5q5yf5+viPyFr3Nwq6FxbNLDcK979Xrt2c4fyVCUbGF/n7v/aRFTOUO6HpCrlMiQtnRrAsh1RTBEGN0xEO+xi6MYOiyriOS7FQZWZyiamxNJNjaZpao+j69l3WfrrgoQiD7vAXUSQDVdpcialiWwxmauPOh7p6OJhsJKLp96z7LpMCfUKlJRjis7v38p8uvMtkPsd8qUSqN8g/+1ufoLM5Tizkw29oaPfIvC0jFQ3y5JFeulvuL+bmeA5Zq8APZt7cESJdV6CFh+L9fLz5CVShrjyfjuuRK1d488YIaj0FrqkyslTjUIR8OgMzC9iOS9ivU6iayJKEBwQNDVWWqdo2vY1x9HVU+ZbHRF3WeTx1BMuzeXH2zIZ5RzsNSQh0TaExGuKJvTqu59bOW1HqnIBb7z3U1UxjNMTnH+4nFvTTkYoSMjbX1r/p2apGYPFYqBSJ6f5ad/8do0VzKryhm24z8PkakRUJyatQS/c343lVhBRCICNEDISG7FURUgwhFCQpCig1sY9lpqjcUdclkJHlXXVOwS3MVZa2NfC2+5vYG+6mybc1S0qg1tYlCVzHw+fXVmrz0ZifctlEEoJiqYosS1vqR161q/qAZcg6hqwT08K0eCnKThVd3mGNiPcZftmgO9DKF9o+SFeghQ5/M1E1iLYGz8VyLSZKMzQYceaqaZb7Acr12mHGzGF5Dn7ZQJc0ruWHsVyTsl0lK+VrFrR6FL9i1OqWQsbxHAxJY7Q4QUgNYrsOE6VpFElGkzQMWUddx/tdFhJBxc9D8f1cyt7kWm5kW57vBbtUq4XaFXyyvm6Qly9WmZrJcHNknnslYZpaY/iDOp7rYfg1whEftu2iaQr+uvKkqsnYlos/oGH4NI6e7CEQ0O9KTd8LrudRKpnMLeTp7kj8VAWnt8PxqphujoDaXreK3lyJzHZdctXavbYrliDh92+YACYJgU9R2Z9sQBaCkmVRti18usKJ/g6Cd+hx3A8+Q6MlFSEZvX+aOWcVGSxMMFGe3bZ7a0wLcyTax8n4QRqN1ZwgSQh0RSERCpAMBbAcl1jAQFVkDrQ34dfU21r8IlQtG0kIehoSmHEbRZIwbYeGSBBNvXe2VhKCmBbmRHw/GbOA7dmMlWa2dW5bhSQEmiKjBe8tIR32GQR1nZZYCJ+mbknSfdMzdtm2qDg2KV8tfbzW7h470kOxvLOtOpFQK7Lio7aSX/4ynfoRSMhKF7eEhwS1yf/297q3vVaDovbdtZ+0maNgb53J3Rtsoy/UsSLduxVomkJHZ5JyyaS5JUo+X4E6LyMWC6DpCo7tUCmbhCN33ySuZ1O0xnC8KsvXRBY6Ia3nvvuuMbcVQg+g02G7MJ0slptDIOFTanKu97rhFUkmpof5YvuHgHvbrtbkUzMcjPYxUpxEETISYqUVreRUiKhBGo0EAcXPTGWehBbF8izKVn0QD3aiyzqqkCloJUJqEL/iI2PlaTYaKFImbWYJqn7afM0k9Ohdtqi3QxKCNn8DJxMHyFtFclZhywOu6VpkrDxpM1frKljnfQGfRkMyTLFk3jPL3dgSpaE5UlMsrKecXdddGYSWX+O2luKm1tjmj9u0mZnPceb8KN0d669UHbcICCRh4HoVJHGrJc7zHGy3gCI9uEyX61lYbp6SPY0sDHxKA5LYnAfA8lwW1Q2Me9gQrwVJCOJGTSd/uUVOCEHAp5HOlShXLCzHwV2n3UwIQXdLvPb8yxKKLOG7T/bGw2OxmuVidoCKs73xXkKwN9TJiXg/e8Kdd71uqArNsRD7Whtqo/ht3+PRrhagFjje+Vpfc21R5nneSnC70Xug3d/IQ/F+qq7JXDW97XPcLDzPo2o75MsVihUT21n/6Y/6DVKRIIFNZgNux6ZH/IxVxnIdPtnWDwLuLhLAF549suUD2hxuj/BWT/R3R+Ybm5wXzQz5LbK4BYIOfxOdgZYtfX4ZgYDOQw/3rHnzfvDD+wHw6g+1WKP2Z7sF3pv7LfLWIK5nIZAJap080/Zft3VcO4Vb2YzabLHRh3O29BpTxRdQpQBHUv+4bgRzb2w0VSsLmaAa4OzSFQp2ibxdxPYcZioLTJXncDwXVVJQhYqEoNVopOxUSOgxDFkjZxV5beEMu0OdtPmacTyHgl1kvpompAQZqbcg1TgYHrqkrvnsrIUPpI4zXppjtDRDYRs1TMu1GStO0+xLst5M39/XTH/fxngFQohVm7l9xX/na1vF/GKBc5fH+cFLl/jyZ46veq12H9X+5c0rSEIlqO2jYN4grB/E8yTAw3YLpMuv0RD4ODstW74MRfjQpCjZ6gAADf6TKNLGgwFVkkn4/ZRyWawVQ6KNw8Oj6jh4QEQ3CGoaHjVp+O++fpnzNyaZTecpVixYIyDQNYWv/vNf2hSPy/M8pisLnFq8tKljXQtBxc8X2j5Ed7AVaY2FVDLs5+Su9nsSse/12nrlrvvhQHQXCMFoaZoLmYHNb2AbsB2X0bk0P7pwk1M3xlgslOsibXe/9zMP9/NrH7u3quX9sOlgQJMUsmaF/3DjLXyyxuc6DxLR/uJYkxas0pYjwLgWIawG0e+x2tsM7nnz3uM1VQrzWMvvYjlZxgvfY678Jra7vZrzzsLDdHPMll6jLfgJxIYHaK/+/x6u57AFgbR1EVT8PJ16eEWVrwbB/shuJARdgdZVgcVjyaO4nlsXhhIrglCSqJFpuwNtdAVakZBoNlJ3iWVvpp6sSypHo31krTwvzJ7a8jlars1gcYKHEwd+Zpj945NpBobm1nzNw6ZgXqJoDmK5aXSlBUWKUbSuENL7qdjDlK0xKvY0lruEh7uJe21zqEndSFTdRQQSrre5+nlY13m0pYPx3EWuLS6wL9FAa2h9RcE7YToO781O4Xgu/ckGuqNxsvkyX33+Pf74+XdpTUVojAfpbvGtObBoirzpe2K4OMX13Ahz1aXNffAOKELmyx0fod3fhCGtHeCrskzE//61pN6OnkArX+n8JP8w+2/eVwvmly8P8dx713j1yjC7m5Psa22oET/X+J5a4hu/V9bDpoOB7lCcZl8Y07UZLSyhv489wxvB9398EcNQaW+Js7tn8339pmthb1Ewwy/rqNLaQjibxXbEYUCgCD+yoqNJUWRhYPPTEwyYboal6mVGcl+nNfAR2GDHRaP/A8SNYwgkZLGzAehKhwur/8tdv7Py+8p7hQCvll24c1vL/1tpUdzCLCyEoDvYylx1iZdmT2+5VGB5NlPl+VUtU+NTS/z2v/8huUJl1YpD0xT+xT/8POGQsVK7rlYt/uNX32BmLsfnP36EYwc7Vm3/h69c4cKVCUzL4R/+nY+v/D1fqDA+tcSPXrvC8Ngi5YqFqsiEQwadbQk+99HDxGMB1HqrqmU7/PGfvcO1m7NMzmRYXCpSrVr88t/9w5VtNjdGePR4K8dOXiPpf5Zs5Qyy8KNKkZWJuGyNIoRG3PcE88Uf8CAjIMsrUHKm8SvN2G4RIaQ7Ast7I+kL8Pm+fr578xovjQ2zKxanMxIhqt8/u1C0TIYzS3z75jVkIXimo5tjDc2UqhZvXhjmmeO7+MwHDtCSiqzLQ9hKIudqdogb+dFNfmo1VKHQ5EvwZOpYjW+zzrj2k+SKGLJGqy/FE6kjvD5/7n0LCG5MzrNUKPPpE/v4pWdOoCryut9RwNicwNVa2FQwMF8psFQtUbRNbNflWnaWvkgDxroaa+8/Tp8doaUpSmSLnt2O525KlnL1Zzef3nsQqD04orZaETJiB9UZdwJVZ4m8OUDV2VxPsiZH0OQdtMO+A5udqG8foO6Sf10jeNgOgoqfBiNOyogzu0VRF9dzWTJzq4KBgF/j0eM95ItVbNthbiHP6ESawZF5HMdZFSC4nsf0XI6xyTSF4t0yz9lcmcmZDFXz1mDpui4Dw3O88Po1ZudztDRG8TwP23YwLYexyXRNlOq27QghaGqI4Lq1DqRq1UIIOHybYFU8GqAhFQIEVWcGxyuBJ2O7War2LFV7FtczwbOoOrN4KxyiBwNZGBhyEkkoWE4ONjkOGIrC7niCL+45wItjg7w4OkTRMjnS0ExnOEpEN9Dkmky5B5iOzVKlwnQhz8DSImdmJlksl/hU7x6ONDST9PuZKxWYS+f55BP99LQmSEZ3pk3Yq0v8jpdnmdqmrkBQ9XM0upeoFuKd1wZYXMgTTwR5/D7aKQBTE2kunR+nkC/zqS+cQNMeDM9JEhI+xeDRxGFOp69g2e9PMJApVfCodQp0NWyeb7NZbOrqVWyLxWqRdLUECBYrRdwHZLKyVQyNzhMI6Nt77Lf44bJTxfacTa0INgLXc3DcMhVnHsvN4eEioaBIQXQ5UWtj2mbk7HoWlpOj6i5hu0U8z0GWdFQpiiZHUKW7B5LlSaVqL2K5OWy3tLIqk4SKKofRpNoE7nkeJXsCy82TqV5lqXIJ1zNZrJxFqitASkLFkJP4lOaV87HdMkVrHMu9xaTX5DBhbfe652K7ZarOIpabr5EoPQ9F8tevVRBZupVVKFmTOJ6JJGrXs2zP4HjVWiglNDQpgk9pYjPchgcBVVIIKwE6/E1bDgYcz72rUyYa8fOFTxylWrUxLZvLN6Z54/QgY5M7oy1vWg6Dowu8eWaIowfa+PATe1E1mXLZYmGpwOjEIn6ftkqiWJIER/a3c2BPCz5DpWpazC8W+OInjq0sX1VFxucDW2rHdnJIwo8iBXA9C0UK43oVVDmO61Zw3AK60rijz+SdkIWOLsew3Dy2V66XJJYJjB7vzkytv1Cor8o9D3bH4rw6Pszp6UlGsxnGclkOJBtp8AcwFLXeSudRti1mikVuLi1weX6Oi/OzCAFHG1vwqSq269ZLVx7xsH9F236nUPM/SZM1t97hIiGIqSEeSuxHQvDuO4MMDszSvathQ8HA7EyWV1+8wuxMlo99+ugDCwagVsrYH+khoYWpOub7kh3wPA9VlogH/fd/8w5gU1evPRgjpvuxXIeI5qM/2khQvZu9aNd90qFG6lgWr3g/BlPH8Qj4dELBraWRFUlBlmRwNv9l5+0SVde8S5FxO/A8D9vNk61eZzT/DRYr72F7VXQpRlTfR1vwEzT4nwBva17ky2S+qpNmvvQW06UXWapexnGr+NVmGnyP0eh/koRxFLhjHx44VJkuvsx8+e36an8REOhKkpTvJI3+J2n0Pw7AcPZrLFbeo2RPYro5wOWd2d9Y2ZwuJ+gMfY6+6K+wTPQq2VNcSf8rFivv4Xo2Hg4Nvsd4tPnfrnMuHnlrmMnCcyxW3qNoTYDnENJ20xb6BAnjGCG1C1FP6Y/m/5yiNY4mR0noR7mZ/UNK9jRCyPiURlK+k/RF/0a9LHH/6+t53qqSwE7e8wHFx65gO6fTG1eGux2u55K18nVmde0elYRAU5WVVuBYxE/At3MtpcWySaFY67Y4cqCD/j3NqHW54vWujSQETalaDTQe9WPotZ7ytuboGp/5AKu7iCCg7brt9WWS4YPNjjmeScVZpGxN43jVVdkX1/P4pe/+V8r23TyC5e4LSQgUIeFXVSqOTcW2GcykV7QH7gcBhDWdt6bGyFTLPNTcRqsSorUhwmy6QNW0VwRqdgLX8yNk6robW4VP1mkyEhyKrh/Y/7RAEhIRNcj+yC6KToWFauaB7zMe9DOfKzKzlMdxXaQttAtuBpsOpQLKrdpEV3BtffjvvnyJM5fHMG2HI32tfP7DhzH096dVrbszie04pJe21hFQ6yNXt+R2Z3sO48VZxkuzO6b0ZntFhnJ/ynj+O0hCY2/s11GlMFnzGouVs7w394840fgvSRgnkMXWBnHLy3Fx4V+QNW9gKCn2xf42mhRmrvIms6XXmCu9xYHE3yXpO7nqc45X4cLi/85U4QUi+h6agx8irO1BRiVrXsdQUmjSLWJLV+SLtIc/RbpynunCC+StIR5q/G2kuqGLJBQ0KcrtA3dI7ebhxt+m6mQYyX+dmeIr656Hh0PBGubUzN9FkfzE9AP0hP8yklCZKv6Y60u/S1w/SG/kr5LwHVv5XNGeZKFyhuniy/RG/ip+pZmSM0O6cp7BzB+hiABd4b903xKF53m42EyXB3E8m6jaQETbOT8Kv2zQtY37ysUjb5WwPQftfSrtxSN+utuTpOJB/vV/eokbgzM89tAudnWlaEiE7r+B+2Ijg+ODX4TYbpGCNUquOkBb6GOr7nsAp16ouAuet1JSMHEorREwbAQekDWrfOfmdb578zq/dPAof//4k/zSp07yr776CgGfxskDnbQ1RLe0/TvxbvoqaTO7rW10B9s4GtuL/FNWxrwXHkkcYKgw8b4EA08f6MHD409eO0dXQ4xjPa0rvJoHgU3P0BuJTC7fnObq4Az5UoV0tsiXPn7svp8xHYfRbIYr6Tn6Ew2ENZ2iZXJpYZaTzTU52LF8hrxZZX+ikYVyiclCluZAiLZQhJhR4wh84ZNHeeHVa5x6b5jujiRNDZtjWUbUIH7ZR2aLwkMDhTFa/akdCwZmii+TrpxDlcMcjP99wtpuhJBJ+I4T1fdxJf2vGcz+MRF9HzJbCQY8xvPfJWcNkTCO0x3+EkGtC4FEzHeIEfnrTBdfZCj3pyR9D7M8sFpukZw5wHTxBZoDH6Qt+HGi+j4koSEQJHzHEMgrEz2AX2nFw6Niz6HIASRbJaT2IEm3jvtutnetd9xQGtCk6Krt3QnHqzCY+WPApTfyV2n0P4YiBWvHYxzl6tK/I2cOMJz/+qpgwHEryMLH3tiv0+h/HEkouJ5DRNtNwRpmoXKG9tCngPvwFQQIT0YSEjlriYKd3tFgQJdVGo37q8LdCx41IqGLx/p0pK3Brfdyr4bgoSOdtDZHef2dAd45N8KZ82OEgga7ulJ89Ol+DvW3bXPFusW2mx2EIvnwK83geWQqVwkobWhybewRQvBISzume2+X0J1EdyROxbQ4c2Ucy3H4z99+h6/+8D2CPp2QX7+roUBXFf7l3/7MfR0llzMe1/Oj2zbSavc1sjfcfRf3ZrPlnJ3KdmwEe8PdpPQYQ8XJDbkbbgdDs2luTC2QLVX4rT95nmjAR9Cnoa3h1vrMgV6+/Pjhbe3vgSzXZxdz5IoVFFkiGQ3cUwd7GZIQxAyDznCU2WKe8XwGWUj0xZNcWpjF8zwihkHS5+fi/AwVxyZq+AjrBsZt8pKFYhXLdpiayfD7X32D9rY4fp+25u312EO9NKRWBwtxLUxA2Rr5EGCsNMPV3DCHon00bXPgBsiaN7DcAj65ibDWhyIFasIg+PApzQTUdpYql3HcCp7kIrYgdrRUuYhAJqC2E9J6UKRajUrBVxvUpChZ8wYeNng1dSu7HgzYbpGU72HCWi+6fDvJZXWdq/aQq/WfVZZX/5LQ7mn1uooMicy90r2uZ7FYOUdAbSOodmDIDSvXQ5ECRLQ9lOwpCuYIVWepnoUAISRUESLpO456mzCNLifwyQ2U7Tm8NVwd5yojVJwCplvG8WxafH0ElCia5EMWCmWnlpK33AozlSGKdgZJSLT4+ggqcdLmJCU7S9UtY7olGo0egkqMJXOGJXMaSUg0+XYTVGLIQqnLGW/NsvV22K5d93jY6hbWTg1XTZuqZd9BBgS/T6OjNc4HHu2joy3B2GSa8aklJmcyfO0775KIBWh8AKql7yckoeGTUwTVdorW9CqNAQH89YPHcN5HcnFbKIzjemSLZVqTkZXvRRK1bo0758+Npp89zyNtZslZBextBDdBxUeDEb9baVASVMoWVy9N8O6pIQr5Crqu0tgcYU9/C727V3tciHoZenBglpGhOeZnc5imTShkcOxkL13dKXz+1eOL53nMTGUYGZ5nZHCO7FIJBOi6SiwRoLu3kUNHO+4mBQuBT645Hca18I65M66HYtXEw6MtEaFq11QUXc/Dcu7OMdlr/G2zeCBPXyZfxrRsIsEg7U2xDd1oshDEfX7SlTI5s8JSpYwiJJ5o7eStyTFcz+OEv5UGf5AzM1M0BYKENR3HdSlaJj6lNtEMDM2RL1TJ5MoMDM3RMDxHMLC2/Ore3c13BQNJPbqtAXfJzHEzP8H5pRvEGo6jSeq26jxlewbbLeJIIdLVc6teK9nTuJ6F6aZxvEqdtLT5YKBoT+DhUHHmWay8t+q1Ql3J0HSWcLwqiqgZsDhemZJdF9LRdq1MrD8p1OrgDmV7ipixvx40rb4WfqUZTQpTsiYwnQyaVFvpS0JFkQL4lNUOjQIZVQpR8EbWTPIuVieouiXcOml0unyTnuAxFKGi3GaLXfM+z7JkTeO4FprkI1Cf9PP2Io5n4XouTvkGbb59LFbHma7cJKo1kbqtX10WEoasr0gdbxWW62zRkU2gaXLNvthxcV1vZSVpOy65fIVisUowsDpDJeoa672dKXo7U8wt5Lk6MM3b7w3zg5cu8/Fn9hOLBtYMBpZr6s46ynk/DaipDxYw3SxBOVYnqN5qLRRC8FRH9/t+XJl8md7WJL2t95dGV5WNWco7nstUeYGqa23L1S+pR0nokbsWXo7jsriQ5/Tbg1y/Mkk+X8GxXCIxP4sLBVINYYIh38p953kelmXz3qkhJifSLC0WKJctXMelUKyif+wQLe1xDOPW8zg5lub82REuX5hgdiqDZdkISSDJEslkCE1VOHR0ddvsMiQh0WjESerRBx4MNEaDHOjYmMHXTnQbPJBgoFyxsG0XXVM23M5Sa5dxeGNqlCdbu9Blhelivp5aq6lHuZ6H7brIQnCkoZmR7BKDmUXCms6HOmukoeGxBXL5Mn6fRmd7bWXuOHcbEsHaVsHNRpKYtj0Bh4nyLN+afJm+UAdt/gYUlC0HBE6dGV+0xklXzq/5Hln4cT2ztnoVm/9KbbdIxZ5lLP9NxvPfXWcfBo5bRZb9deazU5eABVUEVzoCfnLw8LBwMVGEv+4atxqyZCAJHQ8P27uV4pSEhiLWCgCXv7O1B72ykyegREnorfjkMC/N/gGdgYN3vU+VdFrrWYO0OcVk+RodgQNUnSKKUGkwukhpHfxo5j8Q11qxPRtdDtIbfIiwmkDiln6BLCQ0SaFSV5vbCu60qt4ohIB4JEChZJIrlCmVTQxdwfMgmy8zPZclnSmtCgYcx639cz1kuUaUi0f9PHy0m3DIxw9eusz41BIH9rSA/+4yl6rKKIpUyzqYNspt7ixCEhvW73+QsN0CebPAXOktQtokVSeDX2mslwl+ch0o0ZCPX/7Myfu/cRNwPJeR4uS2u8g6/M3E1xhnC4UKs9MZTNPm0184js+vcfXSJBfOjvLNr51iz94Wjj7UhVTvHHAdj4X5PO+8OcCTz+zjiaf3YlsOb752g29+7RTxeBBNU2jrTNQ4Pa7H8989x6m3blIqVXn0yT0cOtqJqsrMz+UwqzbxxL3nrCYjSYMeBwa3dQ3uh6f39/L0/t6V3++cr3aaTPhAgoGgX0dVZGRJQtc2RnjIVav82cBlFsolhrJpDEVBk2T+7MZlWoJBhBCM5JYYWFqgORhiIp9lIp9DkSTCtykg/tP/8TPbOvbuYCstviSapGBuUXzIdC3GSjP840v/F7+59xfZFWwjqGytPUSRg/iUFgJqG92hL6/7Pr/avuUJWZXDSEKnwfcIDb7H13yPEFIthc5yZ4iCImoEsKqbQfeSKJvUYt9ZCCShIws/ppfD5e56nuXmcdwiAmlVJuN2AaHNQhYKstCQhYLL2qv167m3KNpLqJKBJvlXDaSqZOCXwyiShodHUImR0NtYqI7zbvo7dAeO0R7oR6uLLAkEuqTVVTK3Fg64nrvZNngAFFni4aNdPP/yZb79wwuMTS5x/GA7xZLJd354gbnFPOodJjADw3OcvTzOzeF5Du1rpTEVwnFchscXOX9lEiEJDu5txbeOaEpTKkxLQ4Q3zwzxH//kdY4c6EASoCoKyXiA7o5tGILtEDQ5Rtxox680E1K7VzpV/iLC8Ryu58e2VSKAmodLUo/e9XezatPaHuc3f+szNDbXXn/k8T5OvXmTf/evnufrX32LfYdaUZeDAc+jWKjyd//hp2nrSKDXiepPfbCfv/mV3+X1V64RChu0ddYWhjevz/DGq9dJpEJ89ksP8/FPH1nz+O410bb7G2n17RwXaCPwPA/TcShXrZoxla6i7nC76KaDgZlcntFMlrGlmgTlx/b03WWV2NwQYTadp2LaLGyQ1R/Wdb605yCu56LKck21zfOwPRelHv279ZYoWZKQhGBfoqHWknOnJvo2oAiZNl8j/eEezmVubHk7Hh4ZM8//59of8UjiIA/F93M42oe+hmPevRDR9lGx57HdIiGtZ81+f6AeCGzt3BP6UWZKryKQCWu7VjgDqyFWBRuqFCJq7IcszJVeQ5X8hLTeNT63xrEiIwvtNiOl7aPGSVBIGifIVC5RDswR1navOuZM9TJlZxa/2ryiHbBdTJavM1a6hCI0eoLHyduLjBcvM1cdwXKrxLQakbRoZ3G9RXTZv6b2+jKy1hzlSp756iiOZ7PW9ZFFzdXg/U6cy7LE4f42/tqXHuX0+VHefneIl9+6QTho8MSJXhRFZnwqzfTcLZZ5NFyzzl1I5/mTb56iUrHQNZVQUKe5Kco/+PWP0NfbuK6TXG9XA5IkUapYvHFmiBdfv46myezqauCjT+//qQgGCuYoJaumiinuUKH8ScPzPIan0gxOzLOUL68ShLodiizz8x85et/jdjyXkcIkzjYzA53+ZuLa3YRcv18nngyRaqy9JkRNvTMS99O3r5lzZ4Zxb6uPS5IgngjQ0BhB12sZWM/zELLE/sPtXDw7ylK6Ngd5rselC+OUyyY9uxs5eqJ7S99TXAsT18IoQsbeRrluI0gXSlwem+X/fuM8Q7OLWLbLiV1tfOWp4+xrS/G1N87TFA3T3RinIxXd1r42b1RUqVA0TY621sx49DXqfIf6WpiYWWI+XWBgdA7X9e7rFiUJQVC7m+ixjOUveflnAE2694PneR6O41I17Vq6UZbua58qhKDV38CByK5tBQNQCwiWzBxn0leYKs9zMTPAsfhe+sM9qJK6IRZsyvcwJXuCpcoFBrN/SKP/CVQphEvNia3iLCALH42+x5Alo5b+9Vwcr4rjVbHdIq5n4noOFXu+nirXkFDqA5egKfA0GfMKGfMyw/mvkTSOo8phHLeKWRchkoWPJv9TK9darRPyIno/M6VXcT2LmHGwXneXqDoLeJ6LT2kkoq8WEFGkED6lCcstMFn8ISG1u94lIFDlMD65cWUQ8DwH1zNr5+IVaqUAz7ztXPT6uUjIQqct9AmupP8VU4XnqToL9YBAIV25xEL5XRQRoMn/dN1mdvuIa8345QiGHCKoxtElH+2B/aSMTlzPJaY1EVEbiGpN4LnIklanQ0q0+vcgkDDkIAKJI7GPENUasdwqITVZ62hQU6v4B8D7mHm+m0ClawqPnuihpzNFts4N0lWFtuYYsixRKFUp3qZOGA37OXawA8On8nvfeocPPbKbntYEkZBBKGjQ2hRFU2vs6HcujTIyleZEfzs9rTW7YlWp6Qt8+tmDnDjUiWnZyJJEOGzQ3rx2a7PnefzBd06xr6eJntYEqdjOKO+th4qzQKZawfbKlJ3Z2nnr/ehydNvb9jwP5zbtio1CQmDZDtdGZvmTH7zHYq6IZTt4Xo3fsRxMKrJE0K/T2RS7yz9jrWOxPZuFama9RskNwScbRLTgmj4EqiZj6OqKNg3U7jtVVQiGDIqFKrbtrrgvSkJg+DQU5ZYGyvJcEY74sCybasXCqQcQuUwJx3YJBnRisa1la2Uh41cMYlqY+W36MtwLM0t5Tt+c4FunL5MplmmOhbk5vUimWMFyakHIQr7E2EIWy3He32BgvlBkNl8kW65ANIIire27dnB3C5cGpmoKY1NpRiYX6WyJI2/SWWYtNue9fl9GoVglnSkyO5cjmytRqdr09TbS3BTB0FUmppYIBw3CIQN1jWAmoUXZFWwnpoZY2oaHPNQCgrlqmrSZZbw0Q94pkbdKNPuSxLUIETWIcg9/h6DaScI4hu0WyVSv4GLXBXBcHLeK7ZUJa3149RS17ZYo29MsVS/jehUy1ctUnMWViVcWBpoUJqh1raj4RbQ+Ur6TdWXAC9huHlny4boWtldCIN216peEhiGnaA18hOnii2TMa1ScRXQ5jhAytptHlxNrTrq6HCei7cGvtDBbeo28Mogk9Jo2gHEIn9wACCwnT9meJmtew/EqZKvXMZ0crmczVT8XRQoS0w/iV1uQhErCOEbSOEHFnmeu9CYFaxQJmZw1hCx0YsYhksbD2/pOl6FKBhE1RVxrwa9EV1b8hnz35BP2UvXrdmuQi2qryUFt/n1ALY0fUWvXYKeFi+4Hx3GxbAdJqk3Ea+26pTFKS2N0Q9ur+YTEkFUJ48caRw+2c3hPK+HA3aJgo9Np3rs6QVdznJ7WWlpXCAj4dfb0NrGnd2NkKoC3L43iNzQaYsEHHgwoUgBZ8mHZBdyVleLm8zY1wSqYyGdZKJXIVitUbLvOm9rc9rojcdqNMG9eHOHizSnam2IkEwEqps07l0d58nAPFctmMVMkX6qSjATuG2PankPJrlJyKtvKSsW0EIak18Td1sB6265li1crPd/rOASAt/qsVtxetyHgI4TAkHXiWuSBBgM3ZxY4OzzJbCbPB/p76ExFyZcvrnqPT1O5Mj5HKrL9LqNNBQODi2lm8nmylQqvD42iyBKf2b/3rtrFvt4mDu9pYy5dYGRyke+/dpmf//hxwkEDRZbv28u6HViWw/DYAmfOjfDa2wNMTC1Rqdr88l9+nGee2EM8GuDF167R05nk8IF24tG7L0FI9dPub+RApJfXF85vizW7DNtzWDSz/GjmbU4tXuR4fD9Hon30h7vrTodaLf17xw2qSD6a/U8TUnuYKHyP+fIpTDeDJFQ0KUJA7SCi7ULUJ13TWWKu9BY3s79/1zEMZP4zUGuZaw9+uq5ZUDM16g59mbR6ltnyayyUz1B10ygigCGnCGu7CGk9d21PEhq7Il9Bl+MslE+Ts25iVpYQSPiURhp8DajS3cIyPiVFyvcwRWuC6dKLZKpX8DyBLrcg0URYdcFzKdozzJXf4Hr6D2uMcs9FQmAKk8uL/xHHdTHUMPtif4dGGvDwUKQEveFfZSz/AxYr77FYeRFF8ghqPbSHPk3cOEJQvcUUVqSauY0q3U1mkkStm0CTYmvoH0BYTeKTwyhCu2fqv7atjRPdNvPe7cC2a10BUi1th+u6ZHJlsvkyPp9Wa8n9KUl3bxbv51FH9b00+Tuo2PP41ZZa3uce7bJrYXmEWSyX+MHQAG9PjXN9cYHZYgF7Cyn5XzpwlL+55zivvDtIV2uCX/nMI+ztamBiLsN71yf4R7/8ETL5Ej965zoj00sc39d+322arkXazG5LdVBQI+Cp0tpTj2naVKsmjuOuqFR6nodtORTyFYJBA1WRVnUTlEomtuOuqGouZ5Bz2TKqJqMbtUyD47gEwwaSLCiVquSyJVLG1rxOfLJOSo9yfXtrxXvi/PA0I3NLPL63m9/87FMAvHV9jIp1q9TTnozyyuVhsnWVz+1gU8HAI53tdMWiFE2L7niUSzNz6Moa7UDAZ585SFdrgv/ynVP88XfPMJ8u8tEn9rGvp5HoFkyENjooXbw6yX/5+ttcG5ihuSnCRz94gOdeuOW37XkehWKF7/7oEs1NUeLRtSOquB7h5zs+xqn0Zao7LC6RtYq8OHuKl2fPEFGDnEwe4OPNj9Pma0C/LXW2fM6yZBDR+4jofffddkBtY1f0K+yKfmVTx6TKQRoDT9IYeHLDn6kdn0xH6DN0hDZD3BRocoy+2K/QF/sVoJZ1Gl3K8NbQAkupWQqmSXe8gUbjF5j0niagqkzlCoR8BlHdwPVcBheX6EjGGZqvcLY8gOU49CRiGIrBaPppPJ6iKxbjYHPjukeyO/rL674WUNs5nPqtdV9fq3PgZwmDo/PcGJojEvJhGCrjU0u8eXqQkfFFTh7pQpbXlrheqwsHVj+ja73nzi3dxY7ewHvW2t+93vN+wcPDdPPM5L6GISdp8J1EV5KbIqZWHYe/9+JznJ6eoLwNMxxZSMhCwnJcphay/PJnTrKrLUnQpyMQaIpMxbRoa4jy+JEeFGWU//Ttt/n//b0v3rO9sOJUt+yLcQuCVl9q3WCgXDJZmC8wPbVEa1u83jIMS+ki169O0b27AUm5FSy7rkcmXWR6Io3WnUKvtxC6rsul8+NEon5i8doYL4Sg/2Abz337LEM3Zzn37ggf/vihtY/yPvONIWvE9O3bBt8L87kC4PFEf9e630vYp2PZzqoAYavYPIEwX2C+WKIjFuH81DTtsQhq3U1rGRcHpklni2RyJXZ3NjAwOs8rZwZ489wQQb9OLOwnGvahyGuXGdbC3/nK07RuQErzW8+dQ1Ykvvz5h/jcx4+gKBKvvHF95XVFkenraeSt00NUyutP8qpQaPU38IW2D/Hc9BtktlkuWAsuLhkrz4uzp3l9/hydgWb2hbs5GNnFweguNLanUfCzhOGlJd6dmKIpFOTa/AKu55II+DEUhYlMlpjfx55UgqZQiEK1yhujY3x+fz/funKVQtWkPRrh4fYuvnHxCkXLJOn3o8oyNxcX7xkM/LeMmbkcr749wODoApZlEwwYtLfE+NzHjvDxD+5fdwB69b1BXn1vkKGJRWzHRZUlvvjhwzy0v4OGeAjTcrg5Ps/vfONNcoUKiYifZDSAuC0juJApcnlwmq/+4D3KFZM9XQ1kC5W7soa5QoUrwzN8++VLTC/kkCSJ4/vaOXmwkxP97Xiex8D4At96+SI3RuYA6O9pqgvrvH/PjuXkqDgL9IS/zHTx1Xo28X5V+FvIlMu8NjHK21PjmI6DKskkfX72xBOkAkG+d/M6Lh4twRAHU43YrsdcscBwdomlShkhBF2RGH/v4cfZHUuQ8gfIpEu1Mfa2KEuWBEGfTjpbIhryEQv5iAR93JxYuO/xVhyT+W3K8ApqGgPKOh0XhqEyM7nEb/+zb/PZv/QQ/oDOtSuTXHhvlEK+wq/9xkcw9FscGkkShMI+/s9/8T0+8KF+2joT2LbL268PMD25xEc/+TT7D9ayHkLA3v5Wjj/cy7l3h/mTP3id8dEF+g91IEsSmaUihXwZRZX51OeP3/P+0SWNuPpggwG1rjRYKK8vjT+fLaCpMn59+63dmw4GkoEAZcvmpZtDRHzGmryBP/neaeYWC9iOQ9W0KZarmFbt51LFJJMvoc0qbCYbWq5sbHU+Mb3Enl1N7N3VRDjkA1abBkmSIBQyKJbNe6o21VT+ZJ5uOM5YaZoruWGWzNzGD3iDqK0oLEzXYqgwyWI1w+XsIC/OnqY/0sOeUCdNRoLQDijP3QnLLTNdOk9QbSCstmB7JpZbJqTee/K03DIle5GsOU574JEdGXT9qkrEMDAdh/ZohMVikalcjtl8gaJpEjZ0fKqKrsiYtowuK5yfnkGRZBzPYzZf4OLMHIaq4FNVhABdUWgMPth6MdS6XBzPoWiXKdplTNfCcm0sz1n5bm3PxnYdHM/F9hxs18bxXBzPwV7+b/1vy7/XXqv9c+qfNV2TvFXcVqp2Gf19zUQjfgrFKo7joqkygYBOLOInGlmfXNWUCPGhh/v4wDEHWZZIZ0v88K1rtKQipGJB8qUKf/S9M+zvaaK7NYGmykzN53jpzM2Vmu+FG1O8+t5NHtrfwb7uRkoVi5fPDDCzcOsZcz2Pdy6NMjA2zweO9RIN+zFNm1OXx3jrwjDNyRDNyQhf++FZQn6djz62j+ZkmEK5younblAxH6xc7O2QJQNVBJgrv1Mn162V51gfObPKW1NjmI5DUNX4WE8fT7V30RoKYygKr4+PUrYt9iUa+NXDD+F6HhXHZr5U5MLcDC+PDTNVyDFVyHMo1URI08nLFZLRINMLOcz6ylFRZFKxIFdGZmhJRaiYNqWyuaEjtTybgl3a0vW5BUFMCyOvEwy0dyUJBHSisQAv/vASpWIF03QIhnx86gvH2XugFfk2fX5NUzh+soemlijDQ3Oce2+EStnCshw++fljHH24m1TjLVloRZX5+GeP0Nwa5erlSS5fnODiuXFkWaCoColEkANH1hYcuh26pBFRH+zY0hKPML6Q5aVLg5zobVvlXuh5HsWqxatXR4j4DVpi2w9MNh0M+FSFiM+g6tiEjXowcMdkcG1oltnFtVfSrutRqdpUqptLazjuxupm1aqNrisE/BpCrGEr7oFtL9ej7rMxAa3+Bh5JHMLxXC5lB3fgYVgfZadC2akwW1lkSJpgwcwwVZ6j1ddAQ131qklPoMv3r1HfD7ZrUrLTzFWuokl+LLlCxclQcXMElQby1kydrKihCB1JyJTtJTQ5gCQUbK9Kzppad/sL83mCIWOl3ed+SAYC7E0lqTg2bZEIUcPAch1s12V3Kknc7yNs6KiyjF/T6I7HcD2P7ngMx3WxXRdDkelLJVGEhOk4GIpCKri9IGrZhbBoV6g4VSquScWpkajKThXLtTBdG9OxKDkVSk6lNvnXgwHLtbFcC6s+uTuuuzLJ14KB5YnfvRUgcNvf3Vvvdet/24lAACCVCJHaglnQcvnAu03L68boPLliBct2yRYqnLs+wV/60GH2dNX6sX36DK5TO/JK1WJ8domJuSxf+shR9nU3MpcucGFgkqn5W22JlarFjbF5Lt6YIhL0oSgyVdNmZjGHLEvMLOaIBH1cHJji8x88xKOHumhMhJiuBxTvZ/VAFhqqHMG2Z1f09TdTIijZFtfTCwDsiiV4vK2Dpzu6Cem1tm2/qmI6DhHd4EDqVrBetizaQmFUSeYPL53lzYlR+hMpIrqBpsj0tCQYn82stBQamkJPa4JTl8cIGDrZQpmBifm6ONx92grdWsC7HQhR83+R1xi/9h1ow/M8QhEfgaCBB+SzJQxDo6k1Sv/BdqKxW89zPBHk2MM9xGIBEqkgVy9PMjeTxazahMI+Hn58N61tcQxfrfTqeR7Tc1naOpKoqkI8GWJ4cJbR0QUiYR/BgEGqMUxnd+q+56FIyrZk6zeCPS0pppdynLk5wXfOXKUjGWU+V8R2Xc6NTDMwvcjQ7CJP9ffQ27R96ftNBQOO61KyatH2sdZmRpcySMt9nbcN+A3x0I6n6Dbq1hQI6FQqFtl8GdtxV/kieF5Nl3tmLkc04r9LIOVOLD/MT6aO1gx2XJOr2WGqrrn1E9kAPKDqWlzODnI5O0hQ8dNsJDkU3c3Dif006HF8soEmKaiSWluDbPJ6216FsrNIyV5AlQM4nkXZyVB2lgCP2fIlarV9H5oUQpeDzJYvE1SbiGtdqPcQGPI8j/NnR2lti5NMhTB8KkadxLPecTaFgjQGA7jU3Ac6opEVdvWdLZiaLHOkpWklqWk5taDheFvrynuXveM3Y2KyPPFbrr2yIrddh4pTZbqywGI1S9rMsljNMltNs1BdIm8VKTmVHeeV/DTC8zxcz2N4Ms2FgSnmlwpIksBzPfKlWstX1bTI1tUJ93Q1EKyrCrakIsQifoSAQrlKvlhBeB79PbUOgcZEiMZ4iLHALXZ2rlhlMVNkaHJhpZVqGZ3NcUoVi6V8iVLZpCUZprWhRgZra4gQjwQeqMPb3ZBQJJ2ItouMWStLbsbKvGrbTOZrQczJljb6YomVQABq97zjuVh3iP34VJWDqSaSvgBvTo7x8tgwRxtbSPr9NOpBju9r4/zA1Iqcs9/QOLa3jf/vn7zMXDpPqWKBgBN72++byLC87QcDABE1sGYwcGf9/uDhe6/QO7tTqybu/oN3kyAdx6VateoKmBIXr05y9EA7Dc0RmltjPPH0Xq7cmGbPrkZkScK2a8qellXrqLFtt0ZMluVV84UqyfiUuztidhIHO5somyaXRmf4zy+cpikaZCZTe+a+e+YKuXKVpmiIh/va2dN6/wDmfthUMLBYLPHW6DhXZ+foiEZYKJZpDodJ+FdPDL/7v/7Ctg9sq9i/p5mrN2ZwXY/uzhTNDeGVdZRlOSws5nnuhUsc3NdaLyPcH6qk8FTqGN2BFv507Ie8sXDugR3/WijYJQYKYwwUxvjGxAvsCrZxKNrHochuDkR3YUjaXRoM94Mhh4lqXcT0LmJaJwKZkj2P69Wkagv2LHsiHydnTpKxxomLHnxKjEx1BJ8cQZOCK9d1rUHv3//rH2PbDl3dKU6c7OHpD/bT1BLldpmHtVpF5Tt+X+9sbn9tf2PqrqBho0HAneSzkl3hen6E4cIUY6UZRktTjBSnH7i4yM8CPCBfrPJ/ff11Pv/MIb7yyRMko0Ey+RLvXZtY8/0r11fc8Z3Uf1ytJXLnPeEhCcHh3a38y//nZ1axy6GWZZxevFVWuH1bkvT+dhSYbpalyhUsr4AmRerGVhvnDNiuS7ZSY4R3hCOE9dUTjS4r2K5Lxbbvet4kIYjqBl/ed4Ari3O8OjFCYyDIF/v286knDvD5pw+tyDiH/AYffmgPgxMLvHt1nETEz4HeZr787LENtBbuRJkAAvcR3tpJLKYLDI8tsrBUoLsjyfRslmKxWsuKJUNEw36GRufp6UwyMrPI6Pgilu3Q0RonHDIYm1zCNG3aWmLs3XWrtVURMj55a5bxG0XIp/OB/h72dzTx9TcvcubmOLFgbc7y6xof6O/hF548TDTg2xE1wk0FA8lggMe62jnU3EhnLIokxI5LIm4XX/rsQ/zR19/irdNDvHl6kK72BIVChR+/cpVX37pBvlBFkSW+9NkTm7I3loVMV6CF3+j7BXaF2vjO5KssmfkdaTvcLIYKk4wUp3lu+g2Cio8T8f08njxCd6CVqLa1OlbRnmfJHCNjjhLTulAkfUV62HJLzFauoEl+EFB2lijZi2SqwyyZQ0S0TuQ7bqX/6Z98hrNnRrh6eZJv/Ok7/N9//Bb9+1t56JFeDhxsp3tXA5q2M8I/AW1zbVy3I2cVGS5OciU3xNXcEDfy4yvpehcPr/7f/w7Ag1LFxPNqkuMhv07VtPn2K5dW0tA+XaUhFiQcNHjj3DAP7+9ACLg5Ns/EXAbP84iH/SQitSzQq+8N8eTRHsZnlhieTDO/dMs6PBkL0tIQYX6pwJ+/dIEvfqhm0Tq/VMB2XJKxII2xEJGQj+uj8zTEQ/S2JxmaWGR8NvO+cgZUKXhb+61bV/HcRFYKb6WF0K9oqHf04Ac1DUkIKrZN3jIJa6snIk2WOdzQjCwEQ0tpJvJZhACfvoZHhyz4G599lL/+6ZM1J0NZ2lAWxXId8tsMBgSCoOpfMzPwIHDx6iSJeJCPPNUPwLvnR3ni5C6mZ3PMzObo7Uzh2HVBolwZWZZ48pHdPPfCJfp6GjF0lUrF4vrNmTuCAQW//ODl1xVZoiES5G8++zC/8qETK6UvIUCWat/bTgW9mxqNpbpRSq5S5aWBISRJ4smezm0NxjuNeCzAZz92hN3djZy7NMbkTIZI2IcQgnDIR39fC0+c3EVDKoSyiTTich3QLxs8nTpBo57gncWLXM4OsWBmHtwJrQEXD9dzsJ0aQe2dxYvcyI/SZCRrHQmhbvZHeu7rmKhJfrqCTyKQMeQIbf7jNPr245fj6HIITQ4S07sJqLW6r0DCw0URBuARVJsIqo1IdzglCiHYs7eFpuYoj39gDwvzea5cmuDmjRleeP4Sr71yjYbGCP0H2jj+UDcNjWH0bbBhN1Mi8TyPilNloDDOucx1hgtTpM0sebtEwS7tSBr0LypqRkV+HjnUyekrY5y9NoHfUGlOhYlH/Oh1JcFI0McvffokL52+wRvnhogGDXRNYW9XY20MkSSO7GnDtBy+88pFXjo9QGMiSNWy6WyOrexPFoInjvYQCfq4dHOKCwNTuK6Hrin09zTxySf70VWFn//oMS4MTPEH3zlFwKfTnAzR25og4HuwK7fbUQsGuuq/ecjCt6n7UhYSfkUlZ1bJm9W7fFGihoGuKJQsk8l8lnBitTa+EOBTFASCgmlStux19l8ze6upPm4uGHc9l6qzvRKpJGreGlv1AtkswiEf6aUiZ86P0twYQVOVFTEt07SZX8wzOZNhYnqJcsVCVWpKta7rMTa5iKLIK8qNt0MWErqkIiEe6GJhOQO6ltLvTmPTeyiaJkulMnnTXKnVbqY29qChqTLdHUlCQYNkIsDEdIZKxUJRZCIhg+bGKAf2tqBrW3MSFELQYMQxZG3F2/paboQb+dGfSN3Y8VzSZo60mWOmvMhkaY7J0hxjpRm6gy20+lKE1SCadPdkKwmFkFqLdhWho0i36k6aXCPqyLKKLodWtUotP8g+JXbnJlcQDBkEQwbNLS5d3SlSqRDNLVEmxtMsLuTJZkqceWeQmekM7R0JmluiNDXX/t1Punqz8DwPF4/5yhIzlQXGS7MMFMa4kR9lrpL+b6LevxMQQmBoKk8d38XoVJp8qYqhKXWSoKC1MVp7j67y6KEuqqZFxbQJ+nWiQYP2xigtDRE0VaatIQIHOsGrcT6ak2E6mmJoqkJTMrSyv47GGKosI0uChUwB1/Xw+zSaEqEVx8Lj/e3IkmBmMY8Q0NoQIRkN0t2WIBx8sHXdZUhC3ZZzpypJJHx+cmaV2VKtg+Z2pPwBfIpCzqxyfXGBfXcEA64HBdPEw1shpkLt3p9fKjA0tUg6W6JUMalaDj5NIRQwaElFaE6GiYfvL83r4m6rXCaorahlIXA8m/HiDYp2lgPRx7a8zfuhpTlKNlvCcT0MXWHPrkZ0XSUZD6IqMpqm0L+nmaBfR26Sar43ssSu7gbAW3fBKIRAEhKSkG5TnXzwGEqn8YDWUAhDvft+8zyPpUqFXKVCSNdJ+DcuubzpYKBq25iOTSoYYLGwMROi9xuqKtPcGKG5bnax2Xr6RhBWgzyU2M+uUDudgRZsz2GmskjRLmO51k8ksVx0yhRLZUZKU7yzeJGTiYMci++ly99CQo8QVPwoQt5aELTJVqllSJKEz6+xp7+F3XubmZ3JcvPGDOfeG+XtNwc4/94oDU0Rdu1uZP/Bdo491E1jU2SV1vh24HoeVddksZrhQmaA85kbXMoOPhDdiP9W8MjBLh452LXqbw/t71z5WULQ2hDhr3zixLrbMHSVfd0G+7rv3cbqM1R62hL0tK3Plm5Ohml+on9jB/9TCl1RaA9FGM4uMZrNkKmuVpRrD0UIqjqL5RKnpyf5cNcuAqq6wqGoOjYDS4s4nociSSiShOu6FCsWZ66O8crZQcZmMuQKFUzbxtAUkpEgh3a3cKK/nWN72vAb91ac9Lyaa+FWIRB1sSGB5Va4nH2bqfLQAw0GWpuitDRG8LxaW3lTnWTq92m0NEUBePapu++do3Uy4r2IyIJa+fj95BO9PT6O58Gzu3rXDAYApnI5Ls3OsiuReLDBQHMohCYrFEyT0XQGvI1NsssTsrfyfzUsf3SnV4LL//W8muyqLEsIaVnrfWf2KRDEtQhPp47zSOIgz02/wZvz5xkuTlJ5wB0H90PFNXll/l1em3+PJiPJ0dhePtX6JC1GCnnFhvjBZ3Nu/y6W0gXOnx3l3LsjXLk0weJ8noOH21FUmRvXp3nz9Ru0fifOb/2vX6CxKbxiU7qd/VZdkwuZAX5/+FvMVNKY/z0L8DMHb6WH0YP38d59PxFQNQ41NPLaxAjn5mb4UFfvqozr4YYmXh4b5uriHN8fusEH2rv4QEcXmiRjug5ThTy/f/E9TMehNRQmqhuUKhavnR3kn//ej4kEDbpbEhzb00bAp5EtVLg0OM13X7/MmxeG+dXPP8azD99b4dTD3aZ1sXjfiIOr9io20Ea+Du5HRFaEzPqSQDuP10ZGcTyPh9vaaFhHQ2W2UOAbly7zwd5eTrS2bnjbmx5t5wpFbswvULFt/NrGFPI8zyOdLXFzbJ6puSyLmSLFchXbcXniWC+7OlMkdsBoYRljk2nOXRrn5ddvMDS2QLlcRdcUYrEAPZ0pPvexIxzsb920cdK9YEgan2l5ig83nmSkOMXr82d5YfbUTzwocPGYrswzO7PIj+fe4fHkYT7U8DA9wVbCD1g0A+oyotemeelHlzn9ziB4Hm3tcZ76YD9Pfaifjs6aO10uW+b6lSl+/z++wu/8mx/z67/xLM2t65ch7oeKU+VSdpDvz7zBe+mrOJ7z32mAP6MwnSkWS88zW/gq+xp+D11uhjW8In6WETMMPtjZy7997x0m81muLc4z3NBET7TmzHi4oZn+RKrmV1Aq8P/40XfpikQJqBpl22K2mCdbreIBJ5pa2ZtoIFso80ffP83xfe382hcfp6clsUrh0XFcnnvrKj965zp//NwZPvxQ3z0nTdfztlcmEKD8BIKBBwWBeN+IkJuBoSiUbJvKJiWtt8QZyFQqaLJ8z9YvqLXypXMlvv3SBd69Mk6uUKFq2ti2g+N6eJ5HW2OM5lRkVTAwNL7AteFZRqfSqIrML3724RVpxvvh/OUJvv/ji1y9MQ1CcHBvC7quYtk22VyZm0Nz/B+/82P+5lee5MC+ViLh7TNCl49LETJB4WNXsJ2UHuPR5CFOpy9zITPAdHmRivt+xpC34FHjFjiOyZn0FYbyk/QG2zgS28NjyUM1Qs8OZ2been2Ai+fHGBmeZ2E+j6rKfPijB9l/sI3mlijRWIBI1I9W525EY35272ni2Y8f5JtfP02lurUVvOd5TJbn+fHsO1zI3GCiPPff2wJ/xlFXf8D1qnieg4f3vrYNvh/QFYWeaJwPd+2iOxLlqY5uGgO3gnVZCD7cvYuK4/BfLp/Dch1GcxlkIeF6LpZbo7G1hyJ8sLOHQw2NFDIVZtN5Pvf0IVLR4IpV9DIUWWJ3e4rJuSzfff0y92uFrHU8bK9McLvyoBCCkpPn3fSL3Cycw/EcOvx76A7sp9Vfc0m9kj1F2SlgumUKdpbZyhgeHo8lP0mHfw+SkEmbM7yz8DwFOwN4BJUYh6JP0ObfjSQkLmbeYKx4jY+2/BKWU+V7U/+JpNHKvvBDJLRmvj35O5xMfpyRwhXS5gztvt3cKJyl6lZoNjrpCuxnV+jwmuckC3nFDvqnAR5waXYWQS0o2Aw2HQwIIWoMSq/WA7zeRShVTEYmF3nt3UFee3eQ0an0mvK/5ap1l7qgJElMzGZ49cxNVFXm6Yd30d4UR99A2vjUu8NMzWRIJUM8fKyblsYIuqZg2Q75QoXJ6QwvvHaVN04N0toS25FgYNWxCwm/YuCTdRJaBEPWafc3MV6aZbw0w0hxiiUz9xO7eXJWkbxVIm8XyVh5Fs0sD8X7aTaS6PLOdYW88tIVFucLqKrMnr0t9OxuoG9PM20dCUIhY5VfOdRkUv1BneaWGOWKtWI1uhk4nsN0eYHnZ97i3fRVpisLP/GygECgyyqqUFCEjCLJKEJBkWRkIa+YysjI9b9JSLf9XRG33icJCdtzeHnuzE/0nN5vKFKIkHYUEZJRpPBKy+tfJMhCIqRp/NzeAzT4A7SFIviUWzVhIQS90Tgf7OyhaJqcmp5gplig6tgoQiKqG3RFYny4q5eDqUaiukFRVNE1lZBfR5HvtuwVQqCpCn5DQ7uPANsDgQeOZ1NxirT5dlN0cixUJ7Fdi4TejC75KNgZxssDCM+jyeiiw78Hy63il0OAoOwUeDf9Ioqk0WR0ghBYrsm5pVeIaY34lRBVp0TWWqRgLVG0c+TsRURVYrE6Q1RNMVEa4IT7LAvmFBOlASJqglbfLspOgUVzhpq5Ui8+5Y5MqmDL5YeNIlepMpJZ4tz0NABj2Sye5/Hta9dIBVbzATwPLNfl5eHhmujUJqXYNx0MhHSdhlAQx3VrIjFr2BF7nsfkbIY3zw7zjR+dJ1+soKkyYZ+x0s+6mFmffBiL+PA8mJzL4rguVwdnScVDGwoGzl4ax+/XeOyhHj73iaOraj6e55HJlplbyHH20hgf/WA/thOtSdmuQ8ZYhuO6mI5D0bRI+O/fNiSEQJNVWrVmuv1tZO0813MjnE5f4mZhgrxVouxUfiJlBA+PRTPLkpnjRn4Mx3U4GttLsy9JUPHtSF1vcb5AqiHE3v5WDhxup3fX/c2CZEkiFDbo7Epumi9guzZZq8ipxUs8P/0WJaf8wAOuZXa0IsmokoIqFFRJuW2Sr/09qPjQZQ1dUtGEhiGr6JKGKikokrLyWV1SV/6m1D+rSWpNaVLUtlt2qv8NBgMRwsbDhI2Hf9KH8kAhSxIf6d617usxw8exxhYSho+gpnF1cZ6KbaHJMil/gBPNbXx+dz8hTUOWJDRVprc1wfxSgWLFIuh3V8SHAEzbYSlfolCu0N0c534E4e2mxT1uEfJqv3vIQiampdgXPknOWuSVuT9jvHSd/faj6FptoZa30gSUCHvCx4lrTVTdEqrQcXHJW0ucz7zGF9v+Nq3+XiQkZioj/Nn4v+Vw7ANoko5PCRJUoqTNGTLmPEm9pd6FNUO721cbqyW91q7tWgSUKMdiz1B2Cjw/80fMVSco2Jm7g4H3AWXbYmQpw3M3BiiaJpO5HK7r8r3r19Hu0PjxuKXG+pm9e+mJb67MuulgYHcqwe7UvXWQTcvh+69e4fk3rpIv1pzI2pti7OttorWhJjP7u19/c93PR4I+mhIhGhNBJudyXLgxxUMHO4kE77+KX8oU2bu7ib27mu8ifwgh8PlUnji5m9feuolpOiyVyyyUSuxNperkRm9VuLdM4imaJhPZLO+MT/LXjh9dkcpdeX/99zttXN8cG+dwUxPN4TiNRpwnU0cZKk7y9sIFLmQHuFmYwHQsXDbvWb5duHjk7SJ/NPo9zi5d45nGh3g8eZigUos4t1M6+J/+yWcJhYwVS9H7wfM8NF1mV18jv/mPPk3DJgShPM8jbeY4k77C7w1/+8H2/XKLhKpJKkk9SqMRp9lI0WjEadQTxPUwYTVISPETUHyIOvFtu6UY13N3sAvCw6urTQpkPGxqZydRmxRcPNz677d5EeCsfEbcMTHUUvi1+/j2NrtlAmDttdvv8+U21dqgduf1WT6+25Ow4p5Onl79GN36vm6/D8Sqc/lZRVDT2J9qZH/q/sF1yK/zuacP8vvfPUVbUwxNlYneNoZOL+R459IoV4fn+NzTB2u+Gbd9PQJWSYgLsTrNv2l43krL4zJ8cpBdoSMARLQkCb2ZqltirjJGUmsGIK41ktCaSei1341623PJWiJdnUGXDNr9u9Dl2rgV0xpp9e9iojRATGsgqESJqEnmKhPMVSboDh5gsjzITGWEdrOPlN6GXL9fQ2qc/ZFHAPArIUJKvGZC5uTYvuDv5pEKBHh2Vy8toRCnJib45tWrlC2LvakkYX21joYkBIaicKi5mSc7O4lvopMAthAMbAQvvnOdiwNTLOVK+AyVv/HFR/nMM4fw6SoImJnP3TMYAEjEAnS1JJiYzTIwOreicHY/pJIhZElQXUd9zHU9coUKjakwZddmNJNlJp9nbyrF2ckppvJ5/KpKMhBAkQQXpmfojMXoiceI+f0rccJELsfAwiJF06QtHGYyl6NsWaSCQcK6juO63EynWSyW2J2IrzqGnkArXYEWvuh+iCUzz6nFy7w0d5rx0sxPjHB4KTfIaGma1xfO8cvdn6U70LLlbQkhSCQ3F0WnzSkWqrV6YG/L8U0RjSbL87yxcI6vjj73wAIBGYkWfwPdgRZ2hdo5EO6lzd+IJqn1oPMWf8ayR0CU0OQYtjtLvvQcQd+zaMrd2uk/KZSsG0yVv02++h6toV9laOl/QRERGoM/R0A/yGzhj8mUXiXie4Km0M8TMR7F9SrcXPwfqdjDtEd+g7j/2VXbnMn/EUvll3G9Kgeavrryd9vNUDKvMZ3/AwrmRRyvgCQ0VClJQOunLfK30ZUWBKvLVIOL/4hs5S1MZ2blb8daX0ZX1r83HS/PfPGbLBS/Q8Uaw/UqCKGgySlSgc+TCnwOTflvw9K6XLV55/IY6VyJ/+V3vo+qyAT9OkGfTqFcZSlfRlBTk/zTH77HH33/TC34qj9CAZ/G7/zPX1rZnoS4SxlxM6hpIDh3/I1VKq7eGpquilBRpLVLmJ64f70+pMaI640MFy4zUxnlZPKj5K00c5VxZiojtPp2o9Z1WGpiQrcCpq01VO8clmv/x1pbONrSzFg2i+W6/OqJE3fNKyufuQ+Xbz08kGDg3LVJFpYKREM+Duxu4eNP7sdvaCtM1js9y9eC36cRq5MK59IFbHtjxJWPPNPPuUvjvP3uEO2tcZLxW5NSqWwyOr7I9354gWef3kdva5IlUcWqcxbmi0XawmEaQ0Fcz+N7166TCgQYz2aJ+gzivls3SdmyUGUJz/O4PDtHybb45J49DKbTXJufRxYSH9u9m29eubpqJbL8s4xAkjRSepSnG49zIr6Pqco8g4UJrudGuZIbeqAOiWuhaJe5nhvh/7zxJ/ytXX+JnkArxhZ4BJ7n8fz3z7Orr4mW1hh+/91KcJblMDudYXJyif79rUSCKTTJx0J1lJdmf58Go5s2/14Sevs91cryVpEXZk/x+sJZrB0mCmqSSrOR5LHkYfaFu4lrYQKKD11SMWQdVVJxnGmqzhSOm0ZVOlGkGJY9iiQFQO5AkZL89NCLboeH6xapWmNkKq/SGv4V5grfYK74DfTq2+hKK42hX2Ch9F2ylW40uRlNbgKc+or97kxWTdrJqWcZanA9m0zlNaZzf4AsBWgJ/zICFdcrYjoLVOwxFCm4Jg+gNfLrNAR/jpJ1k6XyCyyVX+Be17JQvcx88c9ZLD1HSD9KxHgUWYqA51B1JlHlxKZV97aDbLbE7HSWubkcbW0xMpky5bKJEJBqCKOqMhMTaXyGhs+nkcuVqFZsGhrDNDVHiUQ3t7K7E67rkiuW6WlN1Mq61Mq6kiSIR/y0NkRq+RJJoMoSt2g6tR98xupnf7uZAQ/qRku3vsOyU+BG7iwHIo+SMedJV6epOiUajNtNitZ+/g05QEJrwnKrjJUGaPPvQkIibc4yURrg4cRH0SUfBj4iapJFc7pewlMIqXF02c9g/gIPxT+ykhlYPs8NnY8Hlms/0Kd7JStT+4VdiTiWU3NolaWd5c48kCdjcjZDoVQlEQ2wv7eJ2AbUre6Epsj46inmUsVcVWtaxnMvXCJfuCXOIRBMz2WYnM4wNpGmXLFoSIRQVRnbdimUqszN55mZy+F6ULRM0naZ2UKB2UIRy3XxaxpBTcdyHHSlZg4S1nVcz2OhWGK+WGSuUGQim6Vs2ViuS8WpDX4RQ6/JXDoOuiyYzOWwnLulLFeOVwgUoRCRgkTUIBEtSIMepyfQyuHoboaLk4yVZpitpMlZxQfug+DiUXIqjBan+O7Uq3yq+Uk6Ak0rZYPN4LWXr6PrKolEcM1gwHEc5udzvPTjy7S0xpD9CiUng4tLu38/qqTjeWA65ZX031p4Z/ESl3ODzFeW1n3PZhFVQ3QGmukJttEdaKE70EqTUbOOvrNmWnVmcdwcitxE1bqGqj+Jh4nnyfXUek1k5acRHh5CKPjUXqK+J8lXz1M0r2C7OZr9v4QiwiyWnsN05jGdhXowsDm4XgnTnqZqT5IKfJao8QGE0PC8Krabw3TmkEUQ1ggGDKUNTW5AEgZF88o992M5SxTNq2Qrb2GoPSQDn0FXWpGFD8/zsNxFVCmOJLY3wW4G+VyFubkcTc1RRkcWKBarxOJBwhEfI0PzOK6D43gUlSpCQKlk0t/fysxMlkDI2HYwEPDpfPSRvVv+vHJHTVpCoG4jmKp1I9gr2TtdMtAknYw1z+sL36JkF3Dx6AjsJXQPddNlLE/qR6JPMVg4z3jpOgKB5VXpDR0ioTWhSBoC0CSdslOk0ehAESpBJUpQiXAjf5aE3oy8pfOqOZy+n3i0vR3H84j5dt4X4YEEA0u5ElXTJujT6Wi+/5e6FiRJWiG7OK675oT6xqmbzM7fci0TCBRFolisUixVefn164RDBrquYloOlYpJpWoT8GvMzecolKtIGgRUlbJlEff5COoaqiwhCcHuRIKiZdEWCeNXVbKVKmHdoGxZuJ6HrsgYioKhKHUypURYN2gOhdBlhcVSiWTAv+EWj6DiJ6j46fA34XguA4UxrmSHGCpMMlWZI2sWWLLyD5whb3kOr8+fpVGPIwuJnmBbXTls47hxbYqjxzuxrLVX665bG/zOnhnms184QcAVFOwlNMlPd+AIJTtbrzKv/XnP88jbJd5cOMd4aQbL2/5DKRA0GQn6Qp0cie3hQKSXFt+9K4WOV8DzTBQpTsl5Bw+B61bwhIPrFgGB65Xq/0wk8dPj4wEgCT8h/Rg+pRef2kPVnkKRwkSMRwEJWQrjelUcN3ffba0Fr56tkYSGEDoICUWKIEv++14LIWRk4UOWAsjSvWWFTXuasnUD05mhMfgLxHzP3LH99Yl5Dwpm1aJUqhJPBLh0YYxKxSKRDBEIGKTTRapVk1g8iOu6WPXxqXd3I4ODc1Qrm3/GPc9jupCn6jiENI2kP8AHT9xbSGgzkISEJm9dchlqHT+WayMLiZTRRtWr4JeDDBYuULJMeoL76A70g6uABCElhus5hNcIDiQh4ZMDHIs/w6nF55mvTuJ5LkE1yuHokwSUyAoZWpf9NBrtdAT2oEkGYTVOk9HFQnWKsBrH9RySWgs+ebXeTVRLoUgaunQnc7/e8Po+ty3vb7xV4lrmrbmuC0Ig1f9tFQ8kGKiaNo7rIisSfv/WBj/P9WonCSvGEneiMRW+q0UNaryB+yGbLxNWdY51NfBEVxcA3XewLx/r7MCltmZZUQJrrq2OuuKxNaUqDzc3cbi5aeWLEmw87bSMWsZAZl+4m33hbspOlblKmneXrvLi7ClmKouYbi0geVDZAsdz+c7Uq7h4RNQgjUZi5dh2AkLU2gnLJRPXdVGEj6Aco8HoAgQBJbbuvpZrjxcyA1zNjZCzty+LLSERUAy+0PYMj6eOEFaDGzJTkUQQhyVK1dMocgO2M4flTCGEjGmPIISK5Uxj2WPIUgRpC6vrBwkhZFS5xiSXRQBVjqFIUZbNpCVh1PuotxZsqXIUn9qDprQwk/9DLHeOmO9p/OpedKUZgcJOkPrK9ggVewJJGMT9H6pv9ycL3VDRNIWz744QifopT2cYH18klysRjflR1RCVskk06kc3VMbH08Bq0t5m8fVrl5ks5DjZ0sYX+moyu663TKz01q+y1F3w7rVfRSgEtunU51GzCY9pYY7EnuJI7Ck8z+No7IMMLqZrhkyyzEQ2R28izt7wiXsekywUknoLn2j56/fcb0xr4Ivtf3vld78SIqE3czz+wfpfVJ5s+Nxdnzse/9C623Q9d1vyzFuF63k1ASjXpWJZlGwbTZII6TqaLGPXO/02Gxw8kCcmFDDQlor13v6tCe3kS5UVO9NEJHBXygrgf/iVZ7ZVjt3IdbpXVeZ+H9+p5LAhaXT4m+jwN/HZ1qe4kh3i3fRVzmcGuFEY3aG93I3lNra8VeR/2P1lpB1Md1fKFmfPDBOO+GtBgZ0jbU6iyT5UoeNTwnfZIt+OqmvxX0a+R9HZvsugJql0B1r4tV0/R2+wbVOOaj7tCD4Oc7tUrq7urr9a205T9J/x01oqEEi3raAFAmWdFftGHrTVzP9lxHwfJKgdZbH0feYKX2Oh+F1kyU9AO0Bz6K8RNZ5gu9fH8Yo4XgmBjCo3bHt7O4GW1hgtrTE810NIgldevkoyGaK/vxVR5025rrcil3v4SM3f4UPP7t/yPn84PMBQNo1fVfhCXz+u53HhxhST81mKFRPbcVekum+HIkt8+dmj99y2KimE1e0qxXoU7DIRLchyF4kHTOfy/Nn5y+iKQkBTqdoOv/b4w9ta6T5I2J5N2f7JiMjNFYucmZjgP515l6vz8ziex0d29fK3HnqIA01N/IfTZ+iOx9iXStEV23hm/oEEA62NEebSeTK5MhcHpnj2sc3VrVzXZWouy5XBGYSAvq6GNTUGJCEe6DO/ES2BrX52K8fh4SEh0RfqpCPQzIeaTjJVnuOVufc4k75MyanueKYgbea4mhvmxdlTfLjx5Lrvu3FtirPvjnD67SEACvkK3/7z93j1pWtod3x3juNSKVvMzmZ4+kP9hMM+dFlCkwLMlocAQVfwELK8dkdCwSpxLTfKdGXhrlalzUITKk8kj/DR5sfoDDTX2wY3/t3deu/tvOM7P//TOaBtDevrrTluAcct3kHSE4CEKsdIBT9HzP9BytYwueop8pXTDC3+FntS/xqf2ocsbX3VKQkDSeh1/fwlVCnBT/q6L5sILU/8Bw601RQ3pVv32DIHbKfGi6JtYbu1TMBSvswf/+AMPz51A2DFnnct6JrClz589J4LJFWSt8QfuhMZK0+Td6s9XQANwQDP7tlFxNCJ+X01YuNPaSAAYLo2eef9JXgDDCws8PzNm3zt4iWCmsazu3o5PTGJe1vSJ2dW+fHNQRzXfbDBgOcWAAtEFNwFkGJ3MXT7OhsYnlhkej7HpYEpBsfn6WxJrHsj3onz1ye5MjhDOltCCMHhPa0rZML7wfU8ljJFMtkS+UIVZw3Vw2Xs6mkgEtp5IkbByjFRHmGoeJ2nU5/AJ/t35GFfXrHqsoYuawRkHxE1gF822B3qYCA/xnBxkonS7I612Lmey2I1w2vzZ3k8eQRD1tYUJYrFg3T3NlIsVJmYSN9KU8niLg8IWVbw+zV6djXwzIcPEIn6cYVHQImgSg0U7QzSPbTns1aRc5nrOyIzfCLRz8OJ/XQHWta0ed4Y3p9Bq0bAev/1KG5BIIsQjlvC9Sp4no0QSs0i2qtiOnNYbhpNXm2vW7v3ZRQRRpHCKFIIVYqhiAijmf83JesGutKJzNafRV1uQpMbyHvvka+eIeb78E+FUuHtz3044keSVgebO615ULatlQC5XLV4+9IIezsb2d/TRCRoIK3DQK8pFN5727KQCSjbLBN4kLXyq9LrQghUWSaoayyWyswXSoQMnajP2Pb1cTwX07FxPI+gejeReauwXYeiXbn/G3cY705NcX1+gfZIhC8fOkjK72c8u5rP0xQMcnFmloXS5oKVTQUDnlcFZxLPXUTIbXjOOEI7Si3dc+tL27+rmfPXJ5mYzTA6lealUwM8ecwlGQsS8Gl3kwG92mqxWDaZns/yypmbXB2awbIdosFae6JP39hAPTqxyI2bs0xOZ8gXKivOhWshGQ8+kGCg7JYYLw1zOv0ajyY+iCH7NpV63igUSSYsBTkY3U13oJV2fyNXsgkuK4OMl2d3rAOh5FS5kR9lojxLV6AFbY1gIJEMYRga8XiQocFZLpwdo7M7SXdPiuAd11iSBIah0tIao/9AK56wWbIKlJ08hhyomwp5dXGb1dfN9TyyVoHL2cFtn1dUDfFQfD97Ql1rDnKW65AxS0wUl1b6of2KTrs/RkCp+TksVAqkq0XyVqXO9ZBoC8TQJIW8VSFrldkdbqhryHsU7SqXM1McT3SiCImyY5E1S0yXaxLVnueRMoKkjBB+ZXW6vlYnfH/Zy7dDINCVNlyvQsUapmTdRJOTuJ5D2Rqkak/guqVVHkK2m8N2sjheAUWK1FfvHrIURJWTCASuZ7E627DsdOng4eB6lfp7wPEquG4FhIxAQtRb3XSlFUPtQhZ+0qUX0OV2VDmOEBrg1kideOhKI5K4NxnxQeHODNlOwqvXkU3bWeEyea5HuWKxpzPFRx/dS0MsuK3JVRU1Nc1tHilZs3CXBD1AybQoVE0qlkW6VKa/MbXKuXErsF2HomNiOc6OBgOWZ1Oyt1+i3CyuzS+wVC7zSHs7n95by7hHjFv3swCaQiGKpknB3JxmzebuTjeLZ18G6xqe3AFuGtS9IFbfIIf3tvHulXFGptJMz2X5z3/2FuMzGU4e7KS3I7WqZmVaNsVylYVMkcGxeb75wgXOXZ8gX6wS9Osc2N1MX1fDhrMK3/r+eU6fG2EpU8Tv0/D71g8iPvzU1ttu7gWBQJFUfHUG6oNuCQQIqn5OxPs5EOllpnyMr43/iHeXrlKyK9tWN/TqLYdvLpyn0UigSspdk7QkCUJhg1DYoHd3A3/+9dOceLiXk4/10tAYuef2i3aRucowk6UbJPQWinaWpN6GtgZZyXJtMmaOocLEts4J4HCsj/3hHhqMtcU78laFt+eH+NrIuyybPfUGU3yl9xH6wrXV76XMJG/MDnIpM4UiSWhC4a/2nqTRF+Z6doY35gb5p0c/g09WMV2b4fwC//C9b/LNZ36doKozXcrw9vwwz01dBsB2XT7UvIdnW/rpCq5W+vQ8D/MnGQwIhbB+nLnCn5Iuv4TlLhHWT+C4RRaK38V0ZpHuGAsq1ijZ6jsUzcuEtINociOe51K2hylUzyOLAAG1f9UE7XngYWI5izheiao9ge2k69sbBs9GqncZqHLtGulKC0HtEAF9P4ul5wAIaAdQ5Sie51C1JxFCIRX4PLrS/D5dseXz8TCdB0s08/CoOs6qbJmhKxzta2MhUySTL+PXtXU1XoQAn35vF1pNUomp9ydn3w9LZm7NrN5svkBXPEZAUzk/NbPif3Mnaq2izkr2U0KgSDJ2/W+C2kJJQlB2LPJmGct1aaY2Dtmus0K+9qg5KUpCqpm5ebWtynWBpfWuh+la5N9nHRiAdLmEIkkcaW5ad3npV1VM18G6R1Z8LWwuGJBSCO0xUA+D3Ml6NqKyJPjis0dIxYL8+6+9Tq5Q4YdvXOWHb1xFiNWpsT997l3+9Ln3sCx7lZFRwKfR39vEP/iVZ9f0P1gPZ86Psn9PC888sYdHjndv6vR2CgmtgadTH+fp1Mff933rkkZnoJm/v/cXeXvxIt+cfJkbuRHMbbbeOZ7Lj2be4SNNj26IRHToaAfJVAh5DeLnnfDLUTr8B2nQu4lrLUj3EDaZrswzUpzC2WaAIyHxpbaPkNLXr6nNVfJ8ffQ9/sGBj9IXbkASAsfzVqkjHo93cizeQVDVEQh+58ar3MzPIUsSx5Od/JtrL5O3KmiSwkRxiYuZSZ5s2IVPURHAhcwkZ9Kj/M8HP8a+SPNK3/JaGvCO51D+CaQmb0EmbDxCR/Q3WSz9gHTpR8wVvo4qJ2kIfBlZGJTtm5SsmyufUOUksghg2tNMlF/FdnNIwo8qpwhoe9md+j8I6odYXWpxWCw9x8jS/47lzK86gmvzfxMAgYahdHC09Ycrr4X1E/i1PmbzX2Wx+D2Wyj/G9apI6GhKM82hX+T/z95/h0lyZued6C98pLeV5V1XdbU3aDS8HWC8d/QURZGUKHKv3FJLiY9Wd6W7uzJ7JV2ZS3HFlUQjcoYzQ443HGAADDwaaKC9N+V9VnoXfv+IrOqqrqruMt3AzGre52mgMjMyXEZ8cb5z3vO+grC91ritwAP++OyJu1ri8TyPumVjLAs6YuEAv/KpB/g7//prPHvsErqmEA3raMrqYV9TJH73t3/qlqWCgKzRFkhvbz+Bqcb8mq3APYkYl+fmKTUMUqHQuoFAyarzzNR5rpbncD2PvnCKD3Ts4/npC0zU8kQUnQ917KM9EOe56Qs0HIveUIqhmN+Wdyw7zEQ1z4JRoWDVeLptD/3hNG8tjHAmP4nlOuyIpPl0zz2E5LWzCXXHYN7IbetcbAWqJIEAZWP9Wf9suUJQUQmqm7vWNxUMCILgq4w5s2AeAyQE/SMgRFctF48GePL+nXS1xvn9P3+VC9dncRxfL2B5ZqDR7KddPnce7Enz9IO7ePqBXcQ36Sp4YE8HoihQLNXwvI2pHd5pvJfa58u3fSSxm4ye5LXsSb41+fK2mfdFq8K1ygQBSSOhru8dIAgCv/jLj6KqMrJ8+2DAVzaTkUSZmltipnaV7tC+JQ3y5ZhtLDBen93WcWiiwu5oP22B5C31E5JaiA+27+Gfn/lLHs0Mcn+6j53RDJpyYxZ7vTLPidw4ZwtTBCSVa+V5dkYz9IXTRGSdJ1qHeH3+Og9nBpioFTiTn+JjXfsRBb92PBRtZbZe4p+d/h4f6NjD4607aQvEUNeo7ZqeTc4sbuvYFxFQBuiN/zYeZpNsB62RnyMT/hzLe2h2tfwuApLfYti8tpLBDxIPPI7rmYDrfy4Gmyl/G48bPfKqlKEl9EmSwQ/gLZUDhGaKX0ESgv7rFfeMRDLwQaLag7COzsRi58PKtwRkorRFfpFM+HNNjYPF7UlIYug9KRF4nsfvnzq+aX/5rWxneQZioVjlf/8vz1KsNhjqzZCKhQho8ipPCQBFFm9LfdFFjYy2dhZtw/uIx3htFmsNrZT5Sg1dUUiEgsQ0nS+8c5pP7NtFRNNWXB+XSrNElQCHk93IgojpOnx38gx7Yu08khnE9hy+MnKcv7P3/RxJ9XKtNEfOvNF+nDerBGWVgUg/HcE4f3TtNe5L9aGKMgcTXSiihOnat+yeajgGWaOwrXOxFexKp8mOjvGVs2c51NZKe/TGOOx6HsWGwVfPn6MjEqY3Ht/UujdfxHJL4C74pQGvwY12optIYqJINKSze0crv/a5hzl3dZrrE1mm50ssFKqUqwamZSNJIpoqEw5qdLTE2DPQxp7+VnZ0p2lNRzctufjkw7t4+Y0rPP/yJc5enCIWCfglhjV+1w8+uZfOm0SRnp/9NpIg0x/aSU/I99R+Zf4ZZhtTRJQYT2U+jiTIlO0ipwpv0nBqPJR+ipAU4dXsD5g1JqkvSx99rvuXCayhoHemcJyilUMTA7QGOjlbPE7ZKiEIAiEpQmeglwPxexFZP1V1O2iSSmeghftTB/AQ+NrE89sSLPLwGKlO0RXI3DIYAIhskothuFVyxiS2Z2N7Bq7nrFkvLFlV8ubWBHAWoYkqe2M7kAX5luc2pug81rqTmBok26jww5nLnMqN88HOvXQFE+SMKseyI2QbFT7WdYCgpPLM1Hk0SQE8ArLCk207eXb6Av3hNDmjStGssyfevjTQdAcTvK9tNx3BBHP1En82fJyDiU6OpntpC6wsr1iufceMigQUlJuuS1lY3b2hSqtngpIYRGJtVvnNoZ8vHBRCYu1skud5jOeLfPnYGfrScR7Y0U13Ko4kBJHEzTHXBQQ/IBAiwPbT2XcSZcNYUip9t2DZDmOzeT784G6O7ukmEQ02tQRWL7sRPXtJEAlIGhE5RMWubrn4mTUKNBwTx3NXZMAato1p21iOQ65aoyMWoVBvIIvSilluRyDO1dIcBbNGZzDOwUQX35k8gy7JxNUgNdukZPkTH02Ul/g6i3A9j4CsktRCJJrLZwJRrpXmmKjl6QzGuSfZc0sfhoZjsGDcmcB8M3igu5uSYfK9y5f5Zy++xI5kgtFCAV2W+crZsziuR7ZW55N79rCnZXPWSltgtAggaCDEwJm65ZKSJBIJ6dx/oJeOTIzrEwtMzRXJ5iuUqw0M00FuBgORkEZnJsb+nR10ZGLoGyQM3gzTslnIVxkeyzIzVySVCK07O33k/tWqZKO1a3ieR0AK0B3cgYvL+dJJRmtXyWgd3J98gpiSoGDmuFo5T8Op81DaF66wPJO6XWPBnCdvZcmbWT7V+QtrVlMmG2OMV68hCCIdZjdT9XEMp4Ht2QjAbGOSFr2NjNaOsg3VOl3S6Am2ISJwtnCVC6XhbXEIJmtzd6VWtmgfansmiqivS7is2DWKVmVb21JFhZ3hntsGWYoo0RqI8pQe4XRughdmLnGlPM9QZYGuYIKiWWeimkcSRB5u2YEsSrw8d2XF9/fG2/nC8FtcK89TMGvE1QAp7caDMSir9IaTdAbjHF8Y5Uxhkmtllc5gfFUwYLrWezIA3U14QKFa5/kLV7mnp4OhtjTdqfh7vVt3DR3hCD3RONIdzh56+Eqtx2cmcZoPPkWW2NGZojUZobs1QUvcHwvXDAY2sA1BEJBFmZQao2rX1/Sn2AhqToOiVcFwTILyjUyNrsiYtk3dtjEsm2hAX1NYLaYGyJtVJmsFgrKKKsq06hHyRo1r3hyO59GiR2jYFrONErONEjXbZL5RJq35Aa8siKiS3AxGPKKKTsU2GK/m0CXfNny9s+J6HnXHuGNZus1gIJmk2NVgslTi/Nwc46UiVdPEdBzOzs0hInC0s4N72ttpD2/OLG7zwcCi8YpXBs+3Pb2tD7bgWxh3t21Nmngz+Oq3T/hBQDLM7sFWwiFt3f0LBlY/ZBNqioK5QN7y60GGW6NsF3E933t7zpgipiTImfM4nkNIDhOW/VnyY+kPYTh1JuojvJV/hbdyL91yX8t2iZw5x5wxxeMtHyaupKjYJUaqV3gr9zJtgU4eTX8AZR3Hro0iIGl0BVv5cPvDDFcnqTlbrztPNbIbCgZ8QxYBWRKRlduXClQxQERJQVNdUBLXnrWX71AwsCPUcVsRJcN1mK4ViasBdsfayBoVrpfnma37s3Pbc1FFGUkQqNkWltvgamme1oB/PUiCSEoLE1MCnMyNE1P91ObyQKdsGZSsOlFF51Cii6vlOWzXJWusVlU0HJOpxvyq93+CHx881NnDL+47hCbd2c4CD4+aZfFXv/0X1Gw/+xcOaHzwwT28+PYVAppCVyZOOKiumW0VBYGdPbefSYqC7945WZ/D3QYHYqqepTOQWREMtEcjCPgmcKlgkJGFPIfaW1fJuZes+hL/ouHaXCrNcDTVx6tz1ziZGyeiaBxN9VG06lwuzjJSyeLhcb08T0oLo0ny0n2LAGFFp2obmK6DIAhYnsPF0gwpPYy8Rnuq7dqUrdp7EpiHVJUHursZSCb55oWLvD01hSZJiIJAPBDgcHs7P7N/P1FdR95kVn3zV6RXB3cOnBGfSCjcuXaN2276ph7BtR4WhVKVRx4Y4MlHdrFv161teNf6fovWTtkqsmDMAR5jtWGCUpiOgEZcTTFeG2FneB/z5gyKqJLSbmhFy6KMLEaI2nGCa9S7b4blmciCwue7/hqD4b1L/fv9oZ1M1ke4XDrH0cRjRGRvaX/XUg/byPEFJI33ZY7y7OwxrpbHthwQzBk5f1Zwi5Yfz/P46pffJBYLMrCzlT37Om+7XsttULLmKVrzmG6dpNqOepMIjed51OwGFWvrmQlJEAnIGmn99oHpZDXPvzr3LJO1ArIgElF0DiW7eX+H34UyGG1hZ7SFl2av8Ouv/wkhRWNvrH1VevGjXfv5vUsvMhBp4Wf6jq747I3sdf5i5B1mG2VkQSShhfhE9wEebFlJfvU8v6NjuHrrbNxP8KMLEWgPRzjc2nHHG429pjzt8gd9rlTjP3zpRfLlOi+euHUrrq7K/PD//H/dNlsmCyJD4W7eyZ3H2kaT1HB1gv5QB+3LCIk70yl2ppd30KxNAP/a6AkezwwxFGvFcGz+zfln+UcHBvmFgQeWxsfFsfRn+ldzHD7UsX/F63+w/yP8X5dfYm+8nZ/pO4ooCPyzM9/j3lQv+hpeDHNGnnkjv20S81bgeR6KKNIRjfI3H7j/lsttti1zC6JDC+BMAxGwL4LcC4LCrYV77wwKRoOKZaCIEqey0zza0UdIWTlr/uWffYQLl6d5650RREEgmQg12xJXn5RoRF/V+9uitTFSuULWmMPFY6x6nZiSRJd0ZEFhsj4CeMw3ZlAFlbS6dW90CZmgHGFHaPeK2aIsKCTVDBO1EfL1VwkIO9HkDmQxzmz5iySDH6BinKRinkMUVJLBD6BJHRSN16lbVwCZROBpgsqOVYJQj6QPsWAUqNW3FgxUrCp1p4HjOci3cPp66YULHDjUQ2fXxrJBcbWNqNKC6zlcKL2KKEirdAZsz2+d2o6gUkDSSCq35jssoi+c5v84+rmlGZDQbGEKLBsgPtVzmI90HfBvPJr67ggraqGaJNMRjNMfTtMeXJn6f6ptN4+0DKxok1KbM5fl8GuUBaZrP8kM/LgipGrodzgjsBySKBKQZaqWzzRPx0P841/90JqOr6u/uzHlTUmQ2B3tQxIltvMsvFoe50Bs5+0XXANPtu3iQnGatxdGCMgqn+s9shSAb1XP5dHWnVwqzvDF4TfRJJnP9hwhuI59+2R9lulGdkvb2S6qloWAnyG4FS5ns4Q1jc7oxsY62EIwIIhxPDEB9nWQMjRtfFYsky/VCOgK+iZbG26HhUaN4WLe76F0HWzXXdWL+vKxq1y5NottO7xzZgxVldeVtfy1X3yUXYMrjWOSahpdDtBo1KjYJaYbY6TUVhJqGsOpc7F8Gg/ImfO06p3E1dSa694IZFFBFiTkNRjtIiIeDpabxXZbUDy/PNOwh7HdIpZbQEAipj+EJndQNk5gOzkkIYokBlmofhs99jdXWXPuifbz/OxbW95n35PcxnTtNfd7EbWqgR5QCIY2ljmq2kWK1ix1p9QkM63+zUzX2rb8sCRIaJK6oUFDFkWit3HL0yVlzdnDcrw5P0JPKLkkPrQcmiRvKGU8a+QYq82+a7MR1/MYmc/x58fPMphJEdJUfnhxmFhQ4+cfPMyV2SznJueYLpTY19nKZ4/uJ6DIuJ5HpWFy7Po4ZydnyVVqGJaNIAikwkGe2N3Pvs5WIvrtr4tKw+CFi9c5MzFDUFX51cePEtLUpfvZ9TxK9QbfP3OFa3MLFOq+yFhrNMwH9u9koCVFWP/RcYkMKorvcHoX1i0IAngeAUVBajSVSlWZ/QM3NBXqhkWlblA3LMIBjVhYX8okbHQCKQki3cE2NFGhRmPLGiozjQXmjTxVu74pVUNBEOgLp4ipAcym+2FSCyMtjhlbPLndwSQxJUDdMREFkZQWQl6HQDhZm2em/t4EA9+5dAlZFPno0BABZfW443kexyYm+MqZszzQ3cVPHziw4XVvPkwVNBCTILsIYisIKjf/Ai8cu0wwoJJJRcgkwrSmo8jbcOJahIeHg4fjuYQVdc2HfCio0tEex7FvL/Kx1v6E5Wgzxe9RsgrMNabpD+0io7VTsBYoWQUqdomKXaJHHCC2Ad/t9SAJIvI6fc8Cgm/h65l4nonn2YCL7ZYRkNCkdgTEpr1sHcMex8NDFmPIYgxTWLv9rlVLoq8T8W4UjufcVgo41BxoNjIrKVsL5MwpcuYUhlNDFpQ11Qcdz8HbZjAgCuItMxp3ChXLYLZRYricZaKW57HWnfRHtt6jPV3PMlabuYN7eGt4nkeh1uClSyPMlqp0J2PkKjXeHp2kJRIiX60zmS+xUKkxlivy0GAPnYkYiiTSsGyOXRtjrlzFdlwc18N2HK7P56gYJlFdo78lSWCdyYLneTQsm9eujPLqlVHmShXu7etccb+6rkvVtHjm7FVeujSM6Ti+a4IH2XINy3H58IEhBjIpooF3r5S5FgRB4EM7dhJVNQbi22vNux0CkryUIpdEkVhY9wXdJheYXSiRL9dpGJY/PifCdGXitKejpGIbMyASEIgoIZJNEqGxxe6kmtNgrpFjprHAQLhrU98NKzph5c60iLqex3ShREskTHsgdttnlOXazDYWyJqFO7L9zeJKNku+3iARCPB4X58fBAkCHmDYNpeyWb514SJjxeKSw+5GsYVR0UIQgqC9n8V+4Zvxx998E0EQGOxt4Z7dXTxwsJd4JIiuKaiKtOXAIKxotAXDfktKUyHq5oDgEx88iLWBQACgoy2+6j1dChCSI6iixoIxR87MEleSZPR23wTFs5iqj9Fw6gTlEDFlO6TIjZAvVVzPwnYLiK6Oh4skhgiqe8ASyddfwvNcPM/1+4cFDwSZeOCJphTrys2FlACqqCAhbnmW6Vto3vq7OwYyGIZFLlvBtp1b6g0UzBlyxhQ1p4guhanbZVzPXRUQCBs4X7eDwMZnQdtB0apzOj/Jt8dPsyOcZm+sna7g1q4V27WZqM8yXJ28w3t5e9RMk6lCie5kjM8e3cc//86L/OnrJ7mvv5sD3W0oosi/+M6LXJ7JEg/qJEJBREEgX6tzqLud7lScoKpQrDX47qlLfOOd8+zvbCURCqwKBvzZra/CODyf48+OncbxPI72dfLLj95LUL2hkGfYDuO5Ar/3/Bu0xcI8tWeAgYyfpXvt6hhfffscQVVBlSX2dW69lHcnIAC/89DjSIK4qqx5p9EWjlCzLcKq6ssTWw7HL47z7ZfPMTqTp2FYiIKA67mEAxpH93Tz2D0DPLi/D1W5fRuzIAiIHvSFOsiZJYxtMOqn6vNcLY+xI+xzijab4rccB9P2O9Ic54ZtLwI0LNtvIVR8cS/HdXE8D8d10WQZRZIQRQHDsnnj2jgPDfYQC+iosoSyjlCaL3hUYdZYIG/emRbfzSKsqpyZneULJ08xmEzR2SRdNmybiWKJL585y7PXrvKRoSF2pTc3+dgCZyAPziSCGAICeGJytfgHMJMtMZMt8crb1/g/vyTx0OE+HjjYz/6d7Qx0pVlOdNxoYOC4LgWjwUgxz+EWP+q5mSQx0Le53sq1EJajRJU416oXcD2XhNpCVI5Tkgqk1Azniu+giioROUZADt5Ila0zCV5O+ttsEBRUBjDs6+Trz6FKnUiCjmFPYjgTTaU3C0GAeOAJ8vUXqBmn0KQ2Quoebi7qLd5suqiiigp1d2sWnJIgId9CJRDg8z/3IF/+09d5+81h+ndkaO+Mr7tsV3AvsqhRMGfZFX2Is8UfrqlCKIs3Zj1bhe061J27bz3aGYzzmZ7DfKbn8LbXNVWf51p5gqn6e8MXONjVxkcP7qI7GeO5C9d44+oYj+7s5WOHdpOv1fn3P3idyXyJmmmRCgu0REP8m5/7+Ip12I7DPT3tfPzf/hFX5xbY2ZamPb6ynimKoq9Qly/zW1/6LulQiJ9/6BDv2zOAfpNq3kS+yDdOXCRbqfHvf/HjDGRSBFXf9+TJ3Ts4PznDy5eGiegaezsyd10IbPEeX76d5fd9a3B9X4DtjA8343964DFqlkk6GMIwbc5dm+af/+EP6M7EeHB/Lzu7W4iGNPKlOm+dH+ONs6OcH54lqKkc3du94e0cig9xvTK5rfa6kdo0p4tX+EDbg1s67vFckXdGJ9nRkmQsVySkKsSCOpIg8OLlEYq1Bh/YN4giicwUK8wUy8yUKjw80MO+jlZiQZ3Ls1n+/PgZxnMFdre3MJhJMdi6/kP0eP4CWaPwrkjMr4W/du+9dMVi/Ofjx/mdZ57hP3z8YwQUhVdGRvmTUyc5Nj7B//zkE3x4aIiW0ObspjfPGRDieEIRzNOAANqTcJNYyaHdnZy4MEE2X/V1pG2HV08M88apUYK6QksyzCP37OB9DwzR1RonuMG63ny9ylSlSERVOZebozeWRJWkFfHkb/0vXyGXX92WtRZ+6zc/yIE9q5nuUSVORI5ztvgOXcE+9GbdWBcDdAX6OFt6m4zWsdRSuIiGW8d0DYpWjprjt79ljTkcz0YVNRRRQ9mkHGpAGaJVexRwmg5xIAoyYe8AyYDf2ikKGiDSFvmFJdU135N+7Qe24VrbkidWRAn1NnXys6fG8DyP8+cm+Lu/cYGunhTxRGjJznU5fvlXHyfaHqMmFjme+xbqOmIzqqiskALeCkzPovoeGIxsB8/MHONadfteDFtFRNeIB3UEAWIBndZYmJCmNqXFIaQqWI6D664/QEqiSGssgq7I1EyLhrX6+nNclwvTc3zp2Bk641F++2NP0JOMo62RVVqo1Dg1NsVgJklXIr6ifioIsL+7jefOX2OqWMJyHFT57pWGKnaBsp1Hl4Koyz0WcH3fe6dCVF6/PFB3q5hug4AUQmV7Tn27k2k8/FbBuVyZP/rOWxzd08Xf+PTD9HUkEQVfdMjz4DPvO8gzb1zkh29f5QvfP869e7o3nDU7nBjixbm3ub6xoXZNlKwKw5UpTuYvc09i16aTfnXTYqpQompYNGxfzn5fR4YHdnSTDoc4NTHD1bkFXNcjoCrs72zjZ+7P8KdvnERXFB7d2cvuthYGMik+f/QAmWjotiJ3r8yfYN7Ib/2gt4mopvHBnTvpjcf5h99/ht955llqlsV81Tel+/cf/xhP9PVt6Xrfwh3SQPDqIIRATOCZr4C8C0HuZbGj4Jc++QAffnQvU3NFLg3PcerSBBOzBUzLxrId6oZFuXqBt86O0d0Wp78rxa6+VvYOtBEOauveDEk9QDoQ4lI+S3so0iSNrEQ6FVmVkvY8j0bDolCqky9UURWJxx4cWtexMCRFiKtJylaRfdEjKKKfnlQljVa9k+P5V9kdOUhQ9iMv13O5Vr7AS9nvU3OqGE6dkl0A4KuTf4QiKCii3yHwMz2/tqmzLSAuSagKS1lyAd9KQ13Kefv0GbUZsXprSo56nkfJrtJwjRUWopuBLEgoTeLjrXD61DjzcyX/RgxpVKsmpumsecObloMgiM3WSRer6TC3ygwJAVVS0ERly7VKy7WpNbshRLbPY7mbcD2X0doMZ4pX3hPp00VIorjUsywKAqosrbDiXaxZ+q6Lfor2+MgEJ0anmC9VqRgmRnOwrhpWk0eyOnB4Z2SS63M5JvJFdEVGldYvKVqOQ75aJ1et8/e++O1VZK+RbI6FSg3TdmhY9l0NBuYak0w3wmS0TlT1RjDgeh6Wa1K1y0TlBOs97fLmHAUzS7ves+L7m4Uv631jG6blcHUiy1/56FHS8dAahG6J/s4UY7N5fvDm5U1tJyKH6Ai0kK7Gt1U/L1hlXpx/m8OJXZsuAIriokGRC81unrlyhbdHJsnV6gRVhWzFb7kUBYFEKEBY13Bdv8wpCAKSKCKKAoosIkury86LsF2H4cok47UZau9CZnE9CIJASFEYSqf57ccf4/eOvclMpcL+1gyf3rOH+7u60ORbK6uuhy3oDFh4XsOXI3ZzvvCQV8NzqwiiLwE60J2mqy3Ojq4Uve1J+jqTjEwuMJ0tMZ+rMJ+vLJURJmYLDE8ucG0sy8XhWfo7U3RkYqQTYaIhbYX/tibJpANBRFr8C0FY3Q7zyH0DNIyVDwrP8zBMm1K5wcxskRNnxsikI6jq2g+0iBKlPzTEw+mn2RU5gN50z9NEnf7wEI+kn2YwvJe44kf7AqBLQVr1Tsx1Uu+SIBKR40uve4MDBKUQ0k2z92K5zmy+gVrsZECNYFYlpopFTNshHNRwXJda3URVJFRFRpJEypUG8ViQkK6iKCvtpG/GbGOBhrM5a8vlCMtBNFG9bbp+/8FujMbGHtjRaADbLeN6Dm0BXxVyLdVFQRDQRY2ApG85GHA9F8MxyZkl0mp8/eWcWTy3iCBGEcTWdz1oWHQofC17itnGwrZkpLcLn2dx4/jFtaRrm8/2hUqN81OzfOvkRSRRRJMlEiG9uRYPrrFuOQ0gFtR5cvcOzoxP8+b1CSRBpCsZWzVjFRCQRAFNlgiqKspNrqaLPIE97ZlNS5pvFg2nRt6cw3QNcuYsSbWNkBylZpdZMGd8aW3A8SxKVo6CuYDrOQTlMC1aB6bToGBlcTybnDVPQskQlqNoa7h2bgae51E3TOKLkuxrIKAphHSVunHDN2IjUESZ3lA7o7WpbQUDNbvBxdIwE/VZ2vX0Lb1CboYqSUR0DdNxiOha0/7YZr5cZSxXpDMRwWma3wmCgCJJq5QfBQFSoSCXpuepNExS4SDx4E36JnhYnsWbubOUrNq2xJY2g3y9Ts1a+753PY/+eJyDbW2URkdRRImQqlKzrKXvRFSVqL7x4HJrcsR44FV9jwJ5FyCCV2O5HrimyLSmorSmoty7r5v5fIVzV6c5f22Gi9dnmZgtUKub1Ooml0fmuDQ8hyQKHBjq5MiebnbtyNDbniQc1AgFVVRZxnJddFnhfd3tvDU7sSTCs3ygeuLhoXX33LYdZufLzMyXqNYMbHvtHzUsRxkMRxkM71nxvi4F6A8N0R9auQ1RkOgJ7aAntGPDZ3FP9BB7OLTq/flchbHRCi3KXqKixsKsQ6E0S92wyKQimJbD9HyRaEgnGQsR0BVGJhbYM9iGkhabwcD6GK5Mbcv5LqFGCUi3ZmcLgsCnP3f0lsvcjJl6Fss16AgMISAir6O6GJR0wnJwyxr9HmC4JhO1WVJqbE3SkueZOPZVXHsYSdmNJESb3RwSCH6w5XkNPzODy5KZjqD4y3jGstfgeYbPqxHUDbvmLbKWn599a1uKke82JnIFvnvqEj+8eJ3ffOpBDna3kYmGCGkqrgffeOf8us+brmSMp/fu4EBXO7/9pe/y3dMXCSgysaC+qiNAlSWSoSCKJPHzDx4momtrpriTocCaZYY7jbpTpdzMBtbsCr2hXVTsIjON0abhlkfDqTHTGGOsdhnHdUioLcQUn/RouSazxjg0oKqX6AoMbjsYEASBgKZSKNexbHfVWOl6HrWGSaVuEtQUNpun3xHuZKw2w5nC1S1rf1iezUxjgeO587y/9X5kIbThwDuoqXQkohSqdVqiIebLVWRRJKgqPjnQdgjrGrIoEtE11OZ1kAwFlrwOBEFgqC3NtbkcluOiyNKqYMB2HQpmmVezp5dcRd8NXM5mGc6vXZIQBAFFFNmbaeHC/Dzz1Sqvjo4xW7mhzro3k+Fg28Y7CrYgR5xAkNp8XwKpB8+6AOpRBHH9mpggCGSSETL3R3jf/UOYls2V0XmeP3aZt86OMjVXpNYwcVyPkxcnOHlxAlWRaEmEefBQPx94eDdDvS3M1SrM1asMxlKcXZhlKNGCJm28b1eWJZLxIB98ci9//OU3eODefro6EhtSNny3UK2bWJbDBx7dzV/85UkKpTrRiE4kqDE8voAsi4xM5EjFg4iiQCAQo1o3UWTpljOgxWN8K3eewjbkfDNaclN9wRuFJMhYXoN38n+JKursiz1BaFkmZRERJUhcDTOxDedCw7W4WB7hQGwngrC6HOE647j2FVxnFkk9jG2dwnOmEcQ4gphCEHRs801EeSeeV/bNu7ARpH5EMYljX/CPSe7F88CxTiNKnYjyDiS597b753kes0aOL409w8x7JG6yVWQrNc5PzbG3o4Wfvv/A0qBrWDaXZrK+bv46z41MJMSuthZ2tqb4Bx97kn/y9R/wjRPnMWybzx3dv+K+TIQCHOhq42vvnKcjHqEzEV0a7OHGJm7OatwthOU4LVo7HfoOvjv9x7TonU11Uo+rlTN4eGSNaTzPpTOwA0XQMN06YrO0qksBurUBugNDPDf3FUJSZIW66VagqTJ7+lr5wZuXOTjQQSysIy9jyhuWzfnrs5y9Ns3e/s21oQEMhLuYrM8TUULbkgi3PYevjj/HgegAWkhFlZQNdRa0xyK0x1YbUnmex5O7dyCLa5eYfuGhe5b+lkWRjxzYhWk7SKKwagz1PI+skefV7ClGa++u+udfnDvP186f3/Dyp2ZWth7/xv333+1goA3EDChNa1DNYy3hoVtBkSX27GhjqC/Dr33uYWYXSlwameP4mVHeODNCoVTHtBym5op8/blTfPOFM/zn//Xn6Wv1W7P++MI7dIVjaDeRBzcCQRQIhzQKpRqWdaNu7uDiei6yIG1ZxepOQJbEFeWLrrY4jushSiK7B1o5fmaUh4/0k81VKZYbtKWjRMM6l4dnkSWR9kxszfVans3V8hinCpe2NdMcDHeRvI1j4VaQ1rpIaZ14uMw1RlHXEftJawna9RbOFm8tr3or1G2DV+dP8dmup5DWaI0VpX4kOYsgJpHkIazGd5CU3YhSByDg2NcRxAyudQ7PayDK/SjaY5j1L+N6NrL2NKLcj+eVMCv/F6Lcj+uMIwgBX7HzNjhTvMpLc2/z8vw7Wz7G9woRXaMzEeWd0Sl+cO4qnYkoDcvm2lyOb528gK4ot03bi4LAgwPd/K33P8xXj5/li2+cIqipfOTA0NLg3pOK81P3H+C589f4+1/+Lk/s6meoNU0koFOqNbgwPYeuyBzqaefRnX13/bgVQSEkRZs+ImtHOx2BPs4X3+Jy+SRteg/3pZ5Ga5JlJUEmLMe27UOyHPFwgF/91AP8vX/zNX77//9NetoS9HckCQd0CpUaZ65MUSg36G1P8CuffGDTLbciIt2BVh5N38N3pl/e1r7mrTJ/OPItPt31Po4m925rXcCmdflvLjMtomhVOFm4zJfHnt32Pm0Wv37/fXx239bPxWbUB2FL3QQ+eW07EAQBQQBR9AlCHZkY8WiQfQNtfPr9hxifyXN5ZI7z12Y4fXkSx/btbKOqxq5EmvZQhJlaGUnYHAHMcV0qVYPX3rpGNKQvpdRdz6VglfndK19iKNLLrmgf/aFOEuq7b4Pa35WiozWGKAg89eAQkuQL9wiApim0piIEAyqW5fh9tLpKe0sUURQJrWG8BH6aa8Eo8mdjz2C4W+cLAOyIdG34vCxky8zOFJmZLlCvr7/dPffHkCN1ak4R13OYbQxzNPlxlDUCgpQWpVXfnnCL7dlM1+c5XbjCnugOwjdlOny9hkW2pu9qhhAAVDyvgGNdamo4+JoOwmJ5AA9BzOBY53GdSUQxhSh1g2ciSh0IUua2+/ZW7hwvzb3DO/mL25Jdfq8w2JriZx84SN2y+ZPXTyAgENJU2uMR/tpj9/KNd843U9LrY5EI99hQH1XD5KVLw/zXl4/Tl0qwszWF0uwFb49F+KefeT/fPX2JU+PTvHp5FMtxURWJVCjIQ4M9pEKbs0HeOlaq39XsElW7yEj1ErONccaql0moGRpunapTpuqUmG1M0KH3rVzHHYQiS/R3pPitX3yKl05cY3Q6xysnh33TNVGkPR3liXt3cu/ubrpa40vfmzemsFyTNr3HJyavM8YKgkCrnuKB1H6enX1j27yWy+UxXpx7G9fzeCC1//ZfWAdbyQSt9Z2a3eC52Td5JXviPSnVdUajtG7SeXA51LtuVHSHIQgCqiIjiiKW7dBYKJPN+yTDQvlGC9hCvY5UKlCzLWzX4UJunsF4Cv2mQ3jmhXPU1yCueXhYlkO+UOP0uQn2DLUTCenNz/wa7fnSdbJGgZHqNJ2BFnpCbfQG22nVU2iSukpK9m4gGFCX3BTTSf9CWN7DfPMD3/M8AprcDLDWYF27NhO1WY7lznGpPLotOd+UGiOjJQhItyeljI1kOfbGVYavzlEoVHFsF9txFxknfs1LkWhtizF0KEEwFsT1HCRBwVBqSOvU1uNKlIyeRBFkrC22R3pAwzV5PXuaNj1FQFKRbuqOEMUOBCEKiIjyLgQx0eQAhJDkQV950zNB8PwSmaAgKYcRhCieV0VARRDjSOph8OoIYhJxnVKa53k4uJzMX+LFubc5X7q+ZU7EnYIgCLTFIvzCQ/dwoKsVXZGRRJGHB3sYbE3R07QZ1hWFX3joMAe62ogFdMK6yr6uNj5/dD+T+SK26xJQFTLRMEf7OhEFPzjoSvgZLAHIRMP8zAOH6IhFyERuDH6JUID7d3QRDWiM54pEAjc6jURBQFdkDve047gu4/kipVoD03HQZJlUOMhQW5rW2NYH040irbXTqncRlPwgeShyD1ElheM5dAT6iCtpQnKUWWMCRVTYE72XmJJivHaVtNZOTE2jiBqhZqvyQHg/CfX2gePtIIoCAU3hvr09BHWV6fkihUod03bQVZm2VJQdnSm6WxNLnQYVu8RE7SolK4fpNograapOiaicJChHAI/x2lW6g4NIgkxQ1ugMZtgb3cGZ4pVtjS81p8GF0jCyIBGWA+yN7nhPSraLpmAvz5/geO4847XZ90RX4Ga3xruN9ywYWNQfqNZN6g2LUqXB8OQC75wf59LILDPZEuWqgSBAJKRRcyymqmUWGjU8z2O8XMRyV194z754gYX82vUr03IwDBtFFnnw6A7i8ZWzBtO1uF6d5Hp1El1U6Q21c29iD7uj/SS1GGEpQEDWCEr6LSPmO41bbWe9IACg7hjMNhZ4O3+B52aPUdmA9fB6EBEYCHcRVcK3Zfx6nsfJEyP88LnzzM+ViMdDBAIKly5M09oWQ9NlTMOmWjNpa4sRkuMktQCOa6FLYaJKGk1cm5cQloOk1TgJNcLcNvt9385d4EBsJ0FJJ6mulCIV5RsSqbKyb+lvQYghavdyg3l94zuyukiaXLwuRUQ6mq/XLqU5nkPdMZltLPDMzBuczF+k+iNAGBQFgY5ElF99fCUR9IndK0myQVXh1564b8V76XCQjx/eveZ6P3Jw14rXi0HHX33kyJrLD2RSS8qCN0MQBAKqwqNDfbc6lLuOjN5Fe+DGPuyN3TgfrfqN62i8dgVV1OkJD6EIGiPVSzieQ1LNkFz28B+KHL5j+yYIAslokMcO+7+b53nYjrtuy6bh1Mia02SNacJyDFXUmGmM42h+4C0KIiPVC3QE+pAEGUmQiMohHmk5xKXyyLYFvWYaC1iu3WzFjpHW4n7p9l0aax3XoeY0uFIZ5y9nXmOiNvuuiJT9KOBdDQY8r9kF73m4rsfUXJG3zo1x8sIEZ69MM5e7MRsSBQFZEtFVhYcO7WBfa4ZQTMd0HOKazlgpT0RZzWrvaIsTCq2VLhfQNYVkIsiRAz0cPtCzbrsN+DPHS+VRLpVHkQWJnmA7u6O9HIwPcSg+REDS/D51bij7/Sj0rC+eY9dzuVoe4xuTP+Rs8RrlbQQC4KsOPp65l+AGsgIALz53AQGBD3/0MJ/63FGSqRCf//j/j1//H55mcFcbw9fm+MP//BJPf3A/8UQQVdSgWRaI3WJWJAoCMTXMvtgAc3PHt3VMWbPA1yaep+40eH/bAyjN2+H2v+PtZJGXX1ery2qLmR4Pj6JV4VJ5lD8d+d57Ijf8bsJb0hdwAQHhNloV/09Db2g3k/XrnMy/giTIHIw9tCGr863C87xmiVFoCkT5EwdlGdHSdb2mpof/eUprI612EBBD3JN4HAER27PJm/MUzCwxJdXk89y4/gOSxvta7uObky8yU1/YcsZuEQtmke/PvE7eLPErOz7lBwSsbzh3J+B5Hm7zfjxTvMrvX/sqRavynikNrgfHvdEqeafPx7saDNTqFqPTOd4+P8bLx69yaWQO23Gb3ss3ltvRlebAUAf37u3mvgM9hIM6kihwpbDA9VIOPHiiq39Nksjf/utP3XY/FjkLG4XtOVyvTjBcneQvZ15HFWV2RfrYGelhINzFQKiLjsD2ZZDvBKpOndHqNN+afJm3cucwXHPbF7QiyLQGkjycPoi6wda4mekCTz69j4cfGyKZ8gc8WZawbIdQSGPv/i4+9ql7+KP/+jK/+bc/QP/AxtOiSTXGo+nDvLDNYADgWnWCr0/+kLHaDD/X+2Gi8t0bnJej4tR5YfYt3lg4w4XS8HuqI/BuwrFOY1b/BEm9Dy30M+/17ryrSKltJNVWaHJ87zZRuVRt8J1XzpNOhLl/bw/xNUTWTlye4OLILIlokI88tGeNtUBXYIDZ+iiT9WGiapL7k+9fpaSqSyo/1/MhvjT2LKO16W3vu+M5vLFwhquVcZ7M3MuDqQPsjvZve73rYdbI8ebCWY4tnOV04Qruu+QOull859Jl5qpVdiQSPDWw8Vb2jeCuBgOO61JvWJy5PMVbZ0e5MjrPXK5CtW5QqRmYTTa/rsm0JMLcu7ebhw7voKMlRiSsE9QVggHfnVAQBHoicaKqxvVijv/tzRf4u/c8Qktg5eAt3WK2v114eL6aoWNyqTTCSHWKl0QFTdJIqBH6Q5206yla9CQZLUlHoAVNVO5axsB3NXSZaSxwuTzGtfI4Y7UZpurzlKwqhmvckbg2pcX4RMcTKJtw+xMlEUkSlpTqPM8jGNJo1C3qdYtwRKerO8nYyDyNDYoTLSIkBxiK9NIVaGW6Mb9tW+PZxgIvz59gtDbN57ueZne03y8F3eHfrWY3mDVyvJ07z7GFs8wbecpWdVOBgIhAX7iDserMbZ0jfzThe4/e7Jvx3wPWs+a+W6g1TJ4/fpkH9vdxaLBj3eVypRpvnR/nww/uQRB8OfaKnee17PfYEd5PRu8iIEfQpSCWaxKUwizPDCze3/cm93K5PI7t2UzeAR8ND4+cWeSZmTd4J3+J/lAHD6YOcDC+E13StsXhsj2HilXlWmWCd/KXOFe8Rs4sUrXrGw4EREGkRYsz18i/axmEt6cmmS1XSAfvfHv3XQkGpuaKTM4VmJwtMDFbYGwqz+hUjvlCZYncp8gSQ70ZejoSdGbidGZidLcn2NGVIhTQ1nyoVyyDbL2G4Tjc19qFvoYPvOO6jE/mmZzOM5ctU6+b6/5MTz26i/ZlLNrNoOGaNJYx8+caObJGgagcIqIECcsh4kqYmBImJAcISDohWW/+raGKytI/SRCRBNHXDWdR2tW3arZdG9tzsFwbwzWp2Q0qdo2qXads1yhaFRaMInOGv/2CWabq3Dnt/agSoi/UwT2JXZviSUSjARp1i0rlRv07mQyxkC2zkC0TCmsYho1tuat0HhZRLjco5qsUCn6JY+euNjTNP18hOcB9yb385cxr267p2Z5D0apwuTzGd6Ze4VzxOl3BVjoCLbTqSWJKeNMcEddzMV2bglVipr7AnJFjrpFntrHAWG2GsdoMlmttaggJyQEGQl28v+0B/mD4mxTM8rYGIdfJ4TpjONZZwMV15hDEKJK8E0V7DJBwnHEc6wKeMwkICGICWX8SQYiAV/c/N94AXBAjSFI/knoQQfBLeK49gWNfwbWvQtNDwxcou3tYPCeO62J7DrZnY7n+P9v1X9ueg+naTNbmtiUk43oe47UZ4kp4SapbEWVkQUIWJWRBRmm+v9nup+3Csl0mmwJl602SApqCqsiMzdzg36TVdhRBw3AbhOQIIgJteg8hOYrneWuaiAmCQFgOcl9yHzWnTtYobFkldDkcz6VoVajZDQpmmYJZ5lzxGmktQVKNEFejROUQYSWIKvocBj9IELA9B8dzMB3LHzudBkWrQt4oUbDK5M0Ss40cE/VZZhu5TQXXcSVCf7iDfbEBvjT2zLsmRlRqGDRsm7B6590vNx0M2I6D43oIAlQapu8SdVO6/q2zo5y5MsXV0XmGJxeWZQAUUvEQ0bBOSzzM4T1d7N/ZTm97kpZk5Lap+6LRINuoEpBlHukYQhFXX5Sz8yWOnxzh7IUpZueLNAx73QHz0N6uLQcDN8NwTabq80yxMiKOKxFiSpioEiamhIirEcJykKCko0squqj5g4coITW18v2qqovjOpiuhelaGK5vsFOyquTNGxdz1ihuu11wPUiCRE+wjUPxIToDm2M3t3ckqNYM8rkbTiad3UkmJ/IEgpNYlsPli9OEIzrSOgpxpUKNqakC9Zp/fDsGb0TssijxYOoAxxbOYrmbu5HXgodHwzE4ljvLudJ1+kIdDIa76A910qqnCEj+77QYtEnNwG3xu4u2zpbnP3AM16Rq15luLDBcmWSkOsWckduyOIsuqnQFMjyeOcL7Mkf51uRL1LbhJw/gOqPYxiu4ziQgYhsv+loKuousPQrY2OabONZFPHcBQZDwEBGlNkR5EM8rYBsv41gXml4YIp6cRxCjiPIQ4GJbJ3DMk7jODIIYA2Q870awunhvup6H57m4zWyX4/m6H4uvF8+vy+q/F5f1ln3Xaf4Wlms37x8TwzGb91PzPcfkenVyW/eP47mcK16jYJZRRBldVNGawb4iyqiijCaq6JKKLPium6IgILI4ARCWJgGL15XIyvcXr7nF7y1+tvj3eoHqosJgIhpEkdcOBnRVIaDKVGoGi6TYmJomqiRxcRHxyXtprZ201n7b87E72kveKjFSneJyeWzL5/VmWJ7NgllgwSxwonCRjJakVU/RpqdIaTGSagxdUlEEGUn0tWIs18JyHRqOQdWpU7IqzBsFputZskaBkl3ZUlZRFRW6g6081nKEg/GdfHX8eSzenWBAkaQlC+Y7jU0HA6W6QbnWQJYkzozN8NiefoLaylT4H3ztDWYXfDKgIPhCOooksaMrzb7Bdu7d18P9B3rQ1M0ZKmiSTFswwlAivW6y7QcvXuCFVy6RK1Tp60rR05Vadxuh4K1lde8EClb5PW8T2yoSaoQHUwd5uvX+TX/3wOEeLpydILdw4+F3+EgvX/niMU6dGCWRDDExnuOee/uWWimXw3FcisUalmnzyGNDIKwsAUmCxP74ILujfRgFk4VtWKnejIpd42zxKmeLVwFQBYXuUCtJNUZYDqBLKkFJRxVVnGbQZrk2FbtGziyRt0oUzPK2VNmWQ0CgK9jKg6mDfLj9YQB6gq3kzOK2/OQd8yS2+QahxL8DIUW9+I8QpXZk9RFAxnPnsWp/hhL4BErgbyEIOmbtC5j1r6PoH8BzS1j1rxNK/RGCkMI2fohtncSsfQU9+o/wvAZm/duIUoZA7H9GEFtplP4FK8iXnj/Q1+wGDcfEcE0ajkHNMag7DQzHz8A1HJO606DhGDScGw/3xeUajuF/17Wo2w3qjvGupG5dXN7Knectbq8UJyKgiAqapBIQVQKyRkDU0CQVVVTRJY2gpC1NEjRJQVtcTtKa76nN72vLvq+s6Ybqe5oJmJa9rqOk67pLvK0V3xXENQW5bgdd0jgYG0Tw4D9c+bMV2dM7BcdzmW5kmX6P1Dnb9TRHEnv8cdHzfRoER3hXrrfOaATXc5kqlZf0Z+5UtmnTwcBsocylyXlqhkU0qOO47rr2FkFdobM1zlMP7uKp+3fSkgijacqWq2a5Rp3JShFdlgkpKgktgHzTiXjj+HU62uJ8/hP38pGnby1c8SNA/v+RhS6q/HLfJzkY37nhDoLl+OBHDvDhjx1EWopgBZ58eh/1usnbb15nciLPvff182u/8RSp9GoRoxPHh5kYz1EuNxgdmUfXVT7wkQNEbiJB/Xzvh7E9h9ezp7fNYl4PpmdxrTLBNd4bG+HBSA+f63qKB1MHAD84GIj0MFyd2lYQ5OE0PRf8YEwQZG50Qrg41mkEqQdR6m5qLngo+icwa3+OICYQxRZEeQ+C4Pu/y+pRPHcWo/Fn/vqdEQRB9zUbRF8WVdEfx63dkE118fjD4W/y4tzbFK21/HC9Nf768YSL5wcxrkkJYAPVLeEWrwD6Q+28L3Mfn+1+etVnsizRnYnzzsUJDu3sIBFdLcA0OV9keCpHe0t0zfVvBUk1xtHUXn7d+zy/e+VLP6bclrURVUJ8tvsp7k/u94MlAToCLZiu9a60IH56z16eu3aNb1y8wP3dXfTF46vMl7aKTQcDsWCA3kySRfq/Kq+WBH786CBt6Sg7e1voao37Qjq6iiSJ22qHiGgaAUPh/MIsoiDwWGf/KkObYrnOrp1t9HanEMWfPO03CwEBXVL5HwZ/hsOJIaLKxo1DltYhCH7Qd7PTnCDwxFN7efDhnViWg6JIxOLBNeuZ+w92096ZoFG3SLdEmJspoq2hXNeiJXm69X4UUea52Tc3tZ8/6hAQGIz08Kv9n6I/1LFCKrs/1EFU2Z6gjqzeg+fmqeZ/A1HqAc9CUgeQlJ2r92TJFAyWK/8vT1H7Q8Jye2Jv1ff9IWflhbHY1vWj1sb1owDvFq+gWV5Z57yFdJX33TvIl549QTSk8cCBPnb1ZIiFdArlOscvjPODNy8xNlvg/fcN3TEBREEQCEo6D6cPMVqd4sX5d8ibpTuz8vcIAhCUA/zGwE+xLzZAWA4sXdPtegvT9ey7Egxcz+eYqVTwPPjrX/s6g6kkcT2wZtngyf4+PjA4uOF1b0GOGGzHpVCt0RaPril688GH9xAN6yRjQcJ3MBUvN2toBcOgKxxdk5kbCfmCQMt9B36CjUERJNJagsdajnAgPkhEDq1S5tsoVgdivrZEKKQRDt8+0+A47pKUcamUYH62SHtnAkVZKUAiixID4S7qjkHWKHCqsHFf9h9laKJCZyDDZ7qepC/UQVBe2d3QEWjxByS2k550QZAQ5UGfMCjoSFIfgqD5RDGpG8/N47o5PLcGgoJtnkKUOhDlPkDAdaZx3TKCEMJxRnDdImLTf0EQU03L8wKeWwIhjGNfB++9F1X67wFBXeHRwwO8cW6UE5cnGZ7KkYqF0BSJhmkzlS1RN0x2dCR57J4dd7TPQRREQpLOk5mjWJ7DqfzlbZmLvZcQ8cmRH+98jL2xfqJKaIWFe0ZPoIoba7neLq4uLHA1t4Bp25iOw2SpxEKtvmZ2YH/r5nhemw4GDMuhVGuQrzQI65ovWnGTNeb+nbcnmmwFluPQsC2/VtKcody87cH+DPWGxdjkAgN9aaJr9Nb+BKuhSxodepp9sQGebr3Pt/fdZvqpVjMoFevkFirU6yamYSMrEpqmEAyqZFqjRKKBNbfjOJ7fjVBuUK00cNfpOADfVnlXpJeKVWO2scBcI/djqeu/iJAUoCPQwgOp/TyUOogirObWxJukVF1Stzwj8bwGuFUEIep3B+DheWVcJ4sgpv2HvpTBc2axzTdB0LCtE0jKYSR5F55bRhCj2MYxRDGKY58Hr4Gs+gp8gphAlNrwvBqW8TqilMZzZvC8u0N4/QlWQpElBrvTPH10iDfPjzGXKzMxV1hSIAwHNAa60ty7u5ud3ZvXSTEdm3y9wWzV58bsa8ngeh5128KwHVpCIXZGeqg5DUQEHM95z+r8W4UsSMTVCLsifTyVuY+EGlvV0tiivXvBQETT6I7G6I6ubUi3HJnQ5jRTtkAgbJCv1tEUifFsgb1drejq7Vfjeh6O4+K63lIA4TXZBoLgK8uJooAsSUtqWTejaDbINWroksRIqcD+dBs6KzkIjzwwyDe/f4pXj12lNR1l/56OdR9quqbcVV2CHxeookxvsI33ZY7yRObebaefAWzLYXQ4y8m3R3jjtStMTuSo1yw0TSaWCNHbn+ZDHznIkfv6URUZ4aZMQjQWYGh3G51dCTJtMSRJXJUVWI6MnuTJ1qPkrRLfmnyJil37sQwIFFFmINLFI+lDfLT90XWZ4rIok9bipLU447WtzLhsf9buFrDt17DNV8EzEJU9KNrjqIFPghBBDf4sVv27NCq/C4AodaBH/h6i1I7nVlCDn6NR+X3ARRRTyOpDqIFPNfdZRwl8DKvxPEblPyKIUWTtUQQx0jR6+gnuJhYNn372g0d46EAf1ycXGJ/NU6mbhAMau/oy9HekyCS2dr/PV2scmxznL69exbRt/uPHPoHteVxdyDFZLvGJXb4k9aH4EFE5RFyJ8BcTz71r5M7tQkQkoUY4ktjDZzqfon0dYbk2PYUmvTvBwM8ePHjX1r3pYKAtHqHSMHjr6gQ729Ibeph6nkehVOP05Skuj8wxOpUjV6xRrZvoqkw4pJFOhBnqbeHRIwOkYqElR8Hl6AhFKJsGr0+PsSvRgiKIq1Jb75weZW6+xNRMgWPvDBPQVeKxIJK4elD97b/1IQ7u9bXD/3tlF6iiws/2fIj3ZY6S1uJ3RBTF8zxef+0Kf/bfXmNsNEtLJsojj+0iEg1Qr5lMTeW5eH6S1166zG/9zsd54KFB4onV5CbH8ZibK/H2W8MAaxIIl0MXVX6u58Nk9CTfmnyRa5XJH4tBZxGqoPBT3e/nodRB+kK+SMytsjMdgRY6A61bCgYc8ziecxFB7iQS/1fNdw2Myn/Eqn/DDwbAzwIoh9dchyAmkbWnCWuryWuLkJQjSMoRiPz9Te/jT3Dn0NOWoKctcUfX+eXzZ8nV6zzVv4P/duokLhBRVcbLRb5z+fJSMADQF+qgTU+zN7qDf3v5CyyYhR95YuGuaC+f6XwfRxK70aX1y91dwVYCt/j8xwWbDgZSkSCxYBf7utsYmy+grGN4AX7bSqVm8JXvn+AHb1yiWG5gWjZ2M0OwmOIXBJBEkR++eZk//dZb3HeglyeO7uTofr/9cGlnRYnOcJTP7tzPhdwcsHqwbBgWrS1RUsnw0vpFce1HnN4kpEmCSFpL8D/t/qu8PP8OF0ujzBm5zZ6aHyu06WkOxAb5YPuD9Abb0SUVAQHHcylZJU7kz3Bf8h6iytZsnL/7jRPousJP/eyDfPST96Bqvra453nYts8H+K//6QW+/bW3GRxqXTMYKBZq2LbLhz92CAB9HYvmRSxeC4+mD9Oht/Bm7hwvzL5F1ixs6RjeLWiiQm+og1/u/wS9wfYlctLt0Kan6Qikt7RNz2uA5yBwoy3YscbwPANBuJGC3G6p6EfBr+MnuDu/g2HbJHWdh7q6+W+nTi5uiLV0FgXBJybvjvXx/znwN/n6xA85Wbj0I1c2EPDJjx/ueJgPtT5ESouhiuotz19CjRKWgyii/K6JD90NbCoYmCmUWSjVKDcaWLbL5aksPek4mrJ6NZbtcG08yzOvXeC1E8NMzvq1qvXhgAGVmsGx06MsFKqMTOX4zNMHCej+gFUwGoyU/fX4YiO+2MvyDoUPP7V/SeTodmjLNG1UBQEJkb3RHSTUKEeT84zXZrlaHuNSefTHJq11O0iCxM5wN3ui/fSFO+kJttIVaGWqMY3pWuiiSkQOE1bCNFz/mOt2nZJdJmvkSGlJNFGlYBUxXYuEGkcA5o0FAlKAuBIlocYBmJkpcOBgD7v3dpBKh1fcTD45TeDoAzv4yhffwDTWvoEEQaBWMzh5YhRVkdh/sPu2AQH4/IfeUDsBSaMz0MJr2dOcLlzGcK0fqd8xKOn0htrZE+3ncHyIwXC3b4C1QZnVtBajRdvabE+UBxG8LI71Do3yvwUEPK+CIESR9LUdBH+Cn2A5FsWQlmOmXKZQr6OuoQ4rCAIKMm16ig+1P8TOSA+Xy6OcyF9k9kdg8pVW4+wId3E0uYd9sQEyevK2jon+s0MgrkQIS0Hy7t3vmqiaJvPVKqOFAnXLYj061UAqyVB645OFTQUDngembVM37SXttfW6f8emchw7PcLzxy4zmy0TCqh0NNUHg7qKpvr+6IuiF4ZpU64aTGeLZAsVyrUGxUqD3vYED9+zA0HwxSZMx8FwbIKysmab4r5d62tw3wqCIBBXI8TVCD3BNgbC3XQEWkhrCeaNPCWrQtmuUbaqd0VI424hIOmE5QBxNUKrlmRvdAf7YgO0BVKE5SCe5zFVnyEshzBdg4ZrktKSS983XJOiVSZr5mi4Bpqo4XouiijjeA5TtWlKdhld0vE8bykYwINYIkgsHlx1MwmCgCxLZFpvTYLRmtmEkevzhCM6u/d2bvi4Q3KA3lA7LXoCRVQISBqT9TkWzCIlq4rzHqUoRUTCSoCMlqQr2MpQpJe90X4GI93A5sxrwnKQuBIhKOnUNml7LEptSOxsMv3LgABCAEnZg6QcWlpuqlZEbqrfNRwbVZLQRJmi2aBq+8TFrlCcglknrgYISiouHmPVPH3h5Lb043+Cdw8+h8vCw0VARtyAD0l3LEauXuOtqQlqlslr42PMV6tk63WGUuvbTsuCzM5IDy1aYqkr5nzpOnONPGW7ekdkjDcKEYGUFqdVT9IX6mBPpJ8jid1ENtlSHVMiRJQgeevuBgMLtRpXFxZ4e2qK4Vyehm031Tu9pVZHRZKIaRqaLN+9YKA9ESGkKximTVBXaU9GCazR+21aDi+/c41nXr3IbLaMrins6m/l6L4ehvoydLXGScWDaIqCZTtU6gYLhSrXxrL84I1LXB2bp1iuc21snv/2rTc5sq8bXVOIaTq90TgiArbnokrSXbG1DMkB/2ESbOOp1vu4XBplpDbFaHWaK+UxZhoLOJ6zJHu6KIH6Xs86xWWSppIgoUkqHYEW+kOd7I32c19yL4E1DHjKVpldkUFyZoE5Y56d4f7m8TnYTU13XdKZqs8gILA7Okh3oAsXh5err5PR0tScOmX7huJeW0ccz/WoVQ0cx13BLXFdD8tyyOUqtLXHUdchoOq6QjQWpFSqEwyqCKKw4qJ3PQ/TdZCEG33sqiRjOvaSv4MsKDySPsyR+C6O5c5ysnCZS+VRSmYFy7OwXeeuEw1lQUISJGRB8v0Fwl082nKYg/Eh4kp4w5mAm6GIMlElRKue2oL9sYysHkVWj95yqePZUXRJQZNk5hoVYqpOdzDBxeIsw+UFLNfho937uFyaY0+sjc5gHNdzeW7qIn918MGfEHR/jGA6RVwsFDGKKNyeVPhQdzevjY3xF+fPYzou/+n4W1iuy0Nd3Xxq360F34Clydfe2A5O5S/xavY0V8qjzBl5X0q4OQbdafj3oogsygQlnfuT+3kic4TuYOuWydPJpkfC3ca5uTm+ffEi3750mZCiEFAUsrUaIhBSVWzXRRAE7mlvx3A2V7LYNGegXDcZns0xMuenddrjEVR55WpOXpjg+NkxhicX0DWZn/7QPfzSpx4gqKurhGgURSIYUMkkI+zub+WjT+zj68+d4tnXLnLy4iTnrk7z2olhHjjYS2soTGtwe0z35Q+TjUBCpOE2OBAb4KPtj+J5HgtmkeuVCcZrs0zW55iozzFZm3tP/a+FZoTbpqfoDbWzO9rPofgQkWYt61ZouAaniudQRV/+dLoxy2RtmonaFA3HIGcW0Jvp65SaYKw2yURtms5AG12BDmpOg7SapFW7wbb99OeO8sz3TvPqyxat7XHaO+JLn1UrDa5dnuWbf3GcX/jlR0m1hNeQQxXI56tYps3HP3mEF547h7dMUtXFY6JW4M35EVoDEUKyRt2xeCSzgzezo1Qsg4Zr47ou/ZEUBxKdfKDtQZ5uvZ+qXedk/hLHcme5UBohZxbvioWwgEBA0pasrndF+tgd7SOtxVcte3OL7EYRUUIMhru3EAxsDFmjSsOxkEUJy3GYqhV4IN1HayDKUDTDldIcL89e48F0L9fLWUYrOVoDEXRZvbOk3PVkTu/U8u86bjdObH7nNzqmrQ2XinUV0y0RU3cjC/6D7VbXZG8sTte+KJ/YtYvred/oqDMSJappm9LOlwSRI8k9HEnuoWRVmajN8nbuAqeKl7lemcDcpKHXrSALEoORHnaGu9kV6eVwYhdxJbJtTkWLHieubo1ftRm8NDLCxfksj/b08E+efpq2SJhf/drX6IrG+HuPPMxEscg/fvYHfHLPbo50bC5LvulgoFCpYzsOn3vIl0Zdiy/w2snrTGdLRIIau3a0NgOBjbVeCMBHH98HHhRKdUanc7z41hX2DbYRCW1eFnc5FowCM40se6MDnCleRpc0DNekbjfoCbZjeCYTtRlUUSWuRPCAyfoMebO0gi2aVKPEErs5GB+6YaqCS81ukDOL5M0yRbNMwfb16ctWjVpTP71uG9Rdg4ZjYrkWtuf4pDrPn6Eu3tC+jrlvuqE0zU5Csk5YDhKWg8SUMDElQkqL0aolaNNbCMoaStMMxXdMkzaUdg7JIQ7F9hFXY0vZhb/a97OoorJkwoPggeeLibhNcw9JkOgN9eB6DpIgrXAzO3NqnOmpAqdPjvHic+eJJYKEwzpGw6JSMahWDfDge986yfe+fYqbC1//4z/8OLF4kHKpwXe/fZJQSF0hZGS7Lj+cuczHuw8QkTUmagWGF8YBmKoXqVom/eEUBxIdfHP8NPvi7c2zKhCSgzyQPsCR5B5sz6FglpkzckzV5xmrzpAzS5SsKhW7RsWuLZlF+cctIIu+C50myiiCjCaphOUAKTVOUouRUCMk1RiteoquQAty0zxFEkTkNUScbLdEqXGMRODppuHPxtEbbOdvDHyWX+7/xIr3Ha9Oof4yicD7EG/SrY8oQcQN6s5roozlOs3fXkDyRF6euUbdtRCAqKrj1FwGYxkul+YZqSyQCUT4WNd+5DWMxJbDMm0Kc0X+St/H+dneD6/7MMvPFrn09jUe+OiR217NjuNSmCty7LsnOPrBQ2S6105Xv1e4XB7hjYWTPD/3xi2X+3D7YzzRch/t+q2FY0RBRBUVbLfGTPUZQsoOTCeLh4ciRlClFLPVZ3Fxiag7iat7kcQQk5VvYrs1NClNRB0krh1YWmfDmcN08phOnkzwyVs+KEVBIKio7E234OETwbfzWA3LQYYivfSHO/m0+z5qToPJ+hwz9QXmjRyzjRxzxgJlq0bdNXzTKcfEdO2ma6TsT2oklagcIq0lSGsxUmqctkCKwXA3IVlHFuSlMfJO4EhiD/tjg/zG4E9t6nt609Rqo8hWa8QDOp/bv4/WcAgBUESRRc2dPS0t/PSBAzx37TqW4/DZffs2vO5NBwOiKFBtmLx8fhhNkbhvsJugtpLUNTKZo1xtkIgFObyrk6C+qH1+63UvXnS6qtDTnmTPQBsjUzmuT2QxzO2zNBuuP8sFyJp5WtQkpmtSdeqIgsT54jVkQcJwTMpWFReX/dGdnChcWHqoLnqSi4LIzT9hSAoQU8K0KQ0W6gWe+dJLXDpxlVqjgR7V6NjVxsc+/wjhdKjpxuZrLXieR7Vc59h33+HaqRGykznwPLr2dvPoZx6gvauFYCjgX8BNa1TP9FgYzXP62XO8fuY1VE+mtauFnff0c+T9Bwimoxs+L7sjg8SUCKqoLB1nUL7Rwrc82yFwI6tyQ4ZWXvEaoFSqE4kGUFQZ13GRFQlFlpAkEVVTiCeCzb9lPHd1PkUQwGhYzaDBwzIdPO/GNgRBIKEGObEwQWsgguU6CILAm/OjlMw6hmMzXS8iiwKBZT3A/u/nt/EtBjsBSSOpRukJtnMwtpNz2RmCqowmi1xamCesqcxWy8Q0nbCqYjouY8UinZEosiBiOR4N26E7EicoaVRNB9XSiAYTxJqzhVsFZa5n0rBHKTZeQZEyiIKK65lYbg5N6sT16thuCXBRxASSGKNuXUcWo4hiAM+zEJwcitSCIEg4bhXXmUMX8sTVEKKw9banhBbC9lzAIyQHma4XsT2XXKNK3bWI2wFEfHXQFj3MfKNC2WyQ0IK3fCiU81Wmr89y6sXzPPbZBwCPwnyJRtUk1R4nGA0wdnGSUDSIZdrUxmpcf2mEVHsco27SqBm4jkuqI4koChSzZURRINEap6u1jTOuRlQKIddEslN5cjMFWnvSgEe1WKdaqiOrEqqq0Lu3i1KuQr3SoHfPal6K4zlcLg8zGO5F2aa4TEQJokmrpbpvhk/mDW14tmk5Der2DC4OjlvHw0ZAQhJ04vpBZDFKw54h23iDlsDjVK0x2kIfICC3o4g3tmHYcxj2LLrcSkI7dIst+li8n8RlmYDhQp7RQoEn+/o3tO/L4TsySshIIEFQ9q3fu4OtvmmVY9JwDSzXLyG4ntd0r3T9UH+Zq6jSLJVqooomKuiSRlgO3hUraUWUb5uBvVOQBRFdlpfcgnVZBg/KhkEyEKA/keDbly4yV13L6+MW693sjmiKjCSJjM0X0BSJe/pX3zy5UpWG4fMKutsSWzIEikV02lt8glm2UL1NJ8LG4Hkepmsx05inbFVJKrHFDyjbFQzHRJJ0xOZJrtkGLv5AuBHIzdlfZaHC8AsjjD83RlgKEBECuHMujWKVYirP/b94AC1wowbuefDKi8cQj9m0zEdoFWMggnXKxm6pk9HjDBzqXbL69TyPKyeGmXxhguHnR5Bkibpgc21hhMJcEcu0eernHkWSN3bRt+oZFFFd94F18/trEQJvxu69HfQPbE4OczlCIY2ZmSLVSgNRFHCclZwMCYHBaAs5o44siAQUhf5wCs/z2BFJM1Et+DeNpLAzmlm3Li8goIp+YBBR/NToaNagU4uSDARZKMj0hxPIZo6uUJSwqjJaLNCtxkiIARRRQpRB1EV6ozEqhkm5VsRCQkbeICHQxfHqCIKCaU/i4QIykhikbl3Fw8K05/Gw0OUeNBksJ4vt5hEFDQHJ5/8JIoYzg+s2EAQJd5lV8FbRH06SUAN4eIRkjZgaIK2FCMkqdcdaIvLKgkhIUUlqQTxAvU1WQBDAdVwK8yVEUWT04gTFbBnXdqhV6n7XiACSLGEZFkbdRFYlpkfmqeSrSLJEsj3OxOVpBAG0pux5/fosrb3ppWuynK8yMzJPMKIzfmkKSZEwagaNqoEW1JBkiWgqQjFbwmxYsCwYqDsN6nadqlNnojZFWl3s3PDQJI2608DxXN9ZUNJXBNDrISqH2RHq5oHkoabdtY3tOVTtGhP1mS1Z6i7Cw8JyiksGVB42pueSDjyEJrVg2PPU7ElojmkhpYeA7KvFes3avOkWcNw6khhAk7d2/85WKpyeneaRngxS8/q81TjkTy5cHM9EFJQV5EVJEJeyobf/vuW7Ld5lQasT1yeRRJG93a04rssPTl1hsD1NeyKKJAq8dWWc+3Z2Y7sus/ky07kyDctCkSV2tKVoiYYIaAqmZfPyuWG60jHKdZNirYHneXSlY+xsT6OsYe0eVGQqpkDJuKE6GtE0bM9lqlSiNx5HEKBu+XLFm8GmgwFVlghpil8eEJr9BDfVO+uGheO6KLJIOLS1WYmmyISabWT1hrVkwem4LqbtoCurZ6O33XdRISjpDFcnfJKdKKIKCrbnkDOLdOgZDNckIocIyQHmjBwj1Uk//bXBdJJt2QyfGeMbv/t9XMfl83/v40SSIcYvTfHmd0/wZ//yGxx5+gCZ7hSKpuB5HkbN4Kv/7rs0agb7H9nNPU/tR1Zkvvl73+eZP/whoijS3p8hkvT5EmbD4vj3T/HKV48hqzI//fc/iRpQeefZ01x48wrXT49x9IOHiLdEEST//LieR8OxlmbJy2fYgS24Et4KgiDw0U/cs+31jI8tUK9bqJq0SntbEkX2xttxPA+xuU2fEe2Xml6fG0YWRY6kejZNMi2ZBhHTQJEk5ms1+uMJTMfB9lwqpsmlhSw9sTi5eg1VkkgHQwwkknRGIlxcmMdyfFLjZuqcIjJR7X6y1W9huTlUKUNUPcJs5SuIKFhuDs+zEYUAshhDEgOYzhyeZ6NIKcLqQTSpk7p1DRAJKfuomhc2ddxrYU+8bc33HbdZKmoGznXbwnZdUnqIjsDtpVKD0QAt3SlS7QnimShvfG8Ox3IIRQPkpvIYNZPHPnc/0VSE+fEFFE3h4GN7+P4fvkhpoUzHYBu77xvgu//5eZ8w9dQ+LMPmyolh7Ed3Y5k2tmVTLzcoLZQ5+Nhuvv9HLxII68iKhCRLhKIBPATGLk3ieR7Rm5T4CmbRnzjYFQpWian6LJbnc0uCUoCskfM1+OUgGS1122DAw6NVTxFXIxyID1GxatScOnXHYLQ2xdcnf0B9k10hSxAEJMHfvoiEKyyOyzKuZ2J7FTxsxOaQr4hRP4i8eR89G3BxPRvLLaMI0RX0Bcd1ma/desY5VixyPT9PxZomJLcii8E1t7UcrmdjuEUUMYy6jLxouw1EQUa4TcnT9WxMt4QkqEjS3Q0GvvXmBTRFoi+ToGZY/OuvvchPP3qY9x0cIKSp/N73XmdnR5rpfJm3r05w8voUlYaBrio8sW8H9w1105dJUK4b/KuvvcTH799DtlRlbL6AYdkc6Gvj1z/0IPFQYJXHSyoYIt9oMF4sLr3XGgozVizwzvQ0g6kUw/m8n/28TUB+MzYdDOTKNbKlKp2pKOW6seas328bFHBcD3OL6X3bcZe+qyoSYnMWXaw3eHt4kvftGUASN04EBMjoKVr0G21zAsJSqn6xBWqxHi4KIoOR3hXLbgQTl2e48MYVZobn+Edf+DscfHwPiqZw6Im97H1wiN966p/yytff5MmffpjWnjRGzeTsK5e48vZ1fuV//zke/Pi9dA62+TPcAz3840//H5x/4zLtA608/tkHALjyznUuvnkVURb5lf/t5zj05F4EQWD3fYO8/Bdv8F/+0Rd58c9f5yO/8hR60DedaTgWz09c4/3dO/2LZFtko+Y5ucuCMqoqEwgq2LYvUrXW03V5kLCYsgQ4kOxolnM2v48hRWOkUOBSNkvNNHFcj4iqMl+tIggC/fEEZdNkdypNwWggNdN2AK4HAUWhbllcmJ+nM3L7co2AiCgEll5JQggPl3z9eVSpBcOaJKAMIgo6NesCFbOOJIT92ZwgNr8TaP4/guFMkq//8K56ACwGAdBUnJy/zlStSHcowcHk7VtARVH0lUmzJaaHZ0m3JyjnK0iyxM57usjNFHj9W2/T0pVCC6joweYAL4CiKajajaA2mg5z7fQoeBCOh5i8Ms34pWk6dkwSjAaQFYnXv/U2sVQEVVco56vUKw12HOiha6id//QP/pS9Dwxy9P0rpV7njQUajsGDqXv53vTzjNUnORzfhySIvJU7heM5fLT9ac6VLrFg5ukMLs6y17u3fE2UgKQRlAK0ajfuoYQa5btTP2SruRwBiYDcTsOZRxWTOF4d162TDj7GaOkLCIJMUO4ieRsNiai2F0WM4noGY6UvMRD76ys+LxoGv/jVP7/lOqqmSVizmalXaA88RFjpQLgNR8X2DAy7iCgrIN4gFM/WjxNT+wnJ7bcMrh1MTKeMIgZRxbWD0Ts1XrUnIjRMm+szOeaLFfb3tpGr1JhcKNLXmqDasAhqKn/43HEysTC/8ZGHONDXxni2yP/yhWco1Q0+df9eFEVitlihblj89KMH2dmRZmqhxM//6y/ygcND7O5sIRxYOZnel8lQMgwuzt8QazrU3s5wocDvv3WcV0fHuDA3x77WVtqjmyM0bjoY6EzF0FWFmmExlSut+UxpTUaYz1UoVhpcGJ7lyfuHNrsZ5nJlrozO+9vMxFBkiflylZNj03zxtZNM5Irs7czQHotQbpg8f/465YbBx+/ZhSiIGJaN63q0J8KcGZ/lcG8H7fHVJ0dg5YN+u3K8YxcmGLs4STge5NATe5GasspaUCPTk2bnkX7OvHSBI08faAYDBidfOIcaUBm6dwdtfTdSc6nOBDsO9pCdzHH5ratLwcCFN65QnC+R7kiy/5FdS8tHEiHaB1rp3t3JO8+e5gO/+DgENQpmg/O5Wf744juMlYvsTWboicRRRZE/v3YW23V5pL2XrnCMhm3z7ZELlEyDD/fsYmc8he25PDt2hYlqkYwewvY82kMRPt63Z1vn6nYYHGpjx2Ari9RwWd44uS4sb71O/kRvb7N3138tiyJ70i3+tdIcUGzPWyLu0Aw6BEFgdyrNYMIPOKUNWmjLYpLWyM8DIqnQR1lpASzip3X9Y08EHse/am/YA/tXrQQIRPX78Lh36XOBu+8BIAgCj7cOrhIAux2SbXF+4R9+GkVT6Bxsw3X8fZZkP1BwbBexqXB68PG9ADz984+C5yGIIpIs8vG/8fSK7iBBFBBFkd/8N7+0VCZzXQ9cD0EUeOFLr6OHNBKZDMefPY0oiQwe6qVjoG3pXl2ELmnkzALHFt7GdC0yWoqxmp8pjKtRKlZ11Xix6LlyqvAms40JZFHl6cwnqDkVXs3+gIKVIyrHORS/nza9axtnfSUkIUBb6IPcaKNY/P0ldid/y39XEP2UPSKD8b95E7FUJKnft+z7kA48tGo7ogAxTed3Hn2ckLr2tfX6+BhvTF3E8zzGKs/ieBZxbYCUtg9NjHK9/G0st0ZAThNTd5DUdnG19DWCUguyGCBACg+XonmNq6WvE5Y7SGhDxNQdhJR2rpe+RcMpEJY7aAveT0hp51rxm6hSlIS6E1WKMl07RtWexXByhOR2Uvo+ktquNfd3s+hOx5nOl7g0McdYtsAn7t/HS2evc2F8joCqMNSR5spUFjzoyyTY19sKQFcqxqH+dizb5tz4LId3+Gz/Dx0ZYqAthSJJpKIhutIxCtU6DcteFQw81tfLQz3dKyZBD/f2oEgimiRxYmqKB3u6+aV77mFv5i67FlqOQ7FaZzRbQFeVpUFwOXb2tjAxW2AuV+HN06N8+n0HyaQjK2YTt8J8rsL5azOcvjKJIAjs39nh6wwEdToTUVpjYd63Z4BYUEdXZMK6xYOD3VyeznJhap6D3W2ossRLl4YRhuGDB4aIBf1U+Gbq31sJDIrZMrVynURrfMXgIggCsiqT6U4zdmkSs+7P2mzLYXZsnkQmhhbUEKVlREVBINkWZ3Y0y8J0fmld2akcggDRVGTlNkSBQEgj2RZj/NIUbpNnEVZUusNxMoEwT3UNkA4Ema9XObOQJaSopLUgb82NYzg296Q7ebS9j/P5Oa4Ws4RVlYiiMVYpcF9rF7O1CpVGjZp994VBJElkEx1KK7CdWYAsSqtIkosPucUHz+KNczP7XxLFFctubF/FpYe2sERLXR5lr7Ueb53PZIR1P7t7uF3nwFoQBAG1WQqUJRlPXnnOpZteAyg3aVIozQzBzb+XtkypUlw2Yzn4+B4a1QYeHg9+7AiSJHLoib2EE6tFZjoD7aS1JB6wMzKwgvUtALbnoIgyO8P9K8YKx7Np0VqpO1WKVgGAkeoVWrR2eoIDSILMhdJpWvXObU8+lvZHEJZdO8vhIQn6siuieW5vIpX637/5cbD68aCIEo/39rEjmSAgr02mHC0WiC/oCEKDuDZIUG6lZs8yU3+L7tDjlK1J+iIfIiS3IYtBREEhre2nZI3gNO2tBUTCSicBqYW0fpC0vh9FDCGJOpnAEcrmGJZbI2dcIKJ0kdL3UTZHsdwqrudQdxYISRla9P1UrCmyjTN3LBhoS0Qo1RuMzeUZns3zs48f5o1LoyyUqszmywy0p5b4TaIgLD33FmX3XdfFdW9wQ8K6hir7zqSi4JtLeZ63ZoYpoPil5eVBtyyK7MtkyIRCFBr7CCkKndEowXWCtfWw6WCg1jCpmxadySizRV9k5mbOwIGhTs5enWZyrsjEbIHvvnyOR+8dpLMlRjCgruF176/DcT3Gp/O8dXaUExcmyJfqaKrM0X09hAIqmiwT1lRCmkpnMoosiixUamTLVSzHJR0JMrpQ8GUvJYly3aBYb5AKB5fSuHcbjZqBbdoE17DmFUWBcCJEJV/FtvwSiOu4VPJVgtHAmoS/YCSA57rUSjcSiNViHUEU0UPaquVlVSYYCVDKVZYuJkWUCCsqIUWlMxwlJKtMV8vM1SqEFZWUHqJim5iOw3SthOE4pPUQ45UCddsiJKuYjk17MELJ8G/WW1kK/z8BtwwSb/OQvzPpyNut41af/0g32C/hdkTUzZzH20nGLiLdmVi6RyOJMNVSjWA4gLhG1ikkBwlygwcgICwN8ssf4jf7dwiIxNUUeTNL0fKD+Ly1QKveSVptxfFsThXeZKPE5O1BWPbf7UOVJB7r7SWirq8lMJBIYji9wCV0KU1E6aFhL1C35/DwnxUhuZ2Q4s+YPc8lIKcomtfwmmVaQRBQhBCKGCQgpwnKfutixRrH9hooYgjHMzGcAiCgSwnKwhguNuDheia6nCKi9NKwc5TtsTt0BiATDzOxUGQmX6FhWsRDARKhAJMLRUbn8+zsaCEdCSEIUKw1mC9WSEdDVBomc4UKmViIVPSGQJEkCjc9E4V1q7gC0LBt8o0GZcPAdleqAOuyjON5jBWLtIRCtIY3rsuz+WDAtLBcl30dbUzmipTqDWRJRJWlpQho/852dnSlGZ5YIF+q8aXvvYPtuNyzu5vWdMTnFEjiUjLLdT1sx6FWN3n1xDDff/U8U3NFJFGkqzXOPXu7Ceg3aoSaLJGr1IjoGhO5IldnFyjUGvQkYz4hz7J9v25dxfOgZpqENAX1XWj9sE0b1/GWZiwrIAiouoLRMJdSop7nYTYsVF1FWCNzomiKr7TXuDETtxqWn2lYQ+NBFEUUTcGomSsuKEEQ0GWZvFFf2q4iijhNjYN9iQxly+T0wgzz9Qp9kcSSip8o3OiUkMWm6uMmgwHP86hXGhSzZSzDRlYl0p1JFNVvLTQaJo2qgVEzsW0Hz/UQBBAlEUWVCYQDBCK6bzq1iYeE53m4jku1VMeom9imjWO7eK6fgpYUCVVT0AIqgYi+lJFZaz2F+RKVQhU80EMaybb4UofHctTKdWqlOrVyo/mbCKQ7k2jB1YYntmXTqBrkZnxCUCwdIRDRl2riP8GdhU8cvMFMj9zGvndVJvE2j9VFud2gEEKTdBYHass1kfB1JlzPxeHH09BGkSSOtN9azKYrGiWqd5OzLjVbZMu42EiC2uyLDy9l1DzPxfUsTKeM7dWxvTqOayCJfuZCFFQct4HplBAEiZxxgao9jy75pTi/sdDGcqvYbh3HbWC7/n2niEEkQWnyFe5c4JWKBAmoMtlylWhQRxJFWhMRxrIFrk0v8IHDQ3SkoqQiIYq1BqdHZhhsTzFXrJCv1BlsT9GVvj3Jdi0UGg3GikUuzc8zXa5gue66HJUHu7vubjCgSBLVusE33jxH1bB4/vQ19ve2MtiWIhLwU/GxcIAPPLQbQRD482feoVwz+MOvH+MLytskY0E6MzHSiRCqomDZNpWayUKhyvBElkaTNCiKAt1tCX7zZx8jGtKXIqeAqtCTTvBHr7zDIzt7cVwX23WZLpSxbAdREBidz/taBakEvek4z565ypN7+ulrSa57XO8OmhrSawwo6/2gS++vDBzXX57FTM3K9xVRZFc8zZ9ceod7W7roicS5N9PJn1w6Sd6oMxhNLqXHp6tlLNf1S0DNmvRi4Lqos7CV2e/bz53lT//51xk+O05bXwv/5Mt/l97dnVRKNS69dY13nj/HmZcvMje+QL3aQFZloskw3TvbeORT9/HQx48QSYQRpY3NGhcDgWKuwnNfeIVzr11m6voc+bkitVKdcDxEpifNjv3d7H94iEc+eZRARF9bDdCDL//rb/Ot//w8ru1y5Kl9/P3f/xvEW1YTBN9+7iwvfPl1Xv3GcQCC4QD/7z/72xx+cu+qZRemC7z9gzP8u7/1BwD80j/+LI988ih9ezdeT168FjxudFOsqmUvGwxvFudbrqGxWSy/Dm/eBiu2c2OP7vR21jqWjWxnY+dt5X+3et5CUhTTNam7dfA8tG1oP2wGK8/bzf+9c7/Pcpyfn+eNyYs8tROmam/geiYhpY20fqC5nRuTHsezqNrTjFdfpGyNgScgCRop3b9XokovWeMsRes6KW0vgiBTd+ax3AqSoKEIAWr2LBPVlyibY9SV7Ipfs3lAsAGRreXn6mZjteXQFN8zxXFddne3IIkCfS0Jzo3OMDyTY0d7Ek2W+WvvP8pzp6/yBz94i2ypSiig8jOPHuaBoW5aomFylZrPK7o5g7zOhATgGxcu8J1Llzk1PU1QVQkpyrrLxnSNx/r6bnvci9h0MODhE3GiAY1oUOf9BweJBlanq/cNtpOIBmlLR/n9L79Cw7QxLZuZbInZhfLK4/eaN2TznId0lceODvKxx/dxz96uFcumw0E+f98BDNtGk2W/X9nzsBwHRZL8n7/JKvfpVwL7OlvXLE3cDehBDVmVqJdX84I916NebvglgWatXxRFQvEg82MLSzX+5WhUDURBJBC+0f4XjASWWhJvhmM7NKoGoVhwxXkLySo/PXgIo2k2s9g98b8+8EFcz0UW/XPn4vGJvj1LKUCfqCLw9+95AkkQ2RlL47J2QLMZWKbNxKVpqqU6f/rPv86VE8PUSnVc50atzGxY1Mp15saynPjheb73Bz/kk7/xAR762BFC0dv3dM+MzPPWM6f4wr/4JuVCBdd28bwbN3dhvkQxW+bqyRF++JU3+OL/91v8j//x1xg83It+c0usAMn2BK09KSavzDJ6YRJ7HXfM6WuzzI7ML72uVerMji9QyJZJ3BQ8FLNlpq/PLb3u2tm+ZoBxK9iew+nCJX736p9QdeociA3xmc73sze6EweHk/kLnCte5XJlhDkjR92pE5KCZPQkfcEujiYPcCA2tOVf1PZsXs+e4EL5OhO1WbJmjrJdRRYUX4RLTzMY7uXB1CE6A21b2o6Hx4JZ4PXsSa5URpluzFM0S1SdOiEpQEKN0hloZVekn4fTR4hsQCfeA04VLvAHw19l1sjSHWjjr/R9ioOx3Tg4XCoNc7Z4hXOlK8w0stSdBrqkkVRj9AU7eSh9hH3RQZQNmPrsiR7i1ewPOFk4RkSOcSTx8G0Z9ncKtmdzMn+Bi+VhhqsTzDTmKdkVZGRCcpC0Fmcw0rukeLiR47kVcvUaE0WbXbGfwZ8A+QRYX+tD5EDyV5f4CZKgElV6OJT6df/Z4mugLq2rN/IBvGY7pyioeHh0Bh/1iZDNMUxA5EDy1/BNlhYf/F5zPQLtwYdoDz542/0umDUM1yaq6ASkG1k8D98DJW/WyOgRJAQe37+Dh/f0IYqC38I82MmevhYqlkHJqdMiR+huifNL7zvCLzxxz1J4Iomin90EWqIhXv2Xv4m6LMMb1BT+69/5KWRJXJOMe3pmBkkQ+KdPP81n9u5ZFUgsh7zJoG7Tv3rdsCjXDBRZQmTx4FZf1KIokEmFefrBITpborx4/CoXhmeZXShTq5urssySKJJKhLh3XzcPHOxjoDtNWzq6inQoCAKS6GcIFtndouctyWCuFSXdLJDpeR7Pz36ZyfpVGm6NhJLhp3r+zmZPxZqIpiIEwjqTV2Z8LX3hxj45tsv8xAKp9sRSGlhSJDLdaS6/dQ2rYa2aleZni4iSQCJzI62U6khw/fSozwu4aRtGzSQ/WyTTnUZcZhLjW21CQFZWnCefES8uLSN6ns9dYOW5XJIaFm7XMbwxGDWT069c4vrZMUbPT2A0TKKpCP37uom3RHFsh+xUjtnRLNmpPK7rMHJhguf/7FXMuslHf+V9t1z/hTev8sZ3TvDDP3+D4kIZ13FJZGK0dCfJdKfQAxqlfJXR8xO+8l3dYG4sy+/99p/wV/7nz7L76I4VD2VBEEi2xmjpTDFxeYbcbJFGtYFjO6tKBXPjC2Sn8ivfG5snP1NYFQyU81XmJhYAnxzX0pkkFFtfYGVteLiei9GUTS5bVWYaC3QEWvmzse9yrTJKzixRdxpYntXUnDAp2xUmarOcLl7iwdRhPtj6MAk1tmHjpIpdY7Q6ybemXmCiPtOUbjaxXBvHcxAwqNk1Fow81yvjHFs4yRMt9/Nw+ghJNbax7A4eC0aBt/NneWHuGHmzRM1pNKW8bd/J1DUp21VmGlkulq/zWvYEH25/nH0xX1nzdudtUW666tSZaWTpCZb4xuRzXC4PM2fkqDmNJTnqutOgbFWZbSxwoXSd+1MHeLzlProCbSvOm4BId2AHLZrfbhiUQzyQehzLtRAFiYi8uYBvKzBdi7HaFF+b+AGT9RnKdpWGY2C6VvP3Eag6dfJWkYn6DCfzF3gwdYijiQP0hjrWvA7KhsG/eOUlfuexJ/iT0yc5OTO9apnZarVpb+w/XpZ4FovEWlYqgt6cLVgOUZDwlo1P/tKa/50VZO/1r9n1Rqyy1eBScYZ7Uj2ICIxVc2SNCkPRVrqCN8h3judhOBYFo0aLFoYmEXD5s0kSBOquxVSjQNU2SGXC/jmQJBAcCmadN+ZHeKx1gJh0YyKjqyvLgYIgrCnxv4i+RALH8ygZDRRZXtJYuRPYdDCwWFP1PA/vNjuhKjLpRJgje7vRdZX9OztYKFQpVuo0TBvbdpFEAbVpVpSOh9nRnWagO000rK+pwLS0D7d4vRFkjSkmaleoOiXqeuX2X9gg0l1Jku0JLh2/zvTwHK29LUiygG3ZVIs1pq7PcvjJfUszfVVT6NvfzbP/7UXmJxboHGonmgwv1djnxrLIqrKi5bBrZztnXr5AaaHM7Og8rX2+QZBRNylmS2QnF/y2xpsIPmudp7VIW+9GDsWoG5x59SLT1+dItsU4dHAPO+/pp6M/QzAWxHVcyrkK08NznH7lIqdevIBRM7l2aoxEJsZDHz/iiyqtcQ0W5oocf/YMbz17mpmReURJ5L4PHvRbN3vTxNIRFFWhVmkwO5rl6qkRLr99nYkrMwyfHePlr76JqskcemzPim6NWDpCqj0O+NyQ/FyJZFucYOTGzV0t1ihkS9TKdWRVJtUeZ25sgexknsL8anvTarG61CmSaIsTjPiiONtB3WkwVpumZFU4UThP2aqiiDIJNYYuqdiuTcEq+wp7jkHBKiEAMSXModhuOoOtt91G3ixxtTLKq9l3OFO8TM2pIyISkHRatCi6pOF4DiWrSs2pUzXzZM1808NC5EBsiJ7Q7Y1UxqpTnC1e4fWFE1ypjPr22YLsb0dJoogypmtRsir+g9qusmAU0CQVy7XYFemnLdBy2+2A//CcrM3yQ/tN3s6fY8EsIOB71euShu06lOwKNbtO0SpTtMqwAJqoYiYsBsPLdEkEgYAcIoCfoZCQSKjpVV0PdwsVu8ZkfZYfzL7GmeIlqnYd8FBFlbSWICDpOJ5Lza5TtWvkzCI50+etWK5NwzXYEx1YtV5ZFBlIppAEgfmqT9zef1MLmyxKFBo3xJO2e6yrx6jtZ1SqlsFYZYGX5y4jCNCqx1gwK8w2ihiOxUhlgZ5QkpQWpu6YXC7Ngufh4l9LJavObL1ExTao2gZtegzHc6k7FhXbYMGoULVMoqpOXA2iiBJzdb/GX7YMimadstUgpYUoW768siQKRGQdQYCC6WeWo4pOe/DGRPBIezuW43Byeob/cvw4EU1DFsU1M7W7W9Lsb739vbyITQcDuioT0lWqDZONdAqKgkA4pHP/gV440Ith2lRqBqVqA9O0UWSJYEAhHNQJB9+dOtrdRFtvC50DreB5nHzhLA989AhqQKWSr3D99Cj5mSJDR3cQivuzP0VX2HlPP6qucvXkMInWGH37ukGAkbPjzE8s0Le/m959N2rIvfu6SLbHGb80xemXLvBgNIAoisxPLDB2cZJKocqeB4dW9U1vFkZTDU0SJORtarLfDMuwGT47jh7UOPDILh77zP0cemIPqr6yHaYwXyLTneLKOyPUKw0K8yXGL08zeWVmzXS653lcPTXKiRfOcv3MGJIs0tKd4kO/9DgHH9tN7CbPBtuyOf/GFULRAHPjC5gNizf/8iQdAxn693WTaL1xI0bTEZJt8aXX2ckcnQOtS8GA53lkp/OU81UcxyUSD9G/v5vsZJ75yRyFuZXBgOd5VAo1FqYLCAK097Wg6OvXADeKil3jWmWUBbOA4Zi0B1rIaCkyeoqwHMRwTCbrs8waCywYvof8aG2KF+feQhc1klrslqqUtmszXB3n2MIpXp4/jotLWA6SUhNktCRtepqwEsJybeYaC8wZ/r+cWeRyZQQXD1EQSWrxW8rMlq0qpwuXeDn7NlcqIwAk1RgpNUGLlqRVT6FLGvXmjH7eyLNgFChYJY7nzy4RYGNqZEMqmw3HZLg6wfH82aYTp7+djkCGiBLCdCxmjSyzzWMqWmXG69O8lj2BgEiHnrmtCuHdDgIAbNdhtpHlrdwZXpg9hoNDUAoQVyKktSTdwTaichjbs8mbJeaMBaYbc2SNAter4xjN7E5noJWIvLLlUpNlPr93H5oskwmFaY9E+NyelWY4L44O8+rY+F0/zvXgc4U8pkazWKaNHlSJxIJE4jeuNcO1mDcqnC9M0xNKIYsShmNTty2KZg5JECnbDQ7EO7Fch5FKlrCs3SgxmnXOF6Yo2wY122QhUCWmBJAFkYZjcb2cxfYcBCFBSgsTVrSlx3XOqDJWzWPYNkWzwVyjjCQI6JKCLikoosRUrYAoiHSH4iuCgXQohCJKnJmZ4ezsLK3hMKosrcp+A3xqz567GwzEQwFaYmEK1XkiAQ1BWN1aeCtoqoymyqTid9/7+b1AW3+GoaODdA118Kf/7KtYpk00GWb0wiRvfOdtEm0xHvzYvSSavgtaU2xo9/2DvPy1N5kfX+DI+w8iqzJ/8W+/g9Ew2X3fAAcevSHw07+/h11HB5m6OssX/+XXAA8toPHmX57gyjvDtHSleORT922Zkb54wU/Vr/J/t/feYXLd93nv5/TpvezO9oJddIAgAPYmUqREWVSNLblElu3YjvM4sR3nJje590lzih8nubGvc+0UxXGRZcemVSjJlkiJFHtFI+oCWGB7ndnZ6XPq/ePMDnaxBbsgaErmvn+RizPn/E79vb9veV8QCCpREtrW7DA3i74DnXz87z1Gz54OhDXqOiLJEA/+rTt59n+/yplXhqhXdYoLZc6+dok9d68Us1oa99f/x3cZG5rCsR18ER+f/HuPcefjt63Z4SErMvvu2Uk4HuTsqxe5/PYohVyJC28O0zGQ4f5PHm1uG0tFSLZfc8GbGZmjUuxeQRBGz09SKVTxBb209afp3dvJW8+8zfTIPNnp/Ip3xbEdigtl15hKEOgYaL0lXQQLRoEFo4CAwCPpu/hQy/10+FpXGKnYjs25wmVemHuTb8+8hIPDUOkKLfk4SS3Gvsj6QmE5Y5Hvzb7Oq9mT2I087R2xAzzScg89/na064jjRHWGN3On+dLo16nbOpdKIwRlHyElwN3x24C1C7beWjjNK9kTTSKgCAo/1PoQR+P7yXhTK1ZDtmMzVpnmlewxnhx/GtMxeWvhNA4OUTXM/vDgquNcj4pV5XThIgJwX/IIDyXvYHeoH3WZ0dXScZ6be42vTnwHB4erlQkCeR89/jZuj+29wd1591EwixxbOMM3Jp/Fwk0H7I8Mcl/iMIeiu/FIKxddi0aRt3Kn+e9X/qxJFEUEMt40j6TvWvHMioJASHN/f1trK4ooEfWuJEAJn5+k/737vju2a/z2H/7RnzA5Os/g/k4eeuI2Hv747c1tYlqAnkCCrkCcJzoOIgsiVVOnZNbp9ifYH23jt85/h1ZvmIFQmtvj3bw0e7HZUl1vRAD6gyn2R9r405E3mKsV6QskGS3lGC3l+HT37WS8kab/TMOWjrlaiYJe45HMIH94+XUM2+LedB9R1cczUxcIKx5EQcQnq2jSynfpz06f5ruXhxEEgTs7Ogh7PQhrxgUgGdjaPdgyGZgvlNENk8/ce5BvnRj6G99vfjPYebSfX/lvP8cf/tqT/Mm//wq1Sp1wIsjgkT5+9J9+klhrdEVBoyAK/OoX/i5f/f++xfHvvM3v/uofYFs2O4/28fl//SP0H+xeVdD26OceoPdAF1//r0/z3/7xF7EMk9a+NEc/fJDHPv8Qvk0U2G2EBWOWpya/gCpq7AndyX2pj7+j/a0FQRD49C89TrIjvmEjtCiJ3PHhg1w+NUK9qlMt1xi7uDpXaVs2k8OznHlliEK2hKLJpNrjfPgnH0RWN3jUBQgnQzzyY/dx5Z9+CduyGT49xonvnV1BBqLpEOnOBLIqYeoW45dmKOUrK3Z16cRVigtlghEfPXs66N3fiSiJzIzMMTeRwzKtZktodmqB/Owies1AlER69nU2RXjeKRRBZjDYw+e6P4lvjVWxgMDuUD8xNULJqvDy/HEcHM4WLuORPBuSgacmv8uF0lV0x0ARZG6P7eVz3Z9Yd5Wf8aR4KHUHtuPwxdGvYTkW54vDANwR34+0Rk63but8Y+o5RsqTgOsr8vmeT3F/4vCaq3wBgU5fK3HtA3gkD388+nUM2+Bc4TKmbbE3PLDm6mnt69LHj3V+lIQWW51Waxznk22PMl9f4LXsSQzHZLQyxbdnXv6+IAPPzb7O67lT1GxX2OxIbD+f7fwh2r3pNaeNkBzgvuQRimaFvxj/NgWzxHRtnqcmv8ODqSPI6xQUHs6sLTt9d0cnd7V33LoT2iJ03WT43CRXLkxh1E2uXpjixMuBFWRgCddfD1WUiGl+tw19WbHxWhAFgbjmR5Mbbq+Og+XY5PQyoiBg2jYOoNsmI6UFLhbmGFqccdvpHZu/mjhLUguwaNSQm8WQAgFZY7S8QDSUJKKu/I4v1ur0xmJ89sB+Huzp2fCJ3moU6iYiAx4WKzW+deJCo0hk9QHruun2iIsC8s1KyP0AQ1Flkh1xPv8vf5jKr34U23aL8jw+jWhLZM1e+WDUz0d/7oM8/KP3uoWEOHh8HiKpkOtweN32Hr/GjkM9/PS/+Syf/ccfx8HVNvAFvYRigXcUjnRwGCsPUbcqyIKyopXrVkEQBDx+jZ2H+/AFPDcUjYm3Xuvpt4yVIkxLMA2Lc69dwmy0p0aSIQYO9ayt+XDd/jWvSt++jmZxbn52kanh2aYmgiAIiJKIL+ihpTvF+NAU01fnqFzXNTJyboJyoUKqI0H7YCtdu9oQRRHL0CnMF5m+Ok/7Dtf8Z248RyFbaowBOne2rUqT3Cw8ksYDqaOuLfUa13bpbxE1yAfT9/DK/AkcHBaMAmPVKebqORJqdNWKvWSWeTs/RK6eB0CTVD6a+QBeaXVH0fJj+SQv9yRu4y8m/oqyWaNm6czVFxgqjrAr2LtiexubNxdOs6AXMBwTVVRo9aS4K34bnnWOs/Q3r+ThgeQRvj39EnP1HDWrzkxtjlP58xyI7LxhF4wkiDyQPIpf9q35bbt2HI3HWu7j+MJZDMukZFYYq0wyW8uR0CKbLsK8lXDvT4ULxSuMVabd8SLweOv9JNT1xyQIAjIS9yQO8czMy5SsCoZjummd4ggDwS4UYfU7tJ70tCisbpdbCzPjOfLZEsGwj9au+C1LoSiKRHtvkrauBHPTi3TtaGHf0d5V23kkhQ5/lC8Ov8rBWCcLukvsl49i0ahyYXGG1+eHGSrMcCw3yu7ItSjp9WPWJIV90TZSnhBvZUdYNKrsjbTRFYjyi7sfIKS4RNZy7KZ8t+XY+GQVURB5rG0n3xg7w85wmly9wuXiPJllaYIfGhzk7elpXhsbIx0IkPD5kNdpRfQqyrqS0Wthy2RAliRCPg8hr0axqrtV/NcN5I+//iaCCANdKe6+bfVN2CxcVUKbc5dnmJzNU25U2/s8Ch0tUfo6EmiaclNmNNdw63N4giigqDKpzsTmthcE15K1JbIi5LwRRFHE6/fg9d9ax0EXDlO1qxj2u2d0I8oi0VQYX8i7outhPSxZPoMrVLKk4Lgclmkzen4Cy3RbNH1BL6nOxJrph+shyRLhZTUIRt2kWqpRKVYJxV3hjiUC09KVYHxoiuzkArVyvTEmV0t/5uoctXIdX9BDa3eKeEsEr1+jXqlTypeZGZ1rkoHcdJ5ivtw8frorgaLeGvKsiDIDge5mC+l6UEWFTl+GoOKnaJSxHIuyWWW6Or/MsteFjc14dYaCUcJwTBRBJqqE6fS13tDVUxJEomqImBqhbs+5RWpWndHKJDuDK+V8bcfhYnGEmuVeW5/kpS/QQUj23/B1FREIK0F6/O2UzQoFs0TVqnOxNMK+8CA3ehREQWRHsHuF9PDa20l0+TKElAB1W8d0LKpWjanaHHHt5gRlbgWmqrPk9QJ1W0dEJKIE6fS1om3CyS+ihohrYXJ63u3YcExGKhP0BtpR1pA6rpsmU6UiF+bnmS6VEASIe310R6L0RaN4lPWvYblY5cTLlzh/YoTBA520dsXX3XarEEWRUMTPp37mAUrFGsmWMF07VufOg4qHw/Fu8nqFhBZwawJwiGlueP3+9ABpbwhZkDgY66QnkCDjDaOJMnEtwIFoB3HVj4DA7XHXzySuBcj4wgRlL7NqgajmkkqPpKyY1NfTNEhoAfZFMwQUjaCiEVBWRoRH8nlOTE8zXSwyV64QUNWGHsxq3N/TzcN9q4tA18PWvQlM1551sD3FG5fGmqGU5Sf01WdPYdsOD981uIIMXLsAwg2Jo+M41HSTK+NZvvPqBYZGZlks1XBsh6BfY3dfK5WaQW97nGjYt2nfg+ux1NiyDReO47ZbTVWvYDirdQxuFSRJJJoOI65BJlehsSpvbuWAs4Ymg23bzI5lm7rfpmFRyJV4+8XzNxyPZdrk54srWl5Nw6JcuEYGADSv1uzsyM8uUim67YWCKFDKl1mYK6DXDbwBD6nOBL6Ql3AiRGmxQilfYebqNbex7HSeUr7sRhxCXiKJ4JqKhluFgIAmqqQ98U2shCWCsp+4GqZq1dBtt9VupjbP3vCOFdvajsNoZRLDcYmYJqkktdiGRYDNMQkCEhJxNcJ8fQEDE8MxmK7NrYo7ObjHMR1Xx8Ene+jxtzfP7UbHwYEefzuXS6MUzBKGYzSsyDeOcAkIqIJC2hO/IbkRBYGA7COmRlg0ipiWheXYTFfn2B3qQ3qPPilj1WkqjcJfWZRIeWL4Zd8Nz2cpOhBVwngkjYpVw3YcpmpzWM7qdw1gbHGR07OznJyZJlt1V9URj4dctYrjOBsWr2VnCpx+8wpvPn8eSZZ41NlUMGFTEERX6fWRTx7ecDufrLIr3Irp2MjC6u/QHclrc1e7P7rqt0ukAWBfdLVIWMq7flvret88SRA4GGtjvl4mpHjwXef/kKtWKdRrCILApWx2/ZMD+uJbE9nbuoVxqcpcoURPOsalqSw7Myk8a+RjC+Uai8UqlmVjWq5komG6L7csSY0UgriuvKxtO0zNLvL/fvF7nDw/fk39y03NcGpokpdPDPPZxw/zgTsGCAVudoXs9rguERUbC9uxGx8Op/Hv7jaiIG45/Oc4rmCm49g42Cu0zFwi4u5TQHT1AjZJTBzHxm4Ierj7XWlss7Rvt1XQbT1ZT2bXxmq6rTnY1O0aU9VhDFvHwcZyTHR7fZ91RVC5vu/3RhAlkUDEd8s+ALBUkNfQXgAmLk3z5d+e5su//a2b2p9t21RLK8/b49do73dX9pVSjeJCiUqphsenMXp+EqPuCqT4wz7SXW5kqG1Hmuz0AoWFEhPDM819zU/kmrUNmZ4UkizdklCpKsoEZT+atLnuHLe1KsF0bR4dA8M2mNVXf2iWiueWJmlNVIlrkeYK/kZwcPBJ3uY7ZDo2eb3A9VKxjuMwUZ3BbJAOj6jR4tlce+ASMt4knsZq2LBNRiuTNyQDkiASVPxoorrpaGNKizFZnaFi1bAci5n6/A2P825iojpDzXbvhyRIJLRYUyNhM/CIWlN0yMEhrxfW/e03Lg4xkl9gVzLJp3a5ioEnZqY4NzfP6dkZfv2Dj617nKtD0+RmV7fa/nXD9UD4/kllC4KAKskrogjL8bFdO3mgp3tT+2rZghQx3AQZ6ExEwIE/ffEknckI6jrta3XdZHwmz3dfG+LbL5/jwtXZJjnwaAoD3Sl++EO3c3CwjcgaxW6XxuZ57vUhTpwfB0BTJKJhHx5VIbdYoVCuMTq1wJNPn2ChUOHzn7ixwtRaEBCarHnBmONU/gWuls8yWxujYhZQRQ8JLUO3fzd9gX30BQ9set+O41C1ipwvvMXV8hkmqsMsGvMYto5H8hJSErR4utgVPspg8BDiFuR8svo045WLDBWPM1kdpmguYNp64+HWCCkxkloHbb5e2rz9tHn70KS1iwpfn/8Ws/UxcvoMC/oceWMWq/EhnqwOM1kd5jszf7LuWH558LeJqmsXJ60HQRBuWX58Ca42Q32rtgkb7m+5uxi4qYfuZW2e2akF5idytHanGDo2jGlYxNIREpkYvgZB7d3XyaUTIyzOFhg5O9H87fTIHPm5AppXpXdf5y0jRrIg45O3Ro6Dsr/5Hiz1n18PB4dFo9gkzlk9z9MzL/HMzMubPs7yidJ2bMpmZc1tFo1Sc0WqiDKRDcWDViOsBJEb3ROWY1MwyhsWg4FLBvw3aA28HgHF1zyOjUPFqt7wOO8mCkYJ03bJWtWq8eL8W7w0f2zTv19JZNwahPXOp6TX6QhH+OlDh5tv/p5UiqeGLvDNixc2PM7FU2PMXifMtY0boycavfFGN4ktk4GQT2Nne5LudBRFEvFsoI184coMv/lHz1Gu6uiG6fqK4zofnr08zX/8X8/w+H17uPdQH/sGVraujU0vcPycSwTiET+/+KMPsKMriUdTqNR0fv8rr/HyiWFGJnOcOD/O6NQCHS2RLa+sJEFqhCXP8/WJL7BoZNHtGpZjuBYYlsFE9TKz9XHOF9+kv3iQR1t+HEnYeBVn2gZXymf47syfktfnqNtVTMfAdiwcHMqmSdWqkK1Pcql4grPB/dyT+ChJrX3dnv6lF/Wlua9xoXCM2foYhl1fsV8cMDGo21Vy+gyXS6dIedrZHTrKPckn1tznK9lvUDaLWI6J5ZjYrJbZ3Wi18/3ST+ISDKU5qcZaI/Tt66S1Z2u+3ktItMWIJFZOQh6/RqYvjSiJ2JZNfq7QUHyMc+XMGLZlk+5KkOyINcW5une34w14mB2dZ248S7VUQ5REFqYXKReqxFoidO5qu3U96ML6im7rQRGV5gfdjQ4Zq7T4HXDDx9ev5G/6CXCwWEm23DSVg2lfqwkREFa0RW4Gqqg0uwccHOobRLauYb0mrfWhCPK14zSUHd9LVG03QrEc7yRS4UZJ10baH8ByHKaKRdpCbr3NVLGIadt0hNZe2TqOg2lYDJ+fIjtTQPNuG3JtBe+mTsWWycCSDONGkolL0A2LbL6MIEAiGsDvdYUXKjWD7GKJ+YUyL7x1GU2VySRDxJc5iBWKVabnCwgCDPak2dGdpLMliiSJWJbNxz6wn+HxecZn8mTzZc4PT9OxyeK75XAn5gKvzv8leWOOpNZOWE3gkwIICBTNPOOVi5TMPNn6FLZj0e7bwWDw0Lor7ZpVYbY2xsvzX2e6ehUEgZASI6W145NDyIJC3a6S1+fI1qcomDkul97GJwXZFbqDbv+udUP6o5XznC+8yVTtKqZtEFJitHn78Up+JEHGwUa3axSNBbL6FMWGhWpUXTt/JyBwR/xDmI7Z7IO1bIPnZv8cB4eQHCPt6aQ7sGfN3wP45eD3Rd2FILDCOjoUC7DzSB+HH91/U/vz+LRVrnaS7PpExNJhcjOLFHMlCtkitmkzNTyLZdkkMjHiy57Ftv4WPH4N23aoV3Vmx7KoHoVaRce2bDdN0Ju+ZbWsjuNgrUHoNoLlWMs++sKaOWYBd/W8NExVVIgoIRLaza1WNFGly5fh+hMXBPc4S+FpB2fdvPV6sJZNYgLCuu1xy3FrjvPehpwlrqnRSYJITA03WiS3/nCJgkiXvw1ZvHZOFUPnfx4/hoDA2bk5KobOpVyWtmAIB5gplSjpdRK+a/n0i6fHmZvKs5grs5gtkZ0pMHJxuuHeanP6zSv8zr/8yrrP/0MfvY3Bg51IaxQau9E7h9/7jW9SX+bsuhzJ1gh9uzPcft/gDc95birPm8+f58q5KR762CFa2mMUFyucem2YqZF5PF6V3t0ZugdaaOtJ4jgOp14b5tLpcRZzbmdQMhPhnkf3EYr6N1QTdRyHakVn6OQYU2NZFuaKVEo1TMNC86pEYn6SmQgdfSk6+93v9/cVGdgKRFHAqyns6E7R254g3Aiblip1xqYXOHd5mrHpBc5cmmLfjswKMlCtGxRKNQQEetpiBP0e5EZxlSSJ3LarnZ72OPlilXK1zsWROT54906K1TrVRt7Wp6kEbtC3bTs2FavExdJJuvyD9AUOkNAyBCRXoz1vzBGQw4yUzzFXn2C+PsWp/Au0eXuRRQXpuo+M7dgsGlnOF95kqHgMTfTS5u2j3beDNl8/ISWKLKhUrRLZ+hTjlUtcLB6nYGQ5V3gDnxQioWUIKpFVY3WwGS6+zUxtFN2qEVVT7AodpTewF78cQhIUHGxqVoW8PsdMfYRsfZq0p5NWb8+61+BQ7OHm6sFxHOp2he/N/gUOFn45TIdvgKPx9fN/muj7a1FWuxEE0S1KXOoekBWJUCzIzsObr6i94TEEAVmRSHUmyM8XKTY+cKZhMdswm0pkoiu6QhJtUfwhL5IsYegmU1dm8Ye8GLr7nKqaQmt38pZdwyW9/a2gZuvNZ0BERFsWKVgOj6g2JxavpNHha+H26M311suCRFQNr5iolmpcPJLmWlmz5LuwtRV3za43yYSAgGcTCoTus7+161a39SaBEAQ2VbX/bsKtd3AnTVmQyXhSHInvR7wJUyRBEIir4RXGRZbtcHlhAQFQJRHTlpgsFqka7mKiUK8T1FTSywRvTrxyiaFTY2RnCmSn88xPLzajxHXLYGRompGh6XXH0bcrw479HazXpe44Ds89dZxivoJl2ljXFRf37c5gW/amyEBhocwb3zvPK98+Q3tvisJCmamRLM8+dZyrF6bw+T3svr2bIw/txBvQqFcNXvjmSU69eon5mQII0NaVwOvVOPzAIOH42m3ehmFSyJU5e+wqx164yMjFaeam8hQXqxi6ic+vEU+HyHQn2LG3nVpFp3uwFUWV3zXTvXeVDHg1hb7OJP/kpz9IezrSZHaWZZNbrPDrX3iat4cmmZjJ8/rpEY7su6btbZo2tbqBIApEQyu7BQRBQJIE9vZnmJhZZHJukauTOQCuTucYnckD0NMaY1dnqvmbtWBjgQ0+JcDjrT9FWImvCNO3eLvp8e/h1exf8Ub2aRaMGc4VXudI7IN4pQA+eWUY2XR0pqpXeC33VwCktA4eSH2KHv9upOvC/32B/ewKHUUSJN7MPUNOn2akct4N64fvWD1Wx2akcgHD1lFFDx2+HTza+uOIrF+RXzGLrk66tHYxiftv115c27FXiLOIgoQiavjW+f33EyRZpGtnplGRb1AuVJm6OnvL9eBFSaR9RyvDp1y1woWZRWqVOvNTCzi2Q7IjRrw12jxmIOwnkgzhD3kxdZPxi9NEkkGMuokouZGGdNfWCuQ2gumYVM3als7bbSt0P6KSIOJbI+olIBCUA83J2yNpdPha+XDr/bds7O5xIKj4m0V5pmNRMLbmH1IwSs0CRFEQV8nqrgVXW77qxsc2qapaMivN4wiI+CXve0qMA7K/GZ2QBYkWb4rH0vc26xreKXyKwt+/w63PchwHy3HQLQurUVsjiSKyKOJb1lZ4+vVhzh0baU7SHp9KtaLj2K7VuiRLGypvyoq0sbgO0LWjhexsgWq5Tq2iYxomldI764aaGc9x5q0rjF6aYXo0hyAILC6UePnp0xQLFVRNZmo0x7NfO+aem+POW5fOTPAH//lbtHTGCYR9q6IDtmVTyJV547nz/Ld/+xT1qo4oioiyq9ujajKGbjJxdZ7Ry7O88dx5Tr56mV/45x8n3R5D1eR35Rl7V8lAX0eCn/7knXS2rgwjiqJAIurnFz5zH//uf3ybK+NZhq7Orthmuce4qshr9gd3tcUIB70Mj2eZmlsEYGR6Acu23daLiXl2dqRuWJjllfwcjT9GRE2sWcSnil4Gg4cwbYPvzv4pAOcLb+KTgqvIwGj5ApdLp6hZbv/4B1t/jIy3t+ngdT38cogPpH+EYwvPYTs6s7VRhorH1iQDy+EaRkku49/g/NYjAX8TIasy++/f1Xz55idynHz+HEbdRNFu3aMuSiK9+zp4+ak3WcwWmboyw8Ql16VS9SikOxMkMiuf+bb+NIn2GDNX5xg5O06tM4FeMwjFAqS7XFXDWwWzoRVQMEuE5Bvff7d6fxqjsSpWJYU27+q0kiiI9AU7eX7+DbChYtYYr6y/ortZiIJAn7+DBX2RquX2749UJjka23y6Z6wyRbXR5aCKMn2B9huGym1sSmaFglEipLhWtTfCZHWW6lIrnyDR7mvZlNLhu4WeQDtv5c+AjquMWJ5cVePxTiAKAt3hCABV0+TY1CRPnjvDa+NufdeeZIpHevv4UP+1ttR/+Bs/4tp9N4ZRr+n8i5/9X4xemiEU9XP0oV187lc+vO532o2qrR/ZECWRX/u9n2n+v2lazE7m+YXH/yN6fbUeyWbx3NdPoHkU+nZn+LFffJRKqcbv/YdvUsiVuXJ+ij/9ne8yN7nIvR/ex8G7d1Ap1Tj5ymVe/KtTzE3lOfPWFUJRXzPEv4SJq/O8+p2z/N5/+CaO7RCJ+Tlw9w523dZFKhNB86rMTeU5+eplLpwcZfLqPCdfucRv/V9P8rlffow9R3p+8MhA0O9hR+fq8OdSYVVHS5SQ34tp2eSLq6uXm9uv8/doyIdXU7Asi2LFffH72uKcuTpDqarTkQxvqkJbETW6/bubrXirji8IxLRW2n39CAg4OEzXrlIy86u2na2NMVMbQUDAJwVJam14xPVXCwIiPjlIQA5RMBYomXnm6pNrbisi0uHb4VovW2WulM7wzanf497ExwgqkWs2w9eN/f0CSRLpHMiw+44dnH39EsVcidnReZ78rW/yqb//OKrn1hQriaJI9+52ZEWmuFBmZjTL6AVXHrlzMEMw4l8ldNTakyaRiTF+cYrxy9MggKEbhJMhMr3pW36fdMfgzOJFjsb2b5gv122D6do82Xq+2TLoFbVmX/9ySILIrlAfmqhSokLZrHClPHHLVfcERPaGBzm1OETVqrNoFDmRP8en2x9r/Pv612pJJ+NE/nwzmqCJKrtCfZtqF3Qch7OFSxyK7tnQ3MhyLPe66flmSkYWJXr9He+J+uASBoI9bhQEAcM2uFIeZ66eI6XFUG6B2dgKPZnz5xhZzHMw3crP3X4UHIdTszOM5PN84fgxfuWuuwEIhLxNIuAAtUq9GSUWRAFVVQjH/eveH2Gd9vPrx7MEURTx+d55uiY/X+TjP3kf9//QAbp3tGA7rvnR8984yfRYDlO36NyR5if+wWMEIz5s26Z/dxsnXhqiVKwxeXWe3GxhBRmo1wxefvo0X//iK+BA92ALv/DPP0F7TxLNqyBJIoIoYFk2dz2yh6FTYzz1Ry/z+rPnGDo1yhvPX8DjU9mx79bLPb8rZEBu9EtLooC6ria8gCy7egOOw4o8z5Kxw42gKhKSJGA7DkZDka5YqSOJAh5VxrTsTX1kRUEiqqQ2/MgogoJX8uOXQpSsRQpGjrq9msAUzByLRrZRwVzlG5P/E3kNKc/rUbFKONiYjo1h1zBtA0lYGQ4SBJFdoaNcKp1kpjZKycxzrvAGBSNLxttLytNJQs0QU9NItygs+IMEQRCQFIkHf/hO8nMFLi5WqJRqPPu/XyUUC3Lwwd0k2mNoa7Q0OraDoRtMXJrhyukxEu0xOgczazojipJAW18aSZawLIvFuSLDp0YBt1jQG1xN/pLtcaLpMJZhMTs6jzfgwdRNglE/qY5bp762hLql81ruFLdFdyM563e+lM0Kr84fbxKBoOwn7UkQW0NFT0AgpoTpD3RhFE0KRomyWeHpmZf4RNsjeKWNZaU3C1EQ2RvuJ6KEKJsV6pbBdHWOtxeH2BXq21AdULcNzhQuMlvPYtgGsiARVoPsDvVvKm9uOTavZU+xM9iHR1xfYtmwTV6ZP97UWPBKrhZCXIu8p8W0USVEj7+d+foCs/UsdVvnOzOv8FjLvaS0Wyf5CzBZLCIgcH9XN5mgGyENqCr1RsRgCeKyFK/jOK7i6PJhCO42tzIXfivOU1YkMt1xOnpTaF4Vx3Ho293GsReG3DSSJLDrUBehmB/No+A4DuF4gEQmQnlommK+QrW8stbl0ulxrpyfYmGuiCSLPP7Zu+jakSYQ9q06f69Po3d3hgefuI3Xnz2HoVtcOj1OR2/yB4cMeDQFWRIxLJtSRcfvXUv8xKFaM9wiocbkrxsWiiyim9YKcrCeGZLLJF0ysVQEvFCs4jigyhKWvbnwmOsQtXE4VRBEJEHBKwcoWYtUrXIzrNo8I8ehZpWbKQLTMTi9uPke7CXYjoXtmKuKEwUE0p5OdoaO4JH8zNXGWdTnyeuzzNUnSHs6SWkdJD3txNQ0MbUFVdTWjBj8Tca+ewa5ePwq5UKFiUvTjJyb4HtPvspirkh7fwuhWNBtQRQFLNPG1E3qVZ1SvszohSmGT41w4IHdJFqja5IBQRAIJ0P4Ql7ycwWKCyWGT7tkINObbuoLLEciEyWSCGLbDoVsidmxLEbdJQPJ9q0phW0Ghm1yoTDMheIVev3tq1ToHMehYJYYLo9xLH+2WTyY0uL0BjrWNgMSBDRJ5UBkJzl9kbJZQbcNXs+doi/QQZevjcg6dsFL4ltFo0LRLKHbBqqo0OFrXX0cXBGkHcEuKlaVuXqOolnmpfljBGU/LZ4kXllbMem6mh41JquzvDh/jIpZxcYhpoboC3SS9mxOGtzG5kLxCheKVxgIdhNVQ6uuW8WqMVaZ4q2FM816gYgSYiDQvSmr5HcLS/dnZ7CX+foCWT2P5Vi8uXCaFk+CwWAPCS2Kfw3FSPf+OJTNCiWz0iQ5Pf72dSfWmmnikWUyoSCa5H6rUoEAAVWlZt58eP77Bb6Ah2DEv+J9TrSEmu2QsizRtaOlOYm7svIi4agfBIFaVcfQV16Hy2cnmRlfwLZs/EEPh+8fxBfwrEuEQhE/g/s7UNRrdQTTY7l35XzfFTIQCXqYViRK5TqXR+dJRN0QUNO6tTH5j04vUKzUsCwby7aZyRZIx0PkFyuUq9eKPwzTWjNSYJg2tu2aPSx1GpRqdQzTQpEkNtsBLyAgizcOK4mCiCK6D4Zp69isvNEODqZtYDouSRCR1m0/3Aia5FszzycIArKgcG/iCTLePoYKb3Gh+CZls8B8fZKZ2hgiIl7Zz2DwMIeiD5LQMnil4LraBX/TIAgCyfY4D376TiRZ4tt/8D2K+Qonnz/PqRcvEI4HSbbHiKTCSJLYtETOzxXITeexGyS0pSe5qip5+TE0r0oiEyE3naeUr1ApujnT9oGWNR0jYy0Roo1j6jWDycszWKZFMBog1bG5iWrT16ChBjlXz/G1ye/yoZb76PG3uyt3BMDBsE3OF4d5OXucC8UrgFt93hfo5EB454b7vyt+kPHqNPP1HAtGgdHKJH86+k3uTx5hd6ifFk9iRah8qSOgZusMl0YZKo1QMsok1Cif6frI6vE3pIvvTx6hZJbJNwyLvjPzMgHZy5HYPjLeNLIgNdN2hm0yUZ3h9dwpnpt9DQcHRZDp9Xdwb+L2G8rxLkESJObrOf5y+nkqVpV94QH8sreh2+Bg2hZj1Smen3uz6bwoCxJt3jRHblDTYDdUPmmqfdIsVtRtc4UegOVYGLaBbutLd9RVFRWa2qLNa3U99kcGWTSKDJfHmKvnGKtM8bXJ73AosofDsX10+jJIjf0s3R8Hh5pVZ6Q8yZXKOHO1HIog87N9P7Lu+XgVGcO2GS8USHhdgrFQq1LS9S0Z5Hy/Ihzzryps9AU8TclwSRJJLOtegqVvg9uJYxrWqm/IlQtTZGcXESUBb0BD1WQKDX+S9aDXTXxBjULOIjdbIDdf3HSB61bwrpCBga4U0/NFhsfn+f2vvUZvR5xkLNAM0jkOlKt1vvDky4xN57Edh+n5Av/md7/Fz3/mXk6cn+DMpUZhkgPZfAnTWt03PZ8vUanpyLJIKOBGHwIejWKljmU7m44MLB3nRtE998XdqA/Z4Vp7lkSLt4vHWn5iy4RAETRUcf0VhiwqDAQP0hfYx0PW3+LM4iucyr/ETG2EilWkbBY4tvBdTix8j32Re9gfuYfB4OH3Vf3A4OFekh1xdh7p47//sy8xMzKPYzvk5wrk5zaWQZUVCdWj3rDGoGMgw+TwLOXFClZDartnbyf+8OqVl+pRiCRDpLuSTFyaxjJcP4NIMkRr982JIq2HkBKgxZMgW89zYuEcp/NDxLUo7d40ESVE3dG5Uhonq+ebxW8AByM7ORLbS3+wa4O9u4Y2j7c+QEwN8+TYt6naNa5WJrg6MoEiyAQUHyktjiaqGLZJ1apRNMtk9XxzHy2eJLdH19euANgXHqBm1ZCQeCl7DBuHL088w7emXySuRen0tuKTvVTMCqOVKVUYdIkAACKKSURBVOavO59D0T08kDrCwciuTV03r+Sh29fGvL7AucIlzhcuE1aCdPnbiClh6o7ORGWG2XqW0jLlxJ3BXo7G97M7vHELa9mqUjGr1KwaVatO1apRtVwHx4nqNPoyoaXRyhRvLLzNaHUKj6jhkzxokoYmKnglDzE1vC7BCSkB7kocJKj4+d1Lf0LVrjFRnWWiOss3pr5HQPHRoiXQJNUlaZZO0SwxV19ofr9CcoB94fVtrAEe7evn25cv8TNf/Qo7k243zEg+z/5Umh/de3PaHt9P8Pq1VZ0AsiJdiwSIAoGwd1VaaCktYtvOCvVGx3GYuDrHYraEZdrMTeb58Xt+bUtjMg0LvWqg1020W1QDtYR3hQx84I5BrkxkmZzNc/bSFD/7L77E3v5WYmE/ggCFUo3zV2eYnitgmBZtqTBt6QjHzo7xf/zHr2CaNnrj44oALx+/wkfu30s8ElgRTjl+dpzpuQJej9LsWOhMR7k8MU+pquNRN3exbMehZlfwiL4NCYH74rgfAU3yrKoFEBBRRQ1ZULEcE8PWSXu6bpiCWI3NKaGJiPikALdFH2Rf5B4KRpap6lWGisc4nX8FC5Mzi69QNHLU7RoHIvdtcRw/2Igkgxx97AB77uzntb88wbk3LjN2YZKZ0SylhTJ63UDzKPjDPiKpEJmeFLvu3MHeuwdp6Urgj/g33H/HYIazr15kZmTebYFNhYm1hNclEeFkkK6dbUw0iG4iEyOaCqF4bu1rKAkSSS3G53s+xe9c+mOma/PM1uaZr+cQBbHREmZjc60P/674QT7d8aE1uwjWQosnycOpu+j1d/BHI08xUhnHcmwMxySvFykYpcaqnabvxXJ4JI2QcuP34mBkN2lPknZfC1+d+A41u07FqlGrzDBdnWsWIy8/H01U+KHMQ9yXOELGu/mWTRGRgOLjc90f5/evfoWRyiQ5fZFFo9i8bjb2Cq3+o7F9fKT1IQaC3Tfc/5+N/SWn8ufJ6nn3aiyLEFyvpXC2cIkLxSsN35LlUQEIyD5+eeAn6fV3rityFFFCHI7u4//eE+N/Xf0yo+UJarbeSNWUGzLQyzQnr7tHiigTUVenyJZjZyJJWzDE4/0DXFrIIQoCneEIab+fiOe9S5fcKkiytGEdg4AbHdhMiYjjODi2K5eu6+8sheI6pFqwhpPkO8G7Qga622LceaCHSlXn1NAk2YUyx8+PN1ULDdOiWK5hmDaDPWnuua2XPf2tyJLIqyevYjsOXo9CKh1h/0CGZ165wJ8/fYLb93TS0xbHqymcvTzFa29fZTZXJB7xs6vXNY+ZXyzTlY7iALMLRWzHWfEirQUbi4KRxaN5We/OWo6FbtcoW24LY1CJrlq9C4KAXw4TUCLk9VkKRrbh/BfcdJhyK1gKGSqChoJLQlzRojY6fIM8O/tnVMwis/UJzhfefE/IwHy5zGSxyL50ml1H+/m5f/+jlAtVpIaF8WZc+kRRoO9AF7/0X36KetVAUaWmMmDNXGCq+ia5+iV0u4SASMZ3lKRnD145hqIJBBIa0TsnaO8awp+fo7tcwzFl9kd+Co8SwJbq1MQp5ngdITrCdPwEgrSPlLWXsOSukk27zkjpWQrGKDF1gOnqMYyDY9ydDvBw7QHSvgMuCfAXuFh4lkV9FMOuoogeugIPE1F7SO2UufPnA3R8pJMF/TKpcIBY/xhn8l+ioI+yM/JpHMdiQb+EZesMRj7RvAYz1VMU9FFUMUhn4P4Nn2fLsTBti05fK5/r/gRnChe5Uh5nsjrLolFCd3S8kkZYDZLW3FzyoehuWj3JFQIzG2HJ1GdHsIvP93yCc4VhxiqTzNSyzOt5KmaVuq0jCSKqpOKVPESUIBlvmg5fCx2+VjrXqBe4Hoook9bi3Jc8TIsnyenFIcar02T1PCWzgm4ZaJJKRPYRVyO0edPsCfezI9BNcosV9DY2dcugw9fKZzs/woXiFS6XRxmtTLGgFzAcA1VUG0WWcXaH+jkY2Um7twVtE2nGmlWn1MjJ3whmQ19hPbiaEOtHPkVBxCOpdPna+NtdH+dyaYSx6jRT1Vlmam5kQ3cMBNz0kFf2EFXDtHgStHqTtHtb1uwoWY4lPYGE34fjZp8IqCo2UDYMIuupBP2AYDOR1K1EW13S50rGK6pMqi3CBz91eMsFp5070ijrFubfPN4VMhDwaRzc2Y7VqOa/OpFlsVHYBw3ZWI/KYHecew71cWRfJ12tMYrlGl6PQq1uEg54aW+JMNCd5LuvDfHG6VFyixU6W6N4VIULV2cYm1rAtGwSUT97d7jeBgKN3JyzeU9MyzaYq02Q1NrWvS1Vq+hO7g1HsLCSxCOtDgeHlDgRJUFen6VuV5mtjeERfav0CN4NKKKKIqr45TAhJcrb+ReZtnWqVpG52liT+W/08F1PnGzHWqV1vhnkqm7ucLJQYKpUpCcapeB1yBzpah496fczVSwS9/nQ5PUfRUFwV91HHr1mErV0LlPVNygaU4CDInpxHJvJyqtoYgBZ1BCQma+fwYlN0hlP00WGpY/owfjtSIKH+dpZpqsF/GYKSVBxsFk0rmBV6nilBIrow8YkW7/AXPUUiuBFEX1kulvRO8tENYWd4V3IosaFxS9TNqaxHdMdDzZj5ReQBQ01atB6QCKwy4Mq3UXZnEYVs9Qtg6qVZb52hojaS9EYp2TM0G19AFUMAQ4L9Ytk60O0+TbWn3DvmY3pWHhEjf3hQUJKgA5fK9O1eQpGCcM20USVkBIgqcXoC3TQ7m254YfNcdyi3zPnJ6jrFqIo4POqHNizg6gSZtKfYaaaZXxxntdOX+LO/W2osowiyngkDyE5QIs3SZs3RVQJ4dukKZAmqWQ87m8iSpDJ2hwL+iIVq4puGaiSgk/04nG8BAiz1zdA3Bta0armOA6FSg2/R0NeQ9Z2aRvDNvBIGrtD/UTUEBlvmonqNHmjiGmbKKKMX/aR1GIMBHto86bWJfqWZVOu1DlzfpKjh3rYGerFK2mbIgPghpnrusGJt8c4dKALrTEBaKJKWAki3KCF0bagvGjQH+giroTp9rcxXcsyV89RNqsYy8iAR9KIKEGSnjhpT4KEGrlh5Ob8/BwTxQIlXV/VARZUNR7pu3XKn+8FbnVSVRBAbrQOipKAP+jl3g/t33IXhdenbai7cLN41/rPBrtTJCJ+WpNhvvn8aa5MZDEMd1JRZInWZJjH79/N0X3dxBvh2Efv3smBwTYK5Rohv4d42I9umHS3xRken2dyNr/igRNFgXQ8SH9nir073FVGIuxnfD5PtW4Q8ns31VtsODoj5XMMhA6hrKE14Dg2ufo0k9XL16quPR345dVhtKSWIaW1M1I+h4PDheJbBOUIqui5YRGf6xTYMBy+7kVfyj1ZjoEkrG8OBUtKcVESWoacPotu1jZp1OL+WhRkREHEdiwMR6duVxq1EpuzKXYch+GFHHPlCtlKhappMFMuc3xqkoTPhyS4/hYhj4czc7MczrRtSAbWPgjYmAwtPkWL7zY6/Pfik1OYToXnp/45IaUDr5xAlYJcKnwdSfQyGPoYEbUHAYGalUcRAph2hdnqKSbLr3Mo8XdJefZRtXKcz/854+WXSHv3E1Hdj5ppVzGcKooUZE/0x9GtAm9m/wvZ2nkq/jm8cpShxa/SH/oI7f57CCoZKuYc35n8R4SVTjQp3PANMDgY/2menfqnKKKftPcgIbWL2cpJMr6jeKU4BX2MvD5MynMQw65SNKYw7DJp741dM10japeIS4JEX6CTvkDn1q7vGrAdh9n5Ik9+/Ri6buHzqaQSIQ7u7aDNl6bNl8YwLGbEAuPDCj/7xEfw3qK8piAI+GQvB6O7OMjqGgDTspleKDI8nWVR04n5Vi8GLk1lGWhLElyzu6nhTcCSkp5Eh691zW6HzcKybXILZf7y6bc5fLCLD6S25qxqmhb5xQq/+dVn+JmPPEZkjVqUjVDXDc6cm2TPzlbi4RgtviS3MpP/neFh3pxyXTgDysrISEsg8ANPBm4lhEYBvTegoaoyju2g1wzS7TFXW+D7oJ5ry2RgK/acsbCPB4/08+CRfmp1k1KjQ8DnUfAt6/Vevs90PEg67q6iBUFAlkV+9Scf5jf/6DmGrs5SqV3Lq8Ujfj7+8AE+eNcgaiMFIYoChwc6EASByWxhU5KsNavM8fyzHIp9gITWiszKB7tuV7lUOsWp/EvuMQSRgeBBYmrLqn11+AYomQVOL75CxSryZvYZvFIAQRBp9fRseL1y9RlEQcIvh9aMOliOxVjlIm2+fhQ2Dkvqdp3Z+jh1u4IsuNGCzYSjliq5/XKYorFAwcgyWxtDt+uoomfd+3/99T0zO8t9XV3oVoRXxsYo63WiHi/jhQKKJJHy+zk5PYVh36w+mkPFnKOgj5KrX2Ro8asr/rVq5ahZeSRBZar6Fg+0/FtCSjtSo87DJ7sV/AVzHN0u4JGjzYnWJyeIav3UrQIz1VNNMuD+W5Ke4CMAaHIYv5zCcWyq1jxVK4skaES1PsKqO/n65RaSnr2UzVl0u4gkKISUDiRBQxODBORWwoqbijCcCg4Occ8uTKfOlcIzpDwHmK2eBCzCahdeeXOaBO/EqW7VvpaIqGnz+rErZFoiPHz/LnYPtK7aTpZF2loi/Ot//LE193GzEK5b5V8PUXR1TUzL5rULY3Qmo9dFBuAbr58j8Yh/XTLQ2HLDcdzoPJZ3TCmyRFdHnH/5f167Flv5vSSJxGMB/tU/+/iWxrCEcrnOt757ho62KMGgF9kRVx3jZrD0+6ppcLi1jZ87fKTRnbCNG6G1I8Hk1SxzU3mmx3OUFqsEIz4k6b2/flsmA7puMjG5wHeeO0etZtLWGmH3rgyKIvPaG5epVnX6elP0dCcpl+u8+tolHODBB3bSmg6zWKhy8sI0vT1JUskQL748RLFYw+dTyWbLZHNFjh7upbsrQTTqRgx2dCf5d7/8BCNTOcamFiiUqiSiQfYPtBIJ+ppEAGBofA61UfhxbmSGwfbkhtkCSZBRBBXLsfj9q7/GntAdZLx9RNQkkiAxV5/gVP4FZmqjlM0iIhJHoo+Q0jrRxNUTtiQodPl38pHMT/Pk2G9hY/Hi3Fd5O/8Sbd4+uv278MthHMCw61StEjl9mtHyBRaNefZH7uOexA+tQQYcdKfGH179twSVqKsp4OkgoWYIK3EkQcawdSpWkZw+w5nFV5muXcVyTFo83ewN37XpeywgMBA8xOnFV6haRUYq5/njkV/nSOyDBOQoCG5rZc2qULXL7A/fu6pjIurxcnJ6hoqhU9J1TkxN4Vc1bMehPRSiMxzh1194nn/zyAcJ3nQbkoMoSByK/3xjgr52o0VkREGmas5Do25kvXPdCkQkFGGtwkKn2X52w30sy8sLK6JA7m9jWj+WXefS4jfQ7RKj5efxyynS3tu2NNZbhUrV4MLlab7yzeMMj8xjmhbnLk7Rnomxb1eGJx47CMDp85Mcf3uUE2fGmJsv8ru/8eP4fe7Em1+s8KUvv0FPZ5wTZ8aZnS+iyBKHD3Tx6Y8e4q++e4bJmTy5fIWro/M8/sg+Xj92Fcuy2T3Qwo99+tqqulo3uDQ1zwunr+A48Njtg7QlQng1hXjQx1TO7Rap1A1OXZnijaFRon4vtvXOCdIrbwwzdGmaQrHG2fMTPPrwXi5eniG/WCWdCvFLP+8SxZOnxzh+aoy3z44znyvx33/zc2iq7LZbD8/yxT97ldaWCJNTeep19zv6oz98Bx1trvX10OUZTrw9xhvHrjIzu8j/8+8+QyLmhu0N0+KtEyP8xVPHqFZ1bNshEND4O3/7fnq7k+QXKxw/Ncp3nz/PydNj/Kff/rYrq9ud5Lb9ndx9Rz8AI6NZXnz1EidOj6LrJn6fh5/4zJ10dcSZz5b4y2dO090Z59U3hskvVgiHvBza38XHPuLe70OtbTx3dZgnvvRHtAZCKyTjW4NB/tVDD695DWXlmgiWbdlUypuNWv7gY/ftXUyMzDE3lcc0LL7956/z4R+5k2Bka1GfdwNbJgO5hTLZbJlMSwSpYawgCFCp1JnPFvn4Rw/h92tcHp6jVKpx+PYeQiEvr75+mbvv7MdxHBYWytQzEcAhmy2RWyiTSgZpb4tw8EAHb59xe7ajUX8j1CkQ9Gv0dyRpT0UwTQtVkQn6tVUhlr5MgrG5PJW6QSTgvWHZQFCO0u7tp9XXywtzX+Vc4Q0ul95Gabiz6XaNgrGAYdfQRA9tvn6Oxj/cWO2vLV3sk4L0Bvbwoda/zUvzX6dk5lk05qlZFaaqV5rqgLZjYzsWul2nZpUwHcNNFaw7aAfdrrGgz1Ixi0xUht3uhcZYHexGF0OdopHHckxaPd3sDB1hzw28DlacAyKHog8xUxthujZC3aoyXrlE0cw3Oyjc3nELUZDZGTy8ggwIgsCdHR3Yto3lOJi2jSwKjdSD+0Eu1uvsTCTxKcqmUjlrjdIjxfDKSYr6OPn6MCnvfhzHoWzOoIrBRrpDIa7tYrz8IqoUJKS4yl26XUQR/fikJIoYQLcKzNfOE9cGqdt5CvooVStLn/b46iOvdd+RCKkd2Fgs6qMElXb8cpqalWOhfpGEZxeqGKJkTLLKsvf61iQUvHKclHcfY+XnyevDxLQBotp7E3bVNJm+riSf+fgRnn7+HLWazsG9nfR1JwkuE2Tp6ogT8Gu0psP87u9/b8U+bAdqdYM3Toxw3507SMT8yJJEMOiqFi5piSTjAQb70nz7uTN85uNHOf72GHPZEtWqjsfjpsfOjc0yXyhz164uIn4vL5y+wv37eokFV7pnnhubpVzTOdTfTjoSYGgyuyll041gNQTRfF6Vj37oAM++eIEH791JrWbw1skRKlUdr0ehtztJKOilJR3iC3/4IksHdhyHSlXnwqUZHv3AHj5w/y4mpxaYmMrz3IsX+IkfcUl7W2sUn0elNR3mN3/3GZxlbdK6bvL0s2d54J4B0qkwWsPXIp0MIYoCwYCHg/s68Ps0RsezfPyHbiOVDBEOeQmHvDiOg2naPP3sWSIRHz/+w3chSQLnhqZ5/qUh7r6jn2DAQ7Wq8/pbV/jwB/fi86ruN3fZ/T4zO4Nl23zu4G0kfSsJsl9Zn+BLkoQ/6EHVZGpVg9GLM+g1Hc2jrpLx/puGfXf0cenMBBffHqde1Xn6yTdp70my87YuYmsInC3BcRxmxhfw+lW8Pu2WSasvx5bJgKJIiJLAQr5MMhmipSWM369R18tIokhHu8ts3z49TrVqkMlEiEX9PPWNE1RrBqoiYRgWZkMwqFY3qNUMLNshHPbR2RHnldcuUyytZIuSKOL3qvhvYEncGgsCDjXdJOK/sTyqXw7R5utnMHS40Zp3hUUjy6Ixj2HriIKEVwqQ0FpJam30Bw+S9LRtKG0qiwoBIcLO0FF0u85U7Qp5fY6SmadgZhvKhe6qVhYUPJKftKeLiJqg0zfotjiugis4tC98DzljhopZpGZVKFuLmLaBg42AiCTIqKKHqJoioWXo9O2ky7+TiLI1V7y0p5P9kXsJlePM1ccpGjnmahPN48iigip6iChrC+ak/P4VKRq3vcydBou6Ts00ubOjA0nYXB3CGpcDWdDo8N9LzVpgovwai/pogxDptHoPE1TbkEUPncEHmK2eYqz0IpoUBARsx6Qn+CiK6Ceq9lO1skxUXiZXv4jlVNHtEhG1l6DStsnhCGhimIzvCFVznpHSc6iiW5MQUjsJKW7awCUDN9iXIKJJYVp8tzNSehZZ9OGT46jie2M6JUsi4ZAXr0fh1NkJypUaPZ0J+ntW6iOEAh60xvu9HjRNoaczQUcmiigKK9RFPZpCNOKjrzvJl795nO6OOFfHskzN5KnVTTyND2CuVKFSN2hPREiEfPz5i6eo6quth3PFCrbj0BYPk44GmhI77xSKIuH3aewcaOWbT5+mJRWirps4x21qNQOPphAKelEVmWp1tfWyg1tcuHNHKy3pEMGARl03efvcBEt1zwG/hqpKa1axCYJbuDmfLRGN+EklgsRjfryN1KuqyiTiQXTDwqO5xKQ9E0VrCOg4jsNiocKFy9Mk40H8freoMpcrMTqRY1+p1pz0PZrCjt40kbArsW0vIyUODrIoElQ1gqq2gtR71qkBEhqLx5aOOFOjWXKzBeam8rzwl6fo292GP+iK+tiWja6bGLpJoiVMcJP1Em4Ln73iGVySu7dMC/E9zs/H0yF23dbF5NV53nphiMmr8zz39RPMTCzQ0Zcimgg2pI1dm+N61aBSqpGbLTI+PMuh+wbo2Zn5/iADoaAXv0/l/IUpbNthz84MPp/GYqGG33+NNSqKhChCuawjiaLrIyAKSJKIokjk8xVCIS+GYbm9yLZDrWZQKNSQRGHdit8bIRLwEgncuEo5pMSIaxkSWoZWbw8prZ0HU5/iXOFNJqqXydWnqVolZEEhpqZp8/XT6Rug07+xOtsSREEipqa5L/UJxstDTFQvM1MbIatPU7PKONAgAj7CSoI2bx8dvgGianpNXQJBEJBReSD1Ka6UzzBfn6RgZClbBepWtbFKl1BFDwE5TMrTwWDwEElP+5adC11ZUy+3Rx8m7enkavksE5VhCmYWyzGRBAVN9OCXw6Q9nSjrtFWt9FW4tv6VBYGwptETjd50xW6j85q+0IcZK73AbO1tZmsnsR2LgNJCXBsEB2TBQ1fgISrmPPO1M+h2ARBQpQBdgYcQRC9J7x5k0cPFwteYMo8hCxpp70FafUfwyq5+heCIeOX4qlW8JkVc6WjRgygoDISe4ErpGWarpzDsMrLoYTD0CaJaL1UzhyaFXNts3PoDTYogCBKy6MUnJxEbr6Qq+kl59vH67H9iR/gJ/HLLDavHv98hCAJ93QkCjYgesCLX7NEUwo3vSyTkQ5ZFFFlCkiR041pvtiJJiIJAuaa7/ieyhONATTcoVetU6wZV3UAUBCzbplzTqdQMRHFz+h03giLLhEJefF6VcMiLokhYlo2qypvqIZdEgWDQQ2tLGFl2iUXAr1Epb85yV5El7jrSyze+/TaFYpV8vkx/b5ruzvgGXjArkc2VKRRrLC5WKRavLbxCQS/ehtyuKIr098bwaEpTSGd5brslEKSk61zKZlms1ZDEa36NQU1jT2p9Ma2B/e1Mjc6zMFekmK/y5Bee5+4P7iHZGkHVFAzDolysUivr3PnInjXJgGM7GIZFdmYRx3a1EmzLlRfPzRWbi5F6zWBhrsjVC9NNGXJBcOci1SMTTQT/2giCosjsOdyDadiMD7vpghe+eYqhU+N09qfoHmwlFPVhWw61Sp1CvsL8dJ7LZybJzhaIJIO0dd86u/Pl2DIZMEyLStWgVK6zWKgyfGWOjo4YgrAyuj040MqFoWn+4qtv4dFk9uxuIxr1I4oCyVSQp755ks6OKOVyHVVVqFR1nn/hAgjub9vabr1e+xIEQeDDmc+t+ntIiXNH/LFbehwZme7AbroDu9/x/kRBpMXbRYt3Y4W4WwVV8tAb2EdvYN8t3a9PVfHdIrlSn5xgMPIJBvnEutuogp8DsZ9c9989UoRW3+20+m5fdxtF9HJb/O+s+vuuyKdX/H9I7eRA7KfW3IcmhYlo14pIjyT/QfO/fXKSlmZNgAOIiIKC5RhkfEc3HaH4fodrR772h1dc5k4nStfkcmkI9y5hT1easyMzfOm543gUmQN9GQQBzo3N8dr5EaZzRXa0TdGeCHN5MsuTL54iEw+7JOJWhKEFd0K/fsybhSgIeLSbX9kpisTdd/Szf28Hx06O8PpbV/jaX53iF3/2A+waaF1VjLZa8sn9azTs4767d/CRR/ez8lrD1dEsguBOXuudnl9Rml0Ec+WVkro38iZ44CMHWcyVmR7LkZ1ZZGRompGh1XbYvoBGW0+Cvt2ZVf+m6yYTV+f49V/6IpVSnWpFR6/pGPrKyNTElTkmrszxjT9+BVEU8Po1PF6VcDzAzts6+Xv/4pOb7UK/JUi2Rrjrg3uIp4P853/yZ+Tmi8xM5JgZz/HGc+fX/I0rKSMg3WJDp+XYMhk4fXqcsfEc//c/eYJg0MsffPElAkEPB/d3MrDMqjEe83Pn0V6O3N4DuJWxSydx+8Fu9u3uQG70Sl4enuXCxWnuuWsH6XQIRZZWOF1tYxvvN1TMOS4VvkGL9xAhpRNF3FgN8f2ERMjPvXt6uGtXt9vB0Aj9DrY73LO7CwensUoV6M8keOS2Hc1vj3STEcfvJziOQy5fIRrxce+dO0jEA7zw8kXOnp9ioD/dPEdREPB6VRYXa7SmbFjWRNHZkcC0bMYmFhi+OkdvQxK7UKzh0TY3LXyof8e6JnI3IkiBsJdPfP4+jj60i+eeOs6xF4eYm8xTrxluC55fJdESpndXG+l1FoaO7VCvGoxdnt10LYhtO5SLNcrFGrWqTjT57uu/rAVfQGP/nf38l6//Cq88c5pzx0e4OjTN9GiWQt7VofD6PYRjftJtUQYPdHLkwZ109Cbxr+F9ciuwZTKQyUQxTIsXXh5CVSR27Wwl0xpBFAVE8Zr4hiAIjb85K/4GIIrgaUqwCgQCGol4AJ9XQVOVRpThb3YhyTa2sRbmqmeYqLzKfO0sgiCyJ/pZNCn0A/E+fPfF81y4NMPsfIFiqc5//YPnybRE2D3QSltDLvxWYP1vi8D1c73guJoBSyvfv67L+PSzZ7h0ZZbpmQKlUo3f+Z/fI9MSobtzc62hr745zKkzY8zNlyiV6vzeH79EeybKjt40e3a28qU/f41CsYogCFiWg6bKfOiRvSvSq36/xsMP7OLPv/omoaCHvp4Uu3dm2LMzg6bKfPqJ2zl7YZIvPfk6kugW9x4+2M2+3ZuLQkmiyM1qDAqCgMen0tGX4omfuIcPfOwQhm42axIkUURWJbw+jUhi7TSnqsl07UjzG1/6hZsagyiJ+ALaimci053gJ//hh/nUTz2AL+gh2RpZMeZES4Sf/WdPUC5UkRWJ9t7kipV6JB7g8//ocT79sw/h82vrkg33GQZf0MORB3ex90gvtYqOXjea5kaiKLqpMk3B59cIRrwo2sYaM+8EWyYDsZjrQOjxuFXgmdYo4fDaTMUd89oV98sRCnnp6ozj93u2icA23tfQpBARtRtZ8KBKQWLaDiRh45SKKIhkvCk+1vYwhu1K5ia0yC0fmySJ7B5sRddNYtHVkYpkPIhp2rSmw3RkoqSSYaJhb7P48Oht3cSi/mYh4HIM9KbdFjm/Rijo5ZH7d+HzquwaaKGtNdJsUVzCet+W63Ejca4WT5IPt95PySwjCzIRNbhhXUFHe4xw2IffrxEIeHjg3kFSiSCGaXH/3QP4/e7kkkqGcBxoSUfo6oiTToWIhn3EYwE0VeKjHzrQnET8fo2eriQP3e80J6ZY1E9vV5J0KkxrOkwqESQc9hGN+JAkicH+NMVSHQS3hiAU9NKSCq84X4+mcNu+DizLRhIFkskQoaC3WcS3c6AFVZNJTi5gmjaO49DaEsbrVfF6FY7c1k006m86wt5qiKKIx6viaY+xOUeM634vifgCHvYc3li/ZSvw+jQ6+9YfjeZR6B5YrS+zBEWV6ezf3Nks3YdIPEAk/t4UB68Yj/NO1UC2sY1tbGMb29jGDzR+8BNo29jGNraxjW1s4x1hmwxsYxvb2MY2tvE+xzYZ2MY2trGNbWzjfY5tMrCNbWxjG9vYxvsc22RgG9vYxja2sY33ObbJwDa2sY1tbGMb73Nsk4FtbGMb29jGNt7n2CYD29jGNraxjW28z7FNBraxjW1sYxvbeJ9jmwxsYxvb2MY2tvE+xzYZ2MY2trGNbWzjfY7/H/OP68MXYYJJAAAAAElFTkSuQmCC +"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Building-and-training-the-NMF-model">Building and training the NMF model<a class="anchor-link" href="#Building-and-training-the-NMF-model">¶</a></h2><p>In training the model, only the train data is used - the test dataset is not included. This allows the test dataset to do its job of measuring the accuracy of the model on untrained data. Nevertheless, it is very possible to train on the test data as well, as the label assignment can be done based on the <code>train</code> portion of the predictions.</p> +<p>Accuracy will be used as the performance metric as it is the one used by the competition.</p> +<p>The code below gives an accuracy of 90.8%. Surprisingly, when tested using data_test and submitted, it returned an even higher accuracy of 92.2% (V5).</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">nmf</span> <span class="o">=</span> <span class="n">NMF</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> +<span class="n">train_pred</span> <span class="o">=</span> <span class="n">nmf</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">vectorised</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:</span><span class="mi">1440</span><span class="p">]</span> +</pre></div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">label_permute_compare</span><span class="p">(</span><span class="n">ytdf</span><span class="p">,</span><span class="n">yp</span><span class="p">):</span> +<span class="w"> </span><span class="sd">"""</span> +<span class="sd"> ytdf: labels column from dataframe object.</span> +<span class="sd"> yp: label prediction output.</span> +<span class="sd"> Returns permuted label order and accuracy.</span> +<span class="sd"> Example output: ('business', 'politics', 'sport', 'entertainment', 'tech'), 0.74 .</span> +<span class="sd"> """</span> + <span class="n">y_true</span> <span class="o">=</span> <span class="n">ytdf</span> + <span class="n">best_acc</span> <span class="o">=</span> <span class="mf">0.</span> + <span class="k">for</span> <span class="n">perm</span> <span class="ow">in</span> <span class="n">permutations</span><span class="p">(</span><span class="n">ytdf</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span><span class="p">):</span> + <span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="n">perm</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">yp</span><span class="p">]</span> + <span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span> + <span class="k">if</span> <span class="n">accuracy</span> <span class="o">></span> <span class="n">best_acc</span><span class="p">:</span> + <span class="n">best_acc</span> <span class="o">=</span> <span class="n">accuracy</span> + <span class="n">best_perm</span> <span class="o">=</span> <span class="n">perm</span> + <span class="k">return</span> <span class="n">best_perm</span><span class="p">,</span> <span class="n">best_acc</span> + +<span class="n">labels</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">train_pred</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Accuracy: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">acc</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="p">[</span><span class="n">labels</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_pred</span><span class="p">])</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Accuracy: 91.7% +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div> +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"> +<pre>array([[302, 1, 17, 1, 14], + [ 5, 211, 7, 3, 37], + [ 13, 0, 242, 2, 9], + [ 1, 2, 0, 339, 0], + [ 0, 3, 0, 4, 227]])</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Try-replacing-the-numbers-with-NUM">Try replacing the numbers with NUM<a class="anchor-link" href="#Try-replacing-the-numbers-with-NUM">¶</a></h3><p>Here we unfortunately see that reducing the complexity by generalising the numbers actually decreases the performance of the model.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">text_without_nums</span> <span class="o">=</span> <span class="n">combined_text</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="sa">r</span><span class="s1">'\d+'</span><span class="p">,</span> <span class="s1">'NUM'</span><span class="p">,</span> <span class="n">regex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> +<span class="n">vectorised_without_nums</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s2">"english"</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">text_without_nums</span><span class="p">)</span> +<span class="n">without_nums_pred</span> <span class="o">=</span> <span class="n">NMF</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">vectorised_without_nums</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:</span><span class="mi">1440</span><span class="p">]</span> +<span class="n">labels_without_num</span><span class="p">,</span> <span class="n">acc_without_num</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">without_nums_pred</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Accuracy: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">acc_without_num</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="p">[</span><span class="n">labels_without_num</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">without_nums_pred</span><span class="p">])</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Accuracy: 88.0% +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[44]:</div> +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"> +<pre>array([[287, 1, 30, 1, 16], + [ 20, 200, 8, 6, 29], + [ 12, 0, 241, 5, 8], + [ 19, 2, 0, 321, 0], + [ 7, 3, 0, 6, 218]])</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Changing-the-hyperparameters">Changing the hyperparameters<a class="anchor-link" href="#Changing-the-hyperparameters">¶</a></h3><p>In the previous models, parameters such as the maximum number of features and normalisation were ignored. We will now try out different combinations to try and improve the accuracy.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [48]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">gen_model</span><span class="p">(</span><span class="n">vectoriser</span><span class="p">):</span> + <span class="n">model</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"vectoriser"</span><span class="p">:</span> <span class="n">vectoriser</span><span class="p">}</span> + <span class="n">vectorised</span> <span class="o">=</span> <span class="n">vectoriser</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">combined_text</span><span class="p">)</span> + <span class="n">nmf</span> <span class="o">=</span> <span class="n">NMF</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> + <span class="n">model</span><span class="p">[</span><span class="s2">"nmf"</span><span class="p">]</span> <span class="o">=</span> <span class="n">nmf</span> + <span class="n">y_pred</span> <span class="o">=</span> <span class="n">nmf</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">vectorised</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:</span><span class="mi">1440</span><span class="p">]</span> + <span class="n">model</span><span class="p">[</span><span class="s2">"pred"</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_pred</span> + <span class="n">model</span><span class="p">[</span><span class="s2">"labels"</span><span class="p">],</span> <span class="n">model</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span> + <span class="k">return</span> <span class="n">model</span> + +<span class="n">best_accuracy</span> <span class="o">=</span> <span class="mf">0.</span> +<span class="k">for</span> <span class="n">norm</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"l1"</span><span class="p">,</span> <span class="s2">"l2"</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span> + <span class="k">for</span> <span class="n">max_features</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">4000</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">gen_model</span><span class="p">(</span><span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s2">"english"</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="n">max_features</span><span class="p">))</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">"norm=</span><span class="si">%s</span><span class="s2">, max_features=</span><span class="si">%s</span><span class="s2">, acc=</span><span class="si">%.5f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">norm</span><span class="p">,</span> <span class="n">max_features</span><span class="p">,</span> <span class="n">model</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> + <span class="k">if</span> <span class="n">model</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">></span> <span class="n">best_accuracy</span><span class="p">:</span> + <span class="n">best_model</span> <span class="o">=</span> <span class="n">model</span> + <span class="n">best_accuracy</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Best model accuracy = </span><span class="si">%.3f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">best_model</span><span class="p">[</span><span class="s2">"acc"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="p">[</span><span class="n">best_model</span><span class="p">[</span><span class="s2">"labels"</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">best_model</span><span class="p">[</span><span class="s2">"pred"</span><span class="p">]])</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>norm=l1, max_features=2000, acc=90.20833% +norm=l1, max_features=4000, acc=90.27778% +norm=l1, max_features=6000, acc=90.97222% +norm=l1, max_features=8000, acc=90.97222% +norm=l1, max_features=None, acc=90.69444% +norm=l2, max_features=2000, acc=91.25000% +norm=l2, max_features=4000, acc=91.73611% +norm=l2, max_features=6000, acc=91.52778% +norm=l2, max_features=8000, acc=91.45833% +norm=l2, max_features=None, acc=91.73611% +norm=None, max_features=2000, acc=47.22222% +norm=None, max_features=4000, acc=42.29167% +norm=None, max_features=6000, acc=42.01389% +norm=None, max_features=8000, acc=40.69444% +norm=None, max_features=None, acc=45.69444% +Best model accuracy = 91.736% +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[48]:</div> +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"> +<pre>array([[304, 1, 18, 0, 12], + [ 5, 209, 7, 4, 38], + [ 13, 0, 243, 2, 8], + [ 2, 2, 0, 338, 0], + [ 0, 3, 0, 4, 227]])</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Test-on-data_test">Test on data_test<a class="anchor-link" href="#Test-on-data_test">¶</a></h3><p>The model used has been developed on both the train and test datasets together, as NMF does not require labels to train. As the test labels are not provided, there is no risk of "cheating". In a real-life scenario, there is nothing stopping the model from training itself on the test data before providing results, just like we are doing here.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [50]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">vectorised</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">[</span><span class="s2">"vectoriser"</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">combined_text</span><span class="p">)</span> +<span class="n">test_pred</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">[</span><span class="s2">"nmf"</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">vectorised</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1440</span><span class="p">:]</span> +</pre></div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [51]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">sample_solution</span><span class="o">.</span><span class="n">Category</span> <span class="o">=</span> <span class="p">[</span><span class="n">best_model</span><span class="p">[</span><span class="s2">"labels"</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_pred</span><span class="p">]</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"The order of solution df and test df is equivalent:"</span><span class="p">,</span> <span class="nb">all</span><span class="p">(</span><span class="n">sample_solution</span><span class="o">.</span><span class="n">ArticleId</span> <span class="o">==</span> <span class="n">data_test</span><span class="o">.</span><span class="n">ArticleId</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="n">sample_solution</span><span class="p">)</span> +<span class="n">sample_solution</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"submission.csv"</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>The order of solution df and test df is equivalent: True + ArticleId Category +0 1018 sport +1 1319 tech +2 1138 sport +3 459 business +4 1020 sport +.. ... ... +730 1923 business +731 373 entertainment +732 1704 tech +733 206 business +734 471 politics + +[735 rows x 2 columns] +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Building-and-training-the-Supervised-Learning-model">Building and training the Supervised Learning model<a class="anchor-link" href="#Building-and-training-the-Supervised-Learning-model">¶</a></h2><p>We will now compare the performance with that of supervised learning. To demonstrate, the most basic form of supervised learning will be used: Logistic Regression. To start with, the model will be trained on 80% of the data.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="n">train_vectorised</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">[</span><span class="s2">"vectoriser"</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">Text</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Training score: 99.7% +Test score: 96.9% +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div> +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0"> +<pre>array([[69, 0, 0, 0, 1], + [ 0, 46, 0, 0, 0], + [ 4, 0, 54, 0, 0], + [ 0, 0, 0, 65, 0], + [ 0, 3, 0, 1, 45]])</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>It is evident that supervised learning is superior to unsupervised learning for such tasks given the labels are readily available. The disparity between the training score and test score raises concerns however, as such models have a tendency to overfit. We will try reducing the size of the training data in decrements of 20% to see if that affects performance.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Training size: 60%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Training size: 40%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Training size: 20%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.8</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Training size: 60% +Training score: 99.8% +Test score: 97.0% + +Training size: 40% +Training score: 99.8% +Test score: 96.5% + +Training size: 20% +Training score: 100.0% +Test score: 92.4% +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>Interestingly, both training and test scores both increase by 0.1% at first. It is only when the training dataset is at 20% that the model really suffers from overfitting, with training score being 100% while test score being 92.4%. Thus it can be concluded that the model is indeed vulnerable to overfitting, but has good data-efficiency - only needs around 50% of the dataset to achieve high test scores.</p> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Trying-the-same-with-LinearSVC">Trying the same with LinearSVC<a class="anchor-link" href="#Trying-the-same-with-LinearSVC">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Training size: 80%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Training size: 80% +Training score: 100.0% +Test score: 96.9% +</pre> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Training size: 60%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Training size: 40%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Training size: 20%"</span><span class="p">)</span> +<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train_vectorised</span><span class="p">,</span> <span class="n">data_train</span><span class="o">.</span><span class="n">Category</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.8</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> +<span class="n">clf</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Training score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Test score: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Training size: 60% +Training score: 100.0% +Test score: 97.2% + +Training size: 40% +Training score: 100.0% +Test score: 96.9% + +Training size: 20% +Training score: 100.0% +Test score: 95.7% +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<p>It seems from these tests that LinearSVC does not improve much on LogisticRegression in terms of top performance, but wins in terms of data-efficiency, as it was able to achive 95.7% accuracy with just 20% training data.</p> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="References">References<a class="anchor-link" href="#References">¶</a></h2><ol> +<li><a href="https://stackoverflow.com/questions/48925747/how-to-apply-a-custom-stemmer-before-passing-the-training-corpus-to-tfidfvectori">How to apply a custom stemmer before passing the training corpus to TfidfVectorizer in sklearn?</a></li> +<li><a href="https://stats.stackexchange.com/a/430776">How does adding/omitting lemmatization affect TF-IDF?</a></li> +<li><a href="https://mimno.infosci.cornell.edu/papers/schofield_tacl_2016.pdf">Comparing Apples to Apple: The Effects of Stemmers on Topic Models</a></li> +<li><a href="https://stackoverflow.com/questions/47423854/sklearn-adding-lemmatizer-to-countvectorizer">Sklearn: adding lemmatizer to CountVectorizer</a></li> +<li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html">TfidfTransformer</a></li> +</ol> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span> +</pre></div> +</div> +</div> +</div> +</div> +</div> +</main> +</body> +</html> |