From 70bd6016da2e44fe256ec5112dfc3b083e604993 Mon Sep 17 00:00:00 2001
From: GavinOP <HaoxiangZhang@link.cuhk.edu.hk>
Date: Wed, 17 Apr 2024 13:47:40 +0800
Subject: [PATCH] Updates

---
 component/page/d-a-r-strategy.html            | 10275 ++++++++++++++++
 .../page/fina4400-return-visualization.html   |    45 +
 2 files changed, 10320 insertions(+)
 create mode 100644 component/page/d-a-r-strategy.html
 create mode 100644 component/page/fina4400-return-visualization.html
diff --git a/component/page/d-a-r-strategy.html b/component/page/d-a-r-strategy.html
new file mode 100644
index 00000000..a45abc2b
--- /dev/null
+++ b/component/page/d-a-r-strategy.html
@@ -0,0 +1,10275 @@
+<!DOCTYPE html>
+
+<html lang="en">
+<head>
+  <!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-23JZF546MQ"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-23JZF546MQ');
+</script>
+
+  <!-- website meta configuration -->
+  <link rel="icon" href="../../image/avatar.ico" />
+  <!-- navbar -->
+  <link rel="stylesheet" type="text/css" href="../../css/navbar.css">
+  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
+  <script src="https://kit.fontawesome.com/814179a032.js" crossorigin="anonymous"></script>
+  <!-- end of meta configuration-->
+
+
+  <meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
+<title>D-A-R Quant Strategy</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<style type="text/css">
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+}
+
+/*
+ * 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.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| 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: url(data:image/svg+xml;base64,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);
+  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bell: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
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+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
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+  --jp-icon-code-check: url(data:image/svg+xml;base64,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);
+  --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==);
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+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
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+  --jp-icon-expand-all: url(data:image/svg+xml;base64,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);
+  --jp-icon-extension: url(data:image/svg+xml;base64,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);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-filter: url(data:image/svg+xml;base64,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);
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+  --jp-icon-home: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjRweCIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjRweCIgZmlsbD0iIzAwMDAwMCI+CiAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPjxwYXRoIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xMCAyMHYtNmg0djZoNXYtOGgzTDEyIDMgMiAxMmgzdjh6Ii8+Cjwvc3ZnPgo=);
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+  --jp-icon-info: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUwLjk3OCA1MC45NzgiPgoJPGcgY2xhc3M9ImpwLWljb24zIiBmaWxsPSIjNjE2MTYxIj4KCQk8cGF0aCBkPSJNNDMuNTIsNy40NThDMzguNzExLDIuNjQ4LDMyLjMwNywwLDI1LjQ4OSwwQzE4LjY3LDAsMTIuMjY2LDIuNjQ4LDcuNDU4LDcuNDU4CgkJCWMtOS45NDMsOS45NDEtOS45NDMsMjYuMTE5LDAsMzYuMDYyYzQuODA5LDQuODA5LDExLjIxMiw3LjQ1NiwxOC4wMzEsNy40NThjMCwwLDAuMDAxLDAsMC4wMDIsMAoJCQljNi44MTYsMCwxMy4yMjEtMi42NDgsMTguMDI5LTcuNDU4YzQuODA5LTQuODA5LDcuNDU3LTExLjIxMiw3LjQ1Ny0xOC4wM0M1MC45NzcsMTguNjcsNDguMzI4LDEyLjI2Niw0My41Miw3LjQ1OHoKCQkJIE00Mi4xMDYsNDIuMTA1Yy00LjQzMiw0LjQzMS0xMC4zMzIsNi44NzItMTYuNjE1LDYuODcyaC0wLjAwMmMtNi4yODUtMC4wMDEtMTIuMTg3LTIuNDQxLTE2LjYxNy02Ljg3MgoJCQljLTkuMTYyLTkuMTYzLTkuMTYyLTI0LjA3MSwwLTMzLjIzM0MxMy4zMDMsNC40NCwxOS4yMDQsMiwyNS40ODksMmM2LjI4NCwwLDEyLjE4NiwyLjQ0LDE2LjYxNyw2Ljg3MgoJCQljNC40MzEsNC40MzEsNi44NzEsMTAuMzMyLDYuODcxLDE2LjYxN0M0OC45NzcsMzEuNzcyLDQ2LjUzNiwzNy42NzUsNDIuMTA2LDQyLjEwNXoiLz4KCQk8cGF0aCBkPSJNMjMuNTc4LDMyLjIxOGMtMC4wMjMtMS43MzQsMC4xNDMtMy4wNTksMC40OTYtMy45NzJjMC4zNTMtMC45MTMsMS4xMS0xLjk5NywyLjI3Mi0zLjI1MwoJCQljMC40NjgtMC41MzYsMC45MjMtMS4wNjIsMS4zNjctMS41NzVjMC42MjYtMC43NTMsMS4xMDQtMS40NzgsMS40MzYtMi4xNzVjMC4zMzEtMC43MDcsMC40OTUtMS41NDEsMC40OTUtMi41CgkJCWMwLTEuMDk2LTAuMjYtMi4wODgtMC43NzktMi45NzljLTAuNTY1LTAuODc5LTEuNTAxLTEuMzM2LTIuODA2LTEuMzY5Yy0xLjgwMiwwLjA1Ny0yLjk4NSwwLjY2Ny0zLjU1LDEuODMyCgkJCWMtMC4zMDEsMC41MzUtMC41MDMsMS4xNDEtMC42MDcsMS44MTRjLTAuMTM5LDAuNzA3LTAuMjA3LDEuNDMyLTAuMjA3LDIuMTc0aC0yLjkzN2MtMC4wOTEtMi4yMDgsMC40MDctNC4xMTQsMS40OTMtNS43MTkKCQkJYzEuMDYyLTEuNjQsMi44NTUtMi40ODEsNS4zNzgtMi41MjdjMi4xNiwwLjAyMywzLjg3NCwwLjYwOCw1LjE0MSwxLjc1OGMxLjI3OCwxLjE2LDEuOTI5LDIuNzY0LDEuOTUsNC44MTEKCQkJYzAsMS4xNDItMC4xMzcsMi4xMTEtMC40MSwyLjkxMWMtMC4zMDksMC44NDUtMC43MzEsMS41OTMtMS4yNjgsMi4yNDNjLTAuNDkyLDAuNjUtMS4wNjgsMS4zMTgtMS43MywyLjAwMgoJCQljLTAuNjUsMC42OTctMS4zMTMsMS40NzktMS45ODcsMi4zNDZjLTAuMjM5LDAuMzc3LTAuNDI5LDAuNzc3LTAuNTY1LDEuMTk5Yy0wLjE2LDAuOTU5LTAuMjE3LDEuOTUxLTAuMTcxLDIuOTc5CgkJCUMyNi41ODksMzIuMjE4LDIzLjU3OCwzMi4yMTgsMjMuNTc4LDMyLjIxOHogTTIzLjU3OCwzOC4yMnYtMy40ODRoMy4wNzZ2My40ODRIMjMuNTc4eiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaW5zcGVjdG9yLWljb24tY29sb3IganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,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);
+  --jp-icon-launch: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMzIgMzIiIHdpZHRoPSIzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yNiwyOEg2YTIuMDAyNywyLjAwMjcsMCwwLDEtMi0yVjZBMi4wMDI3LDIuMDAyNywwLDAsMSw2LDRIMTZWNkg2VjI2SDI2VjE2aDJWMjZBMi4wMDI3LDIuMDAyNywwLDAsMSwyNiwyOFoiLz4KICAgIDxwb2x5Z29uIHBvaW50cz0iMjAgMiAyMCA0IDI2LjU4NiA0IDE4IDEyLjU4NiAxOS40MTQgMTQgMjggNS40MTQgMjggMTIgMzAgMTIgMzAgMiAyMCAyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4K);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDAganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjN0IxRkEyIiBkPSJNNSAxNC45aDEybC02LjEgNnptOS40LTYuOGMwLTEuMy0uMS0yLjktLjEtNC41LS40IDEuNC0uOSAyLjktMS4zIDQuM2wtMS4zIDQuM2gtMkw4LjUgNy45Yy0uNC0xLjMtLjctMi45LTEtNC4zLS4xIDEuNi0uMSAzLjItLjIgNC42TDcgMTIuNEg0LjhsLjctMTFoMy4zTDEwIDVjLjQgMS4yLjcgMi43IDEgMy45LjMtMS4yLjctMi42IDEtMy45bDEuMi0zLjdoMy4zbC42IDExaC0yLjRsLS4zLTQuMnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-move-down: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-up: url(data:image/svg+xml;base64,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);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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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: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-pdf: url(data:image/svg+xml;base64,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);
+  --jp-icon-python: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iLTEwIC0xMCAxMzEuMTYxMzYxNjk0MzM1OTQgMTMyLjM4ODk5OTkzODk2NDg0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMzA2OTk4IiBkPSJNIDU0LjkxODc4NSw5LjE5Mjc0MjFlLTQgQyA1MC4zMzUxMzIsMC4wMjIyMTcyNyA0NS45NTc4NDYsMC40MTMxMzY5NyA0Mi4xMDYyODUsMS4wOTQ2NjkzIDMwLjc2MDA2OSwzLjA5OTE3MzEgMjguNzAwMDM2LDcuMjk0NzcxNCAyOC43MDAwMzUsMTUuMDMyMTY5IHYgMTAuMjE4NzUgaCAyNi44MTI1IHYgMy40MDYyNSBoIC0yNi44MTI1IC0xMC4wNjI1IGMgLTcuNzkyNDU5LDAgLTE0LjYxNTc1ODgsNC42ODM3MTcgLTE2Ljc0OTk5OTgsMTMuNTkzNzUgLTIuNDYxODE5OTgsMTAuMjEyOTY2IC0yLjU3MTAxNTA4LDE2LjU4NjAyMyAwLDI3LjI1IDEuOTA1OTI4Myw3LjkzNzg1MiA2LjQ1NzU0MzIsMTMuNTkzNzQ4IDE0LjI0OTk5OTgsMTMuNTkzNzUgaCA5LjIxODc1IHYgLTEyLjI1IGMgMCwtOC44NDk5MDIgNy42NTcxNDQsLTE2LjY1NjI0OCAxNi43NSwtMTYuNjU2MjUgaCAyNi43ODEyNSBjIDcuNDU0OTUxLDAgMTMuNDA2MjUzLC02LjEzODE2NCAxMy40MDYyNSwtMTMuNjI1IHYgLTI1LjUzMTI1IGMgMCwtNy4yNjYzMzg2IC02LjEyOTk4LC0xMi43MjQ3NzcxIC0xMy40MDYyNSwtMTMuOTM3NDk5NyBDIDY0LjI4MTU0OCwwLjMyNzk0Mzk3IDU5LjUwMjQzOCwtMC4wMjAzNzkwMyA1NC45MTg3ODUsOS4xOTI3NDIxZS00IFogbSAtMTQuNSw4LjIxODc1MDEyNTc5IGMgMi43Njk1NDcsMCA1LjAzMTI1LDIuMjk4NjQ1NiA1LjAzMTI1LDUuMTI0OTk5NiAtMmUtNiwyLjgxNjMzNiAtMi4yNjE3MDMsNS4wOTM3NSAtNS4wMzEyNSw1LjA5Mzc1IC0yLjc3OTQ3NiwtMWUtNiAtNS4wMzEyNSwtMi4yNzc0MTUgLTUuMDMxMjUsLTUuMDkzNzUgLTEwZS03LC0yLjgyNjM1MyAyLjI1MTc3NCwtNS4xMjQ5OTk2IDUuMDMxMjUsLTUuMTI0OTk5NiB6Ii8+CiAgPHBhdGggY2xhc3M9ImpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iI2ZmZDQzYiIgZD0ibSA4NS42Mzc1MzUsMjguNjU3MTY5IHYgMTEuOTA2MjUgYyAwLDkuMjMwNzU1IC03LjgyNTg5NSwxNi45OTk5OTkgLTE2Ljc1LDE3IGggLTI2Ljc4MTI1IGMgLTcuMzM1ODMzLDAgLTEzLjQwNjI0OSw2LjI3ODQ4MyAtMTMuNDA2MjUsMTMuNjI1IHYgMjUuNTMxMjQ3IGMgMCw3LjI2NjM0NCA2LjMxODU4OCwxMS41NDAzMjQgMTMuNDA2MjUsMTMuNjI1MDA0IDguNDg3MzMxLDIuNDk1NjEgMTYuNjI2MjM3LDIuOTQ2NjMgMjYuNzgxMjUsMCA2Ljc1MDE1NSwtMS45NTQzOSAxMy40MDYyNTMsLTUuODg3NjEgMTMuNDA2MjUsLTEzLjYyNTAwNCBWIDg2LjUwMDkxOSBoIC0yNi43ODEyNSB2IC0zLjQwNjI1IGggMjYuNzgxMjUgMTMuNDA2MjU0IGMgNy43OTI0NjEsMCAxMC42OTYyNTEsLTUuNDM1NDA4IDEzLjQwNjI0MSwtMTMuNTkzNzUgMi43OTkzMywtOC4zOTg4ODYgMi42ODAyMiwtMTYuNDc1Nzc2IDAsLTI3LjI1IC0xLjkyNTc4LC03Ljc1NzQ0MSAtNS42MDM4NywtMTMuNTkzNzUgLTEzLjQwNjI0MSwtMTMuNTkzNzUgeiBtIC0xNS4wNjI1LDY0LjY1NjI1IGMgMi43Nzk0NzgsM2UtNiA1LjAzMTI1LDIuMjc3NDE3IDUuMDMxMjUsNS4wOTM3NDcgLTJlLTYsMi44MjYzNTQgLTIuMjUxNzc1LDUuMTI1MDA0IC01LjAzMTI1LDUuMTI1MDA0IC0yLjc2OTU1LDAgLTUuMDMxMjUsLTIuMjk4NjUgLTUuMDMxMjUsLTUuMTI1MDA0IDJlLTYsLTIuODE2MzMgMi4yNjE2OTcsLTUuMDkzNzQ3IDUuMDMxMjUsLTUuMDkzNzQ3IHoiLz4KPC9zdmc+Cg==);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-share: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-tag: url(data:image/svg+xml;base64,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);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,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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',
+                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/10.5.0/mermaid.esm.min.mjs")).default;
+    const parser = new DOMParser();
+
+    mermaid.initialize({
+      maxTextSize: 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}`;
+      }
+
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+      result.className = 'jp-RenderedMermaid-Details';
+      const summary = document.createElement("summary");
+      summary.className = 'jp-RenderedMermaid-Summary';
+      const pre = document.createElement("pre");
+      const code = document.createElement("code");
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+
+      const warning = document.createElement("pre");
+      warning.innerText = errorMessage;
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+    async function renderOneMarmaid(src) {
+      const id = `jp-mermaid-${_nextMermaidId++}`;
+      const parent = src.parentNode;
+      let raw = src.textContent.trim();
+      const el = document.createElement("div");
+      el.style.visibility = "hidden";
+      document.body.appendChild(el);
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+        const { svg } = await mermaid.render(id, raw, el);
+        results = makeMermaidImage(svg);
+        output = document.createElement("figure");
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+        parent.classList.add("jp-mod-warning");
+        results = await makeMermaidError(raw);
+        output = results[0];
+      } finally {
+        el.remove();
+      }
+      parent.classList.add("jp-RenderedMermaid");
+      parent.appendChild(output);
+    }
+
+    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);
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+    font-size: var(--jp-ui-font-size1);
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+    word-wrap: break-word;
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+    margin: 0;
+    overflow: auto;
+    max-width: 100%;
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+  .jp-RenderedMermaid-Details > pre {
+    margin-top: 1em;
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+    white-space: normal;
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+</style>
+<!-- End of mermaid configuration -->
+</head>
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+       <!-- Navigation Bar -->
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+</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><span class="c1"># import all nessary libraries</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">yfinance</span> <span class="k">as</span> <span class="nn">yf</span>
+<span class="kn">import</span> <span class="nn">plotly.graph_objects</span> <span class="k">as</span> <span class="nn">go</span>
+<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
+<span class="kn">import</span> <span class="nn">statsmodels.formula.api</span> <span class="k">as</span> <span class="nn">smf</span>
+
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_1samp</span>
+<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">argrelextrema</span>
+
+<span class="n">dpi</span> <span class="o">=</span> <span class="mi">600</span>
+<span class="n">transparent</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">plot_folder</span> <span class="o">=</span> <span class="s2">"stop_loss_10"</span>
+<span class="n">stop_loss</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.1</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">
+<h1 id="D-A-R-Stock-Selection-Trading-Strategy">D-A-R Stock Selection Trading Strategy<a class="anchor-link" href="#D-A-R-Stock-Selection-Trading-Strategy">¶</a></h1><h2 id="Strategy">Strategy<a class="anchor-link" href="#Strategy">¶</a></h2><h3 id="Strategy-Overview">Strategy Overview<a class="anchor-link" href="#Strategy-Overview">¶</a></h3><table>
+<thead>
+<tr>
+<th>Parameter</th>
+<th>Details</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Market</td>
+<td>US Stock Market</td>
+</tr>
+<tr>
+<td>Type</td>
+<td>Long/Short (Market Neutral)</td>
+</tr>
+<tr>
+<td>Rebalance Frequency</td>
+<td>Monthly</td>
+</tr>
+<tr>
+<td>Position Sizing</td>
+<td>Equal Weighted</td>
+</tr>
+<tr>
+<td>Leverage</td>
+<td>1x</td>
+</tr>
+<tr>
+<td>Commission</td>
+<td>$0</td>
+</tr>
+<tr>
+<td>Start Date</td>
+<td>1980-01</td>
+</tr>
+<tr>
+<td>End Date</td>
+<td>2022-12</td>
+</tr>
+<tr>
+<td>Predictor</td>
+<td>Dispersions (DISP), Net Operating Assets (NOA), Max Return in 5 Days per Month (RMAX5_21D)</td>
+</tr>
+<tr>
+<td>Alpha</td>
+<td>2.65%</td>
+</tr>
+<tr>
+<td>Win Rate</td>
+<td>61.55%</td>
+</tr>
+<tr>
+<td>VaR</td>
+<td>15.748%</td>
+</tr>
+</tbody>
+</table>
+<h3 id="Strategy-Rules">Strategy Rules<a class="anchor-link" href="#Strategy-Rules">¶</a></h3><p>At month $i$, we rank all stocks in the universe using a hierachical sorting (Multi-level Sorting) method based on three predictors: <strong>DISP</strong>, <strong>NOA</strong>, and <strong>RMAX5_21D</strong>.</p>
+<ol>
+<li><strong>DISP</strong>: xxx. We initially sort all stocks according to their <strong>DISP</strong> values.</li>
+<li><strong>NOA</strong>: xxx. For stocks that fall within the top 20% or bottom 20% based on the <strong>DISP</strong> value, we further sort them according to their <strong>NOA</strong> values.</li>
+<li><strong>RMAX5_21D</strong>: Max. For stocks that fall within the top 20% or bottom 20% based on previous sort, we further sort them acording to their <strong>RMAX5_21d</strong> values.</li>
+</ol>
+<p>Then we long the top 20% stocks and short the bottom 20% stocks, and hold the positions for one month.</p>
+<p>Stop Loss: If the month $i$ return is less than -10%, then stop  trading for $i+1$, $i+2$ months.</p>
+<blockquote>
+<p>Notes: Top means large value, bottom means small value.</p>
+</blockquote>
+</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="Backtest">Backtest<a class="anchor-link" href="#Backtest">¶</a></h2><h3 id="Data">Data<a class="anchor-link" href="#Data">¶</a></h3><p>The dataset spans from January, 1980, to December, 2022, encompassing 43 years of monthly data. The market data includes market excess return, SMB, HML, and UMD factors. The data was provided by Haojun. SPX data was sourced from Yahoo Finance for regime classification.</p>
+<h3 id="Performance">Performance<a class="anchor-link" href="#Performance">¶</a></h3>
+
+<p>Please click <strong><a href="https://gavin-op.github.io/component/page/fina4400-return-visualization.html" style="color: #0091ea;">here</a></strong> to find the interactive return visualization.</p>
+
+<ol>
+<li><p>Positive Performance Edge?</p>
+<ul>
+<li>t-test:<ul>
+<li>Hypothesis: $H_0$: strategy return = 0.</li>
+<li>Test Statistic: $\displaystyle t = \frac{{\bar{\text{portfolio return in each month}} - 0}}{\text{standard deviation of portfolio return in each month}/\sqrt{n}} \approx 8.66$</li>
+<li>P-value: 0</li>
+<li>Decision: Since p-value &lt; 0.05, we reject the null hypothesis, indicating a significant performance edge.</li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p>How Big is the Edge?</p>
+<ul>
+<li>CAPM: $\alpha$: 2.65%</li>
+<li>FF3: $\alpha$: 2.59%</li>
+<li>FF4: $\alpha$: 2.6%</li>
+</ul>
+</li>
+<li><p>How Consistent is the Edge?</p>
+<ul>
+<li>Win Rate: 61.55%</li>
+</ul>
+</li>
+<li><p>What is the potential loss for this edge?</p>
+<ul>
+<li>VaR (Value at Risk): 15.73%</li>
+</ul>
+</li>
+<li><p>How long can the edge last?</p>
+<ul>
+<li>Compare performance from -12 month to 12 month. 0 is where we stand.</li>
+</ul>
+</li>
+<li><p>What is the benifit of this strategy compare with other 2 predictor or 1 predictor strategy?</p>
+<ul>
+<li>Compare the performance of the current strategy with those of strategies based on individual predictors or a combination of two predictors.</li>
+</ul>
+</li>
+<li><p>Performance in different market conditions
+Market downturn
+Market upturn</p>
+</li>
+<li><p>What is the relationship between each factor and predicted return?</p>
+<ul>
+<li>Fama-MacBeth Regression</li>
+</ul>
+</li>
+</ol>
+</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="Implementation">Implementation<a class="anchor-link" href="#Implementation">¶</a></h2><h3 id="Data">Data<a class="anchor-link" href="#Data">¶</a></h3>
+</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><span class="k">def</span> <span class="nf">TrendDetecter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">win</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">plot_trend</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+    <span class="c1"># ignore futurewarning</span>
+    <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s1">'ignore'</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">)</span>
+
+    <span class="c1"># identify all local max and min location according to win</span>
+    <span class="n">local_max_index</span> <span class="o">=</span> <span class="n">argrelextrema</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">greater</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">win</span><span class="p">)</span>
+    <span class="n">local_min_index</span> <span class="o">=</span> <span class="n">argrelextrema</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">less</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">win</span><span class="p">)</span>
+
+    <span class="c1"># delete consecutive max or min points</span>
+    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">j</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="n">max_after_min</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">min_after_max</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="c1"># if first point is the left most point, then we add it as a max or min point.</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]:</span>
+                <span class="n">max_after_min</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+                <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">min_after_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">])</span>
+                <span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># if point at time t is max, then the next point after t should be min. If the next point after t is still max, choose the larger one between max at time t and the next point as the new max. </span>
+            <span class="k">if</span> <span class="n">max_flag</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]:</span>
+                    <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span> <span class="o">&gt;</span> <span class="n">data</span><span class="p">[</span><span class="n">max_after_min</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]:</span>
+                        <span class="n">max_after_min</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+                        <span class="n">max_after_min</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+                    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">min_after_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">])</span>
+                    <span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="c1"># if the point at time t is min, then the new point should be max. If the new point is still min, choose the smaller one between them as the new min.</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]:</span>
+                    <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">]]</span> <span class="o">&lt;</span> <span class="n">data</span><span class="p">[</span><span class="n">min_after_max</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]:</span>
+                        <span class="n">min_after_max</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+                        <span class="n">min_after_max</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_min_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">j</span><span class="p">])</span>
+                    <span class="n">j</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">False</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">max_after_min</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_max_index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
+                    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">max_flag</span> <span class="o">=</span> <span class="kc">True</span>
+        
+    <span class="c1"># downturn</span>
+    <span class="n">downturn</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"period"</span><span class="p">])</span>
+
+    <span class="c1"># if the left most point is min, then discard the first point of min</span>
+    <span class="k">if</span> <span class="n">min_after_max</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">max_after_min</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+        <span class="n">min_after_max_downturn</span> <span class="o">=</span> <span class="n">min_after_max</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+        
+    <span class="c1"># downturn period</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">max_after_min</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">min_after_max_downturn</span><span class="p">):</span>
+            <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">max_after_min</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">min_after_max_downturn</span><span class="p">[</span><span class="n">i</span><span class="p">]]]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">max_after_min</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">min_after_max_downturn</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">max_after_min</span><span class="p">):</span>
+            <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">max_after_min</span><span class="p">)]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">min_after_max_downturn</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">max_after_min</span><span class="p">[</span><span class="n">i</span><span class="p">]]]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">max_after_min</span><span class="p">)]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">min_after_max_downturn</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]]</span>
+            
+    <span class="c1"># discard the pairs that the start date value is smaller than the end date value</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">downturn</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">data</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span> <span class="o">&lt;</span> <span class="n">data</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]]:</span>
+            <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+    
+    <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span> <span class="o">=</span> <span class="s2">"period"</span><span class="p">)</span>
+    <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+    <span class="n">downturn</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"index"</span>
+    <span class="n">downturn</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">downturn</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+   
+    <span class="c1"># upturn</span>
+    <span class="n">upturn</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"period"</span><span class="p">])</span>
+    
+    <span class="c1"># first period</span>
+    <span class="k">if</span> <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+        <span class="n">first_entry</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="s2">"period"</span><span class="p">:</span> <span class="p">[[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]]})</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">first_entry</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="s2">"period"</span><span class="p">:</span> <span class="p">[[</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]]})</span>
+
+    <span class="c1"># last period</span>
+    <span class="k">if</span> <span class="n">downturn</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="n">last_entry</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="s2">"period"</span><span class="p">:</span> <span class="p">[[</span><span class="n">downturn</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">downturn</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]]})</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">last_entry</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="s2">"period"</span><span class="p">:</span> <span class="p">[[</span><span class="n">downturn</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]]})</span>
+    
+    <span class="c1"># period between downturn</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">downturn</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]]</span>
+    
+    <span class="c1"># add the first and last entry</span>
+    <span class="n">upturn</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">first_entry</span><span class="p">,</span> <span class="n">upturn</span><span class="p">,</span> <span class="n">last_entry</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">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span> <span class="o">=</span> <span class="s2">"period"</span><span class="p">)</span>
+    <span class="n">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+    <span class="n">upturn</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"index"</span>
+    <span class="n">upturn</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">upturn</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="n">plot_trend</span><span class="p">:</span>
+        <span class="n">start_date</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+        <span class="n">end_date</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+        <span class="c1"># downturn red, upturn blue</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">downturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Downturn"</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.7</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">upturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Upturn"</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"SPX 500"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"SPX 500 (M)  </span><span class="si">{</span><span class="n">start_date</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">end_date</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+        
+        <span class="c1"># axis color, ticks color， lable color, grid</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+
+        <span class="c1"># save the plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/market_trend.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</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 [ ]:</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"># ff3 market data</span>
+<span class="n">data_ff3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_stata</span><span class="p">(</span><span class="s2">"../../data/ff_factors.dta"</span><span class="p">)</span>
+<span class="n">data_ff3</span> <span class="o">=</span> <span class="n">data_ff3</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"dateff"</span><span class="p">)</span>
+
+<span class="c1"># spx500 data</span>
+<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s2">"../../data/spx19802022.csv"</span><span class="p">):</span>
+    <span class="n">spxdata</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="s1">'../../data/spx19802022.csv'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">spxdata</span> <span class="o">=</span> <span class="n">yf</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">'^GSPC'</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="s1">'1980-01-01'</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">'2023-01-01'</span><span class="p">)</span>
+    <span class="n">spxdata</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'../../data/spx19802022.csv'</span><span class="p">)</span>
+
+<span class="c1"># spx monthly data</span>
+<span class="n">spx</span> <span class="o">=</span> <span class="n">spxdata</span><span class="p">[</span><span class="s1">'Adj Close'</span><span class="p">]</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s1">'M'</span><span class="p">)</span><span class="o">.</span><span class="n">last</span><span class="p">()</span>
+
+<span class="c1"># trend detection</span>
+<span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="n">TrendDetecter</span><span class="p">(</span><span class="n">spx</span><span class="p">,</span> <span class="n">win</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">plot_trend</span> <span class="o">=</span> <span class="kc">True</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">
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"/>
+</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 [ ]:</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"># data</span>
+<span class="n">raw_data</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">"../../data/data19802022.csv"</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># drop na</span>
+<span class="n">rows_before</span> <span class="o">=</span> <span class="n">raw_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">raw_data</span> <span class="o">=</span> <span class="n">raw_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"gvkey"</span><span class="p">,</span> <span class="s2">"permno"</span><span class="p">,</span>
+                           <span class="s1">'ret_12_7'</span><span class="p">,</span>
+                                   <span class="s1">'ret_3_1'</span><span class="p">,</span> <span class="s1">'ret_6_1'</span><span class="p">,</span> <span class="s1">'ret_9_1'</span><span class="p">,</span> <span class="s1">'ret_12_1'</span><span class="p">,</span> <span class="s1">'ret_18_1'</span><span class="p">,</span>
+                                   <span class="s1">'ret_1_0'</span><span class="p">,</span> <span class="s1">'ret_2_0'</span><span class="p">,</span> <span class="s1">'ret_3_0'</span><span class="p">,</span> <span class="s1">'ret_6_0'</span><span class="p">,</span> <span class="s1">'ret_9_0'</span><span class="p">,</span> <span class="s1">'ret_12_0'</span><span class="p">,</span>
+                                   <span class="s1">'ret_lead1m'</span><span class="p">,</span> <span class="s1">'ret_lead2m'</span><span class="p">,</span> <span class="s1">'ret_lead3m'</span><span class="p">,</span> <span class="s1">'ret_lead4m'</span><span class="p">,</span> <span class="s1">'ret_lead5m'</span><span class="p">,</span> <span class="s1">'ret_lead6m'</span><span class="p">,</span> <span class="s1">'ret_lead7m'</span><span class="p">,</span> <span class="s1">'ret_lead8m'</span><span class="p">,</span> <span class="s1">'ret_lead9m'</span><span class="p">,</span> <span class="s1">'ret_lead10m'</span><span class="p">,</span> <span class="s1">'ret_lead11m'</span><span class="p">,</span> <span class="s1">'ret_lead12m'</span><span class="p">,</span>
+                                   <span class="s1">'cum_rawret_1_6m'</span><span class="p">,</span> <span class="s1">'cum_rawret_1_3m'</span><span class="p">,</span> <span class="s1">'cum_rawret_1_12m'</span><span class="p">,</span>
+                                   <span class="s1">'feps'</span><span class="p">,</span> <span class="s1">'fep'</span><span class="p">,</span> <span class="s1">'disp'</span><span class="p">,</span> <span class="s1">'nsi'</span><span class="p">,</span> <span class="s1">'accrual'</span><span class="p">,</span> <span class="s1">'noa'</span><span class="p">,</span> <span class="s1">'ag'</span><span class="p">,</span> <span class="s1">'ia'</span><span class="p">,</span> <span class="s1">'pead'</span><span class="p">,</span> <span class="s1">'rmax5_21d'</span><span class="p">,</span> <span class="s1">'prc_highprc_252d'</span><span class="p">,</span> <span class="s1">'rmax1_21d'</span>
+                                   <span class="p">],</span> <span class="n">how</span><span class="o">=</span><span class="s2">"all"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"ROWS DROPPED NA:"</span><span class="p">,</span> <span class="n">rows_before</span> <span class="o">-</span> <span class="n">raw_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+<span class="c1"># date to datetime</span>
+<span class="n">raw_data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">raw_data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">],</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y%m</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+
+<span class="c1"># reorganize</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">raw_data</span><span class="p">[[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"gvkey"</span><span class="p">,</span> <span class="s2">"permno"</span><span class="p">,</span> <span class="s2">"ff49"</span><span class="p">,</span> <span class="s1">'mv'</span><span class="p">,</span> <span class="s1">'be_me'</span><span class="p">,</span> <span class="s2">"prc"</span><span class="p">,</span>
+                 <span class="s1">'ret_12_7'</span><span class="p">,</span>
+                 <span class="s1">'ret_3_1'</span><span class="p">,</span> <span class="s1">'ret_6_1'</span><span class="p">,</span> <span class="s1">'ret_9_1'</span><span class="p">,</span> <span class="s1">'ret_12_1'</span><span class="p">,</span> <span class="s1">'ret_18_1'</span><span class="p">,</span>
+                 <span class="s1">'ret_1_0'</span><span class="p">,</span> <span class="s1">'ret_2_0'</span><span class="p">,</span> <span class="s1">'ret_3_0'</span><span class="p">,</span> <span class="s1">'ret_6_0'</span><span class="p">,</span> <span class="s1">'ret_9_0'</span><span class="p">,</span> <span class="s1">'ret_12_0'</span><span class="p">,</span>
+                 <span class="s1">'feps'</span><span class="p">,</span> <span class="s1">'fep'</span><span class="p">,</span> <span class="s1">'disp'</span><span class="p">,</span> <span class="s1">'nsi'</span><span class="p">,</span> <span class="s1">'accrual'</span><span class="p">,</span> <span class="s1">'noa'</span><span class="p">,</span> <span class="s1">'ag'</span><span class="p">,</span> <span class="s1">'ia'</span><span class="p">,</span> <span class="s1">'pead'</span><span class="p">,</span> <span class="s1">'rmax5_21d'</span><span class="p">,</span> <span class="s1">'prc_highprc_252d'</span><span class="p">,</span> <span class="s1">'rmax1_21d'</span><span class="p">,</span>
+                 <span class="s1">'cum_rawret_1_6m'</span><span class="p">,</span> <span class="s1">'cum_rawret_1_3m'</span><span class="p">,</span> <span class="s1">'cum_rawret_1_12m'</span><span class="p">,</span>
+                 <span class="s1">'ret_lead1m'</span><span class="p">,</span> <span class="s1">'ret_lead2m'</span><span class="p">,</span> <span class="s1">'ret_lead3m'</span><span class="p">,</span> <span class="s1">'ret_lead4m'</span><span class="p">,</span> <span class="s1">'ret_lead5m'</span><span class="p">,</span> <span class="s1">'ret_lead6m'</span><span class="p">,</span> <span class="s1">'ret_lead7m'</span><span class="p">,</span> <span class="s1">'ret_lead8m'</span><span class="p">,</span> <span class="s1">'ret_lead9m'</span><span class="p">,</span> <span class="s1">'ret_lead10m'</span><span class="p">,</span> <span class="s1">'ret_lead11m'</span><span class="p">,</span> <span class="s1">'ret_lead12m'</span>
+                 <span class="p">]]</span>
+
+<span class="c1"># sort</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"gvkey"</span><span class="p">,</span> <span class="s2">"permno"</span><span class="p">,</span> <span class="s2">"ff49"</span><span class="p">,</span> <span class="s2">"mv"</span><span class="p">])</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># describe data</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"FIELDS:"</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"DIMENSION:"</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"DUPLICATED ROWS:"</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"# OF MISSING VALUES:"</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="s2">", PERCENTAGE OF MISSING VALUES:"</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
+<span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">", # OF COLUMNS HAVE MISSING VALUES:"</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"START DATE:"</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="s2">", END DATE:"</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+
+<span class="c1"># distribution of predictors</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">32</span><span class="p">):</span>
+    <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="nb">range</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.001</span><span class="p">),</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.999</span><span class="p">)))</span>
+    <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+    <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">wspace</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">"Distribution of Each Predictors"</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">20</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span> <span class="o">=</span> <span class="mf">0.94</span><span class="p">)</span>
+
+<span class="c1"># save plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/predictors_distribution.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># boxplot of predictors</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">32</span><span class="p">):</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">])</span>
+    <span class="n">axs</span><span class="p">[(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="mi">7</span><span class="p">)</span> <span class="o">%</span> <span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">wspace</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">"Boxplot of Each Predictors"</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">20</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span> <span class="o">=</span> <span class="mf">0.94</span><span class="p">)</span>
+
+<span class="c1"># save plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/predictors_boxplot.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># disp, noa, rmax5_21d correlation matrix, heatmap</span>
+<span class="n">corr</span> <span class="o">=</span> <span class="n">data</span><span class="p">[[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">]]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">annot</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s2">"coolwarm"</span><span class="p">,</span> <span class="n">cbar</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">center</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Correlation Matrix of disp, noa, rmax5_21d"</span><span class="p">)</span>
+
+<span class="c1"># save plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/disp_noa_rmax5_21d_corr.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">display</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+<span class="c1"># all predictors correlation matrix, heatmap</span>
+<span class="n">predictors_corr</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">7</span><span class="p">:</span><span class="mi">31</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
+<span class="n">data_corr_heatmap</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">predictors_corr</span><span class="p">,</span> <span class="n">annot</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s2">"coolwarm"</span><span class="p">,</span> <span class="n">cbar</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># save plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/predictors_corr_heatmap.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+<span class="n">display</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">predictors_corr</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+<span class="c1"># save clean data</span>
+<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s2">"../../data/data19802022_clean.csv"</span><span class="p">):</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"../../data/data19802022_clean.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>ROWS DROPPED NA: 0
+FIELDS: ['date' 'gvkey' 'permno' 'ff49' 'mv' 'be_me' 'prc' 'ret_12_7' 'ret_3_1'
+ 'ret_6_1' 'ret_9_1' 'ret_12_1' 'ret_18_1' 'ret_1_0' 'ret_2_0' 'ret_3_0'
+ 'ret_6_0' 'ret_9_0' 'ret_12_0' 'feps' 'fep' 'disp' 'nsi' 'accrual' 'noa'
+ 'ag' 'ia' 'pead' 'rmax5_21d' 'prc_highprc_252d' 'rmax1_21d'
+ 'cum_rawret_1_6m' 'cum_rawret_1_3m' 'cum_rawret_1_12m' 'ret_lead1m'
+ 'ret_lead2m' 'ret_lead3m' 'ret_lead4m' 'ret_lead5m' 'ret_lead6m'
+ 'ret_lead7m' 'ret_lead8m' 'ret_lead9m' 'ret_lead10m' 'ret_lead11m'
+ 'ret_lead12m']
+DIMENSION: (2960897, 46)
+DUPLICATED ROWS: 0
+# OF MISSING VALUES: 17954352 , PERCENTAGE OF MISSING VALUES: 0.132 , # OF COLUMNS HAVE MISSING VALUES: 41
+START DATE: 1980-01-31 00:00:00 , END DATE: 2022-12-30 00:00:00
+</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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"/>
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"/>
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+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" 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;
+    }
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+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>disp</th>
+<th>noa</th>
+<th>rmax5_21d</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>disp</th>
+<td>1.00</td>
+<td>-0.0</td>
+<td>0.05</td>
+</tr>
+<tr>
+<th>noa</th>
+<td>-0.00</td>
+<td>1.0</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>rmax5_21d</th>
+<td>0.05</td>
+<td>-0.0</td>
+<td>1.00</td>
+</tr>
+</tbody>
+</table>
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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" 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>ret_12_7</th>
+<th>ret_3_1</th>
+<th>ret_6_1</th>
+<th>ret_9_1</th>
+<th>ret_12_1</th>
+<th>ret_18_1</th>
+<th>ret_1_0</th>
+<th>ret_2_0</th>
+<th>ret_3_0</th>
+<th>ret_6_0</th>
+<th>...</th>
+<th>disp</th>
+<th>nsi</th>
+<th>accrual</th>
+<th>noa</th>
+<th>ag</th>
+<th>ia</th>
+<th>pead</th>
+<th>rmax5_21d</th>
+<th>prc_highprc_252d</th>
+<th>rmax1_21d</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>ret_12_7</th>
+<td>1.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.27</td>
+<td>0.58</td>
+<td>0.40</td>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>...</td>
+<td>-0.02</td>
+<td>-0.01</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.09</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>ret_3_1</th>
+<td>0.01</td>
+<td>1.00</td>
+<td>0.60</td>
+<td>0.45</td>
+<td>0.34</td>
+<td>0.22</td>
+<td>0.01</td>
+<td>0.48</td>
+<td>0.79</td>
+<td>0.54</td>
+<td>...</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.00</td>
+<td>0.29</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>ret_6_1</th>
+<td>0.01</td>
+<td>0.60</td>
+<td>1.00</td>
+<td>0.75</td>
+<td>0.57</td>
+<td>0.38</td>
+<td>0.01</td>
+<td>0.28</td>
+<td>0.47</td>
+<td>0.88</td>
+<td>...</td>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.00</td>
+<td>0.33</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>ret_9_1</th>
+<td>0.27</td>
+<td>0.45</td>
+<td>0.75</td>
+<td>1.00</td>
+<td>0.78</td>
+<td>0.55</td>
+<td>0.01</td>
+<td>0.22</td>
+<td>0.35</td>
+<td>0.66</td>
+<td>...</td>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.00</td>
+<td>0.31</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>ret_12_1</th>
+<td>0.58</td>
+<td>0.34</td>
+<td>0.57</td>
+<td>0.78</td>
+<td>1.00</td>
+<td>0.71</td>
+<td>0.01</td>
+<td>0.16</td>
+<td>0.27</td>
+<td>0.50</td>
+<td>...</td>
+<td>-0.02</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.24</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>ret_18_1</th>
+<td>0.40</td>
+<td>0.22</td>
+<td>0.38</td>
+<td>0.55</td>
+<td>0.71</td>
+<td>1.00</td>
+<td>-0.01</td>
+<td>0.09</td>
+<td>0.17</td>
+<td>0.32</td>
+<td>...</td>
+<td>-0.02</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.13</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>ret_1_0</th>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>-0.01</td>
+<td>1.00</td>
+<td>0.71</td>
+<td>0.56</td>
+<td>0.37</td>
+<td>...</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.0</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.42</td>
+<td>0.28</td>
+<td>0.37</td>
+</tr>
+<tr>
+<th>ret_2_0</th>
+<td>0.01</td>
+<td>0.48</td>
+<td>0.28</td>
+<td>0.22</td>
+<td>0.16</td>
+<td>0.09</td>
+<td>0.71</td>
+<td>1.00</td>
+<td>0.81</td>
+<td>0.54</td>
+<td>...</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.0</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.30</td>
+<td>0.35</td>
+<td>0.27</td>
+</tr>
+<tr>
+<th>ret_3_0</th>
+<td>0.01</td>
+<td>0.79</td>
+<td>0.47</td>
+<td>0.35</td>
+<td>0.27</td>
+<td>0.17</td>
+<td>0.56</td>
+<td>0.81</td>
+<td>1.00</td>
+<td>0.68</td>
+<td>...</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.25</td>
+<td>0.38</td>
+<td>0.22</td>
+</tr>
+<tr>
+<th>ret_6_0</th>
+<td>0.01</td>
+<td>0.54</td>
+<td>0.88</td>
+<td>0.66</td>
+<td>0.50</td>
+<td>0.32</td>
+<td>0.37</td>
+<td>0.54</td>
+<td>0.68</td>
+<td>1.00</td>
+<td>...</td>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.19</td>
+<td>0.40</td>
+<td>0.16</td>
+</tr>
+<tr>
+<th>ret_9_0</th>
+<td>0.25</td>
+<td>0.42</td>
+<td>0.68</td>
+<td>0.91</td>
+<td>0.72</td>
+<td>0.49</td>
+<td>0.29</td>
+<td>0.42</td>
+<td>0.53</td>
+<td>0.77</td>
+<td>...</td>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.17</td>
+<td>0.37</td>
+<td>0.14</td>
+</tr>
+<tr>
+<th>ret_12_0</th>
+<td>0.52</td>
+<td>0.31</td>
+<td>0.51</td>
+<td>0.69</td>
+<td>0.92</td>
+<td>0.64</td>
+<td>0.21</td>
+<td>0.31</td>
+<td>0.39</td>
+<td>0.58</td>
+<td>...</td>
+<td>-0.02</td>
+<td>-0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.01</td>
+<td>0.14</td>
+<td>0.28</td>
+<td>0.11</td>
+</tr>
+<tr>
+<th>feps</th>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>...</td>
+<td>-0.00</td>
+<td>0.04</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.01</td>
+<td>0.01</td>
+<td>-0.01</td>
+</tr>
+<tr>
+<th>fep</th>
+<td>0.01</td>
+<td>-0.02</td>
+<td>-0.02</td>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>0.02</td>
+<td>-0.02</td>
+<td>-0.02</td>
+<td>-0.02</td>
+<td>-0.02</td>
+<td>...</td>
+<td>-0.04</td>
+<td>-0.01</td>
+<td>0.0</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>0.03</td>
+<td>-0.07</td>
+<td>0.06</td>
+<td>-0.06</td>
+</tr>
+<tr>
+<th>disp</th>
+<td>-0.02</td>
+<td>-0.00</td>
+<td>-0.01</td>
+<td>-0.01</td>
+<td>-0.02</td>
+<td>-0.02</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.01</td>
+<td>...</td>
+<td>1.00</td>
+<td>0.00</td>
+<td>-0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.01</td>
+<td>0.05</td>
+<td>-0.06</td>
+<td>0.04</td>
+</tr>
+<tr>
+<th>nsi</th>
+<td>-0.01</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>...</td>
+<td>0.00</td>
+<td>1.00</td>
+<td>-0.0</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.01</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>accrual</th>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>...</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>1.0</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>noa</th>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>...</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.0</td>
+<td>1.00</td>
+<td>0.09</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>ag</th>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>...</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.0</td>
+<td>0.09</td>
+<td>1.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+</tr>
+<tr>
+<th>ia</th>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>...</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.0</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>1.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>-0.01</td>
+<td>0.00</td>
+</tr>
+<tr>
+<th>pead</th>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>...</td>
+<td>-0.01</td>
+<td>0.00</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>1.00</td>
+<td>-0.01</td>
+<td>0.02</td>
+<td>-0.01</td>
+</tr>
+<tr>
+<th>rmax5_21d</th>
+<td>0.01</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.01</td>
+<td>0.01</td>
+<td>0.42</td>
+<td>0.30</td>
+<td>0.25</td>
+<td>0.19</td>
+<td>...</td>
+<td>0.05</td>
+<td>-0.00</td>
+<td>-0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.01</td>
+<td>1.00</td>
+<td>-0.03</td>
+<td>1.00</td>
+</tr>
+<tr>
+<th>prc_highprc_252d</th>
+<td>0.09</td>
+<td>0.29</td>
+<td>0.33</td>
+<td>0.31</td>
+<td>0.24</td>
+<td>0.13</td>
+<td>0.28</td>
+<td>0.35</td>
+<td>0.38</td>
+<td>0.40</td>
+<td>...</td>
+<td>-0.06</td>
+<td>-0.01</td>
+<td>0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>-0.01</td>
+<td>0.02</td>
+<td>-0.03</td>
+<td>1.00</td>
+<td>-0.01</td>
+</tr>
+<tr>
+<th>rmax1_21d</th>
+<td>0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.00</td>
+<td>0.37</td>
+<td>0.27</td>
+<td>0.22</td>
+<td>0.16</td>
+<td>...</td>
+<td>0.04</td>
+<td>-0.00</td>
+<td>-0.0</td>
+<td>-0.00</td>
+<td>-0.00</td>
+<td>0.00</td>
+<td>-0.01</td>
+<td>1.00</td>
+<td>-0.01</td>
+<td>1.00</td>
+</tr>
+</tbody>
+</table>
+<p>24 rows × 24 columns</p>
+</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 [ ]:</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"># data preprocessing</span>
+<span class="n">start_date</span> <span class="o">=</span> <span class="s2">"1980-01-01"</span>
+<span class="n">end_date</span> <span class="o">=</span> <span class="s2">"2022-12-31"</span>
+
+<span class="c1"># add trend to data, downturn = 0, upturn = 1</span>
+<span class="k">for</span> <span class="n">period</span> <span class="ow">in</span> <span class="n">downturn</span><span class="p">[</span><span class="s2">"period"</span><span class="p">]:</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">period</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">period</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="s2">"regime"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">period</span> <span class="ow">in</span> <span class="n">upturn</span><span class="p">[</span><span class="s2">"period"</span><span class="p">]:</span>
+    <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">period</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">period</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="s2">"regime"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+
+<span class="c1"># data is all the data falls in the range of start_date and end_date</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_date</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">end_date</span><span class="p">)]</span>
+<span class="n">spx</span> <span class="o">=</span> <span class="n">spx</span><span class="p">[(</span><span class="n">spx</span><span class="o">.</span><span class="n">index</span> <span class="o">&gt;=</span> <span class="n">start_date</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">spx</span><span class="o">.</span><span class="n">index</span> <span class="o">&lt;=</span> <span class="n">end_date</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="Stock-Selection">Stock Selection<a class="anchor-link" href="#Stock-Selection">¶</a></h3>
+</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><span class="k">def</span> <span class="nf">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="nb">bin</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="n">data_model</span> <span class="o">=</span> <span class="n">data</span><span class="p">[[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"mv"</span><span class="p">]</span> <span class="o">+</span> <span class="n">predictors</span> <span class="o">+</span> <span class="n">ret_list</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">regime</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">data_model</span> <span class="o">=</span> <span class="n">data_model</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">"regime"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
+    <span class="k">elif</span> <span class="n">regime</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">data_model</span> <span class="o">=</span> <span class="n">data_model</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">"regime"</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span>
+    <span class="n">data_model</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="n">predictors</span> <span class="o">+</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">how</span> <span class="o">=</span> <span class="s2">"any"</span><span class="p">)</span>
+    <span class="n">groupby_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"date"</span><span class="p">]</span>
+    <span class="n">pred_name</span> <span class="o">=</span> <span class="s2">"_"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span>
+    <span class="n">folder_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"../../data/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span><span class="si">}</span><span class="s2">_predictor/</span><span class="si">{</span><span class="n">pred_name</span><span class="si">}</span><span class="s2">"</span>
+    
+    <span class="c1"># add mv as a predictor</span>
+    <span class="k">if</span> <span class="n">mv_factor</span><span class="p">:</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"mv"</span><span class="p">]</span> <span class="o">+</span> <span class="n">predictors</span>
+
+    <span class="c1"># sort by predictors, pred1 is the main predictor, pred2 is the second predictor, etc. {predictor}{1} is the smallest, {predictor}{bin} is the largest. header is the most important predictor</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">predictor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">predictors</span><span class="p">):</span>
+        <span class="n">new_col</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"pred</span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">_sort"</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">data_model</span><span class="p">[</span><span class="n">new_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">groupby_list</span><span class="p">)[</span><span class="n">predictor</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">,</span> <span class="nb">bin</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">predictor</span><span class="si">}{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">bin</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)],</span> <span class="n">duplicates</span> <span class="o">=</span> <span class="s2">"drop"</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+            <span class="n">data_model</span><span class="p">[</span><span class="n">new_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">groupby_list</span><span class="p">)[</span><span class="n">predictor</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">,</span> <span class="nb">bin</span><span class="p">,</span> <span class="n">duplicates</span> <span class="o">=</span> <span class="s2">"drop"</span><span class="p">)</span>
+        <span class="n">groupby_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_col</span><span class="p">)</span>
+    
+    <span class="c1"># frequency table, across time average return table</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">freq_table</span> <span class="o">=</span> <span class="n">data_model</span><span class="p">[</span><span class="s2">"pred1_sort"</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+        <span class="n">freq_table</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">freq_table</span><span class="p">)</span>
+
+        <span class="n">ret_table</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"pred1_sort"</span><span class="p">)[</span><span class="n">ret_list</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="n">ret_table</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">ret_table</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">col_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">data_model</span><span class="p">[</span><span class="sa">f</span><span class="s2">"pred1_sort"</span><span class="p">]]</span>
+        <span class="n">row_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">data_model</span><span class="p">[</span><span class="sa">f</span><span class="s2">"pred</span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">_sort"</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">))]</span>
+
+        <span class="n">freq_table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">row_list</span><span class="p">,</span> <span class="n">col_list</span><span class="p">)</span>
+        <span class="n">ret_table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">row_list</span><span class="p">,</span> <span class="n">col_list</span><span class="p">,</span> <span class="n">values</span> <span class="o">=</span> <span class="n">data_model</span><span class="p">[</span><span class="n">ret_list</span><span class="p">],</span> <span class="n">aggfunc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
+        
+    <span class="c1"># summary table at each time, a time series table</span>
+    <span class="n">ts_table</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">groupby_list</span><span class="p">)[</span><span class="n">ret_list</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+    
+    <span class="c1"># store the frequency table, return table, and time series table. the naming is based on the importance of the predictor, from the most important to the least important. </span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">folder_name</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">folder_name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mv_factor</span><span class="p">:</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="s2">"mv_"</span> <span class="o">+</span> <span class="n">pred_name</span>
+    <span class="k">if</span> <span class="n">regime</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">pred_name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="s1">'downturn'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">regime</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'upturn'</span><span class="si">}</span><span class="s2">"</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">file_name</span> <span class="o">=</span> <span class="n">pred_name</span>
+    <span class="c1"># save files</span>
+    <span class="c1"># if not os.path.exists(f"{folder_name}/{file_name}_{ret_list[0]}_freq.csv"):</span>
+    <span class="n">freq_table</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">_freq.csv"</span><span class="p">)</span>
+    <span class="c1"># if not os.path.exists(f"{folder_name}/{file_name}_{ret_list[0]}.csv"):</span>
+    <span class="n">ret_table</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">.csv"</span><span class="p">)</span>
+    <span class="c1"># if not os.path.exists(f"{folder_name}/{file_name}_{ret_list[0]}_ts.csv"):</span>
+    <span class="n">ts_table</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">_ts.csv"</span><span class="p">)</span>
+        
+    <span class="k">return</span> <span class="n">freq_table</span><span class="p">,</span> <span class="n">ret_table</span><span class="p">,</span> <span class="n">ts_table</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 [ ]:</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"># sort disp, noa, rmax5_21d</span>
+<span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">]</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</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="Portfolio-Construction">Portfolio Construction<a class="anchor-link" href="#Portfolio-Construction">¶</a></h3>
+</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><span class="k">def</span> <span class="nf">FindMaxMin</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">file_name_partial</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]):</span>
+    <span class="n">file_name_partial</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">file_name_partial</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span>
+    
+    <span class="c1"># read the return table</span>
+    <span class="n">ret_table</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="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">file_name_partial</span><span class="si">}</span><span class="s2">.csv"</span><span class="p">)</span>
+
+    <span class="c1"># find the largest and smallest term and the corresponding index</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">ret_table</span> <span class="o">=</span> <span class="n">ret_table</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ret_table</span> <span class="o">=</span> <span class="n">ret_table</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]</span>
+        
+    <span class="n">max_coordinate</span> <span class="o">=</span> <span class="n">ret_table</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span>
+    <span class="n">min_coordinate</span> <span class="o">=</span> <span class="n">ret_table</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()</span>
+    
+    <span class="k">return</span> <span class="n">max_coordinate</span><span class="p">,</span> <span class="n">min_coordinate</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 [ ]:</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="nf">StopLossDate</span><span class="p">(</span><span class="n">port_ret_ts</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">stop_loss</span><span class="p">):</span>
+    <span class="n">date</span> <span class="o">=</span> <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">date</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">date</span><span class="p">)</span>
+
+    <span class="c1"># stop loss location t</span>
+    <span class="n">stop_loss_date</span> <span class="o">=</span> <span class="n">port_ret_ts</span><span class="p">[</span><span class="n">port_ret_ts</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">&lt;</span> <span class="n">stop_loss</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">stop_loss_date_loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">stop_loss_date</span><span class="p">)</span>
+
+    <span class="c1"># find t+1, t+2 month</span>
+    <span class="n">stop_loss_lead1m_date_loc</span> <span class="o">=</span> <span class="n">stop_loss_date_loc</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">stop_loss_lead2m_date_loc</span> <span class="o">=</span> <span class="n">stop_loss_date_loc</span> <span class="o">+</span> <span class="mi">2</span>
+    <span class="n">stop_loss_date_loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">stop_loss_lead1m_date_loc</span><span class="p">,</span> <span class="n">stop_loss_lead2m_date_loc</span><span class="p">])</span>
+    <span class="n">stop_loss_date_loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">stop_loss_date_loc</span><span class="p">)</span>
+    <span class="n">stop_loss_date_loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">stop_loss_date_loc</span><span class="p">)</span>
+    <span class="n">stop_loss_date</span> <span class="o">=</span> <span class="n">date</span><span class="p">[</span><span class="n">stop_loss_date_loc</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">stop_loss_date</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 [ ]:</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="nf">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="n">folder_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"../../data/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span><span class="si">}</span><span class="s2">_predictor/</span><span class="si">{</span><span class="s1">'_'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
+    <span class="n">file_name_partial</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="s1">'_'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
+
+    <span class="c1"># add mv as a predictor</span>
+    <span class="k">if</span> <span class="n">mv_factor</span><span class="p">:</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"mv"</span><span class="p">]</span> <span class="o">+</span> <span class="n">predictors</span>
+
+    <span class="k">if</span> <span class="n">regime</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">file_name_partial</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">folder_name</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="s1">'_'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="s1">'downturn'</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">regime</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">'upturn'</span><span class="si">}</span><span class="s2">"</span>
+    <span class="n">data_ts</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="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">file_name_partial</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">_ts.csv"</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># max and min condition</span>
+    <span class="k">if</span> <span class="n">cond</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">data_ret</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="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">file_name_partial</span><span class="si">}</span><span class="s2">_ret_lead1m.csv"</span><span class="p">)</span>
+
+        <span class="c1"># find the largest and smallest term and corresonding index</span>
+        <span class="n">max_coordinate</span><span class="p">,</span> <span class="n">min_coordinate</span> <span class="o">=</span> <span class="n">FindMaxMin</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">file_name_partial</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="n">regime</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"ts"</span><span class="p">:</span>
+            <span class="n">pred_sort_list</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"pred</span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">_sort"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">))]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">max_cond_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">data_ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">max_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">]}</span>
+                <span class="n">min_cond_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">data_ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">min_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">]}</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)):</span>
+                    <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">max_cond_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">max_coordinate</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
+                        <span class="n">min_cond_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">min_coordinate</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">max_cond_dict</span><span class="p">[</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">data_ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">max_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+                        <span class="n">min_cond_dict</span><span class="p">[</span><span class="n">pred_sort_list</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">data_ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">min_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"ts"</span><span class="p">:</span>
+            <span class="n">max_cond_dict</span> <span class="o">=</span> <span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">min_cond_dict</span> <span class="o">=</span> <span class="n">cond</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"average"</span><span class="p">:</span>
+            <span class="n">max_coordinate</span> <span class="o">=</span> <span class="n">cond</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">min_coordinate</span> <span class="o">=</span> <span class="n">cond</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"ts"</span><span class="p">:</span>
+        <span class="c1"># make the condition for long and short</span>
+        <span class="n">long_cond</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">([</span><span class="n">data_ts</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">==</span> <span class="n">value</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">max_cond_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">short_cond</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">([</span><span class="n">data_ts</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">==</span> <span class="n">value</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">min_cond_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        
+        <span class="c1"># calculate return</span>
+        <span class="n">long_ret_ts</span> <span class="o">=</span> <span class="n">data_ts</span><span class="p">[</span><span class="n">long_cond</span><span class="p">][</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">short_ret_ts</span> <span class="o">=</span> <span class="n">data_ts</span><span class="p">[</span><span class="n">short_cond</span><span class="p">][</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">port_ret_ts</span> <span class="o">=</span> <span class="n">long_ret_ts</span> <span class="o">-</span> <span class="n">short_ret_ts</span>
+        
+        <span class="c1"># change to dataframe</span>
+        <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">data_ts</span><span class="p">[</span><span class="n">long_cond</span><span class="p">][</span><span class="s2">"date"</span><span class="p">]</span>
+        <span class="n">port_ret_ts</span> <span class="o">=</span> <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span>
+        <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">port_ret_ts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+        <span class="n">long_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">data_ts</span><span class="p">[</span><span class="n">long_cond</span><span class="p">][</span><span class="s2">"date"</span><span class="p">]</span>
+        <span class="n">long_ret_ts</span> <span class="o">=</span> <span class="n">long_ret_ts</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span>
+        <span class="n">long_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">long_ret_ts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+        <span class="n">short_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">data_ts</span><span class="p">[</span><span class="n">short_cond</span><span class="p">][</span><span class="s2">"date"</span><span class="p">]</span>
+        <span class="n">short_ret_ts</span> <span class="o">=</span> <span class="n">short_ret_ts</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span>
+        <span class="n">short_ret_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">short_ret_ts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+
+        <span class="c1"># stop loss</span>
+        <span class="k">if</span> <span class="n">stop_loss</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">stop_loss_date</span> <span class="o">=</span> <span class="n">StopLossDate</span><span class="p">(</span><span class="n">port_ret_ts</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">stop_loss</span><span class="p">)</span>
+            <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">port_ret_ts</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">stop_loss_date</span><span class="p">),</span> <span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">stop_loss_date</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">return</span> <span class="n">port_ret_ts</span><span class="p">,</span> <span class="n">stop_loss_date</span><span class="p">,</span> <span class="n">long_ret_ts</span><span class="p">,</span> <span class="n">short_ret_ts</span>
+
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"average"</span><span class="p">:</span>
+        <span class="n">port_ret</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"long_ret"</span><span class="p">,</span> <span class="s2">"short_ret"</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">)</span>
+        <span class="n">data_ret_mean</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="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">file_name_partial</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">.csv"</span><span class="p">)</span>
+
+        <span class="c1"># long short return</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">data_ret_mean</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"long_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">max_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"short_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">min_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data_ret_mean</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"long_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">max_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">max_coordinate</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"short_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_ret_mean</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">min_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">min_coordinate</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+
+        <span class="c1"># standardize return to monthly return</span>
+        <span class="k">if</span> <span class="s2">"_0"</span> <span class="ow">in</span> <span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="n">month</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"long_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"long_ret"</span><span class="p">]</span> <span class="o">/</span> <span class="n">month</span>
+            <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"short_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"short_ret"</span><span class="p">]</span> <span class="o">/</span> <span class="n">month</span>
+        
+        <span class="c1"># portfolio return</span>
+        <span class="n">port_ret</span><span class="p">[</span><span class="s2">"port_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">port_ret</span><span class="p">[</span><span class="s2">"long_ret"</span><span class="p">]</span> <span class="o">-</span> <span class="n">port_ret</span><span class="p">[</span><span class="s2">"short_ret"</span><span class="p">]</span>
+        
+        <span class="k">return</span> <span class="n">port_ret</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 [ ]:</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"># port_ret time series</span>
+<span class="n">disp_noa_rmax5_21d_ts_cond</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"pred1_sort"</span><span class="p">:</span> <span class="s2">"disp1"</span><span class="p">,</span> <span class="s2">"pred2_sort"</span><span class="p">:</span> <span class="s2">"noa1"</span><span class="p">,</span> <span class="s2">"pred3_sort"</span><span class="p">:</span> <span class="s2">"rmax5_21d1"</span><span class="p">},</span> <span class="p">{</span><span class="s2">"pred1_sort"</span><span class="p">:</span> <span class="s2">"disp5"</span><span class="p">,</span> <span class="s2">"pred2_sort"</span><span class="p">:</span> <span class="s2">"noa5"</span><span class="p">,</span> <span class="s2">"pred3_sort"</span><span class="p">:</span> <span class="s2">"rmax5_21d5"</span><span class="p">}]</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">stop_loss_date</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ret_lead1m_ts</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ret_lead1m_ts</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=</span><span class="n">stop_loss</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_downturn_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=</span><span class="n">stop_loss</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_upturn_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=</span><span class="n">stop_loss</span><span class="p">)</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 [ ]:</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"># # update freq table, ret table, ts table, stop loss date return = 0</span>
+<span class="n">ret_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span>
+<span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s2">"date"</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">stop_loss_date</span><span class="p">),</span> <span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_table</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</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 [ ]:</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">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">stop_loss_date</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ret_lead1m_ts</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ret_lead1m_ts</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=-</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_downturn_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=</span><span class="n">stop_loss</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_upturn_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">stop_loss</span><span class="o">=</span><span class="n">stop_loss</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="Performance">Performance<a class="anchor-link" href="#Performance">¶</a></h3>
+</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><span class="k">def</span> <span class="nf">ThreePredictorPlot</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">HTML</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+    <span class="c1"># ignore the first two predictors columns</span>
+    <span class="n">data_model</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span>
+
+    <span class="c1"># find predictors</span>
+    <span class="n">predictors</span> <span class="o">=</span> <span class="n">title</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)</span>
+    <span class="k">if</span> <span class="s2">"prc_highprc_252d"</span> <span class="ow">in</span> <span class="n">title</span><span class="p">:</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"252d"</span><span class="p">,</span> <span class="s2">"prc_highprc_252d"</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span><span class="p">]</span>
+        <span class="n">excluded_items</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"highprc"</span><span class="p">,</span> <span class="s2">"prc"</span><span class="p">]</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">excluded_items</span><span class="p">]</span>
+    <span class="k">if</span> <span class="s2">"rmax5_21d"</span> <span class="ow">in</span> <span class="n">title</span><span class="p">:</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"rmax5"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span><span class="p">]</span>
+        <span class="n">excluded_items</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"21d"</span><span class="p">]</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">excluded_items</span><span class="p">]</span>
+    <span class="k">if</span> <span class="s2">"rmax1_21d"</span> <span class="ow">in</span> <span class="n">title</span><span class="p">:</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"rmax1"</span><span class="p">,</span> <span class="s2">"rmax1_21d"</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span><span class="p">]</span>
+        <span class="n">excluded_items</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"21d"</span><span class="p">]</span>
+        <span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predictors</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">excluded_items</span><span class="p">]</span>
+    
+    <span class="c1"># reshape to 3d matrix, order is [predictor2, predictor3, predictor1]</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">data_model</span> <span class="o">=</span> <span class="n">data_model</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+        <span class="k">return</span>
+    
+    <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">data_model</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">data_model</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">data_model</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+
+    <span class="c1"># point size</span>
+    <span class="n">point_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">data_model</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="o">**</span> <span class="mi">500</span>
+    <span class="n">point_size</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">data_model</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">500</span>
+
+    <span class="c1"># point color</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"#eb9c9e"</span> <span class="k">if</span> <span class="n">value</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">"#5fcd53"</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">data_model</span><span class="o">.</span><span class="n">flatten</span><span class="p">()]</span>
+    <span class="n">colors</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">data_model</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">"red"</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">data_model</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">colors</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">data_model</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">"#519aba"</span>
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">data_model</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">colors</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">data_model</span><span class="p">)]</span> <span class="o">=</span> <span class="s2">"#70a187"</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">HTML</span><span class="p">:</span>
+        <span class="c1"># scatter plot </span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">'3d'</span><span class="p">)</span>
+        <span class="n">sc</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">z</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">s</span> <span class="o">=</span> <span class="n">point_size</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">colors</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+
+        <span class="c1"># title</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
+        
+        <span class="c1"># surface and grid transparent</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">pane</span><span class="o">.</span><span class="n">fill</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">pane</span><span class="o">.</span><span class="n">fill</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">pane</span><span class="o">.</span><span class="n">fill</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">_axinfo</span><span class="p">[</span><span class="s2">"grid"</span><span class="p">][</span><span class="s2">"color"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"#d9d9d9"</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">_axinfo</span><span class="p">[</span><span class="s2">"grid"</span><span class="p">][</span><span class="s2">"color"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"#d9d9d9"</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">_axinfo</span><span class="p">[</span><span class="s2">"grid"</span><span class="p">][</span><span class="s2">"color"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"#d9d9d9"</span>
+
+        <span class="c1"># axis axis color, label, label color, tick color, tick name</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">line</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">line</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">line</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">predictors</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">predictors</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="n">predictors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'z'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#c3c3c3'</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_zticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+
+        <span class="c1"># invert x, y axis and plot angle</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">invert_xaxis</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">view_init</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="mi">125</span><span class="p">)</span>
+
+        <span class="c1"># legend</span>
+        <span class="n">red_patch</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Max'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+        <span class="n">blue_patch</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'#519aba'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Min &amp; +'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+        <span class="n">green_patch</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'#70a187'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Min &amp; -'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+        <span class="n">positive_patch</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'#eb9c9e'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'+'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+        <span class="n">negative_patch</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'w'</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'#5fcd53'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">red_patch</span><span class="p">,</span> <span class="n">blue_patch</span><span class="p">,</span> <span class="n">green_patch</span><span class="p">,</span> <span class="n">positive_patch</span><span class="p">,</span> <span class="n">negative_patch</span><span class="p">],</span> <span class="n">loc</span> <span class="o">=</span> <span class="s1">'upper right'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">),</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="n">frameon</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>
+        
+        <span class="c1"># save the plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s2">.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span>
+            <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+            <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+            <span class="n">z</span><span class="o">=</span><span class="n">z</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+            <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span>
+            <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                <span class="n">size</span><span class="o">=</span><span class="n">point_size</span><span class="o">/</span><span class="mi">20</span><span class="p">,</span>
+                <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>                
+                <span class="n">opacity</span><span class="o">=</span><span class="mf">0.8</span>
+            <span class="p">),</span>
+            <span class="n">name</span> <span class="o">=</span> <span class="s2">"Return"</span>
+        <span class="p">)])</span>
+
+        <span class="n">fig</span><span class="o">.</span><span class="n">update_layout</span><span class="p">(</span>
+            <span class="n">title</span> <span class="o">=</span> <span class="n">title</span><span class="p">,</span> 
+            <span class="n">title_x</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+            <span class="n">scene</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+                <span class="n">xaxis_title</span><span class="o">=</span><span class="n">predictors</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">yaxis_title</span><span class="o">=</span><span class="n">predictors</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                <span class="n">zaxis_title</span><span class="o">=</span><span class="n">predictors</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                <span class="n">xaxis</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">tickvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">ticktext</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span>
+                <span class="n">yaxis</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">tickvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">ticktext</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span>
+                <span class="n">zaxis</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">tickvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">ticktext</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span>
+            <span class="p">),</span>
+            <span class="n">autosize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">width</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span>
+            <span class="n">height</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span>
+            <span class="n">margin</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+                <span class="n">l</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span>
+                <span class="n">r</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span>
+                <span class="n">b</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span>
+                <span class="n">t</span><span class="o">=</span><span class="mi">90</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># add legend</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">z</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'Max'</span><span class="p">))</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">z</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#519aba'</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'Min &amp; +'</span><span class="p">))</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">z</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#70a187'</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'Min &amp; -'</span><span class="p">))</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">z</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#eb9c9e'</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'+'</span><span class="p">))</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">add_trace</span><span class="p">(</span><span class="n">go</span><span class="o">.</span><span class="n">Scatter3d</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">z</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'markers'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#5fcd53'</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'-'</span><span class="p">))</span>
+        <span class="c1"># do not show trace[0]</span>
+        
+        <span class="c1"># save the plot</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">write_html</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../data/3_predictor/</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="s2">.html"</span><span class="p">,</span> <span class="n">config</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">displayModeBar</span> <span class="o">=</span> <span class="kc">False</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 [ ]:</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"># return visualization</span>
+<span class="n">title</span> <span class="o">=</span> <span class="s2">"_"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span>
+
+<span class="n">_</span> <span class="o">=</span> <span class="n">ThreePredictorPlot</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ret_lead1m_table</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span> <span class="n">title</span><span class="p">,</span> <span class="n">HTML</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">ThreePredictorPlot</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ret_lead1m_table</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span> <span class="n">title</span><span class="p">,</span> <span class="n">HTML</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-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
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+<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="nf">LSRetPlot</span><span class="p">(</span><span class="n">port_ret</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="n">downturn</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">spx_data</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">start_date</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">end_date</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">var</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">spx</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+    <span class="n">plot_name</span> <span class="o">=</span> <span class="s2">"_"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">mv_factor</span><span class="p">:</span>
+        <span class="n">plot_name</span> <span class="o">=</span> <span class="s2">"mv_"</span> <span class="o">+</span> <span class="n">plot_name</span>
+    <span class="k">if</span> <span class="n">start_date</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">start_date</span> <span class="o">=</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">start_date</span> <span class="o">=</span> <span class="n">start_date</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">end_date</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">end_date</span> <span class="o">=</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">end_date</span> <span class="o">=</span> <span class="n">end_date</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">"%Y-%m-</span><span class="si">%d</span><span class="s2">"</span><span class="p">)</span>
+
+    <span class="c1"># set the time</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="n">port_ret</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">][</span><span class="n">start_date</span><span class="p">:</span><span class="n">end_date</span><span class="p">]</span>
+     
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s2">"ts"</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">downturn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Portfolio Return"</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Portfolio Return (M) </span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">start_date</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">end_date</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">downturn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">downturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Portfolio Downturn Return"</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span> 
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">upturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Portfolio Upturn Return"</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">)</span>
+        
+        <span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"0"</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.4</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Date"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2"> Return"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">4</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">spx</span><span class="p">:</span>
+            <span class="c1"># axis color, ticks color， lable color, grid</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+            
+            <span class="c1"># plot spx with the same time period in the same plot, left y-axis is for portfolio return, right y-axis is for spx price</span>
+            <span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">downturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spx_data</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Downturn"</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spx_data</span><span class="p">[</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">downturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">upturn</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spx_data</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Upturn"</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spx_data</span><span class="p">[</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span><span class="n">upturn</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">4</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Portfolio Return (M) </span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2"> &amp; SPX500 </span><span class="si">{</span><span class="n">start_date</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">end_date</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+            <span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"SPX 500"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s2">"dist"</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">200</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'grey'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Portfolio Return"</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">var</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">dropna</span><span class="p">(),</span> <span class="mi">1</span><span class="p">),</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">"purple"</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"99% VaR"</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"0"</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Mean"</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2"> Return"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Frequency"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Portfolio Return (M) </span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2"> Distribution"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+ 
+    <span class="c1"># axis color, ticks color， lable color, grid</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+
+    <span class="c1"># save plot</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/Return_</span><span class="si">{</span><span class="n">plot_name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">start_date</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">end_date</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">plot_type</span><span class="si">}</span><span class="s2">.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</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><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 [ ]:</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">predictors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">]</span>
+
+<span class="c1"># drop the rows = 0</span>
+<span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts_plot</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">[</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
+
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"dist"</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="p">,</span> <span class="n">spx_data</span> <span class="o">=</span> <span class="n">spx</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">spx</span> <span class="o">=</span> <span class="kc">True</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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"/>
+</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 [ ]:</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="nf">MultiPredictorP2FRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">):</span>
+    <span class="c1"># construct portfolio return from past to future</span>
+    <span class="n">port_ret_p2f_ts</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">columns</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ret</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ret_list</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"ts"</span><span class="p">:</span>
+            <span class="n">port_ret</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="n">ret</span><span class="p">],</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">"average"</span><span class="p">:</span>
+            <span class="n">port_ret</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="n">ret</span><span class="p">],</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">mv_factor</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+            
+        <span class="n">port_ret_p2f_ts</span><span class="p">[</span><span class="n">ret</span><span class="p">]</span> <span class="o">=</span> <span class="n">port_ret</span><span class="p">[</span><span class="n">ret</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="p">:</span>
+            <span class="n">port_ret_p2f_ts</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">index</span>
+            
+        <span class="c1"># standardize cumulative return to monthly return</span>
+        <span class="k">if</span> <span class="s2">"_0"</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">:</span>
+            <span class="n">month</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">port_ret_p2f_ts</span><span class="p">[</span><span class="n">ret</span><span class="p">]</span> <span class="o">=</span> <span class="n">port_ret_p2f_ts</span><span class="p">[</span><span class="n">ret</span><span class="p">]</span> <span class="o">/</span> <span class="n">month</span>
+    
+    <span class="k">return</span> <span class="n">port_ret_p2f_ts</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 [ ]:</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"># one sample t-test</span>
+<span class="k">def</span> <span class="nf">TTest</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="n">port_ret_p2f_ts</span> <span class="o">=</span> <span class="n">MultiPredictorP2FRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">)</span>
+
+    <span class="c1"># perform the t-test on each column</span>
+    <span class="n">t_test</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"t-statistic"</span><span class="p">,</span> <span class="s2">"p-value"</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">port_ret_p2f_ts</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">port_ret_p2f_ts</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+        <span class="n">t_stat</span><span class="p">,</span> <span class="n">p_val</span> <span class="o">=</span> <span class="n">ttest_1samp</span><span class="p">(</span><span class="n">port_ret_p2f_ts</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">how</span><span class="o">=</span><span class="s2">"any"</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">t_test</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">col</span><span class="p">,</span> <span class="s2">"t-statistic"</span><span class="p">]</span> <span class="o">=</span> <span class="n">t_stat</span>
+        <span class="n">t_test</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">col</span><span class="p">,</span> <span class="s2">"p-value"</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_val</span>
+        
+    <span class="k">return</span> <span class="n">t_test</span><span class="o">.</span><span class="n">T</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 [ ]:</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">ret_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'ret_12_0'</span><span class="p">,</span> <span class="s1">'ret_9_0'</span><span class="p">,</span> <span class="s1">'ret_6_0'</span><span class="p">,</span> <span class="s1">'ret_3_0'</span><span class="p">,</span> <span class="s1">'ret_2_0'</span><span class="p">,</span> <span class="s1">'ret_1_0'</span><span class="p">,</span>
+            <span class="s1">'ret_lead1m'</span><span class="p">,</span> <span class="s1">'ret_lead2m'</span><span class="p">,</span> <span class="s1">'ret_lead3m'</span><span class="p">,</span> <span class="s1">'ret_lead4m'</span><span class="p">,</span> <span class="s1">'ret_lead5m'</span><span class="p">,</span> <span class="s1">'ret_lead6m'</span><span class="p">,</span> <span class="s1">'ret_lead7m'</span><span class="p">,</span> <span class="s1">'ret_lead8m'</span><span class="p">,</span> <span class="s1">'ret_lead9m'</span><span class="p">,</span> <span class="s1">'ret_lead10m'</span><span class="p">,</span> <span class="s1">'ret_lead11m'</span><span class="p">,</span> <span class="s1">'ret_lead12m'</span><span class="p">]</span>
+
+<span class="c1"># generate ts files for different returns</span>
+<span class="k">for</span> <span class="n">ret</span> <span class="ow">in</span> <span class="n">ret_list</span><span class="p">:</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="n">ret</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="n">ret</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="n">ret</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">display</span><span class="p">(</span><span class="n">TTest</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_ts_cond</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-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" 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>ret_12_0</th>
+<th>ret_9_0</th>
+<th>ret_6_0</th>
+<th>ret_3_0</th>
+<th>ret_2_0</th>
+<th>ret_1_0</th>
+<th>ret_lead1m</th>
+<th>ret_lead2m</th>
+<th>ret_lead3m</th>
+<th>ret_lead4m</th>
+<th>ret_lead5m</th>
+<th>ret_lead6m</th>
+<th>ret_lead7m</th>
+<th>ret_lead8m</th>
+<th>ret_lead9m</th>
+<th>ret_lead10m</th>
+<th>ret_lead11m</th>
+<th>ret_lead12m</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>t-statistic</th>
+<td>0.279719</td>
+<td>1.397854</td>
+<td>0.483051</td>
+<td>-5.322571</td>
+<td>-10.240991</td>
+<td>-22.538607</td>
+<td>8.655697</td>
+<td>5.820639</td>
+<td>4.265612</td>
+<td>5.277713</td>
+<td>4.21871</td>
+<td>3.429607</td>
+<td>3.876551</td>
+<td>3.056768</td>
+<td>3.343559</td>
+<td>3.133205</td>
+<td>3.476274</td>
+<td>2.998557</td>
+</tr>
+<tr>
+<th>p-value</th>
+<td>0.779805</td>
+<td>0.162759</td>
+<td>0.629265</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>0.0</td>
+<td>0.000024</td>
+<td>0.0</td>
+<td>0.000029</td>
+<td>0.000654</td>
+<td>0.00012</td>
+<td>0.002355</td>
+<td>0.000888</td>
+<td>0.00183</td>
+<td>0.000552</td>
+<td>0.002847</td>
+</tr>
+</tbody>
+</table>
+</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 [ ]:</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="nf">TestAlpha</span><span class="p">(</span><span class="n">port_ret_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"CAPM"</span><span class="p">):</span>
+    <span class="n">ret_reg</span> <span class="o">=</span> <span class="n">port_ret_ts</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">data_ff3</span><span class="p">,</span> <span class="n">left_index</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">right_index</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># portfolio excess return</span>
+    <span class="n">ret_reg</span><span class="p">[</span><span class="s2">"port_exc_ret"</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="p">[</span><span class="n">ret_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">-</span> <span class="n">ret_reg</span><span class="p">[</span><span class="s2">"rf"</span><span class="p">]</span>
+    <span class="n">ret_reg</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"port_exc_ret"</span><span class="p">,</span> <span class="s2">"mktrf"</span><span class="p">],</span> <span class="n">how</span> <span class="o">=</span> <span class="s2">"any"</span><span class="p">)</span>
+    
+    <span class="c1"># regression</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="p">[</span><span class="s2">"port_exc_ret"</span><span class="p">]</span>
+    
+    <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">"CAPM"</span><span class="p">:</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="p">[</span><span class="s2">"mktrf"</span><span class="p">]</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">LiRegOLS</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="n">LiRegOLS</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+        <span class="c1"># print(results.summary())</span>
+        
+        <span class="c1"># alpha</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_p</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">pvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_t</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">tvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        
+    <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">"ff3"</span><span class="p">:</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="p">[[</span><span class="s2">"mktrf"</span><span class="p">,</span> <span class="s2">"smb"</span><span class="p">,</span> <span class="s2">"hml"</span><span class="p">]]</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">LiRegOLS</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="n">LiRegOLS</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+        <span class="c1"># print(results.summary())</span>
+        
+        <span class="c1"># alpha</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_p</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">pvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_t</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">tvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+    
+    <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s2">"ff4"</span><span class="p">:</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">ret_reg</span><span class="p">[[</span><span class="s2">"mktrf"</span><span class="p">,</span> <span class="s2">"smb"</span><span class="p">,</span> <span class="s2">"hml"</span><span class="p">,</span> <span class="s2">"umd"</span><span class="p">]]</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">LiRegOLS</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
+        <span class="n">results</span> <span class="o">=</span> <span class="n">LiRegOLS</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+        <span class="c1"># print(results.summary())</span>
+        
+        <span class="c1"># alpha</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_p</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">pvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        <span class="n">alpha_t</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">tvalues</span><span class="p">[</span><span class="s2">"const"</span><span class="p">]</span>
+        
+    <span class="k">return</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">alpha_p</span><span class="p">,</span> <span class="n">alpha_t</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 [ ]:</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">disp_noa_rmax5_21d_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_CAPM_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"CAPM"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff3_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff3"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_l_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_CAPM_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_l_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"CAPM"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_l_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff3_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_l_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff3"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_s_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_CAPM_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_s_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"CAPM"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_s_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff3_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_s_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff3"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff4_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff4"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_l_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff4_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_l_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff4"</span><span class="p">)</span>
+<span class="n">disp_noa_rmax5_21d_s_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff4_alpha_t</span> <span class="o">=</span> <span class="n">TestAlpha</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_s_ret_lead1m_ts</span><span class="p">,</span> <span class="n">data_ff3</span><span class="p">,</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">model</span> <span class="o">=</span> <span class="s2">"ff4"</span><span class="p">)</span>
+
+<span class="c1"># table display</span>
+<span class="n">disp_noa_rmax5_21d_alpha_table</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CAPM Alpha"</span><span class="p">,</span> <span class="s2">"CAPM p-value"</span><span class="p">,</span> <span class="s2">"CAPM t-statistic"</span><span class="p">,</span> <span class="s2">"FF3 Alpha"</span><span class="p">,</span> <span class="s2">"FF3 p-value"</span><span class="p">,</span> <span class="s2">"FF3 t-statistic"</span><span class="p">,</span> <span class="s2">"FF4 Alpha"</span><span class="p">,</span> <span class="s2">"FF4 p-value"</span><span class="p">,</span> <span class="s2">"FF4 t-statistic"</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"All"</span><span class="p">,</span> <span class="s2">"Long"</span><span class="p">,</span> <span class="s2">"Short"</span><span class="p">])</span>
+<span class="n">disp_noa_rmax5_21d_alpha_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"All"</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">disp_noa_rmax5_21d_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_CAPM_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff3_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ff4_alpha_t</span><span class="p">]</span>
+<span class="n">disp_noa_rmax5_21d_alpha_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"Long"</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">disp_noa_rmax5_21d_l_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_CAPM_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff3_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_l_ff4_alpha_t</span><span class="p">]</span>
+<span class="n">disp_noa_rmax5_21d_alpha_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"Short"</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">disp_noa_rmax5_21d_s_CAPM_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_CAPM_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_CAPM_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff3_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff3_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff3_alpha_t</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff4_alpha</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff4_alpha_p_value</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_s_ff4_alpha_t</span><span class="p">]</span>
+
+<span class="n">display</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_alpha_table</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-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" 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>CAPM Alpha</th>
+<th>CAPM p-value</th>
+<th>CAPM t-statistic</th>
+<th>FF3 Alpha</th>
+<th>FF3 p-value</th>
+<th>FF3 t-statistic</th>
+<th>FF4 Alpha</th>
+<th>FF4 p-value</th>
+<th>FF4 t-statistic</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>All</th>
+<td>0.026483</td>
+<td>0.0</td>
+<td>7.961592</td>
+<td>0.025897</td>
+<td>0.0</td>
+<td>7.743233</td>
+<td>0.026034</td>
+<td>0.0</td>
+<td>7.644343</td>
+</tr>
+<tr>
+<th>Long</th>
+<td>0.012179</td>
+<td>0.0</td>
+<td>6.509637</td>
+<td>0.012426</td>
+<td>0.0</td>
+<td>6.597473</td>
+<td>0.012761</td>
+<td>0.0</td>
+<td>6.659471</td>
+</tr>
+<tr>
+<th>Short</th>
+<td>-0.017588</td>
+<td>0.000024</td>
+<td>-4.259169</td>
+<td>-0.016739</td>
+<td>0.000063</td>
+<td>-4.03399</td>
+<td>-0.016498</td>
+<td>0.000107</td>
+<td>-3.904767</td>
+</tr>
+</tbody>
+</table>
+</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 [ ]:</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"># 99% VaR in return, win rate</span>
+<span class="k">def</span> <span class="nf">VaRWin</span><span class="p">(</span><span class="n">port_ret</span><span class="p">,</span> <span class="n">var</span> <span class="o">=</span> <span class="mf">0.99</span><span class="p">):</span>
+    <span class="n">var99</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">port_ret</span><span class="o">.</span><span class="n">dropna</span><span class="p">(),</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">var</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+    <span class="n">win_rate</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">port_ret</span><span class="p">[</span><span class="n">port_ret</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">port_ret</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">var99</span><span class="p">,</span> <span class="n">win_rate</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 [ ]:</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">disp_noa_rmax5_21d_ls_ret_lead1m_var99</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_win_rate</span> <span class="o">=</span> <span class="n">VaRWin</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">])</span>
+
+<span class="c1"># build a table</span>
+<span class="n">disp_noa_rmax5_21d_var_win_table</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">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"99% VaR"</span><span class="p">,</span> <span class="s2">"Win Rate"</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Portfolio"</span><span class="p">])</span>
+<span class="n">disp_noa_rmax5_21d_var_win_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"Portfolio"</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_var99</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ls_ret_lead1m_win_rate</span><span class="p">]</span>
+<span class="n">display</span><span class="p">(</span><span class="n">disp_noa_rmax5_21d_var_win_table</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-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" 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>99% VaR</th>
+<th>Win Rate</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Portfolio</th>
+<td>-0.157376</td>
+<td>0.615534</td>
+</tr>
+</tbody>
+</table>
+</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 [ ]:</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="nf">PlotP2F</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">,</span> <span class="n">port_ret</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"LSP"</span><span class="p">,</span> <span class="n">color_scheme</span> <span class="o">=</span>  <span class="p">[</span><span class="s2">"red"</span><span class="p">,</span> <span class="s2">"#e0a794"</span><span class="p">,</span> <span class="s2">"#a5d3eb"</span><span class="p">,</span> <span class="s2">"#3ca7ed"</span><span class="p">,</span> <span class="s2">"#56bb74"</span><span class="p">,</span> <span class="s2">"#c1a3d7"</span><span class="p">,</span> <span class="s2">"#b19d94"</span><span class="p">,</span> <span class="s2">"#361e6a"</span><span class="p">]):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ret</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ret_list</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">"_0"</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">elif</span> <span class="s2">"lead"</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">:</span>
+            <span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"lead"</span><span class="p">)[</span><span class="mi">1</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+    
+    <span class="n">legend_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"_"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="k">for</span> <span class="n">predictors</span> <span class="ow">in</span> <span class="n">predictors_list</span><span class="p">]</span>
+    
+    <span class="k">if</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s2">"LSP"</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">port_ret</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">))):</span>
+            <span class="c1"># subplot</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">port_ret</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">)),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">)):</span>
+                <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">j</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> <span class="p">:],</span> <span class="n">color</span> <span class="o">=</span> <span class="n">color_scheme</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="n">legend_list</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+                    
+            <span class="c1"># axis color, ticks color， lable color, grid</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">locator_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+
+            <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Time (M)"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">port_ret</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+                            
+            <span class="c1"># bad code here, need to be modified. </span>
+            <span class="n">position</span> <span class="o">=</span> <span class="s2">"lower right"</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">position</span> <span class="o">=</span> <span class="s2">"upper left"</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">position</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+        
+        <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Comparison Between Predictors(-12M to 12M)"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+
+        <span class="c1"># save plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/lsp_</span><span class="si">{</span><span class="n">legend_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s2">"port"</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"long_ret"</span><span class="p">,</span> <span class="p">:],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s2">"--"</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Long"</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"short_ret"</span><span class="p">,</span> <span class="p">:],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'red'</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s2">":"</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Short"</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">port_ret</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">"port_ret"</span><span class="p">,</span> <span class="p">:],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#4a8cff'</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">"Portfolio"</span><span class="p">)</span>
+
+        <span class="c1"># axis color, ticks color， lable color, grid</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+            
+        <span class="c1"># x-axis is the -12, 12</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">locator_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+
+        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Time (M)"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Return"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Source of Return Comparison </span><span class="si">{</span><span class="n">legend_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+        
+        <span class="c1"># save plot</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/port_</span><span class="si">{</span><span class="n">legend_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</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><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 [ ]:</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"># -12m to 12m performence comparison</span>
+<span class="n">ret_list_future</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'ret_lead1m'</span><span class="p">,</span> <span class="s1">'ret_lead2m'</span><span class="p">,</span> <span class="s1">'ret_lead3m'</span><span class="p">,</span> <span class="s1">'ret_lead4m'</span><span class="p">,</span> <span class="s1">'ret_lead5m'</span><span class="p">,</span> <span class="s1">'ret_lead6m'</span><span class="p">,</span> <span class="s1">'ret_lead7m'</span><span class="p">,</span> <span class="s1">'ret_lead8m'</span><span class="p">,</span> <span class="s1">'ret_lead9m'</span><span class="p">,</span> <span class="s1">'ret_lead10m'</span><span class="p">,</span> <span class="s1">'ret_lead11m'</span><span class="p">,</span> <span class="s1">'ret_lead12m'</span><span class="p">]</span>
+<span class="n">predictors_list</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">"disp"</span><span class="p">],</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">]]</span>
+<span class="n">disp_noa_rmax5_21d_average_cond</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="s2">"disp1"</span><span class="p">],</span> <span class="p">[</span><span class="mi">24</span><span class="p">,</span> <span class="s2">"disp5"</span><span class="p">]]</span>
+<span class="n">disp_noa_average_cond</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="s2">"disp1"</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="s2">"disp5"</span><span class="p">]]</span>
+<span class="n">disp_rmax5_21d_average_cond</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="s2">"disp1"</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="s2">"disp5"</span><span class="p">]]</span>
+<span class="n">disp_average_cond</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">]]</span>
+
+<span class="c1"># calculate average return for different predictor portfolios</span>
+<span class="n">disp_noa_rmax5_21d_ret_lead1m_p2f</span> <span class="o">=</span> <span class="n">MultiPredictorP2FRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_noa_ret_lead1m_p2f</span> <span class="o">=</span> <span class="n">MultiPredictorP2FRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_rmax5_21d_ret_lead1m_p2f</span> <span class="o">=</span> <span class="n">MultiPredictorP2FRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_ret_lead1m_p2f</span> <span class="o">=</span> <span class="n">MultiPredictorP2FRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">],</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+
+<span class="n">PlotP2F</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">disp_ret_lead1m_p2f</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_p2f</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_p2f</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_p2f</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"LSP"</span><span class="p">)</span>
+<span class="n">PlotP2F</span><span class="p">([</span><span class="n">predictors</span><span class="p">],</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_p2f</span><span class="p">,</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"port"</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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+</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 [ ]:</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="nf">RegimeBarPlot</span><span class="p">(</span><span class="n">ret_comp</span><span class="p">,</span> <span class="n">predictors_list</span><span class="p">):</span>
+    <span class="n">legend_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"_"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictors</span><span class="p">)</span> <span class="k">for</span> <span class="n">predictors</span> <span class="ow">in</span> <span class="n">predictors_list</span><span class="p">]</span>
+    <span class="n">color_scheme</span> <span class="o">=</span>  <span class="p">[</span><span class="s2">"#3366d6"</span><span class="p">,</span> <span class="s2">"#4a8cff"</span><span class="p">,</span> <span class="s2">"#71a3f7"</span><span class="p">,</span> <span class="s2">"#a0c2fa"</span><span class="p">]</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+    <span class="n">barWidth</span> <span class="o">=</span> <span class="mf">0.2</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ret_comp</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">predictors_list</span><span class="p">)):</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">ret_comp</span><span class="p">[</span><span class="n">legend_list</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">color</span> <span class="o">=</span> <span class="n">color_scheme</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">width</span> <span class="o">=</span> <span class="n">barWidth</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="n">color_scheme</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="n">legend_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="o">+</span> <span class="n">barWidth</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">r</span><span class="p">]</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([</span><span class="n">r</span> <span class="o">+</span> <span class="n">barWidth</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ret_comp</span><span class="o">.</span><span class="n">index</span><span class="p">))],</span> <span class="n">ret_comp</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'#595959'</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Group"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Return"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Return Comparison"</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">facecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">'none'</span><span class="p">,</span> <span class="n">labelcolor</span> <span class="o">=</span> <span class="s1">'#595959'</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="c1"># axis color, ticks color， lable color, grid</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'top'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'left'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'bottom'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'#d9d9d9'</span><span class="p">)</span>
+
+    <span class="c1"># save plot</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"../../plot/</span><span class="si">{</span><span class="n">plot_folder</span><span class="si">}</span><span class="s2">/ret_comp_</span><span class="si">{</span><span class="n">legend_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">.png"</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="n">dpi</span><span class="p">,</span> <span class="n">transparent</span> <span class="o">=</span> <span class="n">transparent</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><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><span class="c1"># generate files</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_downturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_upturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_downturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_upturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_ret_lead1m_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_ret_lead1m_downturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">disp_ret_lead1m_upturn_table</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</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 [ ]:</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">disp_noa_rmax5_21d_ret_lead1m_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_noa_ret_lead1m_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_rmax5_21d_ret_lead1m_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+<span class="n">disp_ret_lead1m_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">)</span>
+
+<span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">disp_noa_ret_lead1m_downturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">disp_rmax5_21d_ret_lead1m_downturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">disp_ret_lead1m_downturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+
+<span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">disp_noa_ret_lead1m_upturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_noa_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">disp_rmax5_21d_ret_lead1m_upturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_rmax5_21d_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">disp_ret_lead1m_upturn_average</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">([</span><span class="s2">"disp"</span><span class="p">],</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cond</span> <span class="o">=</span> <span class="n">disp_average_cond</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s2">"average"</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># the order between rmax5_21d and noa is different against the others</span>
+<span class="n">multi_predictor_ret_lead1m_average</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">disp_ret_lead1m_average</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_average</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_average</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_average</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">multi_predictor_ret_lead1m_downturn_average</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">disp_ret_lead1m_downturn_average</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_downturn_average</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_downturn_average</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_downturn_average</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">multi_predictor_ret_lead1m_upturn_average</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">disp_ret_lead1m_upturn_average</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_upturn_average</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_upturn_average</span><span class="p">,</span> <span class="n">disp_noa_rmax5_21d_ret_lead1m_upturn_average</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+
+<span class="n">multi_predictor_ret_lead1m_average_comp</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">multi_predictor_ret_lead1m_average</span><span class="p">[</span><span class="s2">"port_ret"</span><span class="p">],</span> <span class="n">multi_predictor_ret_lead1m_downturn_average</span><span class="p">[</span><span class="s2">"port_ret"</span><span class="p">],</span> <span class="n">multi_predictor_ret_lead1m_upturn_average</span><span class="p">[</span><span class="s2">"port_ret"</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">multi_predictor_ret_lead1m_average_comp</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"all"</span><span class="p">,</span> <span class="s2">"downturn"</span><span class="p">,</span> <span class="s2">"upturn"</span><span class="p">]</span>
+<span class="n">multi_predictor_ret_lead1m_average_comp</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">drop</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">multi_predictor_ret_lead1m_average_comp</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"disp_noa"</span><span class="p">,</span> <span class="s2">"disp_rmax5_21d"</span><span class="p">,</span> <span class="s2">"disp_noa_rmax5_21d"</span><span class="p">]</span>
+<span class="n">multi_predictor_ret_lead1m_average_comp</span> <span class="o">=</span> <span class="n">multi_predictor_ret_lead1m_average_comp</span><span class="o">.</span><span class="n">T</span>
+
+<span class="n">RegimeBarPlot</span><span class="p">(</span><span class="n">multi_predictor_ret_lead1m_average_comp</span><span class="p">,</span> <span class="n">predictors_list</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">
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"/>
+</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 [ ]:</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"># Fama-Macbeth regression</span>
+<span class="c1"># step 1: cross sectional regression</span>
+<span class="n">data_fmb</span> <span class="o">=</span> <span class="n">data</span><span class="p">[[</span><span class="s2">"date"</span><span class="p">,</span> <span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">+</span> <span class="n">predictors</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">data_fmb</span> <span class="o">=</span> <span class="n">data_fmb</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">+</span> <span class="n">predictors</span><span class="p">,</span> <span class="n">how</span> <span class="o">=</span> <span class="s2">"any"</span><span class="p">)</span>
+<span class="n">predictor_exposure</span> <span class="o">=</span> <span class="n">data_fmb</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"date"</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">],</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">predictors</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span> <span class="o">=</span> <span class="n">predictors</span><span class="p">,</span> <span class="n">how</span> <span class="o">=</span> <span class="s2">"any"</span><span class="p">)))</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span><span class="o">.</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+
+<span class="c1"># step 2: time series regression</span>
+<span class="n">Y</span> <span class="o">=</span> <span class="n">data_fmb</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"date"</span><span class="p">)[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">predictor_exposure</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">predictor_exposure</span><span class="p">,</span> <span class="n">Y</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">model1</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">"ret_lead1m ~ disp"</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">predictor_exposure</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">model2</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">"ret_lead1m ~ disp + rmax5_21d"</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">predictor_exposure</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">model3</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">"ret_lead1m ~ disp + noa"</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">predictor_exposure</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">model4</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">"ret_lead1m ~ disp + noa + rmax5_21d"</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">predictor_exposure</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+
+<span class="n">anova_results</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">anova_lm</span><span class="p">(</span><span class="n">model3</span><span class="p">,</span> <span class="n">model4</span><span class="p">)</span>
+<span class="c1"># print(model1.summary())</span>
+<span class="c1"># print(model2.summary())</span>
+<span class="c1"># print(model3.summary())</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">model4</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
+<span class="c1"># print(sm.stats.anova_lm(model4, typ = 2))</span>
+<span class="c1"># print(sm.stats.anova_lm(model1, model4))</span>
+</pre></div>
+</div>
+</div>
+</div>
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+</div>
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+<pre>                            OLS Regression Results                            
+==============================================================================
+Dep. Variable:             ret_lead1m   R-squared:                       0.442
+Model:                            OLS   Adj. R-squared:                  0.438
+Method:                 Least Squares   F-statistic:                     134.7
+Date:                Wed, 17 Apr 2024   Prob (F-statistic):           2.69e-64
+Time:                        13:09:16   Log-Likelihood:                 928.37
+No. Observations:                 515   AIC:                            -1849.
+Df Residuals:                     511   BIC:                            -1832.
+Df Model:                           3                                         
+Covariance Type:            nonrobust                                         
+==============================================================================
+                 coef    std err          t      P&gt;|t|      [0.025      0.975]
+------------------------------------------------------------------------------
+Intercept      0.0179      0.002      9.556      0.000       0.014       0.022
+disp           0.6227      0.424      1.470      0.142      -0.209       1.455
+noa            0.2795      0.179      1.558      0.120      -0.073       0.632
+rmax5_21d      0.0446      0.002     18.390      0.000       0.040       0.049
+==============================================================================
+Omnibus:                      104.845   Durbin-Watson:                   1.822
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):              513.784
+Skew:                          -0.797   Prob(JB):                    2.71e-112
+Kurtosis:                       7.626   Cond. No.                         248.
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
+</pre>
+</div>
+</div>
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># return visualization of disp, disp_noa, disp_rmax5_21d, disp_noa_rmax5_21d</span>
+<span class="n">ret_list</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span>
+<span class="n">predictors1</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">]</span>
+<span class="n">predictors2</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"noa"</span><span class="p">]</span>
+<span class="n">predictors3</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"disp"</span><span class="p">,</span> <span class="s2">"rmax5_21d"</span><span class="p">]</span>
+
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">disp_ret_lead1m_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="n">ret_list</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">disp_noa_ret_lead1m_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="n">ret_list</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">disp_rmax5_21d_ret_lead1m_ts_table</span> <span class="o">=</span> <span class="n">MultiPredictorSort</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">predictors3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">ret_list</span><span class="o">=</span><span class="n">ret_list</span><span class="p">,</span> <span class="n">regime</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span>
+
+<span class="n">disp_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors1</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">"ts"</span><span class="p">)</span>
+<span class="n">disp_noa_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors2</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">"ts"</span><span class="p">)</span>
+<span class="n">disp_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">MultiPredictorLSRet</span><span class="p">(</span><span class="n">predictors3</span><span class="p">,</span> <span class="n">ret_list</span> <span class="o">=</span> <span class="n">ret_list</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">"ts"</span><span class="p">)</span>
+
+<span class="n">disp_ls_ret_lead1m_ts_plot</span> <span class="o">=</span> <span class="n">disp_ls_ret_lead1m_ts</span><span class="p">[</span><span class="n">disp_ls_ret_lead1m_ts</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
+<span class="n">disp_noa_ls_ret_lead1m_ts_plot</span> <span class="o">=</span> <span class="n">disp_noa_ls_ret_lead1m_ts</span><span class="p">[</span><span class="n">disp_noa_ls_ret_lead1m_ts</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
+<span class="n">disp_rmax5_21d_ls_ret_lead1m_ts_plot</span> <span class="o">=</span> <span class="n">disp_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">[</span><span class="n">disp_rmax5_21d_ls_ret_lead1m_ts</span><span class="p">[</span><span class="s2">"ret_lead1m"</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
+
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors1</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"dist"</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_noa_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors2</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"dist"</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_rmax5_21d_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors3</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"dist"</span><span class="p">,</span> <span class="n">var</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors1</span><span class="p">,</span> <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="p">,</span> <span class="n">spx_data</span> <span class="o">=</span> <span class="n">spx</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">spx</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_noa_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors2</span><span class="p">,</span> <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="p">,</span> <span class="n">spx_data</span> <span class="o">=</span> <span class="n">spx</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">spx</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_rmax5_21d_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors3</span><span class="p">,</span> <span class="n">downturn</span> <span class="o">=</span> <span class="n">downturn</span><span class="p">,</span> <span class="n">upturn</span> <span class="o">=</span> <span class="n">upturn</span><span class="p">,</span> <span class="n">spx_data</span> <span class="o">=</span> <span class="n">spx</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">,</span> <span class="n">spx</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors1</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_noa_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors2</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">)</span>
+<span class="n">LSRetPlot</span><span class="p">(</span><span class="n">disp_rmax5_21d_ls_ret_lead1m_ts_plot</span><span class="p">,</span> <span class="n">predictors3</span><span class="p">,</span> <span class="n">mv_factor</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">plot_type</span> <span class="o">=</span> <span class="s2">"ts"</span><span class="p">)</span>
+</pre></div>
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+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
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"/>
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"/>
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"/>
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+</div>
+</div>
+</div>
+</div>
+</main>
+</body>
+</html>
diff --git a/component/page/fina4400-return-visualization.html b/component/page/fina4400-return-visualization.html
new file mode 100644
index 00000000..04df7b3f
--- /dev/null
+++ b/component/page/fina4400-return-visualization.html
@@ -0,0 +1,45 @@
+<html>
+<head><meta charset="utf-8" />
+  <!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-23JZF546MQ"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-23JZF546MQ');
+</script>
+
+  <!-- website meta configuration -->
+  <link rel="icon" href="../../image/avatar.ico" />
+  <!-- navbar -->
+  <link rel="stylesheet" type="text/css" href="../../css/navbar.css">
+  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
+  <script src="https://kit.fontawesome.com/814179a032.js" crossorigin="anonymous"></script>
+  <!-- end of meta configuration-->
+
+<title>D-A-R Return Visualization</title>
+
+</head>
+<body>
+       <!-- Navigation Bar -->
+   <script>
+      fetch('https://raw.githubusercontent.com/Gavin-OP/Gavin-OP.github.io/master/public/component/common/navbar.html')
+         .then(response => response.text())
+         .then(htmlCode => {
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+         })
+         .catch(error => console.error('Error fetching code:', error));
+   </script>
+   <div id="navBar"></div>
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+        <script type="text/javascript">/**
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+*/
+/*! For license information please see plotly.min.js.LICENSE.txt */
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n=r(94724),i=r(25376),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({orientation:{valType:"enumerated",values:["h","v"],dflt:"v"},thicknessmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"pixels"},thickness:{valType:"number",min:0,dflt:30},lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number"},xref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},xanchor:{valType:"enumerated",values:["left","center","right"]},xpad:{valType:"number",min:0,dflt:10},y:{valType:"number"},yref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},yanchor:{valType:"enumerated",values:["top","middle","bottom"]},ypad:{valType:"number",min:0,dflt:10},outlinecolor:n.linecolor,outlinewidth:n.linewidth,bordercolor:n.linecolor,borderwidth:{valType:"number",min:0,dflt:0},bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)"},tickmode:n.minor.tickmode,nticks:n.nticks,tick0:n.tick0,dtick:n.dtick,tickvals:n.tickvals,ticktext:n.ticktext,ticks:a({},n.ticks,{dflt:""}),ticklabeloverflow:a({},n.ticklabeloverflow,{}),ticklabelposition:{valType:"enumerated",values:["outside","inside","outside 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strict";t.exports={cn:{colorbar:"colorbar",cbbg:"cbbg",cbfill:"cbfill",cbfills:"cbfills",cbline:"cbline",cblines:"cblines",cbaxis:"cbaxis",cbtitleunshift:"cbtitleunshift",cbtitle:"cbtitle",cboutline:"cboutline",crisp:"crisp",jsPlaceholder:"js-placeholder"}}},64013:function(t,e,r){"use strict";var n=r(3400),i=r(31780),a=r(26332),o=r(25404),s=r(95936),l=r(42568),u=r(42996);t.exports=function(t,e,r){var c=i.newContainer(e,"colorbar"),f=t.colorbar||{};function h(t,e){return n.coerce(f,c,u,t,e)}var p=r.margin||{t:0,b:0,l:0,r:0},d=r.width-p.l-p.r,v=r.height-p.t-p.b,g="v"===h("orientation"),y=h("thicknessmode");h("thickness","fraction"===y?30/(g?d:v):30);var m=h("lenmode");h("len","fraction"===m?1:g?v:d);var x,b,_,w="paper"===h("yref"),T="paper"===h("xref"),k="left";g?(_="middle",k=T?"left":"right",x=T?1.02:1,b=.5):(_=w?"bottom":"top",k="center",x=.5,b=w?1.02:1),n.coerce(f,c,{x:{valType:"number",min:T?-2:0,max:T?3:1,dflt:x}},"x"),n.coerce(f,c,{y:{valType:"number",min:w?-2:0,max:w?3:1,dflt:b}},"y"),h("xanchor",k),h("xpad"),h("yanchor",_),h("ypad"),n.noneOrAll(f,c,["x","y"]),h("outlinecolor"),h("outlinewidth"),h("bordercolor"),h("borderwidth"),h("bgcolor");var A=n.coerce(f,c,{ticklabelposition:{valType:"enumerated",dflt:"outside",values:g?["outside","inside","outside top","inside top","outside bottom","inside bottom"]:["outside","inside","outside left","inside left","outside right","inside right"]}},"ticklabelposition");h("ticklabeloverflow",-1!==A.indexOf("inside")?"hide past domain":"hide past div"),a(f,c,h,"linear");var M=r.font,S={noAutotickangles:!0,outerTicks:!1,font:M};-1!==A.indexOf("inside")&&(S.bgColor="black"),l(f,c,h,"linear",S),s(f,c,h,"linear",S),o(f,c,h,"linear",S),h("title.text",r._dfltTitle.colorbar);var E=c.showticklabels?c.tickfont:M,L=n.extendFlat({},E,{color:M.color,size:n.bigFont(E.size)});n.coerceFont(h,"title.font",L),h("title.side",g?"top":"right")}},37848:function(t,e,r){"use strict";var n=r(33428),i=r(49760),a=r(7316),o=r(24040),s=r(54460),l=r(86476),u=r(3400),c=u.strTranslate,f=r(92880).extendFlat,h=r(93972),p=r(43616),d=r(76308),v=r(81668),g=r(72736),y=r(94288).flipScale,m=r(28336),x=r(37668),b=r(94724),_=r(84284),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=r(63964).cn;t.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll("g."+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function u(){"function"==typeof i.calc?i.calc(t,n,e):(e._fillgradient=r.reversescale?y(r.colorscale):r.colorscale,e._zrange=[r[i.min],r[i.max]])}for(var c=0;c<o.length;c++){var h=o[c];if((n=h[0].trace)._module){var p=n._module.colorbar;if(!0===n.visible&&p)for(var d=Array.isArray(p),v=d?p:[p],g=0;g<v.length;g++){var m=(i=v[g]).container;(r=m?n[m]:n)&&r.showscale&&((e=l(r.colorbar))._id="cb"+n.uid+(d&&m?"-"+m:""),e._traceIndex=n.index,e._propPrefix=(m?m+".":"")+"colorbar.",e._meta=n._meta,u(),s.push(e))}}}for(var x in a._colorAxes)if((r=a[x]).showscale){var b=a._colorAxes[x];(e=l(r.colorbar))._id="cb"+x,e._propPrefix=x+".colorbar.",e._meta=a._meta,i={min:"cmin",max:"cmax"},"heatmap"!==b[0]&&(n=b[1],i.calc=n._module.colorbar.calc),u(),s.push(e)}return s}(t),(function(t){return t._id}));e.enter().append("g").attr("class",(function(t){return t._id})).classed(A.colorbar,!0),e.each((function(e){var r=n.select(this);u.ensureSingle(r,"rect",A.cbbg),u.ensureSingle(r,"g",A.cbfills),u.ensureSingle(r,"g",A.cblines),u.ensureSingle(r,"g",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),u.ensureSingle(r,"g",A.cbtitleunshift,(function(t){t.append("g").classed(A.cbtitle,!0)})),u.ensureSingle(r,"rect",A.cboutline);var y=function(t,e,r){var o="v"===e.orientation,l=e.len,h=e.lenmode,y=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,P=e.xpad,O=e.ypad,I=e.x,D=o?e.y:1-e.y,z="paper"===e.yref,R="paper"===e.xref,F=r._fullLayout,B=F._size,N=e._fillcolor,j=e._line,U=e.title,V=U.side,q=e._zrange||n.extent(("function"==typeof N?N:j.color).domain()),H="function"==typeof j.color?j.color:function(){return j.color},G="function"==typeof N?N:function(){return N},W=e._levels,Y=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,u=a.size,c=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*u,!(u>0?n>=l:n<=l));i++)n>c&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if("function"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,u=h.size,i=0;i<1e5&&(n=h.start+i*u,!(u>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&"string"==typeof e._fillcolor&&(s=[0]);return a.size<0&&(o.reverse(),s.reverse()),{line:o,fill:s}}(0,e,q),X=Y.fill,Z=Y.line,K=Math.round(y*("fraction"===_?o?B.w:B.h:1)),J=K/(o?B.w:B.h),$=Math.round(l*("fraction"===h?o?B.h:B.w:1)),Q=$/(o?B.h:B.w),tt=R?B.w:r._fullLayout.width,et=z?B.h:r._fullLayout.height,rt=Math.round(o?I*tt+P:D*et+O),nt={center:.5,right:1}[L]||0,it={top:1,middle:.5}[C]||0,at=o?I-nt*J:D-it*J,ot=o?D-it*Q:I-nt*Q,st=Math.round(o?et*(1-ot):tt*ot);e._lenFrac=Q,e._thickFrac=J,e._uFrac=at,e._vFrac=ot;var lt=e._axis=function(t,e,r){var n=t._fullLayout,i="v"===e.orientation,a={type:"linear",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,labelalias:e.labelalias,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:"free",side:i?"right":"bottom",position:1},o=i?"y":"x",s={type:"linear",_id:o+e._id},l={letter:o,font:n.font,noAutotickangles:"y"===o,noHover:!0,noTickson:!0,noTicklabelmode:!0,noInsideRange:!0,calendar:n.calendar};function c(t,e){return u.coerce(a,s,b,t,e)}return m(a,s,c,l,n),x(a,s,c,l),s}(r,e,q);lt.position=J+(o?I+P/B.w:D+O/B.h);var ut=-1!==["top","bottom"].indexOf(V);if(o&&ut&&(lt.title.side=V,lt.titlex=I+P/B.w,lt.titley=ot+("top"===U.side?Q-O/B.h:O/B.h)),o||ut||(lt.title.side=V,lt.titley=D+O/B.h,lt.titlex=ot+P/B.w),j.color&&"auto"===e.tickmode){lt.tickmode="linear",lt.tick0=W.start;var ct=W.size,ft=u.constrain($/50,4,15)+1,ht=(q[1]-q[0])/((e.nticks||ft)*ct);if(ht>1){var pt=Math.pow(10,Math.floor(Math.log(ht)/Math.LN10));ct*=pt*u.roundUp(ht/pt,[2,5,10]),(Math.abs(W.start)/W.size+1e-6)%1<2e-6&&(lt.tick0=0)}lt.dtick=ct}lt.domain=o?[ot+O/B.h,ot+Q-O/B.h]:[ot+P/B.w,ot+Q-P/B.w],lt.setScale(),t.attr("transform",c(Math.round(B.l),Math.round(B.t)));var dt,vt=t.select("."+A.cbtitleunshift).attr("transform",c(-Math.round(B.l),-Math.round(B.t))),gt=lt.ticklabelposition,yt=lt.title.font.size,mt=t.select("."+A.cbaxis),xt=0,bt=0;function _t(n,i){var a={propContainer:lt,propName:e._propPrefix+"title",traceIndex:e._traceIndex,_meta:e._meta,placeholder:F._dfltTitle.colorbar,containerGroup:t.select("."+A.cbtitle)},o="h"===n.charAt(0)?n.substr(1):"h"+n;t.selectAll("."+o+",."+o+"-math-group").remove(),v.draw(r,n,f(a,i||{}))}return u.syncOrAsync([a.previousPromises,function(){var t,e;(o&&ut||!o&&!ut)&&("top"===V&&(t=P+B.l+tt*I,e=O+B.t+et*(1-ot-Q)+3+.75*yt),"bottom"===V&&(t=P+B.l+tt*I,e=O+B.t+et*(1-ot)-3-.25*yt),"right"===V&&(e=O+B.t+et*D+3+.75*yt,t=P+B.l+tt*ot),_t(lt._id+"title",{attributes:{x:t,y:e,"text-anchor":o?"start":"middle"}}))},function(){if(!o&&!ut||o&&ut){var a,l=t.select("."+A.cbtitle),f=l.select("text"),h=[-M/2,M/2],d=l.select(".h"+lt._id+"title-math-group").node(),v=15.6;if(f.node()&&(v=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),bt=a.width,(xt=a.height)>v&&(h[1]-=(xt-v)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),bt=a.width,xt=a.height),o){if(xt){if(xt+=5,"top"===V)lt.domain[1]-=xt/B.h,h[1]*=-1;else{lt.domain[0]+=xt/B.h;var y=g.lineCount(f);h[1]+=(1-y)*v}l.attr("transform",c(h[0],h[1])),lt.setScale()}}else bt&&("right"===V&&(lt.domain[0]+=(bt+yt/2)/B.w),l.attr("transform",c(h[0],h[1])),lt.setScale())}t.selectAll("."+A.cbfills+",."+A.cblines).attr("transform",o?c(0,Math.round(B.h*(1-lt.domain[1]))):c(Math.round(B.w*lt.domain[0]),0)),mt.attr("transform",o?c(0,Math.round(-B.t)):c(Math.round(-B.l),0));var m=t.select("."+A.cbfills).selectAll("rect."+A.cbfill).attr("style","").data(X);m.enter().append("rect").classed(A.cbfill,!0).attr("style",""),m.exit().remove();var x=q.map(lt.c2p).map(Math.round).sort((function(t,e){return t-e}));m.each((function(t,a){var s=[0===a?q[0]:(X[a]+X[a-1])/2,a===X.length-1?q[1]:(X[a]+X[a+1])/2].map(lt.c2p).map(Math.round);o&&(s[1]=u.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?"x":"y",rt).attr(o?"y":"x",n.min(s)).attr(o?"width":"height",Math.max(K,2)).attr(o?"height":"width",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?"vertical":"horizontalreversed",e._fillgradient,"fill");else{var c=G(t).replace("e-","");l.attr("fill",i(c).toHexString())}}));var b=t.select("."+A.cblines).selectAll("path."+A.cbline).data(j.color&&j.width?Z:[]);b.enter().append("path").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=rt,r=Math.round(lt.c2p(t))+j.width/2%1;n.select(this).attr("d","M"+(o?e+","+r:r+","+e)+(o?"h":"v")+K).call(p.lineGroupStyle,j.width,H(t),j.dash)})),mt.selectAll("g."+lt._id+"tick,path").remove();var _=rt+K+(M||0)/2-("outside"===e.ticks?1:0),T=s.calcTicks(lt),k=s.getTickSigns(lt)[2];return s.drawTicks(r,lt,{vals:"inside"===lt.ticks?s.clipEnds(lt,T):T,layer:mt,path:s.makeTickPath(lt,_,k),transFn:s.makeTransTickFn(lt)}),s.drawLabels(r,lt,{vals:T,layer:mt,transFn:s.makeTransTickLabelFn(lt),labelFns:s.makeLabelFns(lt,_)})},function(){if(o&&!ut||!o&&ut){var t,i,a=lt.position||0,s=lt._offset+lt._length/2;if("right"===V)i=s,t=B.l+tt*a+10+yt*(lt.showticklabels?1:.5);else if(t=s,"bottom"===V&&(i=B.t+et*a+10+(-1===gt.indexOf("inside")?lt.tickfont.size:0)+("intside"!==lt.ticks&&e.ticklen||0)),"top"===V){var l=U.text.split("<br>").length;i=B.t+et*a+10-K-w*yt*l}_t((o?"h":"v")+lt._id+"title",{avoid:{selection:n.select(r).selectAll("g."+lt._id+"tick"),side:V,offsetTop:o?0:B.t,offsetLeft:o?B.l:0,maxShift:o?F.width:F.height},attributes:{x:t,y:i,"text-anchor":"middle"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=K+M/2;-1===gt.indexOf("inside")&&(n=p.bBox(mt.node()),s+=o?n.width:n.height),dt=vt.select("text");var u=0,f=o&&"top"===V,v=!o&&"right"===V,g=0;if(dt.node()&&!dt.classed(A.jsPlaceholder)){var m,x=vt.select(".h"+lt._id+"title-math-group").node();x&&(o&&ut||!o&&!ut)?(u=(n=p.bBox(x)).width,m=n.height):(u=(n=p.bBox(vt.node())).right-B.l-(o?rt:st),m=n.bottom-B.t-(o?st:rt),o||"top"!==V||(s+=n.height,g=n.height)),v&&(dt.attr("transform",c(u/2+yt/2,0)),u*=2),s=Math.max(s,o?u:m)}var b=2*(o?P:O)+s+S+M/2,w=0;!o&&U.text&&"bottom"===C&&D<=0&&(b+=w=b/2,g+=w),F._hColorbarMoveTitle=w,F._hColorbarMoveCBTitle=g;var N=S+M,j=(o?rt:st)-N/2-(o?P:0),q=(o?st:rt)-(o?$:O+g-w);t.select("."+A.cbbg).attr("x",j).attr("y",q).attr(o?"width":"height",Math.max(b-w,2)).attr(o?"height":"width",Math.max($+N,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style("stroke-width",S);var 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n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function dt(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ht();var vt={fullLayout:c,container:c._hoverlayer,event:e},gt=t._spikepoints,yt={vLinePoint:at.vLinePoint,hLinePoint:at.hLinePoint};t._spikepoints=yt;var mt=function(){nt.sort((function(t,e){return t.distance-e.distance})),nt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,"bar-like")||g.traceIs(s.trace,"box-violin")?a.push(s):s.trace[r+"period"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(nt,I)};mt();var xt=I.charAt(0),bt=("x"===xt||"y"===xt)&&nt[0]&&L[nt[0].trace.type];if(m&&0!==q&&0!==nt.length){var _t=pt(nt.filter((function(t){return 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S=w(A*M+p.x),E=S+w(A*t.bx);u=t.crossPos+Math.min(S,E),c=t.crossPos+Math.max(S,E)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&("left"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 0!==x&&Math.min(c,x)-Math.max(u,m)>1&&("top"===i.side?(s=i._mainLinePosition,l=r.height):l=i._mainLinePosition)}g[y++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?T:1)/2,pmin:s,pmax:l}]})),g.sort((function(t,e){return t[0].posref-e[0].posref||d*(e[0].traceIndex-t[0].traceIndex)}));!i&&p<=v;){for(p++,i=!0,s=0;s<g.length-1;){var S=g[s],E=g[s+1],L=S[S.length-1],C=E[0];if((a=L.pos+L.dp+L.size-C.pos-C.dp+C.size)>.01&&L.pmin===C.pmin&&L.pmax===C.pmax){for(l=E.length-1;l>=0;l--)E[l].dp+=a;for(S.push.apply(S,E),g.splice(s+1,1),c=0,l=S.length-1;l>=0;l--)c+=S[l].dp;for(o=c/S.length,l=S.length-1;l>=0;l--)S[l].dp-=o;i=!1}else s++}g.forEach(A)}for(s=g.length-1;s>=0;s--){var P=g[s];for(l=P.length-1;l>=0;l--){var 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0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var f=v.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+f+" / -"+v.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" ± "+f,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var h=v.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+h+" / -"+v.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" ± "+h,"y"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&"all"!==p&&(-1===(p=Array.isArray(p)?p:p.split("+")).indexOf("x")&&(t.xLabel=void 0),-1===p.indexOf("y")&&(t.yLabel=void 0),-1===p.indexOf("z")&&(t.zLabel=void 0),-1===p.indexOf("text")&&(t.text=void 0),-1===p.indexOf("name")&&(t.name=void 0)),t}function F(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,u=r.event,c=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(".spikeline").remove(),f||c){var 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S,E,L,C="h"===p("orientation"),P="paper"===p("yref"),O="paper"===p("xref"),I="left";if(C?(S=0,n.getComponentMethod("rangeslider","isVisible")(e.xaxis)?P?(E=1.1,L="bottom"):(E=1,L="top"):P?(E=-.1,L="top"):(E=0,L="bottom")):(E=1,L="auto",O?S=1.02:(S=1,I="right")),i.coerce(f,h,{x:{valType:"number",editType:"legend",min:O?-2:0,max:O?3:1,dflt:S}},"x"),i.coerce(f,h,{y:{valType:"number",editType:"legend",min:P?-2:0,max:P?3:1,dflt:E}},"y"),p("traceorder",_),u.isGrouped(r[t])&&p("tracegroupgap"),p("entrywidth"),p("entrywidthmode"),p("indentation"),p("itemsizing"),p("itemwidth"),p("itemclick"),p("itemdoubleclick"),p("groupclick"),p("xanchor",I),p("yanchor",L),p("valign"),i.noneOrAll(f,h,["x","y"]),p("title.text")){p("title.side",C?"left":"top");var D=i.extendFlat({},d,{size:i.bigFont(d.size)});i.coerceFont(p,"title.font",D)}}}}t.exports=function(t,e,r){var n,a=r.slice(),o=e.shapes;if(o)for(n=0;n<o.length;n++){var s=o[n];if(s.showlegend){var 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V,q=i.ensureSingle(N,"g","scrollbox"),H=h.title;h._titleWidth=0,h._titleHeight=0,H.text?((V=i.ensureSingle(q,"text",w+"titletext")).attr("text-anchor","start").call(u.font,H.font).text(H.text),L(V,q,t,h,_)):q.selectAll("."+w+"titletext").remove();var G=i.ensureSingle(N,"rect","scrollbar",(function(t){t.attr(p.scrollBarEnterAttrs).call(c.fill,p.scrollBarColor)})),W=q.selectAll("g.groups").data(S);W.enter().append("g").attr("class","groups"),W.exit().remove();var Y=W.selectAll("g.traces").data(i.identity);Y.enter().append("g").attr("class","traces"),Y.exit().remove(),Y.style("opacity",(function(t){var e=t[0].trace;return o.traceIs(e,"pie-like")?-1!==B.indexOf(t[0].label)?.5:1:"legendonly"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,h)})).call(x,t,h).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=O(i);i||(i=a[o]);var 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S=k[a][o].trace.legendrank;M>S&&(M=S)}k[a][0]._groupMinRank=M,k[a][0]._preGroupSort=a}var E=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(k.forEach((function(t,e){t[0]._preGroupSort=e})),k.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),a=0;a<k.length;a++){k[a].forEach((function(t,e){t._preSort=e})),k[a].sort(E);var L=k[a][0].trace,C=null;for(o=0;o<k[a].length;o++){var P=k[a][o].trace.legendgrouptitle;if(P&&P.text){C=P,s&&(P.font=e._groupTitleFont);break}}if(u&&k[a].reverse(),C){var O=!1;for(o=0;o<k[a].length;o++)if(n.traceIs(k[a][o].trace,"pie-like")){O=!0;break}k[a].unshift({i:-1,groupTitle:C,noClick:O,trace:{showlegend:L.showlegend,legendgroup:L.legendgroup,visible:"toggleitem"===e.groupclick||L.visible}})}for(o=0;o<k[a].length;o++)k[a][o]=[k[a][o]]}return e._lgroupsLength=k.length,e._maxNameLength=v,k}},33048:function(t,e,r){"use strict";var n=r(24040),i=r(3400),a=i.pushUnique,o=!0;t.exports=function(t,e,r){var s=e._fullLayout;if(!e._dragged&&!e._editing){var l,u=s.legend.itemclick,c=s.legend.itemdoubleclick,f=s.legend.groupclick;if(1===r&&"toggle"===u&&"toggleothers"===c&&o&&e.data&&e._context.showTips?(i.notifier(i._(e,"Double-click on legend to isolate one trace"),"long"),o=!1):o=!1,1===r?l=u:2===r&&(l=c),l){var h="togglegroup"===f,p=s.hiddenlabels?s.hiddenlabels.slice():[],d=t.data()[0][0];if(!d.groupTitle||!d.noClick){var v=e._fullData,g=(s.shapes||[]).filter((function(t){return t.showlegend})),y=v.concat(g),m=d.trace;m._isShape&&(m=m._fullInput);var x,b,_,w,T,k=m.legendgroup,A={},M=[],S=[],E=[],L=(s.shapes||[]).map((function(t){return t._input})),C=!1,P=m.legend,O=m._fullInput;if(O&&O._isShape||!n.traceIs(m,"pie-like")){var I,D=k&&k.length,z=[];if(D)for(x=0;x<y.length;x++)(I=y[x]).visible&&I.legendgroup===k&&z.push(x);if("toggle"===l){var 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L(D),L(P.concat(I)),L(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},66400:function(t,e,r){"use strict";var n=r(33428),i=r(38248),a=r(3400),o=r(9224),s=r(25788).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement("div"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this._uid=i,this.element.className="modebar","hover"===r.displayModeBar&&(this.element.className+=" modebar--hover ease-bg"),"v"===n.modebar.orientation&&(this.element.className+=" vertical",e=e.reverse());var o=n.modebar,s="hover"===r.displayModeBar?".js-plotly-plot .plotly:hover 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e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute("data-attr"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute("data-val")||!0,o=t.getAttribute("data-attr"),s="true"===t.getAttribute("data-toggle"),l=n.select(t);if(s)o===r&&l.classed("active",!l.classed("active"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed("active",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement("a");return e.href="https://plotly.com/",e.target="_blank",e.setAttribute("data-title",a._(this.graphInfo,"Produced with Plotly.js")+" (v"+s+")"),e.className="modebar-btn plotlyjsicon 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n=r(25376),i=r(22548),a=(0,r(31780).templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});t.exports={visible:{valType:"boolean",editType:"plot"},buttons:a,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:n({editType:"plot"}),bgcolor:{valType:"color",dflt:i.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},85984:function(t){"use 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e=t._fullLayout._infolayer.selectAll(".rangeselector").data(function(t){for(var e=f.list(t,"x",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append("g").classed("rangeselector",!0),e.exit().remove(),e.style({cursor:"pointer","pointer-events":"all"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll("g.button").data(l.filterVisible(f.buttons));h.enter().append("g").classed("button",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if("all"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on("click",(function(){t._dragged||i.call("_guiRelayout",t,a)})),r.on("mouseover",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on("mouseout",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(".selector-text"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(".selector-rect"),i=t.select(".selector-text"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr("transform",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_="left";l.isRightAnchor(r)&&(x-=f,_="right"),l.isCenterAnchor(r)&&(x-=f/2,_="center");var w="top";l.isBottomAnchor(r)&&(b-=h,w="bottom"),l.isMiddleAnchor(r)&&(b-=h/2,w="middle"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+"-range-selector",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr("transform",u(x,b))}(t,h,f,o._name,r)}))}},48040:function(t,e,r){"use strict";var n=r(73220),i=r(3400).titleCase;t.exports=function(t,e){var r=t._name,a={};if("all"===e.step)a[r+".autorange"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n["utc"+i(s)],u=e.count;switch(e.stepmode){case"backward":r=t.l2r(+l.offset(o,-u));break;case"todate":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+".range[0]"]=o[0],a[r+".range[1]"]=o[1]}return a}},41152:function(t,e,r){"use strict";t.exports={moduleType:"component",name:"rangeselector",schema:{subplots:{xaxis:{rangeselector:r(26680)}}},layoutAttributes:r(26680),handleDefaults:r(22148),draw:r(50216)}},11200:function(t,e,r){"use strict";var n=r(22548);t.exports={bgcolor:{valType:"color",dflt:n.background,editType:"plot"},bordercolor:{valType:"color",dflt:n.defaultLine,editType:"plot"},borderwidth:{valType:"integer",dflt:0,min:0,editType:"plot"},autorange:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"range[0]":void 0,"range[1]":void 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n=r(3400),i=r(31780),a=r(79811),o=r(11200),s=r(10936);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,"rangeslider");if(_("visible")){_("bgcolor",e.plot_bgcolor),_("bordercolor"),_("borderwidth"),_("thickness"),_("autorange",!u.isValidRange(h.range)),_("range");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf("y"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf("y"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,"yaxis");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x="fixed"),"match"!==w("rangemode",x)&&w("range",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},20060:function(t,e,r){"use strict";var n=r(33428),i=r(24040),a=r(7316),o=r(3400),s=o.strTranslate,l=r(43616),u=r(76308),c=r(81668),f=r(57952),h=r(79811),p=r(86476),d=r(93972),v=r(74636);function g(t){return"number"==typeof t.clientX?t.clientX:t.touches&&t.touches.length>0?t.touches[0].clientX:0}function y(t,e,r,n){var i=o.ensureSingle(t,"rect",v.bgClassName,(function(t){t.attr({x:0,y:0,"shape-rendering":"crispEdges"})})),a=n.borderwidth%2==0?n.borderwidth:n.borderwidth-1,c=-n._offsetShift,f=l.crispRound(e,n.borderwidth);i.attr({width:n._width+a,height:n._height+a,transform:s(c,c),"stroke-width":f}).call(u.stroke,n.bordercolor).call(u.fill,n.bgcolor)}function m(t,e,r,n){var i=e._fullLayout;o.ensureSingleById(i._topdefs,"clipPath",n._clipId,(function(t){t.append("rect").attr({x:0,y:0})})).select("rect").attr({width:n._width,height:n._height})}function x(t,e,r,i){var s,u=e.calcdata,c=t.selectAll("g."+v.rangePlotClassName).data(r._subplotsWith,o.identity);c.enter().append("g").attr("class",(function(t){return v.rangePlotClassName+" "+t})).call(l.setClipUrl,i._clipId,e),c.order(),c.exit().remove(),c.each((function(t,o){var l=n.select(this),c=0===o,p=h.getFromId(e,t,"y"),d=p._name,v=i[d],g={data:[],layout:{xaxis:{type:r.type,domain:[0,1],range:i.range.slice(),calendar:r.calendar},width:i._width,height:i._height,margin:{t:0,b:0,l:0,r:0}},_context:e._context};r.rangebreaks&&(g.layout.xaxis.rangebreaks=r.rangebreaks),g.layout[d]={type:p.type,domain:[0,1],range:"match"!==v.rangemode?v.range.slice():p.range.slice(),calendar:p.calendar},p.rangebreaks&&(g.layout[d].rangebreaks=p.rangebreaks),a.supplyDefaults(g);var y=g._fullLayout.xaxis,m=g._fullLayout[d];y.clearCalc(),y.setScale(),m.clearCalc(),m.setScale();var x={id:t,plotgroup:l,xaxis:y,yaxis:m,isRangePlot:!0};c?s=x:(x.mainplot="xy",x.mainplotinfo=s),f.rangePlot(e,x,function(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n],a=i[0].trace;a.xaxis+a.yaxis===e&&r.push(i)}return r}(u,t))}))}function b(t,e,r,n,i){o.ensureSingle(t,"rect",v.maskMinClassName,(function(t){t.attr({x:0,y:0,"shape-rendering":"crispEdges"})})).attr("height",n._height).call(u.fill,v.maskColor),o.ensureSingle(t,"rect",v.maskMaxClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("height",n._height).call(u.fill,v.maskColor),"match"!==i.rangemode&&(o.ensureSingle(t,"rect",v.maskMinOppAxisClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("width",n._width).call(u.fill,v.maskOppAxisColor),o.ensureSingle(t,"rect",v.maskMaxOppAxisClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("width",n._width).style("border-top",v.maskOppBorder).call(u.fill,v.maskOppAxisColor))}function _(t,e,r,n){e._context.staticPlot||o.ensureSingle(t,"rect",v.slideBoxClassName,(function(t){t.attr({y:0,cursor:v.slideBoxCursor,"shape-rendering":"crispEdges"})})).attr({height:n._height,fill:v.slideBoxFill})}function w(t,e,r,n){var i=o.ensureSingle(t,"g",v.grabberMinClassName),a=o.ensureSingle(t,"g",v.grabberMaxClassName),s={x:0,width:v.handleWidth,rx:v.handleRadius,fill:u.background,stroke:u.defaultLine,"stroke-width":v.handleStrokeWidth,"shape-rendering":"crispEdges"},l={y:Math.round(n._height/4),height:Math.round(n._height/2)};o.ensureSingle(i,"rect",v.handleMinClassName,(function(t){t.attr(s)})).attr(l),o.ensureSingle(a,"rect",v.handleMaxClassName,(function(t){t.attr(s)})).attr(l);var c={width:v.grabAreaWidth,x:0,y:0,fill:v.grabAreaFill,cursor:e._context.staticPlot?void 0:v.grabAreaCursor};o.ensureSingle(i,"rect",v.grabAreaMinClassName,(function(t){t.attr(c)})).attr("height",n._height),o.ensureSingle(a,"rect",v.grabAreaMaxClassName,(function(t){t.attr(c)})).attr("height",n._height)}t.exports=function(t){for(var e=t._fullLayout,r=e._rangeSliderData,a=0;a<r.length;a++){var l=r[a][v.name];l._clipId=l._id+"-"+e._uid}var u=e._infolayer.selectAll("g."+v.containerClassName).data(r,(function(t){return 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D,z,R=0;for(D=0;D<I.length;D++)R+=(z=I[D]).max-z.min;var F=l._width/(P-C-R),B=[-F*C];for(D=0;D<I.length;D++)z=I[D],B.push(B[B.length-1]-F*(z.max-z.min));for(l.d2p=function(t){for(var e=B[0],r=0;r<I.length;r++){var n=I[r];if(t>=n.max)e=B[r+1];else if(t<n.min)break}return e+F*t},D=0;D<I.length;D++)(z=I[D]).pmin=l.d2p(z.min),z.pmax=l.d2p(z.max);l.p2d=function(t){for(var e=B[0],r=0;r<I.length;r++){var n=I[r];if(t>=n.pmax)e=B[r+1];else if(t<n.pmin)break}return(t-e)/F}}}if("match"!==f.rangemode){var N=u.r2l(f.range[0]),j=u.r2l(f.range[1])-N;l.d2pOppAxis=function(t){return(t-N)/j*l._height}}a.call(y,t,r,l).call(m,t,r,l).call(x,t,r,l).call(b,t,r,l,f).call(_,t,r,l).call(w,t,r,l),function(t,e,r,a){if(!e._context.staticPlot){var s=t.select("rect."+v.slideBoxClassName).node(),l=t.select("rect."+v.grabAreaMinClassName).node(),u=t.select("rect."+v.grabAreaMaxClassName).node();t.on("mousedown",c),t.on("touchstart",c)}function c(){var 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n=r(3400),i=r(11200),a=r(10936),o=r(97944);t.exports={moduleType:"component",name:"rangeslider",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(11200),handleDefaults:r(94040),calcAutorange:r(26652),draw:r(20060),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},10936:function(t){"use strict";t.exports={_isSubplotObj:!0,rangemode:{valType:"enumerated",values:["auto","fixed","match"],dflt:"match",editType:"calc"},range:{valType:"info_array",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},editType:"calc"}},93956:function(t,e,r){"use strict";var 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n=r(98192).u,i=r(92880).extendFlat;t.exports={newselection:{mode:{valType:"enumerated",values:["immediate","gradual"],dflt:"immediate",editType:"none"},line:{color:{valType:"color",editType:"none"},width:{valType:"number",min:1,dflt:1,editType:"none"},dash:i({},n,{dflt:"dot",editType:"none"}),editType:"none"},editType:"none"},activeselection:{fillcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"none"},opacity:{valType:"number",min:0,max:1,dflt:.5,editType:"none"},editType:"none"}}},81004:function(t){"use strict";t.exports=function(t,e,r){r("newselection.mode"),r("newselection.line.width")&&(r("newselection.line.color"),r("newselection.line.dash")),r("activeselection.fillcolor"),r("activeselection.opacity")}},5968:function(t,e,r){"use strict";var n=r(72760).selectMode,i=r(1936).clearOutline,a=r(9856),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var a=r.getAttribute("d"),u=e.gd,c=u._fullLayout.newselection,f=e.plotinfo,h=f.xaxis,p=f.yaxis,d=e.isActiveSelection,v=e.dragmode,g=(u.layout||{}).selections||[];if(!n(v)&&void 0!==d){var y=u._fullLayout._activeSelectionIndex;if(y<g.length)switch(u._fullLayout.selections[y].type){case"rect":v="select";break;case"path":v="lasso"}}var m,x=o(a,u,f,d),b={xref:h._id,yref:p._id,opacity:c.opacity,line:{color:c.line.color,width:c.line.width,dash:c.line.dash}};1===x.length&&(m=x[0]),m&&5===m.length&&"select"===v?(b.type="rect",b.x0=m[0][1],b.y0=m[0][2],b.x1=m[2][1],b.y1=m[2][2]):(b.type="path",h&&p&&l(x,h,p),b.path=s(x),m=null),i(u);for(var _=e.editHelpers,w=(_||{}).modifyItem,T=[],k=0;k<g.length;k++){var A=u._fullLayout.selections[k];if(A){if(T[k]=A._input,void 0!==d&&k===u._fullLayout._activeSelectionIndex){var M=b;switch(A.type){case"rect":w("x0",M.x0),w("x1",M.x1),w("y0",M.y0),w("y1",M.y1);break;case"path":w("path",M.path)}}}else T[k]=A}return void 0===d?(T.push(b),T):_?_.getUpdateObj():{}}}}},5840:function(t,e,r){"use strict";var n=r(3400).strTranslate;function i(t,e){switch(t.type){case"log":return t.p2d(e);case"date":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}t.exports={p2r:i,r2p:function(t,e){switch(t.type){case"log":return t.d2p(e);case"date":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e="y"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},22676:function(t,e,r){"use strict";var n=r(23640),i=r(43156);t.exports={moduleType:"component",name:"selections",layoutAttributes:r(93956),supplyLayoutDefaults:r(74224),supplyDrawNewSelectionDefaults:r(81004),includeBasePlot:r(36632)("selections"),draw:n.draw,drawOne:n.drawOne,reselect:i.reselect,prepSelect:i.prepSelect,clearOutline:i.clearOutline,clearSelectionsCache:i.clearSelectionsCache,selectOnClick:i.selectOnClick}},43156:function(t,e,r){"use strict";var n=r(14756),i=r(61456),a=r(24040),o=r(43616).dashStyle,s=r(76308),l=r(93024),u=r(10624).makeEventData,c=r(72760),f=c.freeMode,h=c.rectMode,p=c.drawMode,d=c.openMode,v=c.selectMode,g=r(65152),y=r(85448),m=r(55496),x=r(1936).clearOutline,b=r(9856),_=b.handleEllipse,w=b.readPaths,T=r(93940).newShapes,k=r(5968),A=r(23640).activateLastSelection,M=r(3400),S=M.sorterAsc,E=r(92065),L=r(91200),C=r(79811).getFromId,P=r(73696),O=r(39172).redrawReglTraces,I=r(83280),D=I.MINSELECT,z=E.filter,R=E.tester,F=r(5840),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,u,c,f,h,p,v,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,b=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){W(t,e,a);var _=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 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i=t[n];r=(r=r.concat(i)).concat([i[0]])}return(a=r).isRect=5===a.length&&a[0][0]===a[4][0]&&a[0][1]===a[4][1]&&a[0][0]===a[1][0]&&a[2][0]===a[3][0]&&a[0][1]===a[3][1]&&a[1][1]===a[2][1]||a[0][1]===a[1][1]&&a[2][1]===a[3][1]&&a[0][0]===a[3][0]&&a[1][0]===a[2][0],a.isRect&&(a.xmin=Math.min(a[0][0],a[2][0]),a.xmax=Math.max(a[0][0],a[2][0]),a.ymin=Math.min(a[0][1],a[2][1]),a.ymax=Math.max(a[0][1],a[2][1])),a;var a}function ct(t){return function(e,r){for(var n,i,a=0;a<t.length;a++){var o=t[a],s=o._id,l=s.charAt(0);if(r.isRect){n||(n={});var u=r[l+"min"],c=r[l+"max"];void 0!==u&&void 0!==c&&(n[s]=[B(o,u),B(o,c)].sort(S))}else i||(i={}),i[s]=r.map(N(o))}n&&(e.range=n),i&&(e.lassoPoints=i)}}function ft(t,e){e&&(e.selections=(t.layout||{}).selections||[]),t.emit("plotly_selected",e)}function ht(t){t.emit("plotly_deselect",null)}t.exports={reselect:nt,prepSelect:function(t,e,r,n,i){var 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Lt=function(t){return t.plotinfo.fillRangeItems||ct(t.xaxes.concat(t.yaxes))}(n);n.moveFn=function(t,e){n._clearSubplotSelections&&(n._clearSubplotSelections(),n._clearSubplotSelections=void 0),lt=Math.max(0,Math.min(yt,ot*t+F)),pt=Math.max(0,Math.min(mt,st*e+B));var r=Math.abs(lt-F),i=Math.abs(pt-B);if(g){var a,o,s;if(b){var 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n=r(76308),i=r(3400);t.exports=function(t,e,r){if(r("newshape.visible"),r("newshape.name"),r("newshape.showlegend"),r("newshape.legend"),r("newshape.legendwidth"),r("newshape.legendgroup"),r("newshape.legendgrouptitle.text"),i.coerceFont(r,"newshape.legendgrouptitle.font"),r("newshape.legendrank"),r("newshape.drawdirection"),r("newshape.layer"),r("newshape.fillcolor"),r("newshape.fillrule"),r("newshape.opacity"),r("newshape.line.width")){var a=(t||{}).plot_bgcolor||"#FFF";r("newshape.line.color",n.contrast(a)),r("newshape.line.dash")}var o="drawline"===t.dragmode,s=r("newshape.label.text"),l=r("newshape.label.texttemplate");if(s||l){r("newshape.label.textangle");var u=r("newshape.label.textposition",o?"middle":"middle center");r("newshape.label.xanchor"),r("newshape.label.yanchor",function(t,e){return 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p=2*h*Math.PI/a;f.push([s+u*Math.cos(p),l+c*Math.sin(p)])}return f},e.ellipseOver=function(t){var e=t.x0,r=t.y0,n=t.x1,i=t.y1,a=n-e,s=i-r,l=((e-=a)+n)/2,u=((r-=s)+i)/2;return{x0:l-(a*=o),y0:u-(s*=o),x1:l+a,y1:u+s}},e.fixDatesForPaths=function(t,e,r){var n="date"===e.type,i="date"===r.type;if(!n&&!i)return t;for(var a=0;a<t.length;a++)for(var o=0;o<t[a].length;o++)for(var s=0;s+2<t[a][o].length;s+=2)n&&(t[a][o][s+1]=t[a][o][s+1].replace(" ","_")),i&&(t[a][o][s+2]=t[a][o][s+2].replace(" ","_"));return t}},93940:function(t,e,r){"use strict";var n=r(72760),i=n.drawMode,a=n.openMode,o=r(7e3),s=o.i000,l=o.i090,u=o.i180,c=o.i270,f=o.cos45,h=o.sin45,p=r(5840),d=p.p2r,v=p.r2p,g=r(1936).clearOutline,y=r(9856),m=y.readPaths,x=y.writePaths,b=y.ellipseOver,_=y.fixDatesForPaths;function w(t,e,r){var n,i=t[0][0],o=e.gd,p=i.getAttribute("d"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?"paper":T._id,yref:M?"paper":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&"drawrect"===r)L.type="rect",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&"drawline"===r)L.type="line",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&"drawcircle"===r){L.type="circle";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],D=n[s][2],z=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&("date"===y.xaxis.type||"log"===y.xaxis.type),N=y.yaxis&&("date"===y.yaxis.type||"log"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(D=v(y.yaxis,D),z=v(y.yaxis,z),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(D+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-z+R-D)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type="path",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case"rect":a="drawrect";break;case"circle":a="drawcircle";break;case"line":a="drawline";break;case"path":var l=o[s].path||"";a="Z"===l[l.length-1]?"drawclosedpath":"drawopenpath"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case"line":case"rect":case"circle":f("x0",v.x0),f("x1",v.x1),f("y0",v.y0),f("y1",v.y1);break;case"path":f("path",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},1936:function(t){"use strict";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".outline-controllers").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._outlining=!1}}},65152:function(t,e,r){"use strict";var n=r(85448),i=r(3400),a=r(54460);e.rangeToShapePosition=function(t){return"log"===t.type?t.r2d:function(t){return t}},e.shapePositionToRange=function(t){return"log"===t.type?t.d2r:function(t){return t}},e.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace("_"," ")),t(e)}},e.encodeDate=function(t){return function(e){return t(e).replace(" ","_")}},e.extractPathCoords=function(t,e,r){var a=[];return t.match(n.segmentRE).forEach((function(t){var o=e[t.charAt(0)].drawn;if(void 0!==o){var s=t.substr(1).match(n.paramRE);if(s&&!(s.length<o)){var l=s[o],u=r?l:i.cleanNumber(l);a.push(u)}}})),a},e.getDataToPixel=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if("domain"===i)a=function(t){return r._length*(n?1-t:t)+r._offset};else{var s=e.shapePositionToRange(r);a=function(t){return r._offset+r.r2p(s(t,!0))},"date"===r.type&&(a=e.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},e.getPixelToData=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if("domain"===i)a=function(t){var e=(t-r._offset)/r._length;return n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&"paper"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&"paper"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var o,s,l,u,c,f,h,p,d=r.type,v=a.getRefType(r.xref),g=a.getRefType(r.yref),y=a.getFromId(t,r.xref),m=a.getFromId(t,r.yref),x=t._fullLayout._size;if(y?"domain"===v?s=function(t){return y._offset+y._length*t}:(o=e.shapePositionToRange(y),s=function(t){return y._offset+y.r2p(o(t,!0))}):s=function(t){return x.l+x.w*t},m?"domain"===g?u=function(t){return m._offset+m._length*(1-t)}:(l=e.shapePositionToRange(m),u=function(t){return m._offset+m.r2p(l(t,!0))}):u=function(t){return x.t+x.h*(1-t)},"path"===d)return y&&"date"===y.type&&(s=e.decodeDate(s)),m&&"date"===m.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t="pixel"===o?e(l)+Number(t):e(t):h[a]&&(t="pixel"===s?r(u)-Number(t):r(t)),++a>p&&(t="X"),t}));return a>p&&(d=d.replace(/[\s,]*X.*/,""),i.log("Ignoring extra params in segment "+t)),c+d}))}(r,s,u);if("pixel"===r.xsizemode){var b=s(r.xanchor);c=b+r.x0,f=b+r.x1}else c=s(r.x0),f=s(r.x1);if("pixel"===r.ysizemode){var _=u(r.yanchor);h=_-r.y0,p=_-r.y1}else h=u(r.y0),p=u(r.y1);if("line"===d)return"M"+c+","+h+"L"+f+","+p;if("rect"===d)return"M"+c+","+h+"H"+f+"V"+p+"H"+c+"Z";var w=(c+f)/2,T=(h+p)/2,k=Math.abs(w-c),A=Math.abs(T-h),M="A"+k+","+A,S=w+k+","+T;return"M"+S+M+" 0 1,1 "+w+","+(T-A)+M+" 0 0,1 "+S+"Z"}},41592:function(t,e,r){"use strict";var n=r(4016);t.exports={moduleType:"component",name:"shapes",layoutAttributes:r(46056),supplyLayoutDefaults:r(43712),supplyDrawNewShapeDefaults:r(65144),includeBasePlot:r(36632)("shapes"),calcAutorange:r(96084),draw:n.draw,drawOne:n.drawOne}},97728:function(t){"use strict";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t,r){return e(t.x1,r)-e(t.x0,r)}function i(t,r,n){return e(t.y1,n)-e(t.y0,n)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return"line"!==t.type?void 0:i(t,0,r)/n(t,e)},dx:n,dy:i,width:function(t,e){return Math.abs(n(t,e))},height:function(t,e,r){return Math.abs(i(t,0,r))},length:function(t,e,r){return"line"!==t.type?void 0:Math.sqrt(Math.pow(n(t,e),2)+Math.pow(i(t,0,r),2))},xcenter:function(t,n){return r((e(t.x1,n)+e(t.x0,n))/2,n)},ycenter:function(t,n,i){return r((e(t.y1,i)+e(t.y0,i))/2,i)}}},89861:function(t,e,r){"use strict";var n=r(25376),i=r(66741),a=r(92880).extendDeepAll,o=r(67824).overrideAll,s=r(85656),l=r(31780).templatedArray,u=r(60876),c=l("step",{visible:{valType:"boolean",dflt:!0},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string"},value:{valType:"string"},execute:{valType:"boolean",dflt:!0}});t.exports=o(l("slider",{visible:{valType:"boolean",dflt:!0},active:{valType:"number",min:0,dflt:0},steps:c,lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",min:-2,max:3,dflt:0},pad:a(i({editType:"arraydraw"}),{},{t:{dflt:20}}),xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left"},y:{valType:"number",min:-2,max:3,dflt:0},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},transition:{duration:{valType:"number",min:0,dflt:150},easing:{valType:"enumerated",values:s.transition.easing.values,dflt:"cubic-in-out"}},currentvalue:{visible:{valType:"boolean",dflt:!0},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},offset:{valType:"number",dflt:10},prefix:{valType:"string"},suffix:{valType:"string"},font:n({})},font:n({}),activebgcolor:{valType:"color",dflt:u.gripBgActiveColor},bgcolor:{valType:"color",dflt:u.railBgColor},bordercolor:{valType:"color",dflt:u.railBorderColor},borderwidth:{valType:"number",min:0,dflt:u.railBorderWidth},ticklen:{valType:"number",min:0,dflt:u.tickLength},tickcolor:{valType:"color",dflt:u.tickColor},tickwidth:{valType:"number",min:0,dflt:1},minorticklen:{valType:"number",min:0,dflt:u.minorTickLength}}),"arraydraw","from-root")},60876:function(t){"use strict";t.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},8132:function(t,e,r){"use 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h=t._fullLayout._size;c.lx=h.l+h.w*e.x,c.ly=h.t+h.h*(1-e.y),"fraction"===e.lenmode?c.outerLength=Math.round(h.w*e.len):c.outerLength=e.len,c.inputAreaStart=0,c.inputAreaLength=Math.round(c.outerLength-e.pad.l-e.pad.r);var p=(c.inputAreaLength-2*f.stepInset)/(e._stepCount-1),y=a+f.labelPadding;if(c.labelStride=Math.max(1,Math.ceil(y/p)),c.labelHeight=l,c.currentValueMaxWidth=0,c.currentValueHeight=0,c.currentValueTotalHeight=0,c.currentValueMaxLines=1,e.currentvalue.visible){var m=o.tester.append("g");r.each((function(t){var r=x(m,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},i=u.lineCount(r);c.currentValueMaxWidth=Math.max(c.currentValueMaxWidth,Math.ceil(n.width)),c.currentValueHeight=Math.max(c.currentValueHeight,Math.ceil(n.height)),c.currentValueMaxLines=Math.max(c.currentValueMaxLines,i)})),c.currentValueTotalHeight=c.currentValueHeight+e.currentvalue.offset,m.remove()}c.height=c.currentValueTotalHeight+f.tickOffset+e.ticklen+f.labelOffset+c.labelHeight+e.pad.t+e.pad.b;var b="left";s.isRightAnchor(e)&&(c.lx-=c.outerLength,b="right"),s.isCenterAnchor(e)&&(c.lx-=c.outerLength/2,b="center");var w="top";s.isBottomAnchor(e)&&(c.ly-=c.height,w="bottom"),s.isMiddleAnchor(e)&&(c.ly-=c.height/2,w="middle"),c.outerLength=Math.ceil(c.outerLength),c.height=Math.ceil(c.height),c.lx=Math.round(c.lx),c.ly=Math.round(c.ly);var T={y:e.y,b:c.height*v[w],t:c.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*v[b]):(T.x=e.x,T.l=c.outerLength*d[b],T.r=c.outerLength*v[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":i,"data-notex":1})})),c=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)c+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),c+=h}e.currentvalue.suffix&&(c+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(c).call(u.convertToTspans,e._gd);var v=u.lineCount(l),g=(a.currentValueMaxLines+1-v)*e.currentvalue.font.size*p;return u.positionText(l,n,g),l}}function b(t,e,r){s.ensureSingle(t,"rect",f.gripRectClass,(function(n){n.call(A,e,t,r).style("pointer-events","all")})).attr({width:f.gripWidth,height:f.gripHeight,rx:f.gripRadius,ry:f.gripRadius}).call(a.stroke,r.bordercolor).call(a.fill,r.bgcolor).style("stroke-width",r.borderwidth+"px")}function _(t,e,r){var n=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":"middle","data-notex":1})})),i=e.step.label,a=r._gd._fullLayout._meta;return a&&(i=s.templateString(i,a)),n.call(o.font,r.font).text(i).call(u.convertToTspans,r._gd),n}function w(t,e){var r=s.ensureSingle(t,"g",f.labelsClass),i=e._dims,a=r.selectAll("g."+f.labelGroupClass).data(i.labelSteps);a.enter().append("g").classed(f.labelGroupClass,!0),a.exit().remove(),a.each((function(t){var r=n.select(this);r.call(_,t,e),o.setTranslate(r,E(e,t.fraction),f.tickOffset+e.ticklen+e.font.size*p+f.labelOffset+i.currentValueTotalHeight)}))}function T(t,e,r,n,i){var a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&k(t,e,r,o,!0,i)}function k(t,e,r,n,a,o){var s=r.active;r.active=n,c(t.layout,f.name,r).applyUpdate("active",n);var l=r.steps[r.active];e.call(S,r,o),e.call(x,r),t.emit("plotly_sliderchange",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame((function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)}))))}function A(t,e,r){if(!e._context.staticPlot){var i=r.node(),o=n.select(e);t.on("mousedown",l),t.on("touchstart",l)}function s(){return r.data()[0]}function l(){var t=s();e.emit("plotly_sliderstart",{slider:t});var 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u.add(r,c,h,f,p),d},e.prependTraces=function t(r,n,i,a){var s=z(r=o.getGraphDiv(r),n,i,a,(function(t,e,r){var n,i;if(o.isTypedArray(t))if(r<=0){var a=new t.constructor(0),s=R(e,t);r<0?(n=s,i=a):(n=a,i=s)}else if(n=new t.constructor(r),i=new t.constructor(t.length+e.length-r),r===e.length)n.set(e),i.set(t);else if(r<e.length){var l=e.length-r;n.set(e.subarray(0,l)),i.set(e.subarray(l)),i.set(t,l)}else{var u=r-e.length;n.set(e),n.set(t.subarray(0,u),e.length),i.set(t.subarray(u))}else n=e.concat(t),i=r>=0&&r<n.length?n.splice(r,n.length):[];return[n,i]})),l=e.redraw(r),c=[r,s.update,i,s.maxPoints];return u.add(r,e.extendTraces,c,t,arguments),l},e.newPlot=function(t,r,n,i){return t=o.getGraphDiv(t),h.cleanPlot([],{},t._fullData||[],t._fullLayout||{}),h.purge(t),e._doPlot(t,r,n,i)},e._doPlot=function(t,r,i,a){var s;if(t=o.getGraphDiv(t),l.init(t),o.isPlainObject(r)){var u=r;r=u.data,i=u.layout,a=u.config,s=u.frames}if(!1===l.triggerHandler(t,"plotly_beforeplot",[r,i,a]))return Promise.reject();r||i||o.isPlotDiv(t)||o.warn("Calling _doPlot as if redrawing but this container doesn't yet have a plot.",t),P(t,a),i||(i={}),n.select(t).classed("js-plotly-plot",!0),g.makeTester(),Array.isArray(t._promises)||(t._promises=[]);var f=0===(t.data||[]).length&&Array.isArray(r);Array.isArray(r)&&(T.cleanData(r),f?t.data=r:t.data.push.apply(t.data,r),t.empty=!1),t.layout&&!f||(t.layout=T.cleanLayout(i)),h.supplyDefaults(t);var d=t._fullLayout,v=d._has("cartesian");d._replotting=!0,(f||d._shouldCreateBgLayer)&&(function(t){var e=n.select(t),r=t._fullLayout;if(r._calcInverseTransform=lt,r._calcInverseTransform(t),r._container=e.selectAll(".plot-container").data([0]),r._container.enter().insert("div",":first-child").classed("plot-container",!0).classed("plotly",!0),r._paperdiv=r._container.selectAll(".svg-container").data([0]),r._paperdiv.enter().append("div").classed("user-select-none",!0).classed("svg-container",!0).style("position","relative"),r._glcontainer=r._paperdiv.selectAll(".gl-container").data([{}]),r._glcontainer.enter().append("div").classed("gl-container",!0),r._paperdiv.selectAll(".main-svg").remove(),r._paperdiv.select(".modebar-container").remove(),r._paper=r._paperdiv.insert("svg",":first-child").classed("main-svg",!0),r._toppaper=r._paperdiv.append("svg").classed("main-svg",!0),r._modebardiv=r._paperdiv.append("div"),delete r._modeBar,r._hoverpaper=r._paperdiv.append("svg").classed("main-svg",!0),!r._uid){var i={};n.selectAll("defs").each((function(){this.id&&(i[this.id.split("-")[1]]=1)})),r._uid=o.randstr(i)}r._paperdiv.selectAll(".main-svg").attr(x.svgAttrs),r._defs=r._paper.append("defs").attr("id","defs-"+r._uid),r._clips=r._defs.append("g").classed("clips",!0),r._topdefs=r._toppaper.append("defs").attr("id","topdefs-"+r._uid),r._topclips=r._topdefs.append("g").classed("clips",!0),r._bgLayer=r._paper.append("g").classed("bglayer",!0),r._draggers=r._paper.append("g").classed("draglayer",!0);var a=r._paper.append("g").classed("layer-below",!0);r._imageLowerLayer=a.append("g").classed("imagelayer",!0),r._shapeLowerLayer=a.append("g").classed("shapelayer",!0),r._cartesianlayer=r._paper.append("g").classed("cartesianlayer",!0),r._polarlayer=r._paper.append("g").classed("polarlayer",!0),r._smithlayer=r._paper.append("g").classed("smithlayer",!0),r._ternarylayer=r._paper.append("g").classed("ternarylayer",!0),r._geolayer=r._paper.append("g").classed("geolayer",!0),r._funnelarealayer=r._paper.append("g").classed("funnelarealayer",!0),r._pielayer=r._paper.append("g").classed("pielayer",!0),r._iciclelayer=r._paper.append("g").classed("iciclelayer",!0),r._treemaplayer=r._paper.append("g").classed("treemaplayer",!0),r._sunburstlayer=r._paper.append("g").classed("sunburstlayer",!0),r._indicatorlayer=r._toppaper.append("g").classed("indicatorlayer",!0),r._glimages=r._paper.append("g").classed("glimages",!0);var s=r._toppaper.append("g").classed("layer-above",!0);r._imageUpperLayer=s.append("g").classed("imagelayer",!0),r._shapeUpperLayer=s.append("g").classed("shapelayer",!0),r._selectionLayer=r._toppaper.append("g").classed("selectionlayer",!0),r._infolayer=r._toppaper.append("g").classed("infolayer",!0),r._menulayer=r._toppaper.append("g").classed("menulayer",!0),r._zoomlayer=r._toppaper.append("g").classed("zoomlayer",!0),r._hoverlayer=r._hoverpaper.append("g").classed("hoverlayer",!0),r._modebardiv.classed("modebar-container",!0).style("position","absolute").style("top","0px").style("right","0px"),t.emit("plotly_framework")}(t),d._shouldCreateBgLayer&&delete d._shouldCreateBgLayer),g.initGradients(t),g.initPatterns(t),f&&p.saveShowSpikeInitial(t);var y=!t.calcdata||t.calcdata.length!==(t._fullData||[]).length;y&&h.doCalcdata(t);for(var b=0;b<t.calcdata.length;b++)t.calcdata[b][0].trace=t._fullData[b];t._context.responsive?t._responsiveChartHandler||(t._responsiveChartHandler=function(){o.isHidden(t)||h.resize(t)},window.addEventListener("resize",t._responsiveChartHandler)):o.clearResponsive(t);var _=o.extendFlat({},d._size),w=0;function A(){if(h.clearAutoMarginIds(t),k.drawMarginPushers(t),p.allowAutoMargin(t),t._fullLayout.title.text&&t._fullLayout.title.automargin&&h.allowAutoMargin(t,"title.automargin"),d._has("pie"))for(var e=t._fullData,r=0;r<e.length;r++){var n=e[r];"pie"===n.type&&n.automargin&&h.allowAutoMargin(t,"pie."+n.uid+".automargin")}return h.doAutoMargin(t),h.previousPromises(t)}function M(){t._transitioning||(k.doAutoRangeAndConstraints(t),f&&p.saveRangeInitial(t),c.getComponentMethod("rangeslider","calcAutorange")(t))}var S=[h.previousPromises,function(){if(s)return e.addFrames(t,s)},function e(){for(var r=d._basePlotModules,n=0;n<r.length;n++)r[n].drawFramework&&r[n].drawFramework(t);!d._glcanvas&&d._has("gl")&&(d._glcanvas=d._glcontainer.selectAll(".gl-canvas").data([{key:"contextLayer",context:!0,pick:!1},{key:"focusLayer",context:!1,pick:!1},{key:"pickLayer",context:!1,pick:!0}],(function(t){return t.key})),d._glcanvas.enter().append("canvas").attr("class",(function(t){return"gl-canvas gl-canvas-"+t.key.replace("Layer","")})).style({position:"absolute",top:0,left:0,overflow:"visible","pointer-events":"none"}));var i=t._context.plotGlPixelRatio;if(d._glcanvas){d._glcanvas.attr("width",d.width*i).attr("height",d.height*i).style("width",d.width+"px").style("height",d.height+"px");var a=d._glcanvas.data()[0].regl;if(a&&(Math.floor(d.width*i)!==a._gl.drawingBufferWidth||Math.floor(d.height*i)!==a._gl.drawingBufferHeight)){var s="WebGL context buffer and canvas dimensions do not match due to browser/WebGL bug.";if(!w)return o.log(s+" Clearing graph and plotting 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E(t),t}))},e.purge=function(t){var e=(t=o.getGraphDiv(t))._fullLayout||{},r=t._fullData||[];return h.cleanPlot([],{},r,e),h.purge(t),l.purge(t),e._container&&e._container.remove(),delete t._context,t},e.react=function(t,r,n,i){var a,l;t=o.getGraphDiv(t),T.clearPromiseQueue(t);var u=t._fullData,p=t._fullLayout;if(o.isPlotDiv(t)&&u&&p){if(o.isPlainObject(r)){var d=r;r=d.data,n=d.layout,i=d.config,a=d.frames}var v=!1;if(i){var g=o.extendDeep({},t._context);t._context=void 0,P(t,i),v=st(g,t._context)}t.data=r||[],T.cleanData(t.data),t.layout=n||{},T.cleanLayout(t.layout),function(t,e,r,n){var i,a,l,u,c,f,h,p,d,v,g=n._preGUI,y=[],m={},x={};for(i in g){if(c=et(i,Q)){if(d=c.head,v=c.tail,a=c.attr||d+".uirevision",(u=(l=s(n,a).get())&&rt(a,e))&&u===l){if(null===(f=g[i])&&(f=void 0),at(p=(h=s(e,i)).get(),f)){void 0===p&&"autorange"===v&&y.push(d),h.set(B(s(n,i).get()));continue}if("autorange"===v||"range["===v.substr(0,6)){var 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y=t._fullData,m=t._fullLayout,x=void 0===m.datarevision,b=m.transition,_=function(t,e,r,n,i){var a=A.layoutFlags();return a.arrays={},a.rangesAltered={},a.nChanges=0,a.nChangesAnim=0,ot(e,r,[],{getValObject:function(t){return f.getLayoutValObject(r,t)},flags:a,immutable:n,transition:i,gd:t}),(a.plot||a.calc)&&(a.layoutReplot=!0),i&&a.nChanges&&a.nChangesAnim&&(a.anim=a.nChanges===a.nChangesAnim?"all":"some"),a}(t,p,m,x,b),w=_.newDataRevision,M=function(t,e,r,n,i,a){var o=e.length===r.length;if(!i&&!o)return{fullReplot:!0,calc:!0};var s,l,u=A.traceFlags();u.arrays={},u.nChanges=0,u.nChangesAnim=0;var c={getValObject:function(t){var 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if(M.fullReplot||_.layoutReplot||v)t._fullLayout._skipDefaults=!0,D.push(e._doPlot);else{for(var z in _.arrays){var R=_.arrays[z];if(R.length){var F=c.getComponentMethod(z,"drawOne");if(F!==o.noop)for(var N=0;N<R.length;N++)F(t,R[N]);else{var j=c.getComponentMethod(z,"draw");if(j===o.noop)throw new Error("cannot draw components: "+z);j(t)}}}D.push(h.previousPromises),M.style&&D.push(k.doTraceStyle),(M.colorbars||_.colorbars)&&D.push(k.doColorBars),_.legend&&D.push(k.doLegend),_.layoutstyle&&D.push(k.layoutStyles),_.axrange&&G(D),_.ticks&&D.push(k.doTicksRelayout),_.modebar&&D.push(k.doModeBar),_.camera&&D.push(k.doCamera),D.push(E)}D.push(h.rehover,h.redrag,h.reselect),(l=o.syncOrAsync(D,t))&&l.then||(l=Promise.resolve(t))}else l=e.newPlot(t,r,n,i);return l.then((function(){return t.emit("plotly_react",{data:r,layout:n}),t}))},e.redraw=function(t){if(t=o.getGraphDiv(t),!o.isPlotDiv(t))throw new Error("This element is not a Plotly plot: "+t);return T.cleanData(t.data),T.cleanLayout(t.layout),t.calcdata=void 0,e._doPlot(t).then((function(){return t.emit("plotly_redraw"),t}))},e.relayout=q,e.restyle=F,e.setPlotConfig=function(t){return o.extendFlat(_,t)},e.update=J,e._guiRelayout=$(q),e._guiRestyle=$(F),e._guiUpdate=$(J),e._storeDirectGUIEdit=function(t,e,r){for(var n in r)j(n,s(t,n).get(),r[n],e)}},20556:function(t){"use strict";var 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h=s.distinctVals(t),p=Math.pow(10,Math.floor(Math.log(h.minDiff)/Math.LN10)),v=p*s.roundUp(h.minDiff/p,[.9,1.9,4.9,9.9],!0);f=Math.max(v,2*s.stdev(t)/Math.pow(t.length,n?.25:.4)),i(f)||(f=1)}H.autoTicks(l,f)}var g,y=l.dtick,m=H.tickIncrement(H.tickFirst(l),y,"reverse",a);if("number"==typeof y)m=function(t,e,r,n,a){var o=0,s=0,l=0,u=0;function c(e){return(1+100*(e-t)/r.dtick)%100<2}for(var f=0;f<e.length;f++)e[f]%1==0?l++:i(e[f])||u++,c(e[f])&&o++,c(e[f]+r.dtick/2)&&s++;var h=e.length-u;if(l===h&&"date"!==r.type)r.dtick<1?t=n-.5*r.dtick:(t-=.5)+r.dtick<n&&(t+=r.dtick);else if(s<.1*h&&(o>.3*h||c(n)||c(a))){var p=r.dtick/2;t+=t+p<n?p:-p}return t}(m,t,l,u,c),g=m+(1+Math.floor((c-m)/y))*y;else for("M"===l.dtick.charAt(0)&&(m=function(t,e,r,n,i){var a=s.findExactDates(e,i);if(a.exactDays>.8){var o=Number(r.substr(1));a.exactYears>.8&&o%12==0?t=H.tickIncrement(t,"M6","reverse")+1.5*M:a.exactMonths>.8?t=H.tickIncrement(t,"M1","reverse")+15.5*M:t-=S;var l=H.tickIncrement(t,r);if(l<=n)return 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0===e.minor._tick0Init&&(t.tick0=e.tick0)},H.prepTicks=function(t,e){var r=s.simpleMap(t.range,t.r2l,void 0,void 0,e);if("auto"===t.tickmode||!t.dtick){var n,a=t.nticks;a||("category"===t.type||"multicategory"===t.type?(n=t.tickfont?s.bigFont(t.tickfont.size||12):15,a=t._length/n):(n="y"===t._id.charAt(0)?40:80,a=s.constrain(t._length/n,4,9)+1),"radialaxis"===t._name&&(a*=2)),t.minor&&"array"!==t.minor.tickmode||"array"===t.tickmode&&(a*=100),t._roughDTick=Math.abs(r[1]-r[0])/a,H.autoTicks(t,t._roughDTick),t._minDtick>0&&t.dtick<2*t._minDtick&&(t.dtick=t._minDtick,t.tick0=t.l2r(t._forceTick0))}"period"===t.ticklabelmode&&function(t){var e;function r(){return!(i(t.dtick)||"M"!==t.dtick.charAt(0))}var n=r(),a=H.getTickFormat(t);if(a){var o=t._dtickInit!==t.dtick;/%[fLQsSMX]/.test(a)||(/%[HI]/.test(a)?(e=E,o&&!n&&t.dtick<E&&(t.dtick=E)):/%p/.test(a)?(e=S,o&&!n&&t.dtick<S&&(t.dtick=S)):/%[Aadejuwx]/.test(a)?(e=M,o&&!n&&t.dtick<M&&(t.dtick=M)):/%[UVW]/.test(a)?(e=A,o&&!n&&t.dtick<A&&(t.dtick=A)):/%[Bbm]/.test(a)?(e=T,o&&(n?nt(t.dtick)<1:t.dtick<k)&&(t.dtick="M1")):/%[q]/.test(a)?(e=b,o&&(n?nt(t.dtick)<3:t.dtick<_)&&(t.dtick="M3")):/%[Yy]/.test(a)&&(e=y,o&&(n?nt(t.dtick)<12:t.dtick<m)&&(t.dtick="M12")))}(n=r())&&t.tick0===t._dowTick0&&(t.tick0=t._rawTick0),t._definedDelta=e}(t),t.tick0||(t.tick0="date"===t.type?"2000-01-01":0),"date"===t.type&&t.dtick<.1&&(t.dtick=.1),vt(t)},H.calcTicks=function(t,e){for(var r,n,a=t.type,o=t.calendar,l=t.ticklabelstep,u="period"===t.ticklabelmode,c=s.simpleMap(t.range,t.r2l,void 0,void 0,e),f=c[1]<c[0],h=Math.min(c[0],c[1]),p=Math.max(c[0],c[1]),d=Math.max(1e3,t._length||0),v=[],L=[],C=[],P=[],I=t.minor&&(t.minor.ticks||t.minor.showgrid),D=1;D>=(I?0:1);D--){var z=!D;D?(t._dtickInit=t.dtick,t._tick0Init=t.tick0):(t.minor._dtickInit=t.minor.dtick,t.minor._tick0Init=t.minor.tick0);var R=D?t:s.extendFlat({},t,t.minor);if(z?H.prepMinorTicks(R,t,e):H.prepTicks(R,e),"array"!==R.tickmode)if("sync"!==R.tickmode){var F=J(c),B=F[0],N=F[1],j=i(R.dtick),U="log"===a&&!(j||"L"===R.dtick.charAt(0)),V=H.tickFirst(R,e);if(D){if(t._tmin=V,V<B!==f)break;"category"!==a&&"multicategory"!==a||(N=f?Math.max(-.5,N):Math.min(t._categories.length-.5,N))}var q,G,W=null,Y=V;D&&(j?G=t.dtick:"date"===a?"string"==typeof t.dtick&&"M"===t.dtick.charAt(0)&&(G=T*t.dtick.substring(1)):G=t._roughDTick,q=Math.round((t.r2l(Y)-t.r2l(t.tick0))/G)-1);var X=R.dtick;for(R.rangebreaks&&R._tick0Init!==R.tick0&&(Y=zt(Y,t),f||(Y=H.tickIncrement(Y,X,!f,o))),D&&u&&(Y=H.tickIncrement(Y,X,!f,o),q--);f?Y>=N:Y<=N;Y=H.tickIncrement(Y,X,f,o)){if(D&&q++,R.rangebreaks&&!f){if(Y<B)continue;if(R.maskBreaks(Y)===O&&zt(Y,R)>=p)break}if(C.length>d||Y===W)break;W=Y;var Z={value:Y};D?(U&&Y!==(0|Y)&&(Z.simpleLabel=!0),l>1&&q%l&&(Z.skipLabel=!0),C.push(Z)):(Z.minor=!0,P.push(Z))}}else C=[],v=at(t);else D?(C=[],v=ot(t,!z)):(P=[],L=ot(t,!z))}if(I&&!("inside"===t.minor.ticks&&"outside"===t.ticks||"outside"===t.minor.ticks&&"inside"===t.ticks)){for(var K=C.map((function(t){return t.value})),$=[],Q=0;Q<P.length;Q++){var tt=P[Q],et=tt.value;if(-1===K.indexOf(et)){for(var rt=!1,nt=0;!rt&&nt<C.length;nt++)1e7+C[nt].value===1e7+et&&(rt=!0);rt||$.push(tt)}}P=$}if(u&&function(t,e,r){for(var n=0;n<t.length;n++){var i=t[n].value,a=n,o=n+1;n<t.length-1?(a=n,o=n+1):n>0?(a=n-1,o=n):(a=n,o=n);var s,l=t[a].value,u=t[o].value,c=Math.abs(u-l),f=r||c,h=0;f>=m?h=c>=m&&c<=g?c:y:r===b&&f>=_?h=c>=_&&c<=x?c:b:f>=k?h=c>=k&&c<=w?c:T:r===A&&f>=A?h=A:f>=M?h=M:r===S&&f>=S?h=S:r===E&&f>=E&&(h=E),h>=c&&(h=c,s=!0);var p=i+h;if(e.rangebreaks&&h>0){for(var d=0,v=0;v<84;v++){var L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var it="y"===t._id.charAt(0),st=1;"auto"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=zt(C[r].value,t);var ut=t.c2p(C[r].value);(it?lt>ut-st:lt<ut+st)?C.splice(f?r+1:r,1):lt=ut}}Dt(t)&&360===Math.abs(c[1]-c[0])&&C.pop(),t._tmax=(C[C.length-1]||{}).value,t._prevDateHead="",t._inCalcTicks=!0;var ct,ft,ht=function(e){e.text="",t._prevDateHead=n};for(C=C.concat(P),r=0;r<C.length;r++){var pt=C[r].minor,dt=C[r].value;pt?L.push({x:dt,minor:!0}):(n=t._prevDateHead,ct=H.tickText(t,dt,!1,C[r].simpleLabel),void 0!==(ft=C[r].periodX)&&(ct.periodX=ft,(ft>p||ft<h)&&(ft>p&&(ct.periodX=p),ft<h&&(ct.periodX=h),ht(ct))),C[r].skipLabel&&ht(ct),v.push(ct))}return v=v.concat(L),t._inCalcTicks=!1,u&&v.length&&(v[0].noTick=!0),v};var st=[2,5,10],lt=[1,2,3,6,12],ut=[1,2,5,10,15,30],ct=[1,2,3,7,14],ft=[-.046,0,.301,.477,.602,.699,.778,.845,.903,.954,1],ht=[-.301,0,.301,.699,1],pt=[15,30,45,90,180];function dt(t,e,r){return e*s.roundUp(t/e,r)}function vt(t){var e=t.dtick;if(t._tickexponent=0,i(e)||"string"==typeof e||(e=1),"category"!==t.type&&"multicategory"!==t.type||(t._tickround=null),"date"===t.type){var r=t.r2l(t.tick0),n=t.l2r(r).replace(/(^-|i)/g,""),a=n.length;if("M"===String(e).charAt(0))a>10||"01-01"!==n.substr(5)?t._tickround="d":t._tickround=+e.substr(1)%12==0?"y":"m";else if(e>=M&&a<=10||e>=15*M)t._tickround="d";else if(e>=L&&a<=16||e>=E)t._tickround="M";else if(e>=C&&a<=19||e>=L)t._tickround="S";else{var o=t.l2r(r+e).replace(/^-/,"").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||"L"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 0===t.minexponent?3:t.minexponent;Math.abs(u)>c&&(mt(t.exponentformat)&&!xt(u)?t._tickexponent=3*Math.round((u-1)/3):t._tickexponent=u)}else t._tickround=null}function gt(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||"",fontSize:n.size,font:n.family,fontColor:n.color}}H.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if("date"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>y)e/=y,n=a(10),t.dtick="M"+12*dt(e,n,st);else if(o>T)e/=T,t.dtick="M"+dt(e,1,lt);else if(o>M){if(t.dtick=dt(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ct),!r){var l=H.getTickFormat(t),u="period"===t.ticklabelmode;u&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),u&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=dt(e,E,lt):o>L?t.dtick=dt(e,L,ut):o>C?t.dtick=dt(e,C,ut):(n=a(10),t.dtick=dt(e,n,st))}else if("log"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick="L"+dt(e,n,st)}else t.dtick=e>.3?"D2":"D1"}else"category"===t.type||"multicategory"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Dt(t)?(t.tick0=0,n=1,t.dtick=dt(e,n,pt)):(t.tick0=0,n=a(10),t.dtick=dt(e,n,st));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&"string"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,"ax.dtick error: "+String(h)}},H.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),u=o*Number(e.substr(1));if("M"===l)return s.incrementMonth(t,u,a);if("L"===l)return Math.log(Math.pow(10,t)+u)/Math.LN10;if("D"===l){var c="D2"===e?ht:ft,f=t+.01*o,h=s.roundUp(s.mod(f,1),c,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw"unrecognized dtick 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a,o=St(this);o.select(".text-math-group").empty()&&(e._vals[i]&&(a=e._vals[i].bb||h.bBox(o.node()),e._vals[i].bb=a),t=Math.max(t,a.width),r=Math.max(r,a.height))})),{labelsMaxW:t,labelsMaxH:r}},w=e._anchorAxis;if(w&&(w.autorange||w.insiderange)&&Rt(e)&&!X(a,e._id)&&(a._insideTickLabelsUpdaterange||(a._insideTickLabelsUpdaterange={}),w.autorange&&(a._insideTickLabelsUpdaterange[w._name+".autorange"]=w.autorange,b.push(_)),w.insiderange)){var T=_(),k="y"===e._id.charAt(0)?T.labelsMaxW:T.labelsMaxH;k+=6,"inside"===e.ticklabelposition&&(k+=e.ticklen||0);var A="right"===e.side||"top"===e.side?1:-1,M=1===A?1:0,S=1===A?0:1,E=[];E[S]=w.range[S];var L=w.range,C=w.r2p(L[M]),P=w.r2p(L[S]),O=a._insideTickLabelsUpdaterange[w._name+".range"];if(O){var B=w.r2p(O[M]),N=w.r2p(O[S]),j=A*("y"===e._id.charAt(0)?1:-1);j*C<j*B&&(C=B,E[M]=L[M]=O[M]),j*P>j*N&&(P=N,E[S]=L[S]=O[S])}var U=Math.abs(P-C);U-k>0?k*=1+k/(U-=k):k=0,"y"!==e._id.charAt(0)&&(k=-k),E[M]=w.p2r(w.r2p(L[M])+A*k),"min"===w.autorange||"max 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O,I,V,Z,K,J,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y==="nsew",yt=1===(v+y).length;function mt(){if(O=e.xaxis,I=e.yaxis,tt=O._length,et=I._length,$=O._offset,Q=I._offset,(V={})[O._id]=O,(Z={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;Z[a._id]=a}K=X(V),J=X(Z),it=z(K,y),at=z(J,v),ot=!at&&!it,nt=W(t,t._fullLayout._axisMatchGroups,V,Z);var o=(rt=W(t,t._fullLayout._axisConstraintGroups,V,Z,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has("scattergl"),ct=s._has("splom"),ft=s._has("svg")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?"nsew"===t?r?"":"pan"===e?"move":"crosshair":t.toLowerCase()+"-resize":"pointer"}(at+it,t._fullLayout.dragmode,gt),bt=D(e,v+y+"drag",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents="none",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Pt={element:bt,gd:t,plotinfo:e};function Ot(){Pt.plotinfo.selection=!1,M(t)}function It(t,r){var i=Pt.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(q(i),2!==t||yt||Ht(),gt)o.indexOf("select")>-1&&S(r,i,K,J,e.id,Pt),o.indexOf("event")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:O,u="s"===v||"w"===y?0:1,f=s._name+".range["+u+"]",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return"date"===t.type?n:"log"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a("."+r+"g")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a("."+String(r)+"g")(n))}(s,u),d="left",g="middle";if(s.fixedrange)return;v?(g="n"===v?"top":"bottom","right"===s.side&&(d="right")):"e"===y&&(d="right"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:"#444",horizontalAlign:d,verticalAlign:g}).on("edit",(function(t){var e=s.d2r(t);void 0!==e&&l.call("_guiRelayout",i,f,e)}))}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St="",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr("d","M0,0Z")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>P||o>P?(St="xy",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr("d",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>P||o>P){St="xy";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr("d",G(Tt))}else 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Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,"ew"===it||"ns"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(K,e),Vt("x")),at&&(F(J,r),Vt("y")),Wt([n,i,tt,et]),qt(),void t.emit("plotly_relayouting",ht)}var o,s,l="w"===it==("n"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if("w"===it?e=p(K,0,e):"e"===it?e=p(K,1,-e):it||(e=0),"n"===at?r=p(J,1,r):"s"===at?r=p(J,0,-r):at||(r=0),o="w"===it?e:0,s="n"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var 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n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}(r,_),T=function(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}((o=(t=C)[0],l=t[1],u=t[2],c=t[3],[o*(f=(a=w)[0])-l*(p=a[1])-u*(d=a[2])-c*(y=a[3]),o*p+l*f+u*y-c*d,o*d-l*y+u*f+c*p,o*y+l*d-u*p+c*f])),k=i.r=function(t,e,r){var n=m(e,2,t[0]);n=m(n,1,t[1]),n=m(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],u=e[2],c=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var v=180-a-2*p,y=(Math.atan2(h,c)-Math.atan2(u,i))*s,x=(Math.atan2(h,c)-Math.atan2(u,-i))*s;return g(r[0],r[1],a,y)<=g(r[0],r[1],v,x)?[a,y,r[2]]:[v,x,r[2]]}(T,r,E);isFinite(k[0])&&isFinite(k[1])&&isFinite(k[2])||(k=E),e.rotate(k),E=k}}else r=v(e,M=b);h.of(this,arguments)({type:"zoom"})})),A=h.of(this,arguments),p++||A({type:"zoomstart"})})).on("zoomend",(function(){var r;n.select(this).style(u),d.call(a,"zoom",null),r=h.of(this,arguments),--p||r({type:"zoomend"}),f(t,e,y)})).on("zoom.redraw",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.projection.rotation.lon":-r[0],"geo.projection.rotation.lat":-r[1]})})),n.rebind(a,h,"on")}function v(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function m(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function x(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}t.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},84888:function(t,e,r){"use strict";var n=r(24040),i=r(33816).SUBPLOT_PATTERN;e.KY=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},e._M=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module&&l._module.plot===r?i.push(s):a.push(s))}return[i,a]},e.op=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,u=[];if("gl2d"===e){var c=r.match(i);o="x"+c[1],s="y"+c[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},2428:function(t,e,r){"use strict";var n=r(62644),i=r(97264),a=r(29128),o=r(33816),s=r(89184);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,u.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var y=n/(f[2]-f[0])*(l[2]-l[0])+l[0],m=i/(f[3]-f[1])*(l[3]-l[1])+l[1];u.boxInited||(u.boxStart[0]=y,u.boxStart[1]=m,u.dragStart[0]=n,u.dragStart[1]=i),u.boxEnd[0]=y,u.boxEnd[1]=m,u.boxInited=!0,u.boxEnabled||u.boxStart[0]===u.boxEnd[0]&&u.boxStart[1]===u.boxEnd[1]||(u.boxEnabled=!0);var x=Math.abs(u.dragStart[0]-n)<v,b=Math.abs(u.dragStart[1]-i)<v;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(u.boxEnd[0]=u.boxStart[0]),b&&(u.boxEnd[1]=u.boxStart[1]);else{a=u.boxEnd[0]-u.boxStart[0],s=u.boxEnd[1]-u.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]<l[1]?(u.boxEnd[1]=l[1],u.boxEnd[0]=u.boxStart[0]+(l[1]-u.boxStart[1])/Math.abs(_)):u.boxEnd[1]>l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]<l[0]?(u.boxEnd[0]=l[0],u.boxEnd[1]=u.boxStart[1]+(l[0]-u.boxStart[0])*Math.abs(_)):u.boxEnd[0]>l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case"pan":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n)<d&&(n=u.dragStart[0]),Math.abs(u.dragStart[1]-i)<d&&(i=u.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),u.panning=!0,u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations()):u.panning&&(u.panning=!1,t.relayoutCallback())}u.lastPos[0]=n,u.lastPos[1]=i}return u.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,u.lastPos[0],u.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),u.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=u.lastPos[0],s=u.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),u}},92568:function(t,e,r){"use strict";var n=r(54460),i=r(43080);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf("all"):!!a,f=u?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return"xaxis"===t?"top"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:"yaxis"===t?"right"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},39952:function(t,e,r){"use strict";var n=r(67824).overrideAll,i=r(17188),a=r(64859),o=r(9616),s=r(33816),l=r(57952),u=r(65460),c=r(84888).op;e.name="gl2d",e.attr=["xaxis","yaxis"],e.idRoot=["x","y"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(26720),e.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},"plot","nested"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,"gl2d",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,"gl2d",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},17188:function(t,e,r){"use strict";var n,i,a=r(24040),o=r(54460),s=r(93024),l=r(67792).gl_plot2d,u=r(67792).gl_spikes2d,c=r(67792).gl_select_box,f=r(5408),h=r(92568),p=r(2428),d=r(16576),v=r(71888),g=v.enforce,y=v.clean,m=r(19280).doAutoRange,x=r(72760),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=r(33816).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=c.h*(h[1]-h[0])+"px",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+"px",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+"px",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&"all"!==m){var x=m.split("+");-1===x.indexOf("x")&&(v.traceCoord[0]=void 0),-1===x.indexOf("y")&&(v.traceCoord[1]=void 0),-1===x.indexOf("z")&&(v.traceCoord[2]=void 0),-1===x.indexOf("text")&&(v.textLabel=void 0),-1===x.indexOf("name")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter("xaxis",v.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,"bgcolor")||v.color,borderColor:s.castHoverOption(g,y,"bordercolor"),fontFamily:s.castHoverOption(g,y,"font.family"),fontSize:s.castHoverOption(g,y,"font.size"),fontColor:s.castHoverOption(g,y,"font.color"),nameLength:s.castHoverOption(g,y,"namelength"),textAlign:s.castHoverOption(g,y,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},12536:function(t,e,r){"use strict";var n=r(67824).overrideAll,i=r(65460),a=r(98432),o=r(84888).op,s=r(3400),l=r(9616),u="gl3d",c="scene";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex("scene"),e.attributes=r(6636),e.layoutAttributes=r(346),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),e.supplyLayoutDefaults=r(5208),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},6636:function(t){"use strict";t.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},86140:function(t,e,r){"use strict";var n=r(76308),i=r(94724),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:"plot"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},64380:function(t,e,r){"use strict";var n=r(49760).mix,i=r(3400),a=r(31780),o=r(86140),s=r(14944),l=r(28336),u=["xaxis","yaxis","zaxis"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noAutotickangles:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},44728:function(t,e,r){"use strict";var n=r(43080),i=r(3400),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,"font"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size)),"showline"in s&&(r.lineEnable[o]=s.showline),"linecolor"in s&&(r.lineColor[o]=n(s.linecolor)),"linewidth"in s&&(r.lineWidth[o]=s.linewidth),"showgrid"in s&&(r.gridEnable[o]=s.showgrid),"gridcolor"in s&&(r.gridColor[o]=n(s.gridcolor)),"gridwidth"in s&&(r.gridWidth[o]=s.gridwidth),"log"===s.type?r.zeroEnable[o]=!1:"zeroline"in s&&(r.zeroEnable[o]=s.zeroline),"zerolinecolor"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),"zerolinewidth"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),"ticks"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,"ticklen"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),"tickcolor"in s&&(r.lineTickColor[o]=n(s.tickcolor)),"tickwidth"in s&&(r.lineTickWidth[o]=s.tickwidth),"tickangle"in s&&(r.tickAngle[o]="auto"===s.tickangle?-3600:Math.PI*-s.tickangle/180),"showticklabels"in s&&(r.tickEnable[o]=s.showticklabels),"tickfont"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size)),"mirror"in s?-1!==["ticks","all","allticks"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,"showbackground"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},5208:function(t,e,r){"use strict";var n=r(3400),i=r(76308),a=r(24040),o=r(168),s=r(64380),l=r(346),u=r(84888).op,c="gl3d";function f(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),f=["up","center","eye"],h=0;h<f.length;h++)r("camera."+f[h]+".x"),r("camera."+f[h]+".y"),r("camera."+f[h]+".z");r("camera.projection.type");var p=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),d=r("aspectmode",p?"manual":"auto");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===d&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var g=n.getDfltFromLayout("dragmode");if(!1!==g&&!g)if(g="orbit",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g="turntable"):g="turntable")}else g="turntable";r("dragmode",g),r("hovermode",n.getDfltFromLayout("hovermode"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},346:function(t,e,r){"use strict";var n=r(86140),i=r(86968).u,a=r(92880).extendFlat,o=r(3400).counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}t.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},9020:function(t,e,r){"use strict";var n=r(43080),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},87152:function(t,e,r){"use strict";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if("auto"===c.tickmode){c.tickmode="linear";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],"date"===c.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(54460),i=r(3400),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},94424:function(t){"use strict";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},98432:function(t,e,r){"use strict";var n,i,a=r(67792).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(5408),u=r(89184),c=r(24040),f=r(3400),h=f.preserveDrawingBuffer(),p=r(54460),d=r(93024),v=r(43080),g=r(16576),y=r(94424),m=r(44728),x=r(9020),b=r(87152),_=r(19280).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod("annotations3d","convert"),this.drawAnnotations=c.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r="orthographic"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:"orbit"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("touchstart",(function(){w=!0})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)"skip"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||"").split("+"),S=A&&"all"===A;_.hovertemplate||S||(-1===M.indexOf("x")&&(k.xLabel=void 0),-1===M.indexOf("y")&&(k.yLabel=void 0),-1===M.indexOf("z")&&(k.zLabel=void 0),-1===M.indexOf("text")&&(h.textLabel=void 0),-1===M.indexOf("name")&&(c.name=void 0));var E=[];"cone"===t.type||"streamtube"===t.type?(k.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+k.uLabel),k.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+k.vLabel),k.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+k.wLabel),k.normLabel=h.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+k.normLabel),"streamtube"===t.type&&(k.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+k.divergenceLabel)),h.textLabel&&E.push(h.textLabel),x=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join("<br>")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:c.name,color:d.castHoverOption(_,T,"bgcolor")||c.color,borderColor:d.castHoverOption(_,T,"bordercolor"),fontFamily:d.castHoverOption(_,T,"font.family"),fontSize:d.castHoverOption(_,T,"font.size"),fontColor:d.castHoverOption(_,T,"font.color"),nameLength:d.castHoverOption(_,T,"namelength"),textAlign:d.castHoverOption(_,T,"align"),hovertemplate:f.castOption(_,T,"hovertemplate"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}h.distance<5&&(h.buttons||w)?r.emit("plotly_click",C):r.emit("plotly_hover",C),this.oldEventData=C}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var M=["xaxis","yaxis","zaxis"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+"calendar"],c=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,u),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],c-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,u,c=e[n.id],f=r[n.id];n.fullLayout=e,n.fullSceneLayout=c,n.axesOptions.merge(e,c),n.spikeOptions.merge(c),n.setViewport(c),n.updateFx(c.dragmode,c.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(v(c.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&S(this,i,h);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=M[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var u=n[l];if(u.visible){var c=s.r2l(u[o]);!isNaN(c)&&isFinite(c)&&(e[0][i]=Math.min(e[0][i],c),e[1][i]=Math.max(e[1][i],c))}}}(this,h);var 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A=w[s],E=A.bounds,L=A._trace.data._pad||0;"ErrorBars"===A.constructor.name&&l._lowerLogErrorBound?y[0][o]=Math.min(y[0][o],l._lowerLogErrorBound):y[0][o]=Math.min(y[0][o],E[0][o]/p[o]-L),y[1][o]=Math.max(y[1][o],E[1][o]/p[o]+L)}for(s=0;s<T.length;s++){var C=T[s];if(C.visible){var P=l.r2l(C[k]);y[0][o]=Math.min(y[0][o],P),y[1][o]=Math.max(y[1][o],P)}}if("rangemode"in l&&"tozero"===l.rangemode&&(y[0][o]=Math.min(y[0][o],0),y[1][o]=Math.max(y[1][o],0)),y[0][o]>y[1][o])y[0][o]=-1,y[1][o]=1;else{var O=y[1][o]-y[0][o];y[0][o]-=O/32,y[1][o]+=O/32}if(b=[y[0][o],y[1][o]],b=_(b,l),y[0][o]=b[0],y[1][o]=b[1],l.isReversed()){var I=y[0][o];y[0][o]=y[1][o],y[1][o]=I}}else b=l.range,y[0][o]=l.r2l(b[0]),y[1][o]=l.r2l(b[1]);y[0][o]===y[1][o]&&(y[0][o]-=1,y[1][o]+=1),m[o]=y[1][o]-y[0][o],l.range=[y[0][o],y[1][o]],l.limitRange(),n.glplot.setBounds(o,{min:l.range[0]*p[o],max:l.range[1]*p[o]})}var D=c.aspectmode;if("cube"===D)g=[1,1,1];else if("manual"===D){var 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Y=_.selectAll("g.gauge-outline").data([x]);Y.enter().append("g").classed("gauge-outline",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:W,layer:Y,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=I.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=I.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,u,c=r[0].trace,f=n.gauge,p=n.layer,v=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=c.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(y.l,y.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=y.h,M=c.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(c._hasNumber||c._hasDelta?1-h.bulletNumberDomainSize:1);function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return 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b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=c.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+v.left,y:_-d.top+v.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function 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t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return c.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,m(t))}))}));var u={};u[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:u})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,m(t))}));var u={};u[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(u.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:u})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),u=l.node().getBoundingClientRect(),c=l.datum(),f=c.parcatsViewModel,h=f.model.dimensions[c.model.dimensionInd],p=f.trace,d=u.top+u.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=u.left,a="left"):(i=u.left+u.width,a="right");var v=c.model.count,g=c.model.categoryLabel,y=v/c.parcatsViewModel.model.count,m={countLabel:v,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==c.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",m.countLabel].join(" ")),-1!==c.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+m.categoryLabel+"):",m.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:m,eventData:[{data:p._input,fullData:p,count:v,category:g,probability:y}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,u=this;"color"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(u),S(u,"plotly_hover",n.event)):(function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(u),M(u,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")&&("category"===l?e=E(r,a,u):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,v=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,y=u.parcatsViewModel.model.count,m=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(m+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=m/y,w=m/b,T=m/x,k={countLabel:y,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color ∩ "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=c.mostReadable(u.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(v-e.top),text:M,color:u.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:_,categorycount:x,colorcount:b,bandcolorcount:m}]}}(r,a,u):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,u)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function C(t){var e=t.parcatsViewModel;e.dragDimension||(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(v),-1!==e.hoverinfoItems.indexOf("skip"))||("color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event))}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,u=a.categories,c=u[l-1],f=u[l+1];void 0!==c&&s<c.y+c.height/2&&(o.model.displayInd=c.model.displayInd,c.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),z(t.parcatsViewModel)}}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=D(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var u=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),c=u.map((function(t){return t.categoryValue})),f=u.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[c],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function D(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function z(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr("d",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll("g.category").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr("transform",(function(t){return l(t.x,0)})),r(i).attr("transform",(function(t){return l(0,t.y)})),i.select(".dimlabel").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(".catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("x",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r="start"):(e=-5,r="end"),n.select(this).selectAll("tspan").attr("x",e).attr("text-anchor",r)}));var a=i.selectAll("rect.bandrect").data((function(t){return t.bands}),p),o=a.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);a.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,u=e.height,c=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(u*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n="all"===o.hoverinfo?["count","probability"]:(o.hoverinfo||"").split("+");var v={trace:o,key:o.uid,model:i,x:h,y:p,width:c,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(v),N(v)),v}function B(t,e,r,n,a){var o,s,l=[],u=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),u.push(o(1-a));var c="M "+t[0]+","+e[0];for(c+="l"+r[0]+",0 ",s=1;s<r.length;s++)c+="C"+l[s-1]+","+e[s-1]+" "+u[s-1]+","+e[s]+" "+t[s]+","+e[s],c+="l"+r[s]+",0 ";for(c+="l0,"+n+" ",c+="l -"+r[r.length-1]+",0 ",s=r.length-2;s>=0;s--)c+="C"+u[s]+","+(e[s+1]+n)+" "+l[s]+","+(e[s]+n)+" "+(t[s]+r[s])+","+(e[s]+n),c+="l-"+r[s]+",0 ";return c+"Z"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),u=[];for(var c in r.paths)r.paths.hasOwnProperty(c)&&u.push(r.paths[c]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}u.sort((function(e,r){var n=f(e),i=f(r);return"backward"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(u.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),v=0;v<u.length;v++){var g,y=u[v];g=p>0?d*(y.count/p):0;for(var m,x=new Array(n.length),b=0;b<y.categoryInds.length;b++){var _=y.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=g;var 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n=r(49084),i=r(94724),a=r(25376),o=r(86968).u,s=r(92880).extendFlat,l=r(31780).templatedArray;t.exports={domain:o({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:a({editType:"plot"}),tickfont:a({editType:"plot"}),rangefont:a({editType:"plot"}),dimensions:l("dimension",{label:{valType:"string",editType:"plot"},tickvals:s({},i.tickvals,{editType:"plot"}),ticktext:s({},i.ticktext,{editType:"plot"}),tickformat:s({},i.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:s({editType:"calc"},n("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"})),unselected:{line:{color:{valType:"color",dflt:"#7f7f7f",editType:"plot"},opacity:{valType:"number",min:0,max:1,dflt:"auto",editType:"plot"},editType:"plot"},editType:"plot"}}},71864:function(t,e,r){"use 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k(t){t.on("mousemove",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on("mouseleave",(function(t){t.parent.inBrushDrag||m()})).call(i.behavior.drag().on("dragstart",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,u=o.svgBrush;if(u.wasDragged=!1,u.grabbingBar="ns"===s.region,u.grabbingBar){var c=l.map(e.unitToPaddedPx);u.grabPoint=r-c[0]-n.verticalPadding,u.barLength=c[1]-c[0]}u.clickableOrdinalRange=s.clickableOrdinalRange,u.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(u.stayingIntervals=u.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),u.startExtent=s.region?l["s"===s.region?1:0]:a,e.parent.inBrushDrag=!0,u.brushStartCallback()}(this,t)})).on("drag",(function(t){w(this,t)})).on("dragend",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,m(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var u=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(u?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),u?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}t.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll("."+n.cn.axisBrush).data(o,a);i.enter().append("g").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(".background").data(o);a.enter().append("rect").classed("background",!0).call(d).call(v).style("pointer-events",i?"none":"auto").attr("transform",l(0,n.verticalPadding)),a.call(k).attr("height",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(".highlight-shadow").data(o);s.enter().append("line").classed("highlight-shadow",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width+n.bar.strokeWidth).attr("stroke",e).attr("opacity",n.bar.strokeOpacity).attr("stroke-linecap","butt"),s.attr("y1",(function(t){return t.height})).call(x);var u=t.selectAll(".highlight").data(o);u.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),u.attr("y1",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},61664:function(t,e,r){"use strict";t.exports={attributes:r(82296),supplyDefaults:r(60664),calc:r(95044),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:r(19976),categories:["gl","regl","noOpacity","noHover"],meta:{}}},19976:function(t,e,r){"use strict";var n=r(33428),i=r(84888)._M,a=r(24196),o=r(9616);e.name="parcoords",e.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this,r=t.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":r,preserveAspectRatio:"none",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},95044:function(t,e,r){"use strict";var n=r(3400).isArrayOrTypedArray,i=r(8932),a=r(71688).wrap;t.exports=function(t,e){var r,o;return 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n=r(3400),i=r(94288).hasColorscale,a=r(27260),o=r(86968).Q,s=r(51272),l=r(54460),u=r(82296),c=r(71864),f=r(30140).maxDimensionCount,h=r(26284);function p(t,e,r,i){function a(r,i){return n.coerce(t,e,u.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=c.cleanRanges(h,e))}}t.exports=function(t,e,r,l){function c(r,i){return n.coerce(t,e,u,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var v=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,"values",g);var y={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(c,"labelfont",y),n.coerceFont(c,"tickfont",y),n.coerceFont(c,"rangefont",y),c("labelangle"),c("labelside"),c("unselected.line.color"),c("unselected.line.opacity")}},95724:function(t,e,r){"use strict";var n=r(3400).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!("visible"in t)}},29928:function(t,e,r){"use strict";var n=r(61664);n.plot=r(24196),t.exports=n},51352:function(t,e,r){"use strict";var n=["precision highp float;","","varying vec4 fragColor;","","attribute vec4 p01_04, p05_08, p09_12, p13_16,","               p17_20, p21_24, p25_28, p29_32,","               p33_36, p37_40, p41_44, 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c(t,e,r,n,i){n("marker.line.width")&&n("marker.line.color",i?void 0:r.paper_bgcolor);var a=n("marker.colors");l(n,"marker.pattern",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l("labels"),l("values")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l("label0"),l("dlabel")),h){e._length=h,c(t,e,n,l,!0),l("scalegroup");var p,d=l("text"),v=l("texttemplate");if(v||(p=l("textinfo",i.isArrayOrTypedArray(d)?"text+percent":"percent")),l("hovertext"),l("hovertemplate"),v||p&&"none"!==p){var 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b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 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x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?c*=2:-1!==a.title.position.indexOf("right")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;"funnelarea"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function D(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function z(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,u=o.split("+"),c=function(t){return-1!==u.indexOf(t)},f=c("label"),h=c("text"),p=c("value"),d=c("percent"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d("pie",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,"trace").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u="px0",c="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u="px1",c="px0"}for(n=D(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=D(a,s),r.midangle=s,n=D(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr("stroke-linejoin","round"),d.each((function(){var m=n.select(this).selectAll("g.slice").data(e);m.enter().append("g").classed("slice",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll("path.surface").data([i]);if(d.enter().append("path").classed("surface",!0).style({"pointer-events":r?"none":"all"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A="M"+(u+i.px0[0])+","+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+"Z";k?d.attr("d","M"+(u+k*i.px0[0])+","+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+"Z"+A):d.attr("d",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr("d","M"+(u+k*i.px1[0])+","+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+"l"+S*i.px0[0]+","+S*i.px0[1]+M+"Z")}else d.attr("d","M"+u+","+c+"l"+i.px0[0]+","+i.px0[1]+M+"Z")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll("g.slicetext").data(i.text&&"none"!==E?[0]:[]);C.enter().append("g").classed("slicetext",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if("outside"===E)d=L(m,i);else if(d=w(m,i,v),"auto"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*v.r+","+n*v.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var k=n.select(this).selectAll("g.titletext").data(y.title.text?[0]:[]);if(k.enter().append("g").classed("titletext",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(s.font,y.title.font).call(f.convertToTspans,t),e="middle center"===y.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(v):C(v,h),r.attr("transform",c(e.x,e.y)+u(Math.min(1,e.scale))+c(e.tx,e.ty))})),T&&function(t,e){var r,n,i,a,o,s,u,c,f,h,p,d,v;function y(t,e){return t.pxmid[1]-e.pxmid[1]}function m(t,e){return e.pxmid[1]-t.pxmid[1]}function x(t,r){r||(r={});var i,c,f,p,d=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),v=n?t.yLabelMin:t.yLabelMax,y=n?t.yLabelMax:t.yLabelMin,m=t.cyFinal+o(t.px0[1],t.px1[1]),x=d-v;if(x*u>0&&(t.labelExtraY=x),l.isArrayOrTypedArray(e.pull))for(c=0;c<h.length;c++)(f=h[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,f.pts)||0)||((t.pxmid[1]-f.pxmid[1])*u>0?(x=f.cyFinal+o(f.px0[1],f.px1[1])-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x):(y+t.labelExtraY-m)*u>0&&(i=3*s*Math.abs(c-h.indexOf(t)),(p=f.cxFinal+a(f.px0[0],f.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=p)))}for(n=0;n<2;n++)for(i=n?y:m,o=n?Math.max:Math.min,u=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),f=t[1-n][r],h=f.concat(c),d=[],p=0;p<c.length;p++)void 0!==c[p].yLabelMid&&d.push(c[p]);for(v=!1,p=0;n&&p<f.length;p++)if(void 0!==f[p].yLabelMid){v=f[p];break}for(p=0;p<d.length;p++){var b=p&&d[p-1];v&&!p&&(b=v),x(d[p],b)}}}(_,y),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select("g.slicetext text");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,l.setTransormAndDisplay(i,t.transform);var a=t.cxFinal+t.pxmid[0],s="M"+a+","+(t.cyFinal+t.pxmid[1]),u=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var c=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(c)>Math.abs(f)?s+="l"+f*t.pxmid[0]/t.pxmid[1]+","+f+"H"+(a+t.labelExtraX+u):s+="l"+t.labelExtraX+","+c+"v"+(f-c)+"h"+u}else s+="V"+(t.yLabelMid+t.labelExtraY)+"h"+u;l.ensureSingle(r,"path","textline").call(o.stroke,e.outsidetextfont.color).attr({"stroke-width":Math.min(2,e.outsidetextfont.size/8),d:s,fill:"none"})}else r.select("path.textline").remove()}))}(m,y),T&&y.automargin){var A=s.bBox(d.node()),M=y.domain,S=h.w*(M.x[1]-M.x[0]),E=h.h*(M.y[1]-M.y[0]),P=(.5*S-v.r)/h.w,O=(.5*E-v.r)/h.h;i.autoMargin(t,"pie."+y.uid+".automargin",{xl:M.x[0]-P,xr:M.x[1]+P,yb:M.y[0]-O,yt:M.y[1]+O,l:Math.max(v.cx-v.r-A.left,0),r:Math.max(A.right-(v.cx+v.r),0),b:Math.max(A.bottom-(v.cy+v.r),0),t:Math.max(v.cy-v.r-A.top,0),pad:5})}}))}));setTimeout((function(){v.selectAll("tspan").each((function(){var t=n.select(this);t.attr("dy")&&t.attr("dy",t.attr("dy"))}))}),0)},formatSliceLabel:z,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:I,attachFxHandlers:x,computeTransform:R}},22152:function(t,e,r){"use strict";var n=r(33428),i=r(10528),a=r(82744).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(".trace");a(t,e,"pie"),e.each((function(e){var r=e[0].trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll("path.surface").each((function(e){n.select(this).call(i,e,r,t)}))}))}},10528:function(t,e,r){"use strict";var n=r(76308),i=r(69656).castOption,a=r(21552);t.exports=function(t,e,r,o){var s=r.marker.line,l=i(s.color,e.pts)||n.defaultLine,u=i(s.width,e.pts)||0;t.call(a,e,r,o).style("stroke-width",u).call(n.stroke,l)}},35484:function(t,e,r){"use strict";var n=r(52904);t.exports={x:n.x,y:n.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:n.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"},transforms:void 0}},11072:function(t,e,r){"use 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e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:i(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,s,l,u=this.xData=this.pickXData=t.x,c=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)i=n[2*l],s=n[2*l+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=u.length,n=new Float32Array(2*e),r=new 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r.update(e),r}},41904:function(t,e,r){"use strict";var n=r(3400),i=r(35484);t.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a("x"),a("y"),a("xbounds"),a("ybounds"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a("text"),a("marker.color",r),a("marker.opacity"),a("marker.blend"),a("marker.sizemin"),a("marker.sizemax"),a("marker.border.color",r),a("marker.border.arearatio"),e._length=null}},156:function(t,e,r){"use strict";["*pointcloud* trace is deprecated!","Please consider switching to the *scattergl* trace type."].join(" "),t.exports={attributes:r(35484),supplyDefaults:r(41904),calc:r(41484),plot:r(11072),moduleType:"trace",name:"pointcloud",basePlotModule:r(39952),categories:["gl","gl2d","showLegend"],meta:{}}},41440:function(t,e,r){"use strict";var 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:c.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return"top"===t.circularLinkType?"M "+(n.targetX-e)+" "+(n.targetY+r)+" L"+(n.rightInnerExtent-e)+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r-e)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r-e)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 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n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p="M"+n+","+u,d="C"+s+","+u+" "+l+","+f+" "+a+","+f,v="C"+l+","+h+" "+s+","+c+" "+n+","+c,g=e>0?"L"+(a+e)+","+(f+t.link.width/2):"";return p+d+(g+="L"+a+","+h)+v+"Z"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on("dragstart",(function(a){if("fixed"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,"g","dragcover",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),"snap"===a.arrangement)){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force("collide",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force("constrain",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var 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n=r(94288).hasColorscale,i=r(47128),a=r(43028);t.exports=function(t,e){a.hasLines(e)&&n(e,"line")&&i(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),a.hasMarkers(e)&&(n(e,"marker")&&i(t,e,{vals:e.marker.color,containerStr:"marker",cLetter:"c"}),n(e,"marker.line")&&i(t,e,{vals:e.marker.line.color,containerStr:"marker.line",cLetter:"c"}))}},88200:function(t){"use strict";t.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},96664:function(t,e,r){"use strict";var n=r(16356),i=r(96376).setGroupPositions;function a(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return a?i?i.s+(a.s-i.s)*(r-i[n])/(a[n]-i[n]):a.s:i.s}(t,e,r,o));e||(t[0].t=t[1].t,t[0].trace=t[1].trace,delete t[1].t,delete t[1].trace)}t.exports=function(t,e){"group"===t._fullLayout.scattermode&&function(t,e){for(var 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n=r(21776).Ks,i=r(21776).Gw,a=r(98304),o=r(52904),s=r(45464),l=r(49084),u=r(98192).u,c=r(92880).extendFlat,f=r(67824).overrideAll,h=o.marker,p=o.line,d=h.line;t.exports=f({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n=r(3400),i=r(24040),a=r(80088),o=r(2876),s=r(88200),l=r(43028),u=r(43980),c=r(31147),f=r(74428),h=r(66828),p=r(70840),d=r(124);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g("marker.line.width",y||m?1:0)),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,v,g),g("line.shape")),l.hasText(e)&&(g("texttemplate"),d(t,e,v,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},26768:function(t,e,r){"use strict";var n=r(3400),i=r(76308),a=r(13448).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=n.isArrayOrTypedArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},99396:function(t,e,r){"use strict";var n=r(76688);t.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},80088:function(t,e,r){"use strict";var n=r(67072);e.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},41272:function(t,e,r){"use strict";var n=r(24040),i=r(3400),a=r(44928);function 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A=(m.errorXOptions||[]).concat(m.errorYOptions||[]);m.error2d.update(A)}m.scatter2d&&m.scatter2d.update(m.markerOptions),m.fillOrder=s.repeat(null,_),m.fill2d&&(m.fillOptions=m.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,u=m.lineOptions[e],c=[];s._ownfill&&c.push(e),s._nexttrace&&c.push(e+1),c.length&&(m.fillOrder[e]=c);var f,h,p=[],d=u&&u.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if("toself"===s.fill||"tonext"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var v=s._nexttrace;if(v){var g=m.lineOptions[e+1];if(g){var y=g.positions;if("tonexty"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=v.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var _=m.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),m.fill2d.update(m.fillOptions))}var M=y.dragmode,S=l(M),E=y.clickmode.indexOf("select")>-1;for(v=0;v<_;v++){var L=r[v][0],C=L.trace,P=L.t,O=P.index,I=C._length,D=P.x,z=P.y;if(C.selectedpoints||S||E){if(S||(S=!0),C.selectedpoints){var R=m.selectBatch[O]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<I;g++)F[g]||B.push(g);m.unselectBatch[O]=B}var N=P.xpx=new Array(I),j=P.ypx=new Array(I);for(g=0;g<I;g++)N[g]=x.c2p(D[g]),j[g]=b.c2p(z[g])}else P.xpx=P.ypx=null}if(S){if(m.select2d||(m.select2d=n(y._glcanvas.data()[1].regl)),m.scatter2d){var U=new Array(_);for(v=0;v<_;v++)U[v]=m.selectBatch[v].length||m.unselectBatch[v].length?m.markerUnselectedOptions[v]:{};m.scatter2d.update(U)}m.select2d&&(m.select2d.update(m.markerOptions),m.select2d.update(m.markerSelectedOptions)),m.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};c.hasText(e)&&h(t)}))}else m.scatter2d&&m.scatter2d.update(m.markerOptions);var V={viewport:d(y,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},q=s.repeat(V,m.count);m.fill2d&&m.fill2d.update(q),m.line2d&&m.line2d.update(q),m.error2d&&m.error2d.update(q.concat(q)),m.scatter2d&&m.scatter2d.update(q),m.select2d&&m.select2d.update(q),m.glText&&m.glText.forEach((function(t){t.update(V)}))}else m.init()}}).reglPrecompiled=p},74588:function(t,e,r){"use strict";var n=r(3400);t.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,u=r.selectBatch,c=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(c[f].length){var h=n.repeat([],r.count);h[f]=c[f],o.draw(h)}else u[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(u),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},73224:function(t,e,r){"use strict";var n=r(43028),i=r(26768).styleTextSelection;t.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,u=r[0].t,c=l._length,f=u.x,h=u.y,p=u._scene,d=u.index;if(!p)return s;var 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l=s("u"),u=s("v"),c=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&u&&u.length&&c&&c.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},15436:function(t,e,r){"use strict";t.exports={moduleType:"trace",name:"streamtube",basePlotModule:r(12536),categories:["gl3d","showLegend"],attributes:r(90167),supplyDefaults:r(54304),colorbar:{min:"cmin",max:"cmax"},calc:r(3832).calc,plot:r(25668),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},424:function(t,e,r){"use strict";var n=r(45464),i=r(21776).Ks,a=r(21776).Gw,o=r(49084),s=r(86968).u,l=r(74996),u=r(27328),c=r(92880).extendFlat,f=r(98192).c;t.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:c({colors:{valType:"data_array",editType:"calc"},line:{color:c({},l.marker.line.color,{dflt:null}),width:c({},l.marker.line.width,{dflt:1}),editType:"calc"},pattern:f,editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current path","percent root","percent entry","percent 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n=r(74148),i=r(38248),a=r(3400),o=r(8932).makeColorScaleFuncFromTrace,s=r(45768).makePullColorFn,l=r(45768).generateExtendedColors,u=r(8932).calc,c=r(39032).ALMOST_EQUAL,f={},h={},p={};function d(t,e,r){var n=0,i=t.children;if(i){for(var a=i.length,o=0;o<a;o++)n+=d(i[o],e,r);r.branches&&n++}else r.leaves&&n++;return t.value=t.data.data.value=n,e._values||(e._values=[]),e._values[t.data.data.i]=n,n}e.calc=function(t,e){var r,l,f,h,p,v,g=t._fullLayout,y=e.ids,m=a.isArrayOrTypedArray(y),x=e.labels,b=e.parents,_=e.values,w=a.isArrayOrTypedArray(_),T=[],k={},A={},M=function(t){return t||"number"==typeof t},S=function(t){return!w||i(_[t])&&_[t]>=0};m?(r=Math.min(y.length,b.length),l=function(t){return M(y[t])&&S(t)},f=function(t){return String(y[t])}):(r=Math.min(x.length,b.length),l=function(t){return M(x[t])&&S(t)},f=function(t){return String(x[t])}),w&&(r=Math.min(r,_.length));for(var E=0;E<r;E++)if(l(E)){var L=f(E),C=M(b[E])?String(b[E]):"",P={i:E,id:L,pid:C,label:M(x[E])?String(x[E]):""};w&&(P.v=+_[E]),T.push(P),p=L,k[h=C]?k[h].push(p):k[h]=[p],A[p]=1}if(k[""]){if(k[""].length>1){for(var O=a.randstr(),I=0;I<T.length;I++)""===T[I].pid&&(T[I].pid=O);T.unshift({hasMultipleRoots:!0,id:O,pid:"",label:""})}}else{var D,z=[];for(D in k)A[D]||z.push(D);if(1!==z.length)return a.warn(["Multiple implied roots, cannot build",e.type,"hierarchy of",e.name+".","These roots include:",z.join(", ")].join(" "));D=z[0],T.unshift({hasImpliedRoot:!0,id:D,pid:"",label:D})}try{v=n.stratify().id((function(t){return t.id})).parentId((function(t){return t.pid}))(T)}catch(t){return a.warn(["Failed to build",e.type,"hierarchy of",e.name+".","Error:",t.message].join(" "))}var R=n.hierarchy(v),F=!1;if(w)switch(e.branchvalues){case"remainder":R.sum((function(t){return t.data.v}));break;case"total":R.each((function(t){var r=t.data.data,n=r.v;if(t.children){var i=t.children.reduce((function(t,e){return t+e.data.data.v}),0);if((r.hasImpliedRoot||r.hasMultipleRoots)&&(n=i),n<i*c)return F=!0,a.warn(["Total value for node",t.data.data.id,"of",e.name,"is smaller than the sum of its children.","\nparent value =",n,"\nchildren sum =",i].join(" "))}t.value=n}))}else d(R,e,{branches:-1!==e.count.indexOf("branches"),leaves:-1!==e.count.indexOf("leaves")});if(!F){var B,N;e.sort&&R.sort((function(t,e){return e.value-t.value}));var j=e.marker.colors||[],U=!!j.length;return e._hasColorscale?(U||(j=w?e.values:e._values),u(t,e,{vals:j,containerStr:"marker",cLetter:"c"}),N=o(e.marker)):B=s(g["_"+e.type+"colormap"]),R.each((function(t){var r=t.data.data;r.color=e._hasColorscale?N(j[r.i]):B(j[r.i],r.id)})),T[0].hierarchy=R,T}},e._runCrossTraceCalc=function(t,e){var r=e._fullLayout,n=e.calcdata,i=r[t+"colorway"],a=r["_"+t+"colormap"];r["extend"+t+"colors"]&&(i=l(i,"icicle"===t?p:"treemap"===t?h:f));var o,s=0;function u(t){var e=t.data.data,r=e.id;!1===e.color&&(a[r]?e.color=a[r]:t.parent?t.parent.parent?e.color=t.parent.data.data.color:(a[r]=e.color=i[s%i.length],s++):e.color=o)}for(var c=0;c<n.length;c++){var d=n[c][0];d.trace.type===t&&d.hierarchy&&(o=d.trace.root.color,d.hierarchy.each(u))}},e.crossTraceCalc=function(t){return e._runCrossTraceCalc("sunburst",t)}},27328:function(t){"use strict";t.exports={CLICK_TRANSITION_TIME:750,CLICK_TRANSITION_EASING:"linear",eventDataKeys:["currentPath","root","entry","percentRoot","percentEntry","percentParent"]}},25244:function(t,e,r){"use strict";var n=r(3400),i=r(424),a=r(86968).Q,o=r(31508).handleText,s=r(74174).handleMarkerDefaults,l=r(8932),u=l.hasColorscale,c=l.handleDefaults;t.exports=function(t,e,r,l){function f(r,a){return n.coerce(t,e,i,r,a)}var h=f("labels"),p=f("parents");if(h&&h.length&&p&&p.length){var d=f("values");d&&d.length?f("branchvalues"):f("count"),f("level"),f("maxdepth"),s(t,e,l,f);var v=e._hasColorscale=u(t,"marker","colors")||(t.marker||{}).coloraxis;v&&c(t,e,l,f,{prefix:"marker.",cLetter:"c"}),f("leaf.opacity",v?1:.7);var g=f("text");f("texttemplate"),e.texttemplate||f("textinfo",n.isArrayOrTypedArray(g)?"text+label":"label"),f("hovertext"),f("hovertemplate"),o(t,e,l,f,"auto",{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),f("insidetextorientation"),f("sort"),f("rotation"),f("root.color"),a(e,l,f),e._length=null}else e.visible=!1}},60404:function(t,e,r){"use strict";var n=r(43616),i=r(76308);t.exports=function(t,e,r,a,o){var s=e.data.data,l=s.i,u=o||s.color;if(l>=0){e.i=s.i;var c=r.marker;c.pattern&&c.colors&&c.pattern.shape||(c.color=u,e.color=u),n.pointStyle(t,r,a,e)}else i.fill(t,u)}},45716:function(t,e,r){"use strict";var n=r(33428),i=r(24040),a=r(10624).appendArrayPointValue,o=r(93024),s=r(3400),l=r(95924),u=r(78176),c=r(69656).formatPieValue;function f(t,e,r){for(var 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n=r(47128);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},79164:function(t,e,r){"use strict";var n=r(67792).gl_surface3d,i=r(67792).ndarray,a=r(67792).ndarray_linear_interpolate.d2,o=r(70448),s=r(11240),l=r(3400).isArrayOrTypedArray,u=r(33040).parseColorScale,c=r(43080),f=r(8932).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function v(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(v(t,n))}return e}function y(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function m(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++)r(i,[l,u],n),t.set(l,u,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=m(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=y(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,u=1+a+1,c=i(new Float32Array(l*u),[l,u]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(c,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,c,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=u(t),v=l.dataScale,g=t.z[0].length,y=t._ylength,m=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<y;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<y;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=v[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*y),[g,y]),i(new Float32Array(g*y),[g,y]),i(new Float32Array(g*y),[g,y])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<y;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*y),[g,y]);for(r=0;r<g;r++)for(n=0;n<y;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=v[2],w.intensityBounds[1]*=v[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),"lightposition"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},t.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},60192:function(t,e,r){"use strict";var n=r(24040),i=r(3400),a=r(27260),o=r(16716);function s(t,e,r,n){var i=n("opacityscale");"max"===i?e.opacityscale=[[0,.1],[1,1]]:"min"===i?e.opacityscale=[[0,1],[1,.1]]:"extremes"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=.1+.9*(1-Math.pow(Math.sin(1*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}():function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}t.exports={supplyDefaults:function(t,e,r,u){var c,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),v=h("z");if(!v||!v.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?v.length:v[0].length,e._ylength=v.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],u),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),y=["x","y","z"];for(c=0;c<3;++c){var m="contours."+y[c],x=h(m+".show"),b=h(m+".highlight");if(x||b)for(f=0;f<3;++f)h(m+".project."+y[f]);x&&(h(m+".color"),h(m+".width"),h(m+".usecolormap")),b&&(h(m+".highlightcolor"),h(m+".highlightwidth")),h(m+".start"),h(m+".end"),h(m+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,u,h,{prefix:"",cLetter:"c"}),s(0,e,0,h),e._length=null}},opacityscaleDefaults:s}},91304:function(t,e,r){"use strict";t.exports={attributes:r(16716),supplyDefaults:r(60192).supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:r(56576),plot:r(79164),moduleType:"trace",name:"surface",basePlotModule:r(12536),categories:["gl3d","2dMap","showLegend"],meta:{}}},60520:function(t,e,r){"use strict";var n=r(13916),i=r(92880).extendFlat,a=r(67824).overrideAll,o=r(25376),s=r(86968).u,l=r(29736).descriptionOnlyNumbers;(t.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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c=["","0","00","000","0000","00000","000000","0000000","00000000","000000000","0000000000","00000000000","000000000000","0000000000000","00000000000000","000000000000000","0000000000000000","00000000000000000","000000000000000000","0000000000000000000","00000000000000000000","000000000000000000000","0000000000000000000000","00000000000000000000000","000000000000000000000000","0000000000000000000000000"],f=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],h=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function p(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var u=1;u<n;u++){for(var c=l>>>26,f=67108863&l,h=Math.min(u,e.length-1),p=Math.max(0,u-t.length+1);p<=h;p++){var d=u-p|0;c+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[u]=0|f,l=0|c}return 0!==l?r.words[u]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||"hex"===t){r="";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!=(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}if(t===(0|t)&&t>=2&&t<=36){var u=f[t],p=h[t];r="";var d=this.clone();for(d.negative=0;!d.isZero();){var v=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?v+r:c[u-v.length]+v+r}for(this.isZero()&&(r="0"+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}n(!1,"Base should be between 2 and 36")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(void 0!==o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,u=new t(a),c=this.clone();if(l){for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[s]=o;for(;s<a;s++)u[s]=0}else{for(s=0;s<a-i;s++)u[s]=0;for(s=0;!c.isZero();s++)o=c.andln(255),c.iushrn(8),u[a-s-1]=o}return u},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,W=0|s[2],Y=8191&W,X=W>>>13,Z=0|s[3],K=8191&Z,J=Z>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,X)|0;var xt=(u+(n=n+Math.imul(f,K)|0)|0)+((8191&(i=(i=i+Math.imul(f,J)|0)+Math.imul(h,K)|0))<<13)|0;u=((a=a+Math.imul(h,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,X)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(d,K)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(v,K)|0,a=a+Math.imul(v,J)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(y,K)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(m,K)|0,a=a+Math.imul(m,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,K)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,K)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,K)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,H)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,K)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,K)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,H)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(L,K)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(O,K)|0,i=(i=i+Math.imul(O,J)|0)+Math.imul(I,K)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(z,K)|0,i=(i=i+Math.imul(z,J)|0)+Math.imul(R,K)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,K),i=(i=Math.imul(B,J))+Math.imul(N,K)|0,a=Math.imul(N,J),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){m.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function _(){m.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){m.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g<d);var y=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(y),f=y.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var 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p=l[f-2];if(p.left===h)if(h.left===s){if(!(d=p.right)||0!==d._color){p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).left===p?v.left=h:v.right=h);break}h._color=1,p.right=n(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){h.right=s.left,p._color=0,p.left=s.right,s._color=1,s.left=h,s.right=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).left===p?v.left=s:v.right=s);break}h._color=1,p.right=n(1,d),p._color=0,f-=1}else if(h.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).right===p?v.right=h:v.left=h);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var v;h.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=h,s.left=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).right===p?v.right=s:v.left=s);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}}return l[0]._color=1,new 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f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?h(s,s.left):s.right&&h(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return 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f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(c=!0),a("tickSize"),a("tickAngle"),a("tickPad"),u("tickColor");var v=l("labels");l("labelFont")&&(v=!0),o("labelEnable"),a("labelSize"),a("labelPad"),u("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),u("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),u("lineTickColor"),o("gridEnable"),a("gridWidth"),u("gridColor"),o("zeroEnable"),u("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),u("backgroundColor"),this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,D=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,D=(A+1+(1^O))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,P,this.zeroLineColor[D],this.zeroLineWidth[D]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var z=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(q[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,E=1,"auto"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)q[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),"auto"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),O=0;O<3;++O)q[O]+=k*U[O]*this.labelPad[O]/r[5*O];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},1011:function(t,e,r){"use strict";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(5827),i=r(2944),a=r(1943).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},2864:function(t,e,r){"use strict";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);o(u[I],u[P],u[O],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);var I,D=d([l[I],l[P],l[O],l[I+(1<<M)+(1<<S)]]);C?E=D:L=D}L>E&&(w|=1<<A)}}}var z=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==z&&(R<0||u[R][1]>u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<<x)!==w&&N!==z&&(F<0&&(F=N),(S=u[N])[0]<u[F][0]&&(F=N));var B=-1;for(x=0;x<3;++x){var N;(N=R^1<<x)!==w&&N!==z&&N!==F&&(B<0&&(B=N),(S=u[N])[0]>u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1<<A?-1:1;return y};var n=r(2288),i=r(104),a=r(4670),o=r(417),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},1681:function(t,e,r){"use strict";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(5827),i=r(2944),a=r(1943).j,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1943:function(t,e,r){"use strict";var n=r(6832),i=r(5158),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.j=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\n                                    0.0;\n}\n\nfloat roundTo(float a, float b) {\n  return float(b * floor((a + 0.5 * b) / b));\n}\n\nfloat look_round_n_directions(float a, int n) {\n  float b = positive_angle(a);\n  float div = TWO_PI / float(n);\n  float c = roundTo(b, div);\n  return look_upwards(c);\n}\n\nfloat applyAlignOption(float rawAngle, float delta) {\n  return\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\n                    rawAngle;                // otherwise return back raw input angle\n}\n\nbool isAxisTitle = (axis.x == 0.0) &&\n                   (axis.y == 0.0) &&\n                   (axis.z == 0.0);\n\nvoid main() {\n  //Compute world offset\n  float axisDistance = position.z;\n  vec3 dataPosition = axisDistance * axis + offset;\n\n  float beta = angle; // i.e. user defined attributes for each tick\n\n  float axisAngle;\n  float clipAngle;\n  float flip;\n\n  if (enableAlign) {\n    axisAngle = (isAxisTitle) ? HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.f=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);e.bg=function(t){return i(t,u,c,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},9557:function(t,e,r){"use strict";t.exports=function(t,e,r,i,o,l){var u=n(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,i,o,l),p};var n=r(5827),a=r(2944),o=r(875),s=r(1943).f,l=window||i.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=u[r];l||(l=u[r]={});var c=l[e];c||(c=l[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" 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v=0;v<n[d].length;++v)n[d][v].text&&s(n[d][v].x,n[d][v].text,n[d][v].font||i,n[d][v].fontSize||12,1.25,p);c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},8468:function(t,e){"use 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h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=m?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var O=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}}return _};var n=r(5795),i=r(4670),a=r(2864),o=r(104),s=r(2142),l=r(6342),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},5827:function(t,e,r){"use strict";var n=r(5306),i=r(7498),a=r(5050),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16"),r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,"uint16"):c(t,"float32"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},1140:function(t,e,r){"use strict";var n=r(2858);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=0;m<r.length;m++){var x=r[m];s=Math.min(x[0],s),l=Math.max(x[0],l),u=Math.min(x[1],u),c=Math.max(x[1],c),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[m];if(n.length(b)>o&&(o=n.length(b)),m){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),y=!1):y=!0}y||(p=x,d=b),v.push(b)}var w=[s,u,f],T=[l,c,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A,m=0;for(var M=0;m<r.length;m++)for(var S=(x=r[m])[0],E=x[1],L=x[2],C=v[m],P=n.length(C)*k,O=0;O<8;O++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var I=a.positions.length;a.cells.push([I-6,I-5,I-4],[I-3,I-2,I-1])}return a};var i=r(7234);t.exports.createMesh=r(5028),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:"cone"})}},5028:function(t,e,r){"use strict";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(104),l=r(7437),u=r(5050),c=r(9156),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||"cone");return m.update(e),m}},7234:function(t,e,r){var n=r(6832),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},1950:function(t){t.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},6603:function(t,e,r){var n=r(1950);t.exports=function(t){return n[t]}},3110:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=r(5827),i=r(2944),a=r(7667),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var p,d=n[l],v=e[s];Array.isArray(v[0])&&(v=e[l]),3===v.length?v=[v[0],v[1],v[2],1]:4===v.length&&(v=[v[0],v[1],v[2],v[3]],!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0)),isNaN(d[0][s])||isNaN(d[1][s])||(d[0][s]<0&&((p=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)),d[1][s]>0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},7667:function(t,e,r){"use strict";var n=r(6832),i=r(5158),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},4234:function(t,e,r){"use strict";var n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension("WEBGL_draw_buffers");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),"number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!u)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var v=!0;"depth"in n&&(v=!!n.depth);var g=!1;return"stencil"in n&&(g=!!n.stencil),new d(t,e,r,h,f,v,g,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension("WEBGL_depth_texture");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},3530:function(t,e,r){var n=r(8974).sprintf,i=r(6603),a=r(9365),o=r(8008);t.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var u=n("Error compiling %s shader %s:\n",l,s),c=n("%s%s",u,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var v=parseInt(d.split(":")[2]);if(isNaN(v))throw new Error(n("Could not parse error: %s",d));h[v]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+"\n",h[p+1])){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),u+=n("^^^ %s\n\n",y)}return{long:u.trim(),short:c.trim()}}},6386:function(t,e,r){"use strict";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(5070),i=r(9560),a=r(5306),o=r(5158),s=r(5827),l=r(1292);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,O;l?(P=x*(o[C]-c),O=x*(o[C+1]-c)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),O=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var D,z;l?(D=m*(r[I]-u),z=m*(r[I+1]-u)):(D=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),z=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(C+V)*b+(I+U):C*b+I],H=n.le(d,q);if(H<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(H===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),W=1-G,Y=4*H,X=4*(H+1);F=W*v[Y]+G*v[X],B=W*v[Y+1]+G*v[X+1],N=W*v[Y+2]+G*v[X+2],j=W*v[Y+3]+G*v[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*D+.5*z,k[2*S+1]=.5*P+.5*O,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},1292:function(t,e,r){"use strict";var n=r(6832);t.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},248:function(t,e,r){var n=r(6832),i=r(5158),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var y,m,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=u(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return 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= view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},8116:function(t,e,r){"use strict";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(115),l=r(104),u=r(7437),c=r(5050),f=r(9156),h=r(7212),p=r(5306),d=r(2056),v=r(4340),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,D=1/0,z=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];D=Math.min(D,F),z=Math.max(z,F)}else if(I)if(t.cellIntensityBounds)D=+t.cellIntensityBounds[0],z=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],D=Math.min(D,F),z=Math.max(z,F);else for(R=0;R<n.length;++R)F=n[R][2],D=Math.min(D,F),z=Math.max(z,F);this.intensity=P||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===X.length?m.push(X[0],X[1],X[2],this.opacity):(m.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],x.push(Z[0],Z[1]),B?b.push(B[Y]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],v.push(Z[0],Z[1]),g.push(R);q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t;for(U=0;U<3;++U){var Y,X,Z,K;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(X=S?S[Y]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-D)/(z-D),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[Y]:P?[(P[Y]-D)/(z-D),0]:O?O[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],u.push(Z[0],Z[1]),K=w?w[Y]:T[R],l.push(K[0],K[1],K[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=i(t),I=i(t),D=i(t),z=i(t),R=i(t),F=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:z,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,P,O,R,I,D,z,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},4554:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3016:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(5827),i=r(5158),a=r(5070),o=r(2709);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),D=0+6*O,z=6*Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,z&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,z&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},1154:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(5827),i=r(5158),a=r(2709);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2709:function(t,e,r){"use strict";var n=r(6832),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);t.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},5613:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(5827),i=r(5158),a=r(6946),o=r(5070),s=r(2709);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var y=1/(u[o+2]-u[o]),m=u[o],x=v.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-m)*y);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},2117:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(2611),i=r(3016),a=r(5613),o=r(1154),s=r(4554);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},u.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},u.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},u.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=c(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=c(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=c(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=c(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},u.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4296:function(t,e,r){"use strict";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];"distanceLimits"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),"zoomMin"in e&&(r[0]=e.zoomMin),"zoomMax"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),"turntable"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,c="zoom"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(8161),i=r(1152),a=r(6145),o=r(6475),s=r(2565),l=r(5233)},8245:function(t,e,r){var n=r(6832),i=r(5158),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},1059:function(t,e,r){"use strict";var n=r(4296),i=r(7453),a=r(2771),o=r(6496),s=r(2611),l=r(4234),u=r(8126),c=r(6145),f=r(1120),h=r(5268),p=r(8245),d=r(2321)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return"boolean"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement("canvas"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error("webgl not supported");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,z);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener("resize",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener("resize",N),e.removeEventListener("webglcontextlost",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener("webglcontextlost",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],q=[V[0].slice(),V[1].slice()];function H(){if(!U()){N();var t=F.camera.tick();D.view=F.camera.matrix,O=O||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,O=O||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||q[0][p]!==d[0][p]||q[1][p]!==d[1][p],q[0][p]=d[0][p],q[1][p]=d[1][p];if(I=I||y,O=O||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(z[0]=T,z[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(D))}i.end()}}}()),F.axesPixels=a(F.axes,D,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(D)),S.axes=A,x.object&&S.draw(D),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(D),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=z,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(D);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(D)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(O=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(H(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(O=!0,H())},F},createCamera:n}},8023:function(t,e,r){var n=r(6832);e.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),e.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),e.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),e.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},8271:function(t,e,r){"use strict";var n=r(5158),i=r(5827),a=r(5306),o=r(8023);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},6093:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},8240:function(t){"use strict";t.exports=function(t){return t||0===t?t.toString():""}},4123:function(t,e,r){"use strict";var n=r(875);t.exports=function(t,e,r){var a=i[e];if(a||(a=i[e]={}),t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(u=0;u<s.positions[l].length;++u)s.positions[l][u]/=r;for(l=0;l<c.positions.length;++l)for(u=0;u<c.positions[l].length;++u)c.positions[l][u]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=c.positions.length;for(l=0;l<h;++l){var p=c.positions[l];for(u=0;u<2;++u)f[0][u]=Math.min(f[0][u],p[u]),f[1][u]=Math.max(f[1][u],p[u])}return a[t]=[s,c,f]};var i={}},9282:function(t,e,r){var n=r(5158),i=r(6832),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),u=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),c=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},2182:function(t,e,r){"use strict";var n=r(3596),i=r(5827),a=r(2944),o=r(5306),s=r(104),l=r(9282),u=r(4123),c=r(8240),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var D=p(0,0,0,S(_,O)),z=p(0,0,0,S(w,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=O,O=I,I=R;var F=C;C=P,P=F}D[0]<0&&(O[C]=-1),z[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a="▼",o=!1);var s=u(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}y.draw=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},y.drawTransparent=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},y.drawPick=function(t){C(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,y=t.lineColor,m=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),O=o.mallocUint32(S);if(S>0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)z[T]=U[T];z[3]=1}else if(4===U.length){for(T=0;T<4;++T)z[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else z[0]=z[1]=z[2]=0,z[3]=1;else z=[1,1,1,0];if(j)if(Array.isArray(y)){var U;if(3===(U=B?n<y.length?y[n]:[0,0,0,0]:y).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(g)?q=n<g.length?+g[n]:0:g&&(q=+g);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T]);var W=i,Y=a;W=0,Array.isArray(i)?W=n<i.length?i[n]:0:i&&(W=i),Y=0,Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);var X=[W*=W>0?1-M[0][0]:W<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],Z=k.cells||[],K=k.positions||[];for(T=0;T<Z.length;++T)for(var J=Z[T],$=0;$<3;++$){for(var Q=0;Q<3;++Q)E[3*I+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*I+Q]=z[Q];O[I]=m;var tt=K[J[$]];C[2*I]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*I+1]=V*(G*tt[0]+H*tt[1]+X[1]),I+=1}for(Z=A.edges,K=A.positions,T=0;T<Z.length;++T)for(J=Z[T],$=0;$<2;++$){for(Q=0;Q<3;++Q)E[3*D+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*D+Q]=R[Q];O[D]=m,tt=K[J[$]],C[2*D]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*D+1]=V*(G*tt[0]+H*tt[1]+X[1]),D+=1}}}this.bounds=[c,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(O),o.free(E),o.free(L),o.free(C),o.free(O)},y.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},1884:function(t,e,r){"use strict";var n=r(6832);e.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),e.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},6623:function(t,e,r){"use strict";var n=r(5158),i=r(5827),a=r(1884);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var 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s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),u=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),c=a(t);c.attributes.position.location=0,c.attributes.color.location=1,c.attributes.weight.location=2;var f=new s(t,l,u,c);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,u=[0,0,0],c=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=u,p=c,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);for(f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f,d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},9578:function(t,e,r){var n=r(6832),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},7307:function(t,e,r){"use strict";var n=r(2858),i=r(4020),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],P=(l-M)/(S-M),O=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var D=i[b+w+k],z=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,D,B,P),n.lerp(q,z,N,P),n.lerp(H,R,j,P),n.lerp(G,F,U,P);var W=n.create(),Y=n.create();n.lerp(W,V,H,O),n.lerp(Y,q,G,O);var X=n.create();return n.lerp(X,W,Y,I),X}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],I=d(C),D=C;O.push(I);var z=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),z.push(F),g.push({points:P,velocities:O,divergences:z});for(var B=0;B<100*u&&P.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-A>-1e-4*A&&(P.push(N),D=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),z.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(9578),c=r(1140).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:"streamtube"})}},9054:function(t,e,r){var n=r(5158),i=r(6832),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color — in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},3754:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||"jet",g.update(y),g};var n=r(2288),i=r(5827),a=r(2944),o=r(8931),s=r(5306),l=r(9156),u=r(7498),c=r(7382),f=r(5050),h=r(4162),p=r(104),d=r(7437),v=r(5070),g=r(9144),y=r(9054),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},D=T.slice(),z=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=D;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=B(t.contourWidth,Number)),"showContour"in t&&(this.showContour=B(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=B(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=j(t.contourColor)),"contourProject"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=j(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],"mirror");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,y,0,E*P),b.set(o,y,1,L*P),b.set(o,y,2,C*P)}s.free(x.data);var O=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],D=1/0,z=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+y+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var W=o+k[H][0],Y=y+k[H][1],X=this._field[0].get(W+1,Y+1),Z=this._field[1].get(W+1,Y+1);G=this._field[2].get(W+1,Y+1),E=b.get(W+1,Y+1,0),L=b.get(W+1,Y+1,1),C=b.get(W+1,Y+1,2),t.intensity&&(K=t.intensity.get(W,Y));var K=t.intensity?t.intensity.get(W,Y):G+this.objectOffset[2];F[N++]=W,F[N++]=Y,F[N++]=X,F[N++]=Z,F[N++]=G,F[N++]=0,F[N++]=K,F[N++]=E,F[N++]=L,F[N++]=C,O[0]=Math.min(O[0],X+this.objectOffset[0]),O[1]=Math.min(O[1],Z+this.objectOffset[1]),O[2]=Math.min(O[2],G+this.objectOffset[2]),D=Math.min(D,K),I[0]=Math.max(I[0],X+this.objectOffset[0]),I[1]=Math.max(I[1],Z+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),z=Math.max(z,K),U+=1}}for(t.intensityBounds&&(D=+t.intensityBounds[0],z=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-D)/(z-D);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[O,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===D&&this.intensityBounds[1]===z||(r=!0),this.intensityBounds=[D,z]}if("levels"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;o<3;++o)J[o]=J[o].slice(),J[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<J[o].length;++y)J[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(W=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var yt=q?ht:1-ht;if(Y=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[vt].get(W,Y):(this.intensity.get(W,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,O=T*L,I=T*E,D=C*f.get(_,M)+P*f.get(_,S)+O*f.get(w,M)+I*f.get(w,S),z=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+I*p.get(w,S);if(isNaN(D)||isNaN(z)){m&&(n-=1);break}a[2*n+0]=D,a[2*n+1]=z,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},8931:function(t,e,r){"use strict";var n=r(5050),i=r(7498),a=r(5306);t.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),"number"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=d(o,e.stride.slice()),u=0;"float32"===r?u=t.FLOAT:"float64"===r?(u=t.FLOAT,l=!1,r="float32"):"uint8"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r="uint8");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");g=t.RGBA}}u!==t.FLOAT||t.getExtension("OES_texture_float")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);"float32"!==r&&"float64"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var 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e=("property"===t.expressionContext?dn:fn)(Vt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Bt(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Bt(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Xe(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Xe(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ze(r,["zoom","feature-state"]))return[new Bt(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Ye(r))return[new Bt(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function wn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ut(r))&&i.push(new Bt(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" 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An(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};Tn(t)||(t=En(t));var e=fn(t,kn);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:Sn(t)}}function Mn(t,e){return t<e?-1:t>e?1:0}function Sn(t){if(!Array.isArray(t))return!1;if("within"===t[0])return!0;for(var e=1;e<t.length;e++)if(Sn(t[e]))return!0;return!1}function En(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?Ln(t[1],t[2],"=="):"!="===r?On(Ln(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?Ln(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(En))):"all"===r?["all"].concat(t.slice(1).map(En)):"none"===r?["all"].concat(t.slice(1).map(En).map(On)):"in"===r?Cn(t[1],t.slice(2)):"!in"===r?On(Cn(t[1],t.slice(2))):"has"===r?Pn(t[1]):"!has"===r?On(Pn(t[1])):"within"!==r||t}function Ln(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function Cn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(Mn)]]:["filter-in-small",t,["literal",e]]}}function Pn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function On(t){return["!",t]}function In(t){return Tn(Vt(t.value))?_n(jt({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):Dn(t)}function Dn(t){var e=t.value,r=t.key;if("array"!==Qr(e))return[new Bt(r,e,"array expected, "+Qr(e)+" found")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,"filter array must have at least 1 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Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"string"!==n&&a.push(new Bt(r+"[1]",e[1],"string expected, "+n+" found"));break;case"within":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"object"!==n&&a.push(new Bt(r+"[1]",e[1],"object expected, "+n+" found"))}return a}function zn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return Hn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new Bt(r,a,'unknown property "'+o+'"')];if("string"===Qr(a)&&Kr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new Bt(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(u[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Bt(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&tn(Vt(a))&&"identity"===Ut(a.type)&&f.push(new Bt(r,a,'"text-font" does not support identity functions'))),f.concat(Hn({key:t.key,value:a,valueSpec:c,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function Rn(t){return zn(t,"paint")}function Fn(t){return zn(t,"layout")}function Bn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Bt(n,r,'either "type" or "ref" is required'));var o,s=Ut(r.type),l=Ut(r.ref);if(r.id)for(var u=Ut(r.id),c=0;c<t.arrayIndex;c++){var f=i.layers[c];Ut(f.id)===u&&e.push(new Bt(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Bt(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Bt(n,r,'missing required property "source"'));return e=e.concat(yn({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return Hn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:In,layout:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Fn(jt({layerType:s},t))}}})},paint:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Rn(jt({layerType:s},t))}}})}}})),e}function Nn(t){var e=t.value,r=t.key,n=Qr(e);return"string"!==n?[new Bt(r,e,"string expected, "+n+" found")]:[]}var jn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Qr(r))return Nn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,Nn({key:e+"."+i,value:r[i]}));return 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Yn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function Xn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Yn(t.apply(this,e))}}Wn.source=Xn(Un),Wn.light=Xn(Vn),Wn.layer=Xn(Bn),Wn.filter=Xn(In),Wn.paintProperty=Xn(Rn),Wn.layoutProperty=Xn(Fn);var Zn=Wn,Kn=Zn.light,Jn=Zn.paintProperty,$n=Zn.layoutProperty;function Qn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1){var a=i[n];t.fire(new zt(new Error(a.message))),r=!0}return r}var ti=ri,ei=3;function ri(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],e=i[1],r=i[2],this.d=e+2*r;for(var a=0;a<this.d*this.d;a++){var o=i[ei+a],s=i[ei+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[ei+n.length],u=i[ei+n.length+1];this.keys=i.subarray(l,u),this.bboxes=i.subarray(u),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/e*t;this.min=-f,this.max=t+f}ri.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ri.prototype._insertReadonly=function(){throw"Cannot insert into a GridIndex created from an ArrayBuffer."},ri.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},ri.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},ri.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var u=this.keys,c=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(c[p+0],c[p+1],c[p+2],c[p+3]):t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1])?(o[h]=!0,a.push(u[h])):o[h]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[ei+o]=a,i.set(s,a),a+=s.length}return i[ei+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[ei+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var ni=s.ImageData,ii=s.ImageBitmap,ai={};function oi(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,"_classRegistryKey",{value:t,writeable:!1}),ai[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var si in oi("Object",Object),ti.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},ti.deserialize=function(t){return new ti(t.buffer)},oi("Grid",ti),oi("Color",ue),oi("Error",Error),oi("ResolvedImage",pe),oi("StylePropertyFunction",vn),oi("StyleExpression",un,{omit:["_evaluator"]}),oi("ZoomDependentExpression",pn),oi("ZoomConstantExpression",hn),oi("CompoundExpression",Ee,{omit:["_evaluate"]}),qr)qr[si]._classRegistryKey||oi("Expression_"+si,qr[si]);function li(t){return t&&"undefined"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&"ArrayBuffer"===t.constructor.name)}function ui(t){return ii&&t instanceof ii}function ci(t,e){if(null==t||"boolean"==typeof t||"number"==typeof t||"string"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(li(t)||ui(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof ni)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(ci(o,e))}return n}if("object"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error("can't serialize object of unregistered class");var u=s.serialize?s.serialize(t,e):{};if(!s.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(ai[l].omit.indexOf(c)>=0)){var f=t[c];u[c]=ai[l].shallow.indexOf(c)>=0?f:ci(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error("$name property is reserved for worker serialization logic.");return"Object"!==l&&(u.$name=l),u}throw new Error("can't serialize object of type "+typeof t)}function fi(t){if(null==t||"boolean"==typeof t||"number"==typeof t||"string"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||li(t)||ui(t)||ArrayBuffer.isView(t)||t instanceof ni)return t;if(Array.isArray(t))return t.map(fi);if("object"==typeof t){var e=t.$name||"Object",r=ai[e].klass;if(!r)throw new Error("can't deserialize unregistered class "+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if("$name"!==o){var s=t[o];n[o]=ai[e].shallow.indexOf(o)>=0?s:fi(s)}}return n}throw new Error("can't deserialize object of type "+typeof t)}var hi=function(){this.first=!0};hi.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var pi={"Latin-1 Supplement":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},"Arabic Supplement":function(t){return t>=1872&&t<=1919},"Arabic Extended-A":function(t){return t>=2208&&t<=2303},"Hangul Jamo":function(t){return t>=4352&&t<=4607},"Unified Canadian Aboriginal Syllabics":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},"Unified Canadian Aboriginal Syllabics Extended":function(t){return t>=6320&&t<=6399},"General Punctuation":function(t){return t>=8192&&t<=8303},"Letterlike Symbols":function(t){return t>=8448&&t<=8527},"Number Forms":function(t){return t>=8528&&t<=8591},"Miscellaneous Technical":function(t){return t>=8960&&t<=9215},"Control Pictures":function(t){return t>=9216&&t<=9279},"Optical Character Recognition":function(t){return t>=9280&&t<=9311},"Enclosed Alphanumerics":function(t){return t>=9312&&t<=9471},"Geometric Shapes":function(t){return t>=9632&&t<=9727},"Miscellaneous Symbols":function(t){return t>=9728&&t<=9983},"Miscellaneous Symbols and Arrows":function(t){return t>=11008&&t<=11263},"CJK Radicals Supplement":function(t){return t>=11904&&t<=12031},"Kangxi Radicals":function(t){return t>=12032&&t<=12255},"Ideographic Description Characters":function(t){return t>=12272&&t<=12287},"CJK Symbols and 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t>=43360&&t<=43391},"Hangul Syllables":function(t){return t>=44032&&t<=55215},"Hangul Jamo Extended-B":function(t){return t>=55216&&t<=55295},"Private Use Area":function(t){return t>=57344&&t<=63743},"CJK Compatibility Ideographs":function(t){return t>=63744&&t<=64255},"Arabic Presentation Forms-A":function(t){return t>=64336&&t<=65023},"Vertical Forms":function(t){return t>=65040&&t<=65055},"CJK Compatibility Forms":function(t){return t>=65072&&t<=65103},"Small Form Variants":function(t){return t>=65104&&t<=65135},"Arabic Presentation Forms-B":function(t){return t>=65136&&t<=65279},"Halfwidth and Fullwidth Forms":function(t){return t>=65280&&t<=65519}};function di(t){for(var e=0,r=t;e<r.length;e+=1)if(vi(r[e].charCodeAt(0)))return!0;return!1}function vi(t){return!(746!==t&&747!==t&&(t<4352||!(pi["Bopomofo Extended"](t)||pi.Bopomofo(t)||pi["CJK Compatibility Forms"](t)&&!(t>=65097&&t<=65103)||pi["CJK Compatibility Ideographs"](t)||pi["CJK Compatibility"](t)||pi["CJK Radicals 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Syllables"](t)||pi["Yi Radicals"](t))))}function gi(t){return!(vi(t)||function(t){return!!(pi["Latin-1 Supplement"](t)&&(167===t||169===t||174===t||177===t||188===t||189===t||190===t||215===t||247===t)||pi["General Punctuation"](t)&&(8214===t||8224===t||8225===t||8240===t||8241===t||8251===t||8252===t||8258===t||8263===t||8264===t||8265===t||8273===t)||pi["Letterlike Symbols"](t)||pi["Number Forms"](t)||pi["Miscellaneous Technical"](t)&&(t>=8960&&t<=8967||t>=8972&&t<=8991||t>=8996&&t<=9e3||9003===t||t>=9085&&t<=9114||t>=9150&&t<=9165||9167===t||t>=9169&&t<=9179||t>=9186&&t<=9215)||pi["Control Pictures"](t)&&9251!==t||pi["Optical Character Recognition"](t)||pi["Enclosed Alphanumerics"](t)||pi["Geometric Shapes"](t)||pi["Miscellaneous Symbols"](t)&&!(t>=9754&&t<=9759)||pi["Miscellaneous Symbols and Arrows"](t)&&(t>=11026&&t<=11055||t>=11088&&t<=11097||t>=11192&&t<=11243)||pi["CJK Symbols and Punctuation"](t)||pi.Katakana(t)||pi["Private Use Area"](t)||pi["CJK Compatibility 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},Di.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var zi=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new vn(t,e);if(cn(t)){var r=dn(t,e);if("error"===r.result)throw new Error(r.value.map((function(t){return t.key+": "+t.message})).join(", "));return r.value}var n=t;return"string"==typeof t&&"color"===e.type&&(n=ue.parse(t)),{kind:"constant",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};zi.prototype.isDataDriven=function(){return"source"===this.expression.kind||"composite"===this.expression.kind},zi.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new zi(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Fi.prototype.getValue=function(t){return w(this._values[t].value.value)},Fi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].value=new zi(this._values[t].property,null===e?void 0:w(e))},Fi.prototype.getTransition=function(t){return w(this._values[t].transition)},Fi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].transition=w(e)||void 0},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var 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o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ni.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var ji=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ji.prototype.getValue=function(t){return w(this._values[t].value)},ji.prototype.setValue=function(t,e){this._values[t]=new zi(this._values[t].property,null===e?void 0:w(e))},ji.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ji.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Ui=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Ui.prototype.isConstant=function(){return"constant"===this.value.kind},Ui.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},Ui.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Vi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Vi.prototype.get=function(t){return this._values[t]};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},qi.prototype.interpolate=function(t,e,r){var n=rr[this.specification.type];return n?n(t,e,r):t};var Hi=function(t,e){this.specification=t,this.overrides=e};Hi.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new Ui(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new Ui(this,t.expression,e)},Hi.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ui(this,{kind:"constant",value:void 0},t.parameters);var n=rr[this.specification.type];return n?new Ui(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Hi.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 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this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate($n,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Qn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Ft,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof 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e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Qi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi("StructArrayLayout2i4",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return 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this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Qi);ua.prototype.bytesPerElement=12,oi("StructArrayLayout3f12",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Qi);ca.prototype.bytesPerElement=4,oi("StructArrayLayout1ul4",ca);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Qi);Ta.prototype.bytesPerElement=16,oi("StructArrayLayout4f16",Ta);var ka=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}($i);ka.prototype.size=20;var Aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new 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r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(ma);oi("GlyphOffsetArray",Ca);var Pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(xa);oi("SymbolLineVertexArray",Pa);var Oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return 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Float32Array(16),ro=function(t){function e(e,r){t.call(this,e,r),this.current=eo}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Xa);function no(t){return[Ra(255*t.r,255*t.g),Ra(255*t.b,255*t.a)]}var io=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return"u_"+t})),this.type=r};io.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},io.prototype.getBinding=function(t,e,r){return"color"===this.type?new to(t,e):new Ka(t,e)};var ao=function(t,e){this.uniformNames=e.map((function(t){return"u_"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};ao.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},ao.prototype.setUniform=function(t,e,r,n){var i="u_pattern_to"===n?this.patternTo:"u_pattern_from"===n?this.patternFrom:"u_pixel_ratio_to"===n?this.pixelRatioTo:"u_pixel_ratio_from"===n?this.pixelRatioFrom:null;i&&t.set(i)},ao.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new Qa(t,e):new Ka(t,e)};var oo=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?2:1,offset:0}})),this.paintVertexArray=new n};oo.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new Di(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},oo.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},oo.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=no(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},oo.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},oo.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var so=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return"u_"+t+"_t"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?4:2,offset:0}})),this.paintVertexArray=new a};so.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new Di(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Di(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},so.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},so.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=no(r),a=no(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},so.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},so.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=f(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},so.prototype.getBinding=function(t,e,r){return new Ka(t,e)};var lo=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};lo.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},lo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},lo.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},lo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Fa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Fa.members,this.expression.isStateDependent))},lo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var uo=function(t,e,r){this.binders={},this._buffers=[];var n=[];for(var i in t.paint._values)if(r(i)){var a=t.paint.get(i);if(a instanceof Ui&&Kr(a.property.specification)){var o=fo(i,t.type),s=a.value,l=a.property.specification.type,u=a.property.useIntegerZoom,c=a.property.specification["property-type"],f="cross-faded"===c||"cross-faded-data-driven"===c;if("constant"===s.kind)this.binders[i]=f?new ao(s.value,o):new io(s.value,o,l),n.push("/u_"+i);else if("source"===s.kind||f){var h=ho(i,l,"source");this.binders[i]=f?new lo(s,l,u,e,h,t.id):new oo(s,o,l,h),n.push("/a_"+i)}else{var p=ho(i,l,"composite");this.binders[i]=new so(s,o,l,u,e,p),n.push("/z_"+i)}}}this.cacheKey=n.sort().join("")};uo.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof oo||e instanceof so?e.maxValue:0},uo.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof oo||o instanceof so||o instanceof lo)&&o.populatePaintArray(t,e,r,n,i)}},uo.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ao&&n.setConstantPatternPositions(t,e)}},uo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof oo||h instanceof so||h instanceof lo)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},uo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof io||r instanceof ao)&&t.push.apply(t,r.uniformNames.map((function(t){return"#define HAS_UNIFORM_"+t})))}return t},uo.prototype.getBinderAttributes=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof oo||r instanceof so)for(var n=0;n<r.paintVertexAttributes.length;n++)t.push(r.paintVertexAttributes[n].name);else if(r instanceof lo)for(var i=0;i<Fa.members.length;i++)t.push(Fa.members[i].name)}return t},uo.prototype.getBinderUniforms=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof io||r instanceof ao||r instanceof so)for(var n=0,i=r.uniformNames;n<i.length;n+=1){var a=i[n];t.push(a)}}return t},uo.prototype.getPaintVertexBuffers=function(){return this._buffers},uo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof io||i instanceof ao||i instanceof so)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},uo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},uo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof lo){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof oo||r instanceof so)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},uo.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof oo||r instanceof so||r instanceof lo)&&r.upload(t)}this.updatePaintBuffers()},uo.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof oo||e instanceof so||e instanceof lo)&&e.destroy()}};var co=function(t,e,r){void 0===r&&(r=function(){return!0}),this.programConfigurations={};for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.programConfigurations[a.id]=new uo(a,e,r)}this.needsUpload=!1,this._featureMap=new qa,this._bufferOffset=0};function fo(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(e+"-","").replace(/-/g,"_")]}function ho(t,e,r){var n={color:{source:oa,composite:Ta},number:{source:ma,composite:oa}},i=function(t){return{"line-pattern":{source:sa,composite:sa},"fill-pattern":{source:sa,composite:sa},"fill-extrusion-pattern":{source:sa,composite:sa}}[t]}(t);return i&&i[r]||n[e][r]}co.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},co.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},co.prototype.get=function(t){return this.programConfigurations[t]},co.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},co.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},oi("ConstantBinder",io),oi("CrossFadedConstantBinder",ao),oi("SourceExpressionBinder",oo),oi("CrossFadedCompositeBinder",lo),oi("CompositeExpressionBinder",so),oi("ProgramConfiguration",uo,{omit:["_buffers"]}),oi("ProgramConfigurationSet",co);var po=8192,vo=Math.pow(2,14)-1,go=-vo-1;function yo(t){for(var e=po/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a],s=Math.round(o.x*e),l=Math.round(o.y*e);o.x=f(s,go,vo),o.y=f(l,go,vo),(s<o.x||s>o.x+1||l<o.y||l>o.y+1)&&k("Geometry exceeds allowed extent, reduce your vector tile buffer size")}return r}function mo(t,e){return{type:t.type,id:t.id,properties:t.properties,geometry:e?yo(t):[]}}function xo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var bo=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ra,this.indexArray=new va,this.segments=new za,this.programConfigurations=new co(t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function _o(t,e){for(var r=0;r<t.length;r++)if(Co(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Co(t,e[n]))return!0;return!!Ao(t,e)}function wo(t,e,r){return!!Co(t,e)||!!So(e,t,r)}function To(t,e){if(1===t.length)return Lo(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Co(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Lo(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Ao(t,e[o]))return!0;return!1}function ko(t,e,r){if(t.length>1){if(Ao(t,e))return!0;for(var n=0;n<e.length;n++)if(So(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(So(t[i],e,r))return!0;return!1}function Ao(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(Mo(n,i,e[a],e[a+1]))return!0;return!1}function Mo(t,e,r,n){return A(t,r,n)!==A(e,r,n)&&A(t,e,r)!==A(t,e,n)}function So(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Eo(t,e[i-1],e[i])<n)return!0;return!1}function Eo(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function Lo(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Co(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Po(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=A(t,e,r[0]);return a!==A(t,e,r[1])||a!==A(t,e,r[2])||a!==A(t,e,r[3])}function Oo(t,e,r){var n=e.paint.get(t).value;return"constant"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Io(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Do(t,e,r,n,i){if(!e[0]&&!e[1])return t;var o=a.convert(e)._mult(i);"viewport"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var u=t[l];s.push(u.sub(o))}return s}bo.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;"circle"===n.type&&(a=n.layout.get("circle-sort-key"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],u=l.feature,c=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d=mo(u,p);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),d,r)){var v=a?a.evaluate(d,{},r):void 0,g={id:c,properties:u.properties,type:u.type,sourceLayerIndex:h,index:f,geometry:p?d.geometry:yo(u),patterns:{},sortKey:v};i.push(g)}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=x,_=b.geometry,w=b.index,T=b.sourceLayerIndex,k=t[w].feature;this.addFeature(x,_,w,r),e.featureIndex.insert(k,_,w,T,this.index)}},bo.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},bo.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},bo.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},bo.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Da),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},bo.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},bo.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],u=l.x,c=l.y;if(!(u<0||u>=po||c<0||c>=po)){var 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t.id}))};Us.prototype.populate=function(t,e,r){this.hasPattern=Ns("fill",this.layers,e);for(var n=this.layers[0].layout.get("fill-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),p,r)){var d=n?n.evaluate(p,{},r,e.availableImages):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js("fill",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},Us.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Us.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},Us.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Us.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Us.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,is),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0},Us.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())},Us.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray),h=f.vertexLength,p=[],d=[],v=0,g=s;v<g.length;v+=1){var y=g[v];if(0!==y.length){y!==s[0]&&d.push(p.length/2);var 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u=t.readVarint();n=7&u,i=u>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Ys.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,u=-1/0;t.pos<e;){if(n<=0){var c=t.readVarint();r=7&c,n=c>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>u&&(u=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,u]},Ys.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),u=Ys.types[this.type];function c(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];c(l=f);break;case 2:for(n=0;n<l.length;n++)c(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=Zs(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)c(l[n][i])}1===l.length?l=l[0]:u="Multi"+u;var h={type:"Feature",geometry:{type:u,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var Ks=Js;function Js(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields($s,this,e),this.length=this._features.length}function $s(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new Ks(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Js.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Ws(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Ws,VectorTileLayer:Ks},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ia,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new za,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function al(t,e){return t.x===e.x&&(t.x<0||t.x>po)||t.y===e.y&&(t.y<0||t.y>po)}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f=mo(o,c);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:yo(o),properties:o.properties,type:o.type,patterns:{}};this.hasPattern?this.features.push(js("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},il.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((O=d).every((function(t){return t.x<0}))||O.every((function(t){return t.x>po}))||O.every((function(t){return t.y<0}))||O.every((function(t){return t.y>po}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!al(y,m)){f.vertexLength+4>za.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var 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Hi(Ft["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Gi(Ft["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},sl=function(t){function e(e){t.call(this,e,ol)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return Io(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var u=Do(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),c=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];qo(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,P=A+h,O=M+p,I=S+d,D=new a((k+s)/L,(A+l)/L);D.z=E/L,y.push(D);var z=new a(C/I,P/I);z.z=O/I,m.push(z)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;To(r,e)&&(n=ul(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var 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e=this;this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((function(t){e.gradients[t.id]={}})),this.layoutVertexArray=new aa,this.layoutVertexArray2=new oa,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new za,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};vl.prototype.populate=function(t,e,r){this.hasPattern=Ns("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),p,r)){var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js("line",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},vl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},vl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},vl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},vl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},vl.prototype.upload=function(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,fl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,cl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},vl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},vl.prototype.lineFeatureClips=function(t){if(t.properties&&t.properties.hasOwnProperty("mapbox_clip_start")&&t.properties.hasOwnProperty("mapbox_clip_end"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}},vl.prototype.addFeature=function(t,e,r,n,i){var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),u=a.get("line-round-limit");this.lineClips=this.lineFeatureClips(t);for(var c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},vl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var s="Polygon"===hl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var c,f=this.overscaling<=16?15*po/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<pl&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,c),"miter"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if("flipbevel"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,O=k?0:C;if(p&&this.addCurrentVertex(c,v,P,O,h),"fakeround"===E)for(var I=Math.round(180*w/Math.PI/20),D=1;D<I;D++){var z=D/I;if(.5!==z){var R=z-.5;z+=z*R*(z-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(z)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-P,-O,h)}else if("butt"===E)this.addCurrentVertex(c,m,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},vl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,u=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,u,a,!0,-n,i),this.distance>dl/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},vl.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,u=.5*(this.lineClips?this.scaledDistance*(dl-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&u)<<2,u>>6),this.lineClips){var c=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(c,this.lineClipsArray.length)}var f=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,f),o.primitiveLength++),i?this.e2=f:this.e1=f},vl.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},vl.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},oi("LineBucket",vl,{omit:["layers","patternFeatures"]});var gl=new Xi({"line-cap":new qi(Ft.layout_line["line-cap"]),"line-join":new Hi(Ft.layout_line["line-join"]),"line-miter-limit":new qi(Ft.layout_line["line-miter-limit"]),"line-round-limit":new qi(Ft.layout_line["line-round-limit"]),"line-sort-key":new Hi(Ft.layout_line["line-sort-key"])}),yl={paint:new Xi({"line-opacity":new Hi(Ft.paint_line["line-opacity"]),"line-color":new Hi(Ft.paint_line["line-color"]),"line-translate":new qi(Ft.paint_line["line-translate"]),"line-translate-anchor":new qi(Ft.paint_line["line-translate-anchor"]),"line-width":new Hi(Ft.paint_line["line-width"]),"line-gap-width":new Hi(Ft.paint_line["line-gap-width"]),"line-offset":new Hi(Ft.paint_line["line-offset"]),"line-blur":new Hi(Ft.paint_line["line-blur"]),"line-dasharray":new Wi(Ft.paint_line["line-dasharray"]),"line-pattern":new Gi(Ft.paint_line["line-pattern"]),"line-gradient":new Yi(Ft.paint_line["line-gradient"])}),layout:gl},ml=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Di(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Hi),xl=new ml(yl.paint.properties["line-width"].specification);xl.useIntegerZoom=!0;var bl=function(t){function e(e){t.call(this,e,yl),this.gradientVersion=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){if("line-gradient"===t){var e=this._transitionablePaint._values["line-gradient"].value.expression;this.stepInterpolant=e._styleExpression.expression instanceof tr,this.gradientVersion=(this.gradientVersion+1)%l}},e.prototype.gradientExpression=function(){return this._transitionablePaint._values["line-gradient"].value.expression},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values["line-floorwidth"]=xl.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,e)},e.prototype.createBucket=function(t){return new vl(t)},e.prototype.queryRadius=function(t){var e=t,r=_l(Oo("line-width",this,e),Oo("line-gap-width",this,e)),n=Oo("line-offset",this,e);return r/2+Math.abs(n)+Io(this.paint.get("line-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s){var l=Do(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),o.angle,s),u=s/2*_l(this.paint.get("line-width").evaluate(e,r),this.paint.get("line-gap-width").evaluate(e,r)),c=this.paint.get("line-offset").evaluate(e,r);return c&&(n=function(t,e){for(var r=[],n=new a(0,0),i=0;i<t.length;i++){for(var o=t[i],s=[],l=0;l<o.length;l++){var u=o[l-1],c=o[l],f=o[l+1],h=0===l?n:c.sub(u)._unit()._perp(),p=l===o.length-1?n:f.sub(c)._unit()._perp(),d=h._add(p)._unit(),v=d.x*p.x+d.y*p.y;d._mult(1/v),s.push(d._mult(e)._add(c))}r.push(s)}return r}(n,c*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(Co(t,i[a]))return!0;if(ko(t,i,r))return!0}return!1}(l,n,u)},e.prototype.isTileClipped=function(){return!0},e}(Ki);function _l(t,e){return e>0?e+2*t:t}var wl=ta([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"},{name:"a_pixeloffset",components:4,type:"Int16"}],4),Tl=ta([{name:"a_projected_pos",components:3,type:"Float32"}],4),kl=(ta([{name:"a_fade_opacity",components:1,type:"Uint32"}],4),ta([{name:"a_placed",components:2,type:"Uint8"},{name:"a_shift",components:2,type:"Float32"}])),Al=(ta([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"}]),ta([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4)),Ml=ta([{name:"a_pos",components:2,type:"Float32"},{name:"a_radius",components:1,type:"Float32"},{name:"a_flags",components:2,type:"Int16"}],4);function Sl(t,e,r){return 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$o({width:o||1,height:s||1});for(var u in t){var c=t[u],f=r[u].paddedRect;$o.copy(c.data,l,{x:0,y:0},{x:f.x+iu,y:f.y+iu},c.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,v=d.x+iu,g=d.y+iu,y=p.data.width,m=p.data.height;$o.copy(p.data,l,{x:0,y:0},{x:v,y:g},p.data),$o.copy(p.data,l,{x:0,y:m-1},{x:v,y:g-1},{width:y,height:1}),$o.copy(p.data,l,{x:0,y:0},{x:v,y:g+m},{width:y,height:1}),$o.copy(p.data,l,{x:y-1,y:0},{x:v-1,y:g},{width:1,height:m}),$o.copy(p.data,l,{x:0,y:0},{x:v+y,y:g},{width:1,height:m})}this.image=l,this.iconPositions=r,this.patternPositions=n};su.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2*iu,h:i.data.height+2*iu};r.push(a),e[n]=new au(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},su.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},su.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},oi("ImagePosition",au),oi("ImageAtlas",su);var lu={horizontal:1,vertical:2,horizontalOnly:3},uu=-17;var cu=function(){this.scale=1,this.fontStack="",this.imageName=null};cu.forText=function(t,e){var r=new cu;return r.scale=t||1,r.fontStack=e,r},cu.forImage=function(t){var e=new cu;return e.imageName=t,e};var fu=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function hu(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=fu.fromFeature(t,i);f===lu.vertical&&y.verticalizePunctuation();var m=Ii.processBidirectionalText,x=Ii.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),bu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new fu;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,bu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new fu;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,bu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=uu,d=0,v=0,g="right"===s?1:"left"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*Ll,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,O=null,I=null,D=Ll,z=!(l===lu.horizontal||!c&&!vi(L)||c&&(pu[L]||yi(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,O=R.paddedRect;var F=R.displaySize;S.scale=S.scale*Ll/f,P={width:F[0],height:F[1],left:iu,top:-ru,advance:z?F[1]:F[0]},C=w+(Ll-F[1]*S.scale),D=P.advance;var B=z?F[0]*S.scale-Ll*_:F[1]*S.scale-Ll*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)O=j.rect,P=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;P=V.metrics}C=(_-S.scale)*Ll}z?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=D*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=P.advance*S.scale+u)}if(0!==k.length){var q=h-u;d=Math.max(q,d),wu(k,0,k.length-1,g,A)}h=0;var H=a*_+A;T.lineOffset=Math.max(A,w),p+=H,v=Math.max(H,v),++y}else p+=a,++y}var G=p-uu,W=_u(o),Y=W.horizontalAlign,X=W.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-uu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,X,d,v,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}fu.fromFeature=function(t,e){for(var r=new fu,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},fu.prototype.length=function(){return this.text.length},fu.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},fu.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},fu.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},fu.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},fu.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&pu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&pu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},fu.prototype.substring=function(t,e){var r=new fu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},fu.prototype.toString=function(){return this.text},fu.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},fu.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(cu.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},fu.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(cu.forImage(e)),this.sectionIndex.push(this.sections.length-1)):k("Reached maximum number of images 6401")}else k("Can't add FormattedSection with an empty image.")},fu.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var pu={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},du={};function vu(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*Ll/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function gu(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function yu(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function mu(t,e,r,n,i,a){for(var o=null,s=gu(e,r,i,a),l=0,u=n;l<u.length;l+=1){var c=u[l],f=gu(e-c.x,r,i,a)+c.badness;f<=s&&(o=c,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function xu(t){return t?xu(t.priorBreak).concat(t.index):[]}function bu(t,e,r,n,i,a,o){if("point"!==a)return[];if(!t)return[];for(var s=[],l=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=vu(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("​")>=0,c=0,f=0;f<t.length();f++){var h=t.getSection(f),p=t.getCharCode(f);if(pu[p]||(c+=vu(p,h,n,i,e,o)),f<t.length()-1){var d=!((v=p)<11904||!(pi["Bopomofo Extended"](v)||pi.Bopomofo(v)||pi["CJK Compatibility Forms"](v)||pi["CJK Compatibility Ideographs"](v)||pi["CJK 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a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Tu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return"height"===r||"both"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}du[10]=!0,du[32]=!0,du[38]=!0,du[40]=!0,du[41]=!0,du[43]=!0,du[45]=!0,du[47]=!0,du[173]=!0,du[183]=!0,du[8203]=!0,du[8208]=!0,du[8211]=!0,du[8231]=!0;var ku=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(a);oi("Anchor",ku);var Au=128;function Mu(t,e){var r=e.expression;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new Di(t+1))};if("source"===r.kind)return{kind:"source"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return"composite"===r.kind?{kind:"composite",minZoom:s,maxZoom:l,interpolationType:i}:{kind:"camera",minZoom:s,maxZoom:l,minSize:r.evaluate(new Di(s)),maxSize:r.evaluate(new Di(l)),interpolationType:i}}function Su(t,e,r){var n=e.uSize,i=e.uSizeT,a=r.lowerSize,o=r.upperSize;return"source"===t.kind?a/Au:"composite"===t.kind?er(a/Au,o/Au,i):n}function Eu(t,e){var r=0,n=0;if("constant"===t.kind)n=t.layoutSize;else if("source"!==t.kind){var i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?f(wr.interpolationFactor(i,e,a,o),0,1):0;"camera"===t.kind?n=er(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Lu=Object.freeze({__proto__:null,getSizeData:Mu,evaluateSizeForFeature:Su,evaluateSizeForZoom:Eu,SIZE_PACK_FACTOR:Au});function Cu(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=c.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),u+=p;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=f.dist(h)}return!0}function Pu(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Ou(t,e,r){return t?.6*e*r:0}function Iu(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function Du(t,e,r,n,i,a){for(var o=Ou(r,i,a),s=Iu(r,n)*a,l=0,u=Pu(t)/2,c=0;c<t.length-1;c++){var f=t[c],h=t[c+1],p=f.dist(h);if(l+p>u){var d=(u-l)/p,v=er(f.x,h.x,d),g=er(f.y,h.y,d),y=new ku(v,g,h.angleTo(f),c);return y._round(),!o||Cu(t,y,s,o,e)?y:void 0}l+=p}}function zu(t,e,r,n,i,a,o,s,l){var u=Ou(n,a,o),c=Iu(n,i),f=c*o,h=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-f<e/4&&(e=f+e/4),Ru(t,h?e/2*s%e:(c/2+2*a)*o*s%e,e,u,r,f,h,!1,l)}function Ru(t,e,r,n,i,a,o,s,l){for(var u=a/2,c=Pu(t),f=0,h=e-r,p=[],d=0;d<t.length-1;d++){for(var v=t[d],g=t[d+1],y=v.dist(g),m=g.angleTo(v);h+r<f+y;){var x=((h+=r)-f)/y,b=er(v.x,g.x,x),_=er(v.y,g.y,x);if(b>=0&&b<l&&_>=0&&_<l&&h-u>=0&&h+u<=c){var w=new ku(b,_,m,d);w._round(),n&&!Cu(t,w,a,n,i)||p.push(w)}}f+=y}return s||p.length||o||(p=Ru(t,f/2,r,n,i,a,o,!0,l)),p}function Fu(t,e,r,n,i){for(var o=[],s=0;s<t.length;s++)for(var l=t[s],u=void 0,c=0;c<l.length-1;c++){var f=l[c],h=l[c+1];f.x<e&&h.x<e||(f.x<e?f=new a(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round():h.x<e&&(h=new a(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round()),f.y<r&&h.y<r||(f.y<r?f=new a(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round():h.y<r&&(h=new a(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round()),f.x>=n&&h.x>=n||(f.x>=n?f=new a(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round():h.x>=n&&(h=new a(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round()),f.y>=i&&h.y>=i||(f.y>=i?f=new a(f.x+(h.x-f.x)*((i-f.y)/(h.y-f.y)),i)._round():h.y>=i&&(h=new a(f.x+(h.x-f.x)*((i-f.y)/(h.y-f.y)),i)._round()),u&&f.equals(u[u.length-1])||(u=[f],o.push(u)),u.push(h)))))}return o}var Bu=iu;function Nu(t,e,r,n){var i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*Bu,u=o.paddedRect.h-2*Bu,c=t.right-t.left,f=t.bottom-t.top,h=o.stretchX||[[0,l]],p=o.stretchY||[[0,u]],d=function(t,e){return t+e[1]-e[0]},v=h.reduce(d,0),g=p.reduce(d,0),y=l-v,m=u-g,x=0,b=v,_=0,w=g,T=0,k=y,A=0,M=m;if(o.content&&n){var S=o.content;x=ju(h,0,S[0]),_=ju(p,0,S[1]),b=ju(h,S[0],S[2]),w=ju(p,S[1],S[3]),T=S[0]-x,A=S[1]-_,k=S[2]-S[0]-b,M=S[3]-S[1]-w}var E=function(n,i,l,u){var h=Vu(n.stretch-x,b,c,t.left),p=qu(n.fixed-T,k,n.stretch,v),d=Vu(i.stretch-_,w,f,t.top),y=qu(i.fixed-A,M,i.stretch,g),m=Vu(l.stretch-x,b,c,t.left),S=qu(l.fixed-T,k,l.stretch,v),E=Vu(u.stretch-_,w,f,t.top),L=qu(u.fixed-A,M,u.stretch,g),C=new a(h,d),P=new a(m,d),O=new a(m,E),I=new a(h,E),D=new a(p/s,y/s),z=new a(S/s,L/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),I._matMult(N),O._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=i.stretch+i.fixed,q=u.stretch+u.fixed;return{tl:C,tr:P,bl:I,br:O,tex:{x:o.paddedRect.x+Bu+j,y:o.paddedRect.y+Bu+V,w:U-j,h:q-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:D,pixelOffsetBR:z,minFontScaleX:k/s/c,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=Uu(h,y,v),C=Uu(p,m,g),P=0;P<L.length-1;P++)for(var O=L[P],I=L[P+1],D=0;D<C.length-1;D++){var z=C[D],R=C[D+1];i.push(E(O,z,I,R))}else 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a(m,g),k=c*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),y=Math.min(b.x,_.x,w.x,T.x),m=Math.max(b.x,_.x,w.x,T.x),v=Math.min(b.y,_.y,w.y,T.y),g=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,y,v,m,g,r,n,i)}this.boxEndIndex=t.length},Gu=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=Wu),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function Wu(t,e){return t<e?-1:t>e?1:0}function Yu(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,i=1/0,o=-1/0,s=-1/0,l=t[0],u=0;u<l.length;u++){var c=l[u];(!u||c.x<n)&&(n=c.x),(!u||c.y<i)&&(i=c.y),(!u||c.x>o)&&(o=c.x),(!u||c.y>s)&&(s=c.y)}var f=o-n,h=s-i,p=Math.min(f,h),d=p/2,v=new Gu([],Xu);if(0===p)return new a(n,i);for(var g=n;g<o;g+=p)for(var y=i;y<s;y+=p)v.push(new Zu(g+d,y+d,d,t));for(var m=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],u=i[s],c=l.x*u.y-u.x*l.y;r+=(l.x+u.x)*c,n+=(l.y+u.y)*c,e+=3*c}return new Zu(r/e,n/e,0,t)}(t),x=v.length;v.length;){var b=v.pop();(b.d>m.d||!m.d)&&(m=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-m.d<=e||(d=b.h/2,v.push(new Zu(b.p.x-d,b.p.y-d,d,t)),v.push(new Zu(b.p.x+d,b.p.y-d,d,t)),v.push(new Zu(b.p.x-d,b.p.y+d,d,t)),v.push(new Zu(b.p.x+d,b.p.y+d,d,t)),x+=4)}return r&&(console.log("num probes: "+x),console.log("best distance: "+m.d)),m.p}function Xu(t,e){return e.max-t.max}function Zu(t,e,r,n){this.p=new a(t,e),this.h=r,this.d=function(t,e){for(var r=!1,n=1/0,i=0;i<e.length;i++)for(var a=e[i],o=0,s=a.length,l=s-1;o<s;l=o++){var u=a[o],c=a[l];u.y>t.y!=c.y>t.y&&t.x<(c.x-u.x)*(t.y-u.y)/(c.y-u.y)+u.x&&(r=!r),n=Math.min(n,Eo(t,u,c))}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}Gu.prototype.push=function(t){this.data.push(t),this.length++,this._up(this.length-1)},Gu.prototype.pop=function(){if(0!==this.length){var t=this.data[0],e=this.data.pop();return this.length--,this.length>0&&(this.data[0]=e,this._down(0)),t}},Gu.prototype.peek=function(){return this.data[0]},Gu.prototype._up=function(t){for(var e=this.data,r=this.compare,n=e[t];t>0;){var i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},Gu.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var Ku=7,Ju=Number.POSITIVE_INFINITY;function $u(t,e){return e[1]!==Ju?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case"top-right":case"top-left":case"top":i=r-Ku;break;case"bottom-right":case"bottom-left":case"bottom":i=-r+Ku}switch(t){case"top-right":case"bottom-right":case"right":n=-e;break;case"top-left":case"bottom-left":case"left":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case"top-right":case"top-left":n=i-Ku;break;case"bottom-right":case"bottom-left":n=-i+Ku;break;case"bottom":n=-e+Ku;break;case"top":n=e-Ku}switch(t){case"top-right":case"bottom-right":r=-i;break;case"top-left":case"bottom-left":r=i;break;case"left":r=e;break;case"right":r=-e}return[r,n]}(t,e[0])}function Qu(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}var tc=255,ec=tc*Au;function rc(t,e,r,n,i,o,s,l,u,c,f,h,p,d,v){var g=function(t,e,r,n,i,o,s,l){for(var u=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,c=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,v=p.positionedGlyphs;d<v.length;d+=1){var g=v[d];if(g.rect){var y=g.rect||{},m=ru+1,x=!0,b=1,_=0,w=(i||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=(g.scale-1)*Ll,A=(Ll-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,b=M.pixelRatio,m=iu/b}var S=i?[g.x+T,g.y]:[0,0],E=i?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-m)*g.scale-T+E[0],P=(-g.metrics.top-m)*g.scale+E[1],O=C+y.w*g.scale/b,I=P+y.h*g.scale/b,D=new a(C,P),z=new a(O,P),R=new a(C,I),F=new a(O,I);if(w){var B=new a(-T,T-uu),N=-Math.PI/2,j=Ll/2-T,U=g.imageName?j:0,V=new a(5-uu-j,-U),q=new(Function.prototype.bind.apply(a,[null].concat(L)));D._rotateAround(N,B)._add(V)._add(q),z._rotateAround(N,B)._add(V)._add(q),R._rotateAround(N,B)._add(V)._add(q),F._rotateAround(N,B)._add(V)._add(q)}if(u){var H=Math.sin(u),G=Math.cos(u),W=[G,-H,H,G];D._matMult(W),z._matMult(W),R._matMult(W),F._matMult(W)}var Y=new a(0,0),X=new a(0,0);c.push({tl:D,tr:z,bl:R,br:F,tex:y,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:Y,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return c}(0,r,l,i,o,s,n,t.allowVerticalPlacement),y=t.textSizeData,m=null;"source"===y.kind?(m=[Au*i.layout.get("text-size").evaluate(s,{})])[0]>ec&&k(t.layerIds[0]+': Value for "text-size" is >= '+tc+'. 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Reduce your "text-size".'),t.addSymbols(t.text,g,m,l,o,s,c,e,u.lineStartIndex,u.lineLength,p,v);for(var x=0,b=f;x<b.length;x+=1)h[b[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function nc(t){for(var e in t)return t[e];return null}function ic(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var ac=tl.VectorTileFeature.types,oc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function sc(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=s?Math.min(ec,Math.round(s[0])):0,d=s?Math.min(ec,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*u,16*c,256*f,256*h)}function lc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function uc(t){for(var e=0,r=t.sections;e<r.length;e+=1)if(bi(r[e].text))return!0;return!1}var cc=function(t){this.layoutVertexArray=new la,this.indexArray=new va,this.programConfigurations=t,this.segments=new za,this.dynamicLayoutVertexArray=new ua,this.opacityVertexArray=new ca,this.placedSymbolArray=new Sa};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,oc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},oi("SymbolBuffers",cc);var fc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new za,this.collisionVertexArray=new da};fc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,kl.members,!0)},fc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},oi("CollisionBuffers",fc);var hc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Mu(this.zoom,e["text-size"]),this.iconSizeData=Mu(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.canOverlap=r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement"),this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&this.canOverlap,"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return lu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};hc.prototype.createArrays=function(){this.text=new cc(new co(this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new co(this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Ca,this.lineVertexArray=new Pa,this.symbolInstances=new La},hc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=El[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},hc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof he&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),u="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get("symbol-sort-key");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new Di(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T=mo(m,w);if(n._featureFilter.filter(d,T,r)){w||(T.geometry=yo(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=he.factory(A);uc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===Pi()||this.hasRTLText&&Ii.isParsed())&&(k=Sl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof pe?E:pe.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:T.geometry,properties:m.properties,type:ac[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),O="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(lu.vertical)>=0;for(var I=0,D=k.sections;I<D.length;I+=1){var z=D[I];if(z.image)f[z.image.name]=!0;else{var R=di(k.toString()),F=z.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(z.text,B,O,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},hc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},hc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},hc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},hc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},hc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},hc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},hc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},hc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,this.canOverlap?a.sortKey:void 0),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===lu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,O=b.sectionIndex,I=d.vertexLength,D=C[1];sc(p,s.x,s.y,_.x,D+_.y,A.x,A.y,r,P,M.x,M.y,E,L),sc(p,s.x,s.y,w.x,D+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),sc(p,s.x,s.y,T.x,D+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),sc(p,s.x,s.y,k.x,D+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),lc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&O===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[O])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},hc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},hc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},hc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},hc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new fc(ha,Al.members,_a),this.iconCollisionBox=new fc(ha,Al.members,_a);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},hc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},hc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},hc.prototype.hasTextData=function(){return this.text.segments.get().length>0},hc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},hc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},hc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},hc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},hc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},hc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},hc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},hc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi("SymbolBucket",hc,{omit:["layers","collisionBoxArray","features","compareText"]}),hc.MAX_GLYPHS=65535,hc.addDynamicAttributes=lc;var pc=new Xi({"symbol-placement":new qi(Ft.layout_symbol["symbol-placement"]),"symbol-spacing":new qi(Ft.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new qi(Ft.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Hi(Ft.layout_symbol["symbol-sort-key"]),"symbol-z-order":new qi(Ft.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new qi(Ft.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new qi(Ft.layout_symbol["icon-ignore-placement"]),"icon-optional":new qi(Ft.layout_symbol["icon-optional"]),"icon-rotation-alignment":new qi(Ft.layout_symbol["icon-rotation-alignment"]),"icon-size":new Hi(Ft.layout_symbol["icon-size"]),"icon-text-fit":new qi(Ft.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new qi(Ft.layout_symbol["icon-text-fit-padding"]),"icon-image":new Hi(Ft.layout_symbol["icon-image"]),"icon-rotate":new Hi(Ft.layout_symbol["icon-rotate"]),"icon-padding":new qi(Ft.layout_symbol["icon-padding"]),"icon-keep-upright":new qi(Ft.layout_symbol["icon-keep-upright"]),"icon-offset":new Hi(Ft.layout_symbol["icon-offset"]),"icon-anchor":new Hi(Ft.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new qi(Ft.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new qi(Ft.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new qi(Ft.layout_symbol["text-rotation-alignment"]),"text-field":new Hi(Ft.layout_symbol["text-field"]),"text-font":new Hi(Ft.layout_symbol["text-font"]),"text-size":new Hi(Ft.layout_symbol["text-size"]),"text-max-width":new Hi(Ft.layout_symbol["text-max-width"]),"text-line-height":new qi(Ft.layout_symbol["text-line-height"]),"text-letter-spacing":new Hi(Ft.layout_symbol["text-letter-spacing"]),"text-justify":new Hi(Ft.layout_symbol["text-justify"]),"text-radial-offset":new Hi(Ft.layout_symbol["text-radial-offset"]),"text-variable-anchor":new qi(Ft.layout_symbol["text-variable-anchor"]),"text-anchor":new Hi(Ft.layout_symbol["text-anchor"]),"text-max-angle":new qi(Ft.layout_symbol["text-max-angle"]),"text-writing-mode":new qi(Ft.layout_symbol["text-writing-mode"]),"text-rotate":new Hi(Ft.layout_symbol["text-rotate"]),"text-padding":new qi(Ft.layout_symbol["text-padding"]),"text-keep-upright":new qi(Ft.layout_symbol["text-keep-upright"]),"text-transform":new Hi(Ft.layout_symbol["text-transform"]),"text-offset":new Hi(Ft.layout_symbol["text-offset"]),"text-allow-overlap":new qi(Ft.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new qi(Ft.layout_symbol["text-ignore-placement"]),"text-optional":new qi(Ft.layout_symbol["text-optional"])}),dc={paint:new Xi({"icon-opacity":new Hi(Ft.paint_symbol["icon-opacity"]),"icon-color":new Hi(Ft.paint_symbol["icon-color"]),"icon-halo-color":new Hi(Ft.paint_symbol["icon-halo-color"]),"icon-halo-width":new Hi(Ft.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Hi(Ft.paint_symbol["icon-halo-blur"]),"icon-translate":new qi(Ft.paint_symbol["icon-translate"]),"icon-translate-anchor":new qi(Ft.paint_symbol["icon-translate-anchor"]),"text-opacity":new Hi(Ft.paint_symbol["text-opacity"]),"text-color":new Hi(Ft.paint_symbol["text-color"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Hi(Ft.paint_symbol["text-halo-color"]),"text-halo-width":new Hi(Ft.paint_symbol["text-halo-width"]),"text-halo-blur":new Hi(Ft.paint_symbol["text-halo-blur"]),"text-translate":new qi(Ft.paint_symbol["text-translate"]),"text-translate-anchor":new qi(Ft.paint_symbol["text-translate-anchor"])}),layout:pc},vc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};vc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},vc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},vc.prototype.outputDefined=function(){return!1},vc.prototype.serialize=function(){return null},oi("FormatSectionOverride",vc,{omit:["defaultValue"]});var gc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||cn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new hc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=dc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new vc(a),s=new un(o,a.property.specification);i="constant"===a.value.kind||"source"===a.value.kind?new hn("source",s):new pn("composite",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Ui(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=dc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof he)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof me&&ge(t.value)===Qt){var e=t.value;a(e.sections)}else t instanceof we?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ki),yc={paint:new Xi({"background-color":new qi(Ft.paint_background["background-color"]),"background-pattern":new Wi(Ft.paint_background["background-pattern"]),"background-opacity":new qi(Ft.paint_background["background-opacity"])})},mc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki),xc={paint:new Xi({"raster-opacity":new qi(Ft.paint_raster["raster-opacity"]),"raster-hue-rotate":new qi(Ft.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new qi(Ft.paint_raster["raster-brightness-min"]),"raster-brightness-max":new qi(Ft.paint_raster["raster-brightness-max"]),"raster-saturation":new qi(Ft.paint_raster["raster-saturation"]),"raster-contrast":new qi(Ft.paint_raster["raster-contrast"]),"raster-resampling":new qi(Ft.paint_raster["raster-resampling"]),"raster-fade-duration":new qi(Ft.paint_raster["raster-fade-duration"])})},bc=function(t){function e(e){t.call(this,e,xc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki);var _c=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ki),wc={circle:Go,heatmap:es,hillshade:ns,fill:Hs,"fill-extrusion":sl,line:bl,symbol:gc,background:mc,raster:bc};var Tc=s.HTMLImageElement,kc=s.HTMLCanvasElement,Ac=s.HTMLVideoElement,Mc=s.ImageData,Sc=s.ImageBitmap,Ec=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Ec.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Ec.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Ec.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Ec.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Lc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Lc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Lc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Lc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=S()?t:s};function Pc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=C(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:ci(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(fi(e.error)):n(null,fi(e.data)))}else{var i=!1,a=C(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?ci(e):null,data:ci(n,a)},a)}:function(t){i=!0},s=null,l=fi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Oc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Oc.prototype.setNorthEast=function(t){return this._ne=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Oc.prototype.setSouthWest=function(t){return this._sw=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Oc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Dc)e=t,r=t;else{if(!(t instanceof Oc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Oc.convert(a))}var o=t;return this.extend(Dc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Dc(e.lng,e.lat),this._ne=new Dc(r.lng,r.lat)),this},Oc.prototype.getCenter=function(){return new Dc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Oc.prototype.getSouthWest=function(){return this._sw},Oc.prototype.getNorthEast=function(){return this._ne},Oc.prototype.getNorthWest=function(){return new Dc(this.getWest(),this.getNorth())},Oc.prototype.getSouthEast=function(){return new Dc(this.getEast(),this.getSouth())},Oc.prototype.getWest=function(){return this._sw.lng},Oc.prototype.getSouth=function(){return this._sw.lat},Oc.prototype.getEast=function(){return this._ne.lng},Oc.prototype.getNorth=function(){return this._ne.lat},Oc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Oc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Oc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Oc.prototype.contains=function(t){var e=Dc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Oc.convert=function(t){return!t||t instanceof Oc?t:new Oc(t)};var Ic=6371008.8,Dc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Dc.prototype.wrap=function(){return new Dc(h(this.lng,-180,180),this.lat)},Dc.prototype.toArray=function(){return[this.lng,this.lat]},Dc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Dc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ic*Math.acos(Math.min(i,1))},Dc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Oc(new Dc(this.lng-r,this.lat-e),new Dc(this.lng+r,this.lat+e))},Dc.convert=function(t){if(t instanceof Dc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Dc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Dc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var zc=2*Math.PI*Ic;function Rc(t){return zc*Math.cos(t*Math.PI/180)}function Fc(t){return(180+t)/360}function Bc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nc(t,e){return t/Rc(e)}function jc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Dc.convert(t);return new Uc(Fc(r.lng),Bc(r.lat),Nc(e,r.lat))},Uc.prototype.toLngLat=function(){return new Dc(360*this.x-180,jc(this.y))},Uc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Rc(jc(e));var t,e},Uc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=jc(this.y),1/Math.cos(t*Math.PI/180));var t};var Vc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gc(0,t,t,e,r)};Vc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Pc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Pc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},Vc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*po,(t.y*e-this.y)*po)},Vc.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var qc=function(t,e){this.wrap=t,this.canonical=e,this.key=Gc(t,e.z,e.z,e.x,e.y)},Hc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Vc(r,+n,+i),this.key=Gc(e,t,r,n,i)};function Gc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Hc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Hc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Hc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Hc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hc(e,this.wrap,e,r,n),new Hc(e,this.wrap,e,r+1,n),new Hc(e,this.wrap,e,r,n+1),new Hc(e,this.wrap,e,r+1,n+1)]},Hc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Hc.prototype.wrapped=function(){return new Hc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.unwrapTo=function(t){return new Hc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Hc.prototype.toUnwrapped=function(){return new qc(this.wrap,this.canonical)},Hc.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},Hc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Uc(t.x-this.wrap,t.y))},oi("CanonicalTileID",Vc),oi("OverscaledTileID",Hc,{omit:["posMatrix"]});var Wc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return k('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Wc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Wc.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Wc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},Wc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Wc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Wc.prototype.getPixels=function(){return new $o({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Wc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},oi("DEMData",Wc);var Yc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Yc.prototype.encode=function(t){return this._stringToNumber[t]},Yc.prototype.decode=function(t){return this._numberToString[t]};var Xc=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Zc={geometry:{configurable:!0}};Zc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Zc.geometry.set=function(t){this._geometry=t},Xc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Xc.prototype,Zc);var Kc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Kc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},p(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Kc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Kc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=p({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Kc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Kc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),p(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,f=Object.keys(this.deletedStates[o][u]);c<f.length;c+=1){var h=f[c];delete this.state[o][u][h]}s[u]=this.state[o][u]}r[o]=r[o]||{},p(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Jc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ti(po,16,0),this.grid3D=new ti(po,16,0),this.featureIndexArray=new Ia,this.promoteId=e};function $c(t,e,r,n,i){return b(t,(function(t,a){var o=e instanceof Vi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Qc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function tf(t,e){return e-t}Jc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<po&&c[1]<po&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Jc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Ol(this.rawTileData)).layers,this.sourceLayerCoder=new Yc(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},Jc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Qc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Qc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Co(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(Po(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(tf);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=yo(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Jc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),d=this.vtLayers[h].feature(n);if(i.needGeometry){var v=mo(d,!0);if(!i.filter(new Di(this.tileID.overscaledZ),v,this.tileID.canonical))return}else if(!i.filter(new Di(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,h),y=0;y<f.length;y++){var m=f[y];if(!(a&&a.indexOf(m)<0)){var x=s[m];if(x){var b={};void 0!==g&&u&&(b=u.getState(x.sourceLayer||"_geojsonTileLayer",g));var _=p({},l[m]);_.paint=$c(_.paint,x.paint,d,b,o),_.layout=$c(_.layout,x.layout,d,b,o);var w=!c||c(d,x,b);if(w){var T=new Xc(d,this.z,this.x,this.y,g);T.layer=_;var k=t[m];void 0===k&&(k=t[m]=[]),k.push({featureIndex:n,feature:T,intersectionZ:w})}}}}}},Jc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=An(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Jc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Jc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},oi("FeatureIndex",Jc,{omit:["rawTileData","sourceLayerCoder"]});var ef=function(t,e){this.tileID=t,this.uid=v(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};ef.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<N.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},ef.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},ef.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof hc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof hc&&o.hasRTLText){this.hasRTLText=!0,Ii.isLoading()||Ii.isLoaded()||"deferred"!==Pi()||Oi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Aa},ef.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},ef.prototype.getBucket=function(t){return this.buckets[t.id]},ef.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Ec(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Ec(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},ef.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},ef.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},ef.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=An(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.needGeometry){var d=mo(p,!0);if(!o.filter(new Di(this.tileID.overscaledZ),d,this.tileID.canonical))continue}else if(!o.filter(new Di(this.tileID.overscaledZ),p))continue;var v=r.getId(p,i),g=new Xc(p,l,u,c,v);g.tile=f,t.push(g)}}},ef.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},ef.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},ef.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=E(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},ef.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},ef.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},ef.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},ef.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<N.now()},ef.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},ef.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=N.now()+t},ef.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},ef.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var rf=s.performance,nf=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},rf.mark(this._marks.start)};nf.prototype.finish=function(){rf.mark(this._marks.end);var t=rf.getEntriesByName(this._marks.measure);return 0===t.length&&(rf.measure(this._marks.measure,this._marks.start,this._marks.end),t=rf.getEntriesByName(this._marks.measure),rf.clearMarks(this._marks.start),rf.clearMarks(this._marks.end),rf.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=Jo,t.CanonicalTileID=Vc,t.CollisionBoxArray=Aa,t.Color=ue,t.DEMData=Wc,t.DataConstantProperty=qi,t.DictionaryCoder=Yc,t.EXTENT=po,t.ErrorEvent=zt,t.EvaluationParameters=Di,t.Event=Dt,t.Evented=Rt,t.FeatureIndex=Jc,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=su,t.ImagePosition=au,t.LineBucket=vl,t.LngLat=Dc,t.LngLatBounds=Oc,t.MercatorCoordinate=Uc,t.ONE_EM=Ll,t.OverscaledTileID=Hc,t.Point=a,t.Point$1=a,t.Properties=Xi,t.Protobuf=Ol,t.RGBAImage=$o,t.RequestManager=W,t.RequestPerformance=nf,t.ResourceType=_t,t.SegmentVector=za,t.SourceFeatureState=Kc,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=ra,t.StructArrayLayout3ui6=va,t.StructArrayLayout4i8=na,t.SymbolBucket=hc,t.Texture=Ec,t.Tile=ef,t.Transitionable=Fi,t.Uniform1f=Ka,t.Uniform1i=Za,t.Uniform2f=Ja,t.Uniform3f=$a,t.Uniform4f=Qa,t.UniformColor=to,t.UniformMatrix4f=ro,t.UnwrappedTileID=qc,t.ValidationError=Bt,t.WritingMode=lu,t.ZoomHistory=hi,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=lc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=u,t.bindAll=m,t.browser=N,t.cacheEntryPossiblyAdded=function(t){++xt>ht&&(t.getActor().send("enforceCacheSizeLimit",ft),xt=0)},t.clamp=f,t.clearTileCache=function(t){var e=s.caches.delete(ct);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fu,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=fn,t.createLayout=ta,t.createStyleLayer=function(t){return"custom"===t.type?new _c(t):new wc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if("object"==typeof e&&null!==e&&null!==r){if("object"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=c,t.emitValidationErrors=Qn,t.endsWith=x,t.enforceCacheSizeLimit=function(t){dt(),rt&&rt.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Su,t.evaluateSizeForZoom=Eu,t.evaluateVariableOffset=$u,t.evented=Ci,t.extend=p,t.featureFilter=An,t.filterObject=_,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=_u,t.getAnchorJustification=Qu,t.getArrayBuffer=Mt,t.getImage=Pt,t.getJSON=function(t,e){return At(p(t,{type:"json"}),e)},t.getRTLTextPluginStatus=Pi,t.getReferrer=Tt,t.getVideo=function(t,e){var r,n,i=s.document.createElement("video");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=s.document.createElement("source");r=t[a],n=void 0,(n=s.document.createElement("a")).href=r,n.protocol===s.document.location.protocol&&n.host===s.document.location.host||(i.crossOrigin="Anonymous"),o.src=t[a],i.appendChild(o)}return{cancel:function(){}}},t.identity=Bo,t.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,k=c*v-f*d,A=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null},t.isChar=pi,t.isMapboxURL=Y,t.keysDifference=function(t,e){var r=[];for(var n in t)n in e||r.push(n);return r},t.makeRequest=At,t.mapObject=b,t.mercatorXfromLng=Fc,t.mercatorYfromLat=Bc,t.mercatorZfromAltitude=Nc,t.mul=jo,t.multiply=No,t.mvt=tl,t.nextPowerOfTwo=function(t){return t<=1?1:Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))},t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=er,t.offscreenCanvasSupported=bt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},t.parseGlyphPBF=function(t){return new Ol(t).readFields(Ql,[])},t.pbf=Ol,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=po/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,u=t.layers[0]._unevaluatedLayout._values,c={};if("composite"===t.textSizeData.kind){var 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g=d.toString(),_=l.get("text-letter-spacing").evaluate(a,{},o)*Ll,A=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n=r[e].charCodeAt(0),pi.Arabic(n)||pi["Arabic Supplement"](n)||pi["Arabic Extended-A"](n)||pi["Arabic Presentation Forms-A"](n)||pi["Arabic Presentation Forms-B"](n))return!1;var n;return!0}(g)?_:0,M=l.get("text-anchor").evaluate(a,{},o),S=l.get("text-variable-anchor");if(!S){var E=l.get("text-radial-offset").evaluate(a,{},o);v=E?$u(M,[E*Ll,Ju]):l.get("text-offset").evaluate(a,{},o).map((function(t){return t*Ll}))}var L=m?"center":l.get("text-justify").evaluate(a,{},o),C=l.get("symbol-placement"),P="point"===C?l.get("text-max-width").evaluate(a,{},o)*Ll:0,O=function(){t.allowVerticalPlacement&&di(g)&&(p.vertical=hu(d,e,r,i,s,P,y,M,"left",A,v,lu.vertical,!0,C,f,u))};if(!m&&S){for(var I="auto"===L?S.map((function(t){return Qu(t)})):[L],D=!1,z=0;z<I.length;z++){var R=I[z];if(!p.horizontal[R])if(D)p.horizontal[R]=p.horizontal[0];else{var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if("Point"===o||"MultiPoint"===o||"LineString"===o)i=yt(a.geometry,e);else if("MultiLineString"===o||"Polygon"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(yt(a.geometry[s],e))}else if("MultiPolygon"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],u=0;u<a.geometry[s].length;u++)l.push(yt(a.geometry[s][u],e));i.push(l)}r.push(tt(a.id,o,i,a.tags))}return r}function yt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function mt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],u=l.geometry,c=l.type;if(l.geometry=[],1===c)for(n=0;n<u.length;n+=2)l.geometry.push(xt(u[n],u[n+1],e,a,o,s));else for(n=0;n<u.length;n++){var f=[];for(i=0;i<u[n].length;i+=2)f.push(xt(u[n][i],u[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function xt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function bt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,_t(o,t[s],a,i);var l=t[s].minX,u=t[s].minY,c=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),u<o.minY&&(o.minY=u),c>o.maxX&&(o.maxX=c),f>o.maxY&&(o.maxY=f)}return o}function _t(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)wt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)wt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var u=i[l];for(s=0;s<u.length;s++)wt(o,u[s],t,r,!0,0===s)}if(o.length){var c=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in c={},e.tags)c[f]=e.tags[f];c.mapbox_clip_start=i.start/i.size,c.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:c};null!==e.id&&(h.id=e.id),t.features.push(h)}}function wt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function Tt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else"Feature"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new j(s,K,J,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,K,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",u,s.length,+Date.now()-c)}return r&&console.timeEnd("total time"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),X(a),Y(i),X(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?W(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(+t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=n.minPoints,l=i/(a*Math.pow(2,e)),u=0;u<t.length;u++){var c=t[u];if(!(c.zoom<=e)){c.zoom=e;for(var f=this.trees[e+1],h=f.within(c.x,c.y,l),p=c.numPoints||1,d=p,v=0,g=h;v<g.length;v+=1){var y=g[v],m=f.points[y];m.zoom>e&&(d+=m.numPoints||1)}if(d>=s){for(var x=c.x*p,b=c.y*p,_=o&&p>1?this._map(c,!0):null,w=(u<<5)+(e+1)+this.points.length,T=0,k=h;T<k.length;T+=1){var A=k[T],M=f.points[A];if(!(M.zoom<=e)){M.zoom=e;var S=M.numPoints||1;x+=M.x*S,b+=M.y*S,M.parentId=w,o&&(_||(_=this._map(c,!0)),o(_,this._map(M)))}}c.parentId=w,r.push(q(x/d,b/d,w,d,_))}else if(r.push(c),d>1)for(var E=0,L=h;E<L.length;E+=1){var C=L[E],P=f.points[C];P.zoom<=e||(P.zoom=e,r.push(P))}}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?Z({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?Z({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time("creation"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time("clipping");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd("clipping"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",u,c,f),a>1&&console.time("drilling down"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if("error"===s.result)throw new Error(s.value.map((function(t){return t.key+": "+t.message})).join(", "));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:"FeatureCollection",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u<c.length;u+=1){var f=c[u],h=n[f],p=h[0],d=h[1],v=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=v.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var u={};if(i){var c=i.finish();c&&(u.resourceTiming={},u.resourceTiming[n.source]=JSON.parse(JSON.stringify(c)))}r(null,u)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return St.prototype.setReferrer=function(t,e){this.referrer=e},St.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. 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x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w="-transition",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setSourceProperty=function(t){this._tileJSONRequest&&this._tileJSONRequest.cancel(),t(),this.map.style.sourceCaches[this.id].clearTiles(),this.load()},r.prototype.setTiles=function(t){var e=this;return this.setSourceProperty((function(){e._options.tiles=t})),this},r.prototype.setUrl=function(t){var e=this;return this.setSourceProperty((function(){e.url=t,e._options.url=t})),this},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(L),P=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,n.clusterMinPoints||2),extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties,filter:n.filter},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var D=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,"raster-dem":C,geojson:P,video:D,image:I,canvas:z};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var c=o[u],f=l[u]=l[u]||{},h=e[u]=e[u]||[],p=0,d=c;p<d.length;p+=1){var v=d[p];f[v.featureIndex]||(f[v.featureIndex]=!0,h.push(v))}}return e}(u);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function N(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var j=function(t,e){this.max=t,this.onRemove=e,this.reset()};j.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},j.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var U=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};U.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},U.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},U.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var V={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},q=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};q.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},q.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},q.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},q.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[V[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},q.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var H=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};H.prototype.get=function(){return this.current},H.prototype.set=function(t){},H.prototype.getDefault=function(){return this.default},H.prototype.setDefault=function(){this.set(this.default)};var G=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(H),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(H),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(H),K=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(H),J=function(t){function e(){t.apply(this,arguments)}return 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t=this.gl;return[t.ONE,t.ZERO]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),at=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),ot=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.FUNC_ADD},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)},e}(H),st=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.CULL_FACE):e.disable(e.CULL_FACE),this.current=t,this.dirty=!1}},e}(H),lt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.BACK},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.cullFace(t),this.current=t,this.dirty=!1)},e}(H),ut=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.CCW},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.frontFace(t),this.current=t,this.dirty=!1)},e}(H),ct=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)},e}(H),ft=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.TEXTURE0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)},e}(H),ht=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(H),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(H),vt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(H),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}},e}(H),yt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){var e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1},e}(H),mt=function(t){function e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(H),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(H),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),_t=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),wt=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(H),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(wt),kt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(wt),At=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new Tt(t,a),n&&(this.depthAttachment=new kt(t,a))};At.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Pt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new G(this),this.clearDepth=new W(this),this.clearStencil=new Y(this),this.colorMask=new X(this),this.depthMask=new Z(this),this.stencilMask=new K(this),this.stencilFunc=new J(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query"),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE)};Pt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Pt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Pt.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Pt.prototype.createVertexBuffer=function(t,e,r){return new q(this,t,e,r)},Pt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Pt.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Pt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Pt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Pt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Pt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Pt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Pt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ot=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Dt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return"raster"===t||"image"===t||"video"===t}function zt(){return new t.window.Worker(oa.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt="mapboxgl_preloaded_worker_pool",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var qt={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Ht(t,e,r){r.push({command:qt.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:qt.removeSource,args:[t]}),r[t]=!0}function Wt(t,e,r,n){Gt(t,r,n),Ht(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Xt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Zt(t){return t.id}function Kt(t,e){return t[e.id]=e,t}function Jt(e,r){if(!e)return[{command:qt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:qt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:qt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:qt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:qt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:qt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:qt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:qt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:qt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:qt.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Yt(e,r,a)?n.push({command:qt.setGeoJSONSourceData,args:[a,r[a].data]}):Wt(a,r,n,i)):Ht(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:qt.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Zt),h=r.map(Zt),p=e.reduce(Kt,{}),d=r.reduce(Kt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:qt.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(c in Xt(s.layout,l.layout,n,o,null,qt.setLayoutProperty),Xt(s.paint,l.paint,n,o,null,qt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:qt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:qt.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&"layout"!==c&&"paint"!==c&&"filter"!==c&&"metadata"!==c&&"minzoom"!==c&&"maxzoom"!==c&&(0===c.indexOf("paint.")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&"layout"!==c&&"paint"!==c&&"filter"!==c&&"metadata"!==c&&"minzoom"!==c&&"maxzoom"!==c&&(0===c.indexOf("paint.")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:qt.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:qt.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:qt.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),P=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D<z.length;D+=1){var R=z[D];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var O=1;O<L.path.length;O++)C.push(L.path[O]);var I=2.5*k;if(l){var D=C.map((function(t){return re(t,l)}));C=D.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:D.map((function(t){return t.point}))}var z=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);z=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),W=G.x+de,Y=G.y+de;d.push(W,Y,k,0);var X=W-k,Z=Y-k,K=W+k,J=Y+k;if(T=T&&this.isOffscreen(X,Z,K,J),w=w||this.isInsideGrid(X,Z,K,J),!e&&this.grid.hitTestCircle(W,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get("text-optional"),y=s.get("icon-optional"),m=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),W=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,W,l,u,c,n,_,v.predicate,X,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=Z(a.verticalIconBox)).box.length>0:(O=Z(a.iconBox)).box.length>0,S=S&&O.offscreen}var K=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if(K||J?J?K||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get("icon-ignore-placement"),o.bucketInstanceId,z,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<P.circles.length;tt+=4)Q.circles.push(P.circles[tt+0]),Q.circles.push(P.circles[tt+1]),Q.circles.push(P.circles[tt+2]),Q.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),u=a.get("text-variable-anchor"),c="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:ze;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?ze:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function De(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var qe=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},He=t.pick(qt,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ge=t.pick(qt,["setCenter","setZoom","setBearing","setPitch"]),We=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(We,!1)},r.prototype._load=function(e,r){if(!r||!qe(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}var l={};for(var u in this.sourceCaches){var c=this.sourceCaches[u];l[u]=c.used,c.used=!1}for(var f=0,h=this._order;f<h.length;f+=1){var p=h[f],d=this._layers[p];d.recalculate(e,this._availableImages),!d.isHidden(e.zoom)&&d.source&&(this.sourceCaches[d.source].used=!0)}for(var v in l){var g=this.sourceCaches[v];l[v]!==g.used&&g.fire(new t.Event("data",{sourceDataType:"visibility",dataType:"source",sourceId:v}))}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),qe(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Jt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in He)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast("setImages",this._availableImages),this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,"sources."+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is required to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&qe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];"symbol"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var Xe=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ze=_r("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),Ke=_r("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Je=_r("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),$e=_r("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Qe=_r("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),tr=_r("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),er=_r("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),rr=_r("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),nr=_r("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),ir=_r("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),ar=_r("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),or=_r("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),sr=_r("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),lr=_r("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),ur=_r("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),cr=_r("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),fr=_r("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),hr=_r("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),pr=_r("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),dr=_r("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),vr=_r("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),gr=_r("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),yr=_r("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=_r("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),xr=_r("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),br=_r("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function _r(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n=e.match(/attribute ([\w]+) ([\w]+)/g),i=t.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),a=e.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a="float"===n?"vec2":"vec4",o=i.match(/color/)?"color":a;return s[i]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Ze,background:Ke,backgroundPattern:Je,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r<t.length;r++)if(null!==t[r]){var n=t[r].split(" ");e.push(n.pop())}return e}Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var Ar=function(t,e,r,n,i,a){var o=t.gl;this.program=o.createProgram();for(var s=kr(r.staticAttributes),l=n?n.getBinderAttributes():[],u=s.concat(l),c=r.staticUniforms?kr(r.staticUniforms):[],f=n?n.getBinderUniforms():[],h=[],p=0,d=c.concat(f);p<d.length;p+=1){var v=d[p];h.indexOf(v)<0&&h.push(v)}var g=n?n.defines():[];a&&g.push("#define OVERDRAW_INSPECTOR;");var y=g.concat(Ze.fragmentSource,r.fragmentSource).join("\n"),m=g.concat(Ze.vertexSource,r.vertexSource).join("\n"),x=o.createShader(o.FRAGMENT_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(x,y),o.compileShader(x),o.attachShader(this.program,x);var b=o.createShader(o.VERTEX_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(b,m),o.compileShader(b),o.attachShader(this.program,b),this.attributes={};var _={};this.numAttributes=u.length;for(var w=0;w<this.numAttributes;w++)u[w]&&(o.bindAttribLocation(this.program,w,u[w]),this.attributes[u[w]]=w);o.linkProgram(this.program),o.deleteShader(b),o.deleteShader(x);for(var T=0;T<h.length;T++){var k=h[T];if(k&&!_[k]){var A=o.getUniformLocation(this.program,k);A&&(_[k]=A)}}this.fixedUniforms=i(t,_),this.binderUniforms=n?n.getUniforms(t,_):[]}}};function Mr(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Lr=function(t){return{u_matrix:t}},Cr=function(e,r,n,i){return t.extend(Lr(e),Mr(n,r,i))},Pr=function(t,e){return{u_matrix:t,u_world:e}},Or=function(e,r,n,i,a){return t.extend(Cr(e,r,n,i),{u_world:a})},Ir=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+("map"===i.paint.get("circle-pitch-scale")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get("circle-translate"),i.paint.get("circle-translate-anchor")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Dr=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},zr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}},jr=function(e,r,n){var i=n.paint.get("hillshade-shadow-color"),a=n.paint.get("hillshade-highlight-color"),o=n.paint.get("hillshade-accent-color"),s=n.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===n.paint.get("hillshade-illumination-anchor")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get("hillshade-exaggeration"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Yr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Wr(r,a);return{u_matrix:Yr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Wr(r,o),u="round"===n.layout.get("line-cap"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Wr(t,e){return 1/ge(t,1,e.tileZoom)}function Yr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Xr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Zr(i.paint.get("raster-hue-rotate"))};var a,o};function Zr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Kr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},$r=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Qr=function(e,r,n,i,a,o,s,l,u,c){return t.extend($r(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Dr(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],O=E.circleArray[C+1],I=E.circleArray[C+2],D=E.circleArray[C+3];k.emplace(A++,P,O,I,D,0),k.emplace(A++,P,O,I,D,1),k.emplace(A++,P,O,I,D,2),k.emplace(A++,P,O,I,D,3)}(!Kr||Kr.length<2*h)&&(Kr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var z=l.createIndexBuffer(Kr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=zr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,z,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),z.destroy()}}var an=t.identity(new Float32Array(16));function on(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function sn(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=on(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var O=C.get(P);if(O.hidden)he(O.numGlyphs,d);else{var I=v[P];if(I)for(var D=0;D<O.numGlyphs;D++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(O.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function ln(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function un(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g="map"===l,y="map"===u,m=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!y&&!m,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=!1,w=e.depthModeForSublayer(0,Mt.ReadOnly),T=n.layout.get("text-variable-anchor"),k=[],A=0,M=i;A<M.length;A+=1){var S=M[A],E=r.getTile(S),L=E.getBucket(n);if(L){var C=a?L.text:L.icon;if(C&&C.segments.get().length){var P=C.programConfigurations.get(n.id),O=a||L.sdfIcons,I=a?L.textSizeData:L.iconSizeData,D=y||0!==v.pitch,z=e.useProgram(ln(O,a,L),P),R=t.evaluateSizeForZoom(I,v.zoom),F=void 0,B=[0,0],N=void 0,j=void 0,U=null,V=void 0;if(a){if(N=E.glyphAtlasTexture,j=d.LINEAR,F=E.glyphAtlasTexture.size,L.iconsInText){B=E.imageAtlasTexture.size,U=E.imageAtlasTexture;var q="composite"===I.kind||"camera"===I.kind;V=D||e.options.rotating||e.options.zooming||q?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get("icon-size").constantOr(0)||L.iconsNeedLinear;N=E.imageAtlasTexture,j=O||e.options.rotating||e.options.zooming||H||D?d.LINEAR:d.NEAREST,F=E.imageAtlasTexture.size}var G=ge(E,1,e.transform.zoom),W=te(S.posMatrix,y,g,e.transform,G),Y=ee(S.posMatrix,y,g,e.transform,G),X=T&&L.hasTextData(),Z="none"!==n.layout.get("icon-text-fit")&&X&&L.hasIconData();m&&ae(L,S.posMatrix,e,a,W,Y,y,c);var K=e.translatePosMatrix(S.posMatrix,E,o,s),J=m||a&&T||Z?an:W,$=e.translatePosMatrix(Y,E,o,s,!0),Q=O&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1),tt={program:z,buffers:C,uniformValues:O?L.iconsInText?Qr(I.kind,R,x,y,e,K,J,$,F,B):$r(I.kind,R,x,y,e,K,J,$,a,F,!0):Jr(I.kind,R,x,y,e,K,J,$,a,F),atlasTexture:N,atlasTextureIcon:U,atlasInterpolation:j,atlasInterpolationIcon:V,isSDF:O,hasHalo:Q};if(b&&L.canOverlap){_=!0;for(var et=0,rt=C.segments.get();et<rt.length;et+=1){var nt=rt[et];k.push({segments:new t.SegmentVector([nt]),sortKey:nt.sortKey,state:tt})}}else k.push({segments:C.segments,sortKey:0,state:tt})}}}_&&k.sort((function(t,e){return t.sortKey-e.sortKey}));for(var it=0,at=k;it<at.length;it+=1){var ot=at[it],st=ot.state;if(p.activeTexture.set(d.TEXTURE0),st.atlasTexture.bind(st.atlasInterpolation,d.CLAMP_TO_EDGE),st.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),st.atlasTextureIcon&&st.atlasTextureIcon.bind(st.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),st.isSDF){var lt=st.uniformValues;st.hasHalo&&(lt.u_is_halo=1,cn(st.buffers,ot.segments,n,e,st.program,w,f,h,lt)),lt.u_is_halo=0}cn(st.buffers,ot.segments,n,e,st.program,w,f,h,st.uniformValues)}}function cn(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function fn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u="fillOutlinePattern"===l&&d?Or(S,t,v,x,E):Pr(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Cr(S,t,v,x):Lr(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function hn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get("fill-extrusion-pattern"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?"fillExtrusionPattern":"fillExtrusion",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=c?Er(k,t,A,h,v,f,g):Sr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function pn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=jr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function dn(e,r,n,i,a,o){var s=e.context,l=s.gl,u=r.dem;if(u&&u.data){var c=u.dim,f=u.stride,h=u.getPixels();if(s.activeTexture.set(l.TEXTURE1),s.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(f),r.demTexture){var p=r.demTexture;p.update(h,{premultiply:!1}),p.bind(l.NEAREST,l.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(s,h,l.RGBA,{premultiply:!1}),r.demTexture.bind(l.NEAREST,l.CLAMP_TO_EDGE);s.activeTexture.set(l.TEXTURE0);var d=r.fbo;if(!d){var v=new t.Texture(s,{width:c,height:c,data:null},l.RGBA);v.bind(l.LINEAR,l.CLAMP_TO_EDGE),(d=r.fbo=s.createFramebuffer(c,c,!0)).colorAttachment.set(v.texture)}s.bindFramebuffer.set(d.framebuffer),s.viewport.set([0,0,c,c]),e.useProgram("hillshadePrepare").draw(s,l.TRIANGLES,i,a,o,Ct.disabled,Ur(r.tileID,u),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function vn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),mn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),bn=new t.Color(0,1,1,1);function _n(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,mn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,bn)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f="$debug";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,Rr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+" "+d+"kb"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,Rr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if("translucent"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get("text-variable-anchor")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u="map"===a,c="map"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b="none"!==n.layout.get("icon-text-fit")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);sn(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&un(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&un(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(nn(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),nn(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Ir(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,O,n.id,C,P,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Br(c.posMatrix,f,v,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Nr(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1))for(var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get("line-dasharray"),c=n.paint.get("line-pattern"),f=c.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":u?"lineSDF":h?"lineGradient":"line",v=e.context,g=v.gl,y=!0,m=0,x=i;m<x.length;m+=1){var b=x[m],_=r.getTile(b);if(!f||_.patternsLoaded()){var w=_.getBucket(n);if(w){var T=w.programConfigurations.get(n.id),k=e.context.program.get(),A=e.useProgram(d,T),M=y||A.program!==k,S=c.constantOr(null);if(S&&_.imageAtlas){var E=_.imageAtlas,L=E.patternPositions[S.to.toString()],C=E.patternPositions[S.from.toString()];L&&C&&T.setConstantPatternPositions(L,C)}var P=f?Hr(e,_,n,p):u?Gr(e,_,n,u,p):h?qr(e,_,n,w.lineClipsArray.length):Vr(e,_,n);if(f)v.activeTexture.set(g.TEXTURE0),_.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),T.updatePaintBuffers(p);else if(u&&(M||e.lineAtlas.dirty))v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v);else if(h){var O=w.gradients[n.id],I=O.texture;if(n.gradientVersion!==O.version){var D=256;if(n.stepInterpolant){var z=r.getSource().maxzoom,R=b.canonical.z===z?Math.ceil(1<<e.transform.maxZoom-b.canonical.z):1,F=w.maxLineLength/t.EXTENT*1024*R;D=t.clamp(t.nextPowerOfTwo(F),256,v.maxTextureSize)}O.gradient=t.renderColorRamp({expression:n.gradientExpression(),evaluationKey:"lineProgress",resolution:D,image:O.gradient||void 0,clips:w.lineClipsArray}),O.texture?O.texture.update(O.gradient):O.texture=new t.Texture(v,O.gradient,g.RGBA),O.version=n.gradientVersion,I=O.texture}v.activeTexture.set(g.TEXTURE0),I.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}A.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(b),l,Ct.disabled,P,n.id,w.layoutVertexBuffer,w.indexBuffer,w.segments,n.paint,e.transform.zoom,T,w.layoutVertexBuffer2),y=!1}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===u){var c=e.depthModeForSublayer(1,"opaque"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);fn(e,r,n,i,c,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,Mt.ReadOnly);fn(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))hn(t,e,r,n,a,Et.disabled,Lt.disabled),hn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();hn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=t.depthModeForSublayer(0,Mt.ReadOnly),o=t.colorModeForRenderPass(),s="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],l=s[0],u=0,c=s[1];u<c.length;u+=1){var f=c[u],h=e.getTile(f);h.needsHillshadePrepare&&"offscreen"===t.renderPass?dn(t,h,r,a,Et.disabled,o):"translucent"===t.renderPass&&pn(t,h,r,a,l[f.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=vn(m,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Xr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get("background-pattern");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,"opaque"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?"backgroundPattern":"background"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?en(b,i,t,u,{tileID:x,tileSize:l},g):tn(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)An(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o="3d"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Sn=function(t,e){this.context=new Pt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ot.maxUnderzooming+Ot.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Sn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Sn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Xe.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Xe.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,O.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Xe.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Sn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Fr(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Sn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Fr(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Sn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Sn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?Lt.unblended:Lt.alphaBlended},Sn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Sn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Sn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass="offscreen";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&Mn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&_n(this),this.context.setDefault()},Sn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),Mn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Sn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Sn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Sn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Sn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Sn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Sn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Sn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Ln=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Ln.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Ln(n,i)},Ln.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Ln.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Ln.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Cn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};Cn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Cn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Cn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Cn.prototype.clone=function(){return new Cn(this.top,this.bottom,this.left,this.right)},Cn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Pn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Cn,this._posMatrixCache={},this._alignedPosMatrixCache={}},On={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Pn.prototype.clone=function(){var t=new Pn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},On.minZoom.get=function(){return this._minZoom},On.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},On.maxZoom.get=function(){return this._maxZoom},On.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},On.minPitch.get=function(){return this._minPitch},On.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},On.maxPitch.get=function(){return this._maxPitch},On.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},On.renderWorldCopies.get=function(){return this._renderWorldCopies},On.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},On.worldSize.get=function(){return this.tileSize*this.scale},On.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},On.size.get=function(){return new t.Point(this.width,this.height)},On.bearing.get=function(){return-this.angle/Math.PI*180},On.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},On.pitch.get=function(){return this._pitch/Math.PI*180},On.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},On.fov.get=function(){return this._fov/Math.PI*180},On.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},On.zoom.get=function(){return this._zoom},On.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},On.center.get=function(){return this._center},On.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},On.padding.get=function(){return this._edgeInsets.toJSON()},On.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},On.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Pn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Pn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Pn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Pn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Pn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new Ln([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Pn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},On.unmodified.get=function(){return this._unmodified},Pn.prototype.zoomScale=function(t){return Math.pow(2,t)},Pn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Pn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Pn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},On.point.get=function(){return this.project(this.center)},Pn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Pn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Pn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Pn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Pn.prototype.coordinateLocation=function(t){return t.toLngLat()},Pn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Pn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Pn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Pn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Pn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Pn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Pn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Pn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Pn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Pn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Pn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Pn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Pn.prototype,On);var In=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};In.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},In.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},In.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c="";if(c+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||u)&&(c+="/"+Math.round(10*l)/10),u&&(c+="/"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+c):t})).filter((function(t){return t}));return h||p.push(f+"="+c),"#"+p.join("&")}return"#"+c},In.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},In.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},In.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var Dn={linearity:.3,easing:t.bezier(0,0,.3,1)},zn=t.extend({deceleration:2500,maxSpeed:1400},Dn),Rn=t.extend({deceleration:20,maxSpeed:1400},Dn),Fn=t.extend({deceleration:1e3,maxSpeed:360},Dn),Bn=t.extend({deceleration:1e3,maxSpeed:90},Dn),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Un(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Nn.prototype.clear=function(){this._inertiaBuffer=[]},Nn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Nn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Un(r.pan.mag(),o,t.extend({},zn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,jn(s,l)}if(r.zoom){var u=Un(r.zoom,o,Rn);s.zoom=this._map.transform.zoom+u.amount,jn(s,u)}if(r.bearing){var c=Un(r.bearing,o,Fn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),jn(s,c)}if(r.pitch){var f=Un(r.pitch,o,Bn);s.pitch=this._map.transform.pitch+f.amount,jn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Vn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),qn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Gn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Gn.prototype.reset=function(){delete this._mousedownPos},Gn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},Gn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseup=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Wn=function(t){this._map=t};Wn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Wn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Wn.prototype.mousedown=function(){this._delayContextMenu=!0},Wn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Wn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},Wn.prototype.isEnabled=function(){return!0},Wn.prototype.isActive=function(){return!1},Wn.prototype.enable=function(){},Wn.prototype.disable=function(){};var Yn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Xn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Yn.prototype.isEnabled=function(){return!!this._enabled},Yn.prototype.isActive=function(){return!!this._active},Yn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Yn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Yn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Yn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Yn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Yn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Yn.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Yn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Zn=function(t){this.reset(),this.numTouches=t.numTouches};Zn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Zn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Xn(n,r)))},Zn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Xn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Zn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Kn=function(t){this.singleTap=new Zn(t),this.numTaps=t.numTaps,this.reset()};Kn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Kn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Kn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Kn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Kn({numTouches:1,numTaps:2}),this._zoomOut=new Kn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var $n={};$n[0]=1,$n[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=$n[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)<this._clickTolerance))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Qn.prototype.mouseupWindow=function(t){this._lastPoint&&r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Qn.prototype.enable=function(){this._enabled=!0},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Qn),ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ni=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ni.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ni.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ni.prototype.touchmove=function(t,e,r){if(this._active&&!(r.length<this._minTouches))return t.preventDefault(),this._calculateTransform(t,e,r)},ni.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ni.prototype.touchcancel=function(){this.reset()},ni.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Xn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ii=function(){this.reset()};function ai(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ii.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ii.prototype._start=function(t){},ii.prototype._move=function(t,e,r){return{}},ii.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ii.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ai(r,e,i),s=ai(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ii.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ai(n,e,a),l=ai(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ii.prototype.touchcancel=function(){this.reset()},ii.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},ii.prototype.disable=function(){this._enabled=!1,this.reset()},ii.prototype.isEnabled=function(){return this._enabled},ii.prototype.isActive=function(){return this._active};function oi(t,e){return Math.log(t/e)/Math.LN2}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}},e}(ii);function li(t,e){return 180*t.angleWith(e)/Math.PI}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e},e}(ii);function ci(t){return Math.abs(t.y)>Math.abs(t.x)}var fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ci(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ci(t)&&ci(e)&&a}},e}(ii),hi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=hi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var vi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll(["_onTimeout"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%vi==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>vi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if("wheel"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var mi=function(){this.reset()};mi.prototype.reset=function(){this._active=!1},mi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},mi.prototype.enable=function(){this._enabled=!0},mi.prototype.disable=function(){this._enabled=!1,this.reset()},mi.prototype.isEnabled=function(){return this._enabled},mi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new Kn({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var bi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};bi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},bi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},bi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},bi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var _i=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};_i.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},_i.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},_i.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},_i.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!0}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};Mi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},Mi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Gn(e,t));var n=e.boxZoom=new Yn(e,t);this._add("boxZoom",n);var i=new Jn,a=new mi;e.doubleClickZoom=new yi(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new xi;this._add("tapDragZoom",o);var s=e.touchPitch=new fi;this._add("touchPitch",s);var l=new ei(t),u=new ri(t);e.dragRotate=new _i(t,l,u),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",u,["mouseRotate"]);var c=new ti(t),f=new ni(t);e.dragPan=new bi(r,c,f),this._add("mousePan",c),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ui,p=new si;e.touchZoomRotate=new wi(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new gi(e,this);this._add("scrollZoom",d,["mousePan"]);var v=e.keyboard=new pi;this._add("keyboard",v),this._add("blockableMapEvent",new Wn(e));for(var g=0,y=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},Mi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},Mi.prototype.stop=function(t){if(!this._updatingCamera){for(var e=0,r=this._handlers;e<r.length;e+=1)r[e].handler.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}},Mi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},Mi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Mi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Mi.prototype.isMoving=function(){return Boolean(Ti(this._eventsInProgress))||this.isZooming()},Mi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},Mi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},Mi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},Mi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ai(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ai(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],m(this._map))}else this.stop(!0)},Mi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},Mi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},Mi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ai(t))return this._fireEvents(e,r,!0);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)},Mi.prototype._fireEvents=function(e,r,n){var i=this,a=Ti(this._eventsInProgress),o=Ti(e),s={};for(var l in e){var u=e[l].originalEvent;this._eventsInProgress[l]||(s[l+"start"]=u),this._eventsInProgress[l]=e[l]}for(var c in!a&&o&&this._fireEvent("movestart",o.originalEvent),s)this._fireEvent(c,s[c]);for(var f in o&&this._fireEvent("move",o.originalEvent),e){var h=e[f].originalEvent;this._fireEvent(f,h)}var p,d={};for(var v in this._eventsInProgress){var g=this._eventsInProgress[v],y=g.handlerName,m=g.originalEvent;this._handlersById[y].isActive()||(delete this._eventsInProgress[v],p=r[y]||m,d[v+"end"]=p)}for(var x in d)this._fireEvent(x,d[x]);var b=Ti(this._eventsInProgress);if(n&&(a||o)&&!b){this._updatingCamera=!0;var _=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),w=function(t){return 0!==t&&-i._bearingSnap<t&&t<i._bearingSnap};_?(w(_.bearing||this._map.getBearing())&&(_.bearing=0),this._map.easeTo(_,{originalEvent:p})):(this._map.fire(new t.Event("moveend",{originalEvent:p})),w(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Mi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Mi.prototype._requestFrame=function(){var t=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add((function(e){delete t._frameId,t.handleEvent(new ki("renderFrame",{timeStamp:e})),t._applyChanges()}))},Mi.prototype._triggerRenderFrame=function(){void 0===this._frameId&&(this._frameId=this._requestFrame())};var Si=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){e=t.LngLatBounds.convert(e);var n=r&&r.bearing||0;return this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),n,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x="number"==typeof i.offset.x?new t.Point(i.offset.x,i.offset.y):t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(b,_).rotate(n*Math.PI/180),T=x.add(w).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(T)),zoom:m,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u="zoom"in e?+e.zoom:a,c="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},D=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=T<w?-1:1;D=Math.abs(Math.log(T/w))/_,I=function(){return 0},O=function(t){return Math.exp(z*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*D/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_toggleAttribution","_updateEditLink","_updateData","_updateCompact"],this)};Ei.prototype.getDefaultPosition=function(){return"bottom-right"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._compactButton=r.create("button","mapboxgl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),this._innerContainer.setAttribute("role","list"),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString("AttributionControl."+e);t.title=r,t.setAttribute("aria-label",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains("mapboxgl-compact-show")?(this._container.classList.remove("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","false")):(this._container.classList.add("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","true"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow",this._setElementTitle(e,"MapFeedback")}},Ei.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"visibility"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ei.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact","mapboxgl-compact-show")};var Li=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};Li.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},Li.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},Li.prototype.getDefaultPosition=function(){return"bottom-left"},Li.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},Li.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Li.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var Ci=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ci.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ci.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ci.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ci.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Pi={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},Oi=t.window.HTMLImageElement,Ii=t.window.HTMLElement,Di=t.window.ImageBitmap,zi=60,Ri={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:zi,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Fi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Ri,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>zi)throw new Error("maxPitch must be less than or equal to 60");var i=new Pn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ci,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Pi,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ii))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_onMapScroll","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1),t.window.addEventListener("orientationchange",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new In(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Li,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():"top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?zi:t)>zi)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Oi||Di&&r instanceof Di){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof Oi||Di&&r instanceof Di?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof Oi||Di&&r instanceof Di);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map"),this._canvas.setAttribute("role","region");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)})),this._container.addEventListener("scroll",this._onMapScroll,!1)},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new Sn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype._onMapScroll=function(t){if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}var p=this._sourcesDirty||this._styleDirty||this._placementDirty;return p||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event("idle")),!this._loaded||this._fullyLoaded||p||(this._fullyLoaded=!0),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("orientationchange",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext&&n.loseContext(),Bi(this._canvasContainer),Bi(this._controlContainer),Bi(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Si);function Bi(t){t.parentNode&&t.parentNode.removeChild(t)}var Ni={showCompass:!0,showZoom:!0,visualizePitch:!1},ji=function(e){var n=this;this.options=t.extend({},Ni,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};ji.prototype._updateZoomButtons=function(){var t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute("aria-disabled",e.toString()),this._zoomOutButton.setAttribute("aria-disabled",r.toString())},ji.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},ji.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ui(this._map,this._compass,this.options.visualizePitch)),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},ji.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},ji.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ui=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new ei({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ri({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Vi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ui.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var qi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Hi(t,e,r){var n=t.classList;for(var i in qi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Gi,Wi=function(e){function n(n,i){if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._scale=n&&n.scale||1,this._draggable=n&&n.draggable||!1,this._clickTolerance=n&&n.clickTolerance||0,this._isDragging=!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var a=r.createNS("http://www.w3.org/2000/svg","svg");a.setAttributeNS(null,"display","block"),a.setAttributeNS(null,"height","41px"),a.setAttributeNS(null,"width","27px"),a.setAttributeNS(null,"viewBox","0 0 27 41");var o=r.createNS("http://www.w3.org/2000/svg","g");o.setAttributeNS(null,"stroke","none"),o.setAttributeNS(null,"stroke-width","1"),o.setAttributeNS(null,"fill","none"),o.setAttributeNS(null,"fill-rule","evenodd");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"fill-rule","nonzero");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"transform","translate(3.0, 29.0)"),l.setAttributeNS(null,"fill","#000000");for(var u=0,c=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<c.length;u+=1){var f=c[u],h=r.createNS("http://www.w3.org/2000/svg","ellipse");h.setAttributeNS(null,"opacity","0.04"),h.setAttributeNS(null,"cx","10.5"),h.setAttributeNS(null,"cy","5.80029008"),h.setAttributeNS(null,"rx",f.rx),h.setAttributeNS(null,"ry",f.ry),l.appendChild(h)}var p=r.createNS("http://www.w3.org/2000/svg","g");p.setAttributeNS(null,"fill",this._color);var d=r.createNS("http://www.w3.org/2000/svg","path");d.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),p.appendChild(d);var v=r.createNS("http://www.w3.org/2000/svg","g");v.setAttributeNS(null,"opacity","0.25"),v.setAttributeNS(null,"fill","#000000");var g=r.createNS("http://www.w3.org/2000/svg","path");g.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 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b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#FFFFFF"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962"),m.appendChild(x),m.appendChild(b),s.appendChild(l),s.appendChild(p),s.appendChild(v),s.appendChild(y),s.appendChild(m),a.appendChild(s),a.setAttributeNS(null,"height",41*this._scale+"px"),a.setAttributeNS(null,"width",27*this._scale+"px"),this._element.appendChild(a),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),Hi(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[r,-1*(24.6+r)],"bottom-right":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,qi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){if(!this._isDragging){var r=this._clickTolerance||this._map._clickTolerance;this._isDragging=e.point.dist(this._pointerdownPos)>=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag")))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Yi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Xi=0,Zi=!1,Ki=function(e){function n(r){e.call(this),this.options=t.extend({},Yi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Gi="denied"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Xi=0,Zi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Zi)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Wi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Wi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Xi--,Zi=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Xi>1?(e={maximumAge:6e5,timeout:0},Zi=!0):(e=this.options.positionOptions,Zi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:"metric"},$i=function(e){this.options=t.extend({},Ji,e),t.bindAll(["_onMove","setUnit"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString("ScaleControl.Miles")):ta(e,n,l,t._getUIString("ScaleControl.Feet"))}else r&&"nautical"===r.unit?ta(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles")):s>=1e3?ta(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):ta(e,n,s,t._getUIString("ScaleControl.Meters"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(""+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}$i.prototype.getDefaultPosition=function(){return"bottom-left"},$i.prototype._onMove=function(){Qi(this._map,this._container,this.options)},$i.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},$i.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},$i.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},ea.prototype._isFullscreen=function(){return 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S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),c.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=c.mallocUint8(this.count),P=c.mallocFloat(2*this.count);this.textWidth=[];for(var O=0,I=0;O<this.counts.length;O++){for(var D=this.counts[O],z=this.font[O]||this.font[0],R=this.fontAtlas[O]||this.fontAtlas[0],F=0;F<D;F++){var B=this.text.charAt(I),N=this.text.charAt(I-1);if(C[I]=R.ids[B],P[2*I]=z.width[B],F){var j=P[2*I-2],U=P[2*I],V=P[2*I-1]+.5*j+.5*U;if(this.kerning){var q=z.kerning[N+B];q&&(V+=.001*q)}P[2*I+1]=V}else 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u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:"Polygon",coordinates:[]}:1==c.length?{type:"Polygon",coordinates:c[0]}:{type:"MultiPolygon",coordinates:c}}};t.exports=e},72200:function(t,e,r){var n=r(48088);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function u(i,o){var s=n.create();function u(t){return s.findTransition((function(r){var n,i,a,o,s,l;return n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,(e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function c(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,u=a.start,c=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,u,c);if(!1===f){if(!e.pointsCollinear(o,s,u))return!1;if(e.pointsSame(o,c)||e.pointsSame(s,u))return!1;var h=e.pointsSame(o,u),p=e.pointsSame(s,c);if(h&&p)return n;var d=!h&&e.pointBetween(o,u,c),v=!p&&e.pointBetween(s,u,c);if(h)return v?l(n,s):l(t,c),n;d&&(p||(v?l(n,s):l(t,c)),l(n,o))}else 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y,m,x=g();if(x)t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(x.seg.myFill.above=!x.seg.myFill.above):x.seg.otherFill=h.seg.myFill,r&&r.segmentUpdate(x.seg),h.other.remove(),h.remove();if(a.getHead()!==h){r&&r.rewind(h.seg);continue}t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=v?v.seg.myFill.above:i,h.seg.myFill.above=m?!h.seg.myFill.below:h.seg.myFill.below):null===h.seg.otherFill&&(y=v?h.primary===v.primary?v.seg.otherFill.above:v.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:y,below:y}),r&&r.status(h.seg,!!d&&d.seg,!!v&&v.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error("PolyBool: Zero-length segment detected; your epsilon is probably too small or too large");if(s.exists(b.prev)&&s.exists(b.next)&&c(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},82368:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},9696:function(t,e,r){"use 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r=new Error(t);return r.code=e,r}function r(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function n(t,r,n){this.input=t.subarray(r,n),this.start=r;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw e("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}n.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},n.prototype.read_uint16=function(t){var r=this.input;if(t+2>r.length)throw e("unexpected EOF","EBADDATA");return this.big_endian?256*r[t]+r[t+1]:r[t]+256*r[t+1]},n.prototype.read_uint32=function(t){var r=this.input;if(t+4>r.length)throw e("unexpected EOF","EBADDATA");return this.big_endian?16777216*r[t]+65536*r[t+1]+256*r[t+2]+r[t+3]:r[t]+256*r[t+1]+65536*r[t+2]+16777216*r[t+3]},n.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},n.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},n.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return this.read_uint32(e);case 9:return 0|this.read_uint32(e);default:return null}},n.prototype.scan_ifd=function(t,n,i){var a=this.read_uint16(n);n+=2;for(var o=0;o<a;o++){var s=this.read_uint16(n),l=this.read_uint16(n+2),u=this.read_uint32(n+4),c=this.exif_format_length(l),f=u*c,h=f<=4?n+8:this.read_uint32(n+8),p=!1;if(h+f>this.input.length)throw e("unexpected EOF","EBADDATA");for(var d=[],v=h,g=0;g<u;g++,v+=c){var 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t=l(t,"float64"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t="transparent"),!Array.isArray(t)||"number"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a<r;a++)t[a]=n}if(t.length<r)throw Error("Not enough colors");for(var o=new Uint8Array(4*r),s=0;s<r;s++){var l=i(t[s],"uint8");o.set(l,4*s)}return o},range:function(t,e,r){var n=e.bounds;return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},13472:function(t,e,r){"use strict";var n=r(72160),i=r(76752),a=r(50896),o=r(55616),s=r(47520),l=r(28912),u=r(71152),c=r(37816),f=c.float32,h=c.fract32,p=r(60463),d=r(51160),v=r(10272);function g(t,e){if(!(this instanceof g))return new g(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=g.shaders.has(t)?g.shaders.get(t):g.shaders.set(t,g.createShaders(t)).get(t),this.update(e)}t.exports=g,g.dashMult=2,g.maxPatternLength=256,g.precisionThreshold=3e6,g.maxPoints=1e4,g.maxLines=2048,g.shaders=new p,g.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle strip",instances:t.prop("count"),count:4,offset:0,uniforms:{miterMode:function(t,e){return"round"===e.join?2:1},miterLimit:t.prop("miterLimit"),scale:t.prop("scale"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),thickness:t.prop("thickness"),dashTexture:t.prop("dashTexture"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),dashLength:t.prop("dashLength"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop("depth")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport")},i=t(a({vert:"\nprecision highp float;\n\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\nattribute vec4 color;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\n\t// the order is important\n\treturn position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n}\n\nvoid main() {\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineOffset = lineTop * 2. - 1.;\n\n\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\n\ttangent = normalize(diff * scale * viewport.zw);\n\tvec2 normal = vec2(-tangent.y, tangent.x);\n\n\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\n\t\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\n\n\t\t+ thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n",frag:"\nprecision highp float;\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:"\nprecision highp float;\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n",frag:"\nprecision highp float;\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:"\nprecision highp float;\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n",frag:"\nprecision highp float;\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n",uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},g.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},g.prototype.draw=function(){for(var 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*g.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}y.dashLength=tt,y.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=y.count,ut=t.color;ut||(ut="transparent");var ct=new Uint8Array(4*lt+4);if(Array.isArray(ut)&&"number"!=typeof ut[0]){for(var ft=0;ft<lt;ft++){var ht=n(ut[ft],"uint8");ct.set(ht,4*ft)}ct.set(n(ut[0],"uint8"),4*lt)}else for(var pt=n(ut,"uint8"),dt=0;dt<lt+1;dt++)ct.set(pt,4*dt);y.colorBuffer({usage:"dynamic",type:"uint8",data:ct})}}else e.passes[p]=null})),t.length<this.passes.length){for(var p=t.length;p<this.passes.length;p++){var y=this.passes[p];y&&(y.colorBuffer.destroy(),y.positionBuffer.destroy(),y.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&m.push(this.passes[x]);return this.passes=m,this}},g.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},38540:function(t,e,r){"use strict";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){var r=null==t?null:"undefined"!=typeof Symbol&&t[Symbol.iterator]||t["@@iterator"];if(null!=r){var n,i,a,o,s=[],l=!0,u=!1;try{if(a=(r=r.call(t)).next,0===e){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==e);l=!0);}catch(t){u=!0,i=t}finally{try{if(!l&&null!=r.return&&(o=r.return(),Object(o)!==o))return}finally{if(u)throw i}}return s}}(t,e)||i(t,e)||function(){throw new TypeError("Invalid attempt to destructure non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function i(t,e){if(t){if("string"==typeof t)return a(t,e);var 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop("opacity"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context("pixelRatio"),scale:t.prop("scale"),scaleFract:t.prop("scaleFract"),translate:t.prop("translate"),translateFract:t.prop("translateFract"),markerTexture:t.prop("markerTexture"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=c({},o);s.frag=f(["precision highp float;\n#define 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opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=f(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\n// `invariant` effectively turns off optimizations for the position.\n// We need this because -fast-math on M1 Macs is re-ordering\n// floating point operations in a way that causes floating point\n// precision limits to put points in the wrong locations.\ninvariant gl_Position;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 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/ paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),v&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}x.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if("number"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var 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n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Persian",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Persian",epochs:["BP","AP"],monthNames:["Farvardin","Ordibehesht","Khordad","Tir","Mordad","Shahrivar","Mehr","Aban","Azar","Day","Bahman","Esfand"],monthNamesShort:["Far","Ord","Kho","Tir","Mor","Sha","Meh","Aba","Aza","Day","Bah","Esf"],dayNames:["Yekshambe","Doshambe","Seshambe","Chæharshambe","Panjshambe","Jom'e","Shambe"],dayNamesShort:["Yek","Do","Se","Chæ","Panj","Jom","Sha"],dayNamesMin:["Ye","Do","Se","Ch","Pa","Jo","Sh"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var u=t-this.toJD(l,1,1)+1,c=u<=186?Math.ceil(u/31):Math.ceil((u-6)/30),f=t-this.toJD(l,c,1)+1;return this.newDate(l,c,f)}}),n.calendars.persian=a,n.calendars.jalali=a},51456:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}o.prototype=new 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t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},4592:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}o.prototype=new n.baseCalendar,i(o.prototype,{name:"Thai",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Thai",epochs:["BBE","BE"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 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n=r(50896);function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day(),"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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Parameter	Details
Market	US Stock Market
Type	Long/Short (Market Neutral)
Rebalance Frequency	Monthly
Position Sizing	Equal Weighted
Leverage	1x
Commission	$0
Start Date	1980-01
End Date	2022-12
Predictor	Dispersions (DISP), Net Operating Assets (NOA), Max Return in 5 Days per Month (RMAX5_21D)
Alpha	2.65%
Win Rate	61.55%
VaR	15.748%
	ret_12_7	ret_3_1	ret_6_1	ret_9_1	ret_12_1	ret_18_1	ret_1_0	ret_2_0	ret_3_0	ret_6_0	...	disp	nsi	accrual	noa	ag	ia	pead	rmax5_21d	prc_highprc_252d	rmax1_21d
ret_12_7	1.00	0.01	0.01	0.27	0.58	0.40	0.00	0.01	0.01	0.01	...	-0.02	-0.01	0.0	-0.00	-0.00	0.00	0.01	0.01	0.09	0.00
ret_3_1	0.01	1.00	0.60	0.45	0.34	0.22	0.01	0.48	0.79	0.54	...	-0.00	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.00	0.29	-0.00
ret_6_1	0.01	0.60	1.00	0.75	0.57	0.38	0.01	0.28	0.47	0.88	...	-0.01	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.00	0.33	0.00
ret_9_1	0.27	0.45	0.75	1.00	0.78	0.55	0.01	0.22	0.35	0.66	...	-0.01	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.00	0.31	0.00
ret_12_1	0.58	0.34	0.57	0.78	1.00	0.71	0.01	0.16	0.27	0.50	...	-0.02	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.01	0.24	0.00
ret_18_1	0.40	0.22	0.38	0.55	0.71	1.00	-0.01	0.09	0.17	0.32	...	-0.02	-0.00	0.0	-0.00	-0.00	0.00	0.01	0.01	0.13	0.00
ret_1_0	0.00	0.01	0.01	0.01	0.01	-0.01	1.00	0.71	0.56	0.37	...	0.00	-0.00	-0.0	0.00	-0.00	-0.00	0.00	0.42	0.28	0.37
ret_2_0	0.01	0.48	0.28	0.22	0.16	0.09	0.71	1.00	0.81	0.54	...	-0.00	-0.00	-0.0	0.00	-0.00	-0.00	0.01	0.30	0.35	0.27
ret_3_0	0.01	0.79	0.47	0.35	0.27	0.17	0.56	0.81	1.00	0.68	...	-0.00	-0.00	0.0	0.00	-0.00	-0.00	0.01	0.25	0.38	0.22
ret_6_0	0.01	0.54	0.88	0.66	0.50	0.32	0.37	0.54	0.68	1.00	...	-0.01	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.19	0.40	0.16
ret_9_0	0.25	0.42	0.68	0.91	0.72	0.49	0.29	0.42	0.53	0.77	...	-0.01	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.17	0.37	0.14
ret_12_0	0.52	0.31	0.51	0.69	0.92	0.64	0.21	0.31	0.39	0.58	...	-0.02	-0.00	0.0	-0.00	-0.00	-0.00	0.01	0.14	0.28	0.11
feps	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	...	-0.00	0.04	0.0	-0.00	-0.00	-0.00	0.00	-0.01	0.01	-0.01
fep	0.01	-0.02	-0.02	-0.01	-0.00	0.02	-0.02	-0.02	-0.02	-0.02	...	-0.04	-0.01	0.0	0.00	0.00	-0.00	0.03	-0.07	0.06	-0.06
disp	-0.02	-0.00	-0.01	-0.01	-0.02	-0.02	0.00	-0.00	-0.00	-0.01	...	1.00	0.00	-0.0	-0.00	-0.00	-0.00	-0.01	0.05	-0.06	0.04
nsi	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	...	0.00	1.00	-0.0	0.00	0.00	-0.00	0.00	-0.00	-0.01	-0.00
accrual	0.00	0.00	0.00	0.00	0.00	0.00	-0.00	-0.00	0.00	0.00	...	-0.00	-0.00	1.0	0.00	0.00	-0.00	0.00	-0.00	0.00	-0.00
noa	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	-0.00	...	-0.00	0.00	0.0	1.00	0.09	-0.00	-0.00	-0.00	-0.00	-0.00
ag	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	...	-0.00	0.00	0.0	0.09	1.00	0.00	-0.00	-0.00	-0.00	-0.00
ia	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	...	-0.00	-0.00	-0.0	-0.00	0.00	1.00	0.00	0.00	-0.01	0.00
pead	0.01	0.01	0.01	0.01	0.01	0.01	0.00	0.01	0.01	0.01	...	-0.01	0.00	0.0	-0.00	-0.00	0.00	1.00	-0.01	0.02	-0.01
rmax5_21d	0.01	0.00	0.00	0.00	0.01	0.01	0.42	0.30	0.25	0.19	...	0.05	-0.00	-0.0	-0.00	-0.00	0.00	-0.01	1.00	-0.03	1.00
prc_highprc_252d	0.09	0.29	0.33	0.31	0.24	0.13	0.28	0.35	0.38	0.40	...	-0.06	-0.01	0.0	-0.00	-0.00	-0.01	0.02	-0.03	1.00	-0.01
rmax1_21d	0.00	-0.00	0.00	0.00	0.00	0.00	0.37	0.27	0.22	0.16	...	0.04	-0.00	-0.0	-0.00	-0.00	0.00	-0.01	1.00	-0.01	1.00
	ret_12_0	ret_9_0	ret_6_0	ret_3_0	ret_2_0	ret_1_0	ret_lead1m	ret_lead2m	ret_lead3m	ret_lead4m	ret_lead5m	ret_lead6m	ret_lead7m	ret_lead8m	ret_lead9m	ret_lead10m	ret_lead11m	ret_lead12m
t-statistic	0.279719	1.397854	0.483051	-5.322571	-10.240991	-22.538607	8.655697	5.820639	4.265612	5.277713	4.21871	3.429607	3.876551	3.056768	3.343559	3.133205	3.476274	2.998557
p-value	0.779805	0.162759	0.629265	0.0	0.0	0.0	0.0	0.0	0.000024	0.0	0.000029	0.000654	0.00012	0.002355	0.000888	0.00183	0.000552	0.002847
	CAPM Alpha	CAPM p-value	CAPM t-statistic	FF3 Alpha	FF3 p-value	FF3 t-statistic	FF4 Alpha	FF4 p-value	FF4 t-statistic
All	0.026483	0.0	7.961592	0.025897	0.0	7.743233	0.026034	0.0	7.644343
Long	0.012179	0.0	6.509637	0.012426	0.0	6.597473	0.012761	0.0	6.659471
Short	-0.017588	0.000024	-4.259169	-0.016739	0.000063	-4.03399	-0.016498	0.000107	-3.904767