From adfa4d85b309e2785966cbc824b855600ec7d9a4 Mon Sep 17 00:00:00 2001
From: CI <ci-bot@example.com>
Date: Mon, 22 Apr 2024 19:22:43 +0000
Subject: [PATCH] Deployed 3e808db to v0.8.0 with MkDocs 1.5.3 and mike 2.0.0

---
 latest/api/layers/polygon-layer/index.html    |    4 +-
 .../api/layers/polygon-layer/polygon-layer.md |    4 +-
 latest/api/viz/index.html                     |    2 +
 .../images/social/examples/overture-maps.png  |  Bin 0 -> 31794 bytes
 latest/assets/overture.jpg                    |  Bin 0 -> 1033337 bytes
 .../examples/data-filter-extension/index.html |    8 +-
 latest/examples/global-boundaries/index.html  |    4 +-
 latest/examples/migration/index.html          |    4 +-
 latest/examples/overture-maps/index.html      | 2911 +++++++++++++++++
 .../overture-maps/overture-maps.ipynb         |  247 ++
 latest/search/search_index.json               |    2 +-
 latest/sitemap.xml                            |   93 +-
 latest/sitemap.xml.gz                         |  Bin 574 -> 583 bytes
 v0.8.0/api/layers/polygon-layer/index.html    |    4 +-
 .../api/layers/polygon-layer/polygon-layer.md |    4 +-
 v0.8.0/api/viz/index.html                     |    2 +
 .../images/social/examples/overture-maps.png  |  Bin 0 -> 31794 bytes
 v0.8.0/assets/overture.jpg                    |  Bin 0 -> 1033337 bytes
 .../examples/data-filter-extension/index.html |    8 +-
 v0.8.0/examples/global-boundaries/index.html  |    4 +-
 v0.8.0/examples/migration/index.html          |    4 +-
 v0.8.0/examples/overture-maps/index.html      | 2911 +++++++++++++++++
 .../overture-maps/overture-maps.ipynb         |  247 ++
 v0.8.0/search/search_index.json               |    2 +-
 v0.8.0/sitemap.xml                            |   93 +-
 v0.8.0/sitemap.xml.gz                         |  Bin 574 -> 583 bytes
 26 files changed, 6444 insertions(+), 114 deletions(-)
 create mode 100644 latest/assets/images/social/examples/overture-maps.png
 create mode 100644 latest/assets/overture.jpg
 create mode 100644 latest/examples/overture-maps/index.html
 create mode 100644 latest/examples/overture-maps/overture-maps.ipynb
 create mode 100644 v0.8.0/assets/images/social/examples/overture-maps.png
 create mode 100644 v0.8.0/assets/overture.jpg
 create mode 100644 v0.8.0/examples/overture-maps/index.html
 create mode 100644 v0.8.0/examples/overture-maps/overture-maps.ipynb

diff --git a/latest/api/layers/polygon-layer/index.html b/latest/api/layers/polygon-layer/index.html
index 1d227e59..2f93aa01 100644
--- a/latest/api/layers/polygon-layer/index.html
+++ b/latest/api/layers/polygon-layer/index.html
@@ -2307,9 +2307,9 @@
 
 
 <h1 id="polygonlayer">PolygonLayer<a class="headerlink" href="#polygonlayer" title="Permanent link">&para;</a></h1>
-<p><img alt="" src="../../../assets/boundaries.png" /></p>
+<p><img alt="" src="../../../assets/overture.jpg" /></p>
 <blockquote>
-<p>Screenshot from <a href="../../examples/global-boundaries">Global Boundaries</a> example</p>
+<p>Screenshot from <a href="../../examples/overture-maps">Overture Maps</a> example</p>
 </blockquote>
 
 
diff --git a/latest/api/layers/polygon-layer/polygon-layer.md b/latest/api/layers/polygon-layer/polygon-layer.md
index b7c4dd20..af13025a 100644
--- a/latest/api/layers/polygon-layer/polygon-layer.md
+++ b/latest/api/layers/polygon-layer/polygon-layer.md
@@ -1,8 +1,8 @@
 # PolygonLayer
 
-![](../../assets/boundaries.png)
+![](../../assets/overture.jpg)
 
-> Screenshot from [Global Boundaries](../../examples/global-boundaries) example
+> Screenshot from [Overture Maps](../../examples/overture-maps) example
 
 ::: lonboard.PolygonLayer
     options:
diff --git a/latest/api/viz/index.html b/latest/api/viz/index.html
index 4fae5f8e..f44b167f 100644
--- a/latest/api/viz/index.html
+++ b/latest/api/viz/index.html
@@ -1954,6 +1954,7 @@ <h2 id="lonboard.viz" class="doc doc-heading">
     <a href="https://gist.github.com/sgillies/2217756">Geo Interface protocol</a>.</li>
 <li><code>dict</code> holding GeoJSON-like data.</li>
 <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li>
+<li>pyarrow <code>Array</code> marked with a <a href="https://geoarrow.org/geoarrow-python/main/pyarrow.html#geoarrow.pyarrow.GeometryExtensionType">GeoArrow extension type defined by geoarrow-pyarrow</a></li>
 </ul>
 <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p>
 
@@ -2050,6 +2051,7 @@ <h2 id="lonboard._viz.VizDataInput" class="doc doc-heading">
     <span class="n">Table</span><span class="p">,</span>
     <span class="n">NDArray</span><span class="p">[</span><span class="n">object_</span><span class="p">],</span>
     <span class="n">BaseGeometry</span><span class="p">,</span>
+    <span class="n">ArrowArrayExportable</span><span class="p">,</span>
     <span class="n">ArrowStreamExportable</span><span class="p">,</span>
     <span class="n">GeoInterfaceProtocol</span><span class="p">,</span>
     <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
diff --git a/latest/assets/images/social/examples/overture-maps.png b/latest/assets/images/social/examples/overture-maps.png
new file mode 100644
index 0000000000000000000000000000000000000000..3c7d83ae9098f393c0069f09c9181b925c5df6df
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+      <a href="../integrations/color-picker/" class="md-nav__link">
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+    ColorPicker
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+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../integrations/sidecar/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Sidecar
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2_8" >
+        
+          
+          <label class="md-nav__link" for="__nav_2_8" id="__nav_2_8_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_2_8_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_2_8">
+            <span class="md-nav__icon md-icon"></span>
+            30 Day Map Challenge
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../map_challenge/1-points/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge: Day 1 (Points)
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../map_challenge/6-asia/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge: Day 6 (Asia)
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
+          
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            API Reference
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/viz/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    viz
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/map/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Map
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Layers
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Layers
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/bitmap-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BitmapLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/bitmap-tile-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BitmapTileLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/heatmap-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    HeatmapLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/path-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PathLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/point-cloud-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PointCloudLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/polygon-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PolygonLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/scatterplot-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ScatterplotLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/solid-polygon-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    SolidPolygonLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/base-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Base Classes
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../api/layer-extensions/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    Layer Extensions
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Layer Extensions
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/brushing-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BrushingExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/collision-filter-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    CollisionFilterExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/data-filter-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    DataFilterExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/basemap/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.basemap
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/colormap/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.colormap
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/controls/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.controls
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/traits/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.traits
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_9" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_9" id="__nav_3_9_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Experimental
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_9_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_9">
+            <span class="md-nav__icon md-icon"></span>
+            Experimental
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_9_1" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_9_1" id="__nav_3_9_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Layers
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="3" aria-labelledby="__nav_3_9_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_9_1">
+            <span class="md-nav__icon md-icon"></span>
+            Layers
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/arc-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ArcLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/text-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    TextLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../cli/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Command-line Interface
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../ecosystem/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    Ecosystem
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_5" id="__nav_5_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            Ecosystem
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/geoarrow/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    GeoArrow
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/geopandas/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    GeoPandas
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/jupyter-widgets/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Jupyter Widgets
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/panel/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Panel
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/shiny/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Shiny
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../performance/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Performance
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../CHANGELOG/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Changelog
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../alternatives/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Alternatives
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_9" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../how-it-works/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    How it works?
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_9" id="__nav_9_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_9_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_9">
+            <span class="md-nav__icon md-icon"></span>
+            How it works?
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../how-it-works/internals/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Internal Architecture
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#dependendencies" class="md-nav__link">
+    <span class="md-ellipsis">
+      Dependendencies
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#imports" class="md-nav__link">
+    <span class="md-ellipsis">
+      Imports
+    </span>
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+
+    <a href="/lonboard/latest/examples/overture-maps/overture-maps.ipynb" title="Download Notebook" class="md-content__button md-icon">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 20h14v-2H5m14-9h-4V3H9v6H5l7 7 7-7Z"/></svg>
+    </a>
+
+
+
+                  
+
+  
+  
+
+
+<script>
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+  }(this, function () { 'use strict';
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+    function createNode(text) {
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+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
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+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
+
+      if (!body) {
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+      copyNode(node);
+      body.removeChild(node);
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+      if (text) {
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+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
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+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
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+        return copyNode(content);
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+
+    function clicked(event) {
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+        if (button instanceof HTMLElement) {
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+          copy(button);
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+      event.currentTarget.addEventListener('keydown', keydown);
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+    function blurred(event) {
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+
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+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
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+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
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#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: #eeeeee;--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}.jupyter-wrapper{--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), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--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;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--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, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--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);--jp-inverse-layout-color0: #111111;--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);--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);--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);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .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: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--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 );--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);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--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, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--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: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--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;--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;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .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);--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);--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)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--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-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--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: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--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 );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: 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+<h1 id="overture-maps-buildings">Overture Maps Buildings<a class="anchor-link" href="#overture-maps-buildings">¶</a></h1><p><img alt="overture.jpg" 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"/></p>
+<p>This notebook will give a quick overview of using the new <a href="https://github.com/OvertureMaps/overturemaps-py">Overture Maps Python library</a> with Lonboard.</p>
+<p>We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).</p>
+<p>This uses the latest advancements of the <a href="https://geoparquet.org/">GeoParquet specification</a>. Data is fetched <em>directly from the cloud</em> with no server in the middle!</p>
+<p>You can view a <a href="https://notebooksharing.space/view/f16f8f4c7133c4576938b262f17ce617cf112106bc25a24a08b528f8f0d11828#displayOptions=">hosted version of this notebook on Notebook Sharing Space</a> (26MB download).</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="dependendencies">Dependendencies<a class="anchor-link" href="#dependendencies">¶</a></h2><p>This notebook requires Lonboard (tested with version 0.8) and <code>overturemaps</code> (tested with version 0.4). To install <code>overturemaps</code>, run:</p>
+<pre><code>pip install overturemaps
+</code></pre>
+</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="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-1">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></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">overturemaps</span>
+<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">LogNorm</span>
+<span class="kn">from</span> <span class="nn">palettable.colorbrewer.sequential</span> <span class="kn">import</span> <span class="n">Oranges_9</span>
+
+<span class="kn">from</span> <span class="nn">lonboard</span> <span class="kn">import</span> <span class="n">Map</span><span class="p">,</span> <span class="n">PolygonLayer</span>
+<span class="kn">from</span> <span class="nn">lonboard.colormap</span> <span class="kn">import</span> <span class="n">apply_continuous_cmap</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-1">import numpy as np
+import overturemaps
+from matplotlib.colors import LogNorm
+from palettable.colorbrewer.sequential import Oranges_9
+
+from lonboard import Map, PolygonLayer
+from lonboard.colormap import apply_continuous_cmap</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Using <a href="https://boundingbox.klokantech.com/">this bounding box tool</a> we can find a bounding box covering Manhattan. Choose "CSV" in the drop down box, then paste the result in the following cell:</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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>
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">bbox</span> <span class="o">=</span> <span class="o">-</span><span class="mf">74.02169</span><span class="p">,</span> <span class="mf">40.696423</span><span class="p">,</span> <span class="o">-</span><span class="mf">73.891338</span><span class="p">,</span> <span class="mf">40.831263</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-2">bbox = -74.02169, 40.696423, -73.891338, 40.831263</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Ensure you pass the bbox into <code>overturemaps.record_batch_reader</code> as in the next line.</p>
+<p><code>record_batch_reader</code> returns a <a href="https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html">RecordBatchReader</a>, an iterator of Arrow RecordBatches. We call <code>.read_all()</code> to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">overturemaps</span><span class="o">.</span><span class="n">record_batch_reader</span><span class="p">(</span><span class="s2">"building"</span><span class="p">,</span> <span class="n">bbox</span><span class="p">)</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span>
+
+<span class="c1"># Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug</span>
+<span class="n">table</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-3">table = overturemaps.record_batch_reader("building", bbox).read_all()
+
+# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug
+table = table.combine_chunks()</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Let's color buildings by their heights. First, convert the "height" column to a numpy array, then replace any null values with <code>1</code>.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">heights</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s2">"height"</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+<span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan_to_num</span><span class="p">(</span><span class="n">heights</span><span class="p">,</span> <span class="n">nan</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-4">heights = table["height"].to_numpy()
+heights = np.nan_to_num(heights, nan=1)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.LogNorm.html"><code>LogNorm</code></a> to normalize these values according to a log scale.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">normalizer</span> <span class="o">=</span> <span class="n">LogNorm</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">normalized_heights</span> <span class="o">=</span> <span class="n">normalizer</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-5">normalizer = LogNorm(1, heights.max(), clip=True)
+normalized_heights = normalizer(heights)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Then we can apply a colormap to these heights. For this case, we'll use the <a href="https://jiffyclub.github.io/palettable/colorbrewer/sequential/#oranges_9"><code>Oranges_9</code></a> colormap.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-6">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">Oranges_9</span><span class="o">.</span><span class="n">mpl_colormap</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-6">Oranges_9.mpl_colormap</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Using <a href="https://developmentseed.org/lonboard/latest/api/colormap/#lonboard.colormap.apply_continuous_cmap"><code>apply_continuous_cmap</code></a>, we can apply our values onto this colormap.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="n">apply_continuous_cmap</span><span class="p">(</span><span class="n">normalized_heights</span><span class="p">,</span> <span class="n">Oranges_9</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-7">colors = apply_continuous_cmap(normalized_heights, Oranges_9)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>We create a <code>PolygonLayer</code> with our data, passing in the heights and colors from before.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-8">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">PolygonLayer</span><span class="p">(</span>
+    <span class="c1"># Select only a few attribute columns from the table</span>
+    <span class="n">table</span><span class="o">=</span><span class="n">table</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="s2">"id"</span><span class="p">,</span> <span class="s2">"height"</span><span class="p">,</span> <span class="s2">"geometry"</span><span class="p">,</span> <span class="s2">"names"</span><span class="p">]),</span>
+    <span class="n">extruded</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">get_elevation</span><span class="o">=</span><span class="n">heights</span><span class="p">,</span>
+    <span class="n">get_fill_color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-8">layer = PolygonLayer(
+    # Select only a few attribute columns from the table
+    table=table.select(["id", "height", "geometry", "names"]),
+    extruded=True,
+    get_elevation=heights,
+    get_fill_color=colors,
+)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this <code>view_state</code> call if you change the input dataset.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-9">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">view_state</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">"longitude"</span><span class="p">:</span> <span class="o">-</span><span class="mf">73.98416810282863</span><span class="p">,</span>
+    <span class="s2">"latitude"</span><span class="p">:</span> <span class="mf">40.72651721370669</span><span class="p">,</span>
+    <span class="s2">"zoom"</span><span class="p">:</span> <span class="mf">12.726630492730596</span><span class="p">,</span>
+    <span class="s2">"pitch"</span><span class="p">:</span> <span class="mf">59.80465353190481</span><span class="p">,</span>
+    <span class="s2">"bearing"</span><span class="p">:</span> <span class="mf">13.243243243243244</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">Map</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="n">view_state</span><span class="o">=</span><span class="n">view_state</span><span class="p">)</span>
+<span class="n">m</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-9">view_state = {
+    "longitude": -73.98416810282863,
+    "latitude": 40.72651721370669,
+    "zoom": 12.726630492730596,
+    "pitch": 59.80465353190481,
+    "bearing": 13.243243243243244,
+}
+m = Map(layer, view_state=view_state)
+m</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-10">
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+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-10"></div>
+</div>
+</div>
+</div>
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+</div>
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\ No newline at end of file
diff --git a/latest/examples/overture-maps/overture-maps.ipynb b/latest/examples/overture-maps/overture-maps.ipynb
new file mode 100644
index 00000000..25fb999b
--- /dev/null
+++ b/latest/examples/overture-maps/overture-maps.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overture Maps Buildings\n",
+    "\n",
+    "![overture.jpg](attachment:2b842dc7-1483-4955-ac16-ad554dd43db8.jpg)\n",
+    "\n",
+    "This notebook will give a quick overview of using the new [Overture Maps Python library](https://github.com/OvertureMaps/overturemaps-py) with Lonboard.\n",
+    "\n",
+    "We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).\n",
+    "\n",
+    "This uses the latest advancements of the [GeoParquet specification](https://geoparquet.org/). Data is fetched _directly from the cloud_ with no server in the middle!\n",
+    "\n",
+    "You can view a [hosted version of this notebook on Notebook Sharing Space](https://notebooksharing.space/view/f16f8f4c7133c4576938b262f17ce617cf112106bc25a24a08b528f8f0d11828#displayOptions=) (26MB download)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Dependendencies\n",
+    "\n",
+    "This notebook requires Lonboard (tested with version 0.8) and `overturemaps` (tested with version 0.4). To install `overturemaps`, run:\n",
+    "\n",
+    "```\n",
+    "pip install overturemaps\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import overturemaps\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from palettable.colorbrewer.sequential import Oranges_9\n",
+    "\n",
+    "from lonboard import Map, PolygonLayer\n",
+    "from lonboard.colormap import apply_continuous_cmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using [this bounding box tool](https://boundingbox.klokantech.com/) we can find a bounding box covering Manhattan. Choose \"CSV\" in the drop down box, then paste the result in the following cell:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = -74.02169, 40.696423, -73.891338, 40.831263"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ensure you pass the bbox into `overturemaps.record_batch_reader` as in the next line.\n",
+    "\n",
+    "`record_batch_reader` returns a [RecordBatchReader](https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html), an iterator of Arrow RecordBatches. We call `.read_all()` to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = overturemaps.record_batch_reader(\"building\", bbox).read_all()\n",
+    "\n",
+    "# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug\n",
+    "table = table.combine_chunks()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's color buildings by their heights. First, convert the \"height\" column to a numpy array, then replace any null values with `1`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heights = table[\"height\"].to_numpy()\n",
+    "heights = np.nan_to_num(heights, nan=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's [`LogNorm`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.LogNorm.html) to normalize these values according to a log scale.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalizer = LogNorm(1, heights.max(), clip=True)\n",
+    "normalized_heights = normalizer(heights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can apply a colormap to these heights. For this case, we'll use the [`Oranges_9`](https://jiffyclub.github.io/palettable/colorbrewer/sequential/#oranges_9) colormap.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Oranges_9.mpl_colormap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using [`apply_continuous_cmap`](https://developmentseed.org/lonboard/latest/api/colormap/#lonboard.colormap.apply_continuous_cmap), we can apply our values onto this colormap.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = apply_continuous_cmap(normalized_heights, Oranges_9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create a `PolygonLayer` with our data, passing in the heights and colors from before.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = PolygonLayer(\n",
+    "    # Select only a few attribute columns from the table\n",
+    "    table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),\n",
+    "    extruded=True,\n",
+    "    get_elevation=heights,\n",
+    "    get_fill_color=colors,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this `view_state` call if you change the input dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view_state = {\n",
+    "    \"longitude\": -73.98416810282863,\n",
+    "    \"latitude\": 40.72651721370669,\n",
+    "    \"zoom\": 12.726630492730596,\n",
+    "    \"pitch\": 59.80465353190481,\n",
+    "    \"bearing\": 13.243243243243244,\n",
+    "}\n",
+    "m = Map(layer, view_state=view_state)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/latest/search/search_index.json b/latest/search/search_index.json
index 06c09265..8ec36e24 100644
--- a/latest/search/search_index.json
+++ b/latest/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Lonboard","text":"<p>A Python library for fast, interactive geospatial vector data visualization in Jupyter.</p> <p>Building on cutting-edge technologies like GeoArrow and GeoParquet in conjunction with GPU-based map rendering, Lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.</p> <p></p> <p>3 million points rendered from a GeoPandas <code>GeoDataFrame</code> in JupyterLab.  Example notebook.</p>"},{"location":"#install","title":"Install","text":"<p>To install Lonboard using pip:</p> <pre><code>pip install lonboard\n</code></pre> <p>Lonboard is on conda-forge and can be installed using conda, mamba, or pixi. To install Lonboard using conda:</p> <pre><code>conda install -c conda-forge lonboard\n</code></pre> <p>To install from source, refer to the developer documentation.</p>"},{"location":"#get-started","title":"Get Started","text":"<p>For the simplest rendering, pass geospatial data into the top-level <code>viz</code> function.</p> <pre><code>import geopandas as gpd\nfrom lonboard import viz\n\ngdf = gpd.GeoDataFrame(...)\nviz(gdf)\n</code></pre> <p>Under the hood, this delegates to a <code>ScatterplotLayer</code>, <code>PathLayer</code>, or <code>PolygonLayer</code>. Refer to the documentation and examples for more control over rendering.</p>"},{"location":"#documentation","title":"Documentation","text":"<p>Refer to the documentation at developmentseed.org/lonboard.</p>"},{"location":"#why-the-name","title":"Why the name?","text":"<p>This is a new binding to the deck.gl geospatial data visualization library. A \"deck\" is the part of a skateboard you ride on. What's a fast, geospatial skateboard? A lonboard.</p> <p></p>"},{"location":"CHANGELOG/","title":"Changelog","text":""},{"location":"CHANGELOG/#080-2024-04-05","title":"[0.8.0] - 2024-04-05","text":""},{"location":"CHANGELOG/#new","title":"New!","text":"<ul> <li>A new <code>PolygonLayer</code>! This layer renders polygon outlines for easier visibility. @kylebarron in developmentseed/lonboard!330</li> <li>An example using <code>PolygonLayer</code> by @naomatheus in developmentseed/lonboard!351</li> <li>Sync view state between JS and Python by @kylebarron in developmentseed/lonboard!448</li> <li>Support geoarrow array input into <code>viz()</code> by @kylebarron in developmentseed/lonboard!427</li> <li>Internal architecture documentation by @kylebarron in developmentseed/lonboard!450</li> </ul>"},{"location":"CHANGELOG/#fixes","title":"Fixes","text":"<ul> <li>Fix CLI with unset <code>geometry_name</code> by @kylebarron in developmentseed/lonboard!451</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.1...v0.8.0</p>"},{"location":"CHANGELOG/#071-2024-03-22","title":"[0.7.1] - 2024-03-22","text":""},{"location":"CHANGELOG/#fixes_1","title":"Fixes","text":"<ul> <li>Fix CLI with geopackage files by @kylebarron in developmentseed/lonboard!434</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.0...v0.7.1</p>"},{"location":"CHANGELOG/#070-2024-03-21","title":"[0.7.0] - 2024-03-21","text":""},{"location":"CHANGELOG/#new_1","title":"New!","text":"<ul> <li>There's a new command-line interface (CLI)! Use the <code>lonboard</code> command to quickly visualize one or more data files readable by GDAL! For example: <code>lonboard admins.geojson features.gpkg</code>. By @kylebarron in developmentseed/lonboard!379</li> <li>Type hinting for constructors and <code>from_geopandas</code> method. This should make it easier to pass the correct parameters into layers. This has been tested to work in IDEs like VSCode, but unfortunately appears not to work in JupyterLab. By @kylebarron in developmentseed/lonboard!399</li> </ul> <ul> <li>Warn on missing CRS. One of the most common reasons that you might see an empty map is from accidentally visualizing data that is not in EPSG 4326 (longitude-latitude). We now emit a warning for data that doesn't have a CRS defined on the data. By @kylebarron in developmentseed/lonboard!395.</li> <li>Lonboard is now on <code>conda-forge</code>! Install with <code>conda install -c conda-forge lonboard</code>. By @giswqs in developmentseed/lonboard!223</li> <li>Add PointCloudLayer. By @kylebarron in developmentseed/lonboard!396</li> <li>Add fly-to map action to \"fly\" the map to a new location. By @kylebarron in developmentseed/lonboard!408</li> <li>Docs showcase page by @kylebarron in developmentseed/lonboard!401</li> <li>Improve default colors in <code>viz</code>. We now attempt to apply some basic styling onto data passed into <code>viz</code>. This will likely further improve in the future. By @kylebarron in developmentseed/lonboard!389</li> </ul>"},{"location":"CHANGELOG/#fixes_2","title":"Fixes","text":"<ul> <li>Set exported HTML height to 100% by @kylebarron in developmentseed/lonboard!377</li> <li>Raise error on single input to MultiRangeSlider by @kylebarron in developmentseed/lonboard!367</li> <li>Fix pandas <code>to_numeric</code> FutureWarning by @kylebarron in developmentseed/lonboard!368</li> <li>Fix viewing polygons in local html files by @kylebarron in developmentseed/lonboard!387</li> <li>Fix: fix sliced array input for reprojection by @kylebarron in developmentseed/lonboard!391</li> <li>Fix: Don't reproject for epsg:4326 input by @kylebarron in developmentseed/lonboard!392</li> <li>Fix: Fix weighted centroid calculation by @kylebarron in developmentseed/lonboard!393</li> <li>Fix <code>viz()</code> with <code>__geo_interface__</code> input by @kylebarron in developmentseed/lonboard!426</li> <li>Add DataFilterExtension notebook to website by @kylebarron in developmentseed/lonboard!362</li> <li>Allow non-compliant geoarrow CRS metadata by @kylebarron in developmentseed/lonboard!369</li> <li>Automatically parse geoarrow.wkb to native geoarrow by @kylebarron in developmentseed/lonboard!372</li> <li>Parse GeoParquet metadata by @kylebarron in developmentseed/lonboard!407</li> <li>CLI: 'crs' in geoparquet metadata should be optional. by @jwass in developmentseed/lonboard!411</li> </ul>"},{"location":"CHANGELOG/#other-changes","title":"Other changes","text":"<ul> <li>Creating a new user bug report by @emmalu in developmentseed/lonboard!386</li> <li>Update epic template by @emmalu in developmentseed/lonboard!382</li> <li>NormalAccessor by @naomatheus in developmentseed/lonboard!376</li> <li>Conda: Try including <code>manifest.in</code> file for <code>static</code> folder inclusion by @kylebarron in developmentseed/lonboard!421</li> <li>Switch to animated hero image by @kylebarron in developmentseed/lonboard!423</li> <li>Add CRS to GeoDataFrame in notebook examples by @kylebarron in developmentseed/lonboard!419</li> </ul>"},{"location":"CHANGELOG/#new-contributors","title":"New Contributors","text":"<ul> <li>@emmalu made their first contribution in developmentseed/lonboard!382</li> <li>@naomatheus made their first contribution in developmentseed/lonboard!376</li> <li>@jwass made their first contribution in developmentseed/lonboard!411</li> <li>@giswqs made their first contribution in developmentseed/lonboard!223</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.6.0...v0.7.0</p>"},{"location":"CHANGELOG/#060-2024-02-13","title":"[0.6.0] - 2024-02-13","text":""},{"location":"CHANGELOG/#new_2","title":"New!","text":"<ul> <li>DataFilterExtension by @kylebarron in developmentseed/lonboard!278</li> <li>Multi-range sliders for DataFilterExtension by @kylebarron in developmentseed/lonboard!340</li> <li>BitmapLayer and BitmapTileLayer by @kylebarron in developmentseed/lonboard!288</li> <li>Improved GeoArrow interop by @kylebarron in developmentseed/lonboard!308</li> <li>Allow passing a positional <code>layers</code> object into <code>Map</code> by @kylebarron in developmentseed/lonboard!319</li> <li>GeoArrow-based multithreaded coordinate reprojection by @kylebarron in developmentseed/lonboard!337</li> <li>Support <code>pyarrow.Table</code> with <code>geoarrow.pyarrow</code> extension types as geometry columns by @jorisvandenbossche in developmentseed/lonboard!218</li> <li>Add ecosystem/integrations documentation by @kylebarron in developmentseed/lonboard!350</li> </ul>"},{"location":"CHANGELOG/#fixes_3","title":"Fixes","text":"<ul> <li>Add font to index.css to fix static HTML export by @jtmiclat in developmentseed/lonboard!284</li> <li>Fix displaying tooltip for first row by @kylebarron in developmentseed/lonboard!287</li> <li>accept matplotlib colormap input to apply_cmap by @kylebarron in developmentseed/lonboard!289</li> <li>Use preferred OSM tile url by @kylebarron in developmentseed/lonboard!290</li> <li>set max zoom on osm layer by @kylebarron in developmentseed/lonboard!291</li> <li>Update contributor docs by @kylebarron in developmentseed/lonboard!316</li> <li>Check epsg:4326 bounds in layer creation by @kylebarron in developmentseed/lonboard!317</li> <li>add reference for installing from source by @kylebarron in developmentseed/lonboard!318</li> <li>Fix inferring number of rows per chunk by @kylebarron in developmentseed/lonboard!327</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!331</li> <li>Set max number of chunks per layer by @kylebarron in developmentseed/lonboard!332</li> <li>Move accessor length validation to serialization by @kylebarron in developmentseed/lonboard!333</li> <li>Deduplicate serialization for accessors by @kylebarron in developmentseed/lonboard!334</li> <li>Multi-dimensional GPU-based data filtering by @kylebarron in developmentseed/lonboard!335</li> <li>Bump anywidget to 0.9 &amp; simplify Wasm initialization by @kylebarron in developmentseed/lonboard!344</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!348</li> <li>docs fixes by @kylebarron in developmentseed/lonboard!354</li> <li>Add <code>DataFilterExtension</code> example by @kylebarron in developmentseed/lonboard!358</li> <li>fix arc layer default arguments by @kylebarron in developmentseed/lonboard!359</li> </ul>"},{"location":"CHANGELOG/#new-contributors_1","title":"New Contributors","text":"<ul> <li>@jtmiclat made their first contribution in developmentseed/lonboard!284</li> <li>@jorisvandenbossche made their first contribution in developmentseed/lonboard!218</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.5.0...v0.6.0</p>"},{"location":"CHANGELOG/#050-2023-12-01","title":"[0.5.0] - 2023-12-01","text":""},{"location":"CHANGELOG/#new-features","title":"New Features","text":"<ul> <li>Improve map height by @vgeorge in developmentseed/lonboard!220</li> <li>Add method to apply a categorical colormap by @kylebarron in developmentseed/lonboard!251</li> <li>Deduce environment and set map height in colab and vscode by @kylebarron in developmentseed/lonboard!252</li> <li>Add various carto basemaps as options by @kylebarron in developmentseed/lonboard!268</li> <li>Sync the clicked index back to Python by @kylebarron in developmentseed/lonboard!270</li> <li>Set <code>width_min_pixels</code> in PathLayer example by @kylebarron in developmentseed/lonboard!276</li> <li>Bump deck.gl layers for performance benefits by @kylebarron in developmentseed/lonboard!277</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.2...v0.5.0</p>"},{"location":"CHANGELOG/#042-2023-11-13","title":"[0.4.2] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed","title":"Fixed","text":"<ul> <li>Fixed ScatterplotLayer rendering by @kylebarron in developmentseed/lonboard!246</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.1...v0.4.2</p>"},{"location":"CHANGELOG/#041-2023-11-13","title":"[0.4.1] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed_1","title":"Fixed","text":"<ul> <li>Fixed Polygon rendering by @kylebarron in developmentseed/lonboard!243</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.0...v0.4.1</p>"},{"location":"CHANGELOG/#040-2023-11-10","title":"[0.4.0] - 2023-11-10","text":""},{"location":"CHANGELOG/#new-features_1","title":"New Features","text":"<ul> <li>New <code>HeatmapLayer</code></li> <li>New <code>experimental</code> module, with new layers (<code>ArcLayer</code>, <code>TextLayer</code>) and \"layer extensions\" (<code>BrushingExtension</code>, <code>CollisionFilterExtension</code>).</li> <li>New \"migration\" notebook using the experimental <code>ArcLayer</code>.</li> </ul>"},{"location":"CHANGELOG/#fixed_2","title":"Fixed","text":"<ul> <li>Add pandas v2 requirement by @kylebarron in developmentseed/lonboard!229</li> <li>bump anywidget to 0.7.1 by @kylebarron in developmentseed/lonboard!233</li> </ul> <p>This should error when the JS files have not been included when packaging.</p> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.3.0...v0.4.0</p>"},{"location":"CHANGELOG/#030-2023-11-07","title":"[0.3.0] - 2023-11-07","text":""},{"location":"CHANGELOG/#new-features_2","title":"New Features","text":"<ul> <li>Save widget to standalone HTML file by @kylebarron in developmentseed/lonboard!199</li> <li>Support for rendering inside Visual Studio Code</li> <li>Allow customized picking radius by @kylebarron in developmentseed/lonboard!212</li> <li>New example notebooks</li> <li>Automatically downcast data types in <code>from_geopandas</code> by @kylebarron in developmentseed/lonboard!195</li> </ul>"},{"location":"CHANGELOG/#fixed_3","title":"Fixed","text":"<ul> <li>Allow pandas series as accessor to FloatAccessor by @kylebarron in developmentseed/lonboard!208</li> <li>Raise error when creating class with unknown keyword argument by @kylebarron in developmentseed/lonboard!209</li> <li>fix tooltip rendering when not hovering over an object by @kylebarron in developmentseed/lonboard!215</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.2.0...v0.3.0</p>"},{"location":"CHANGELOG/#020-2023-11-01","title":"[0.2.0] - 2023-11-01","text":""},{"location":"CHANGELOG/#breaking-changes","title":"Breaking Changes","text":"<ul> <li>Layers no longer render a map object. Instead, pass one or more layer instances into a <code>lonboard.Map</code> and display that.</li> </ul>"},{"location":"CHANGELOG/#new-features_3","title":"New Features","text":"<ul> <li>Support multiple layers on a single map.</li> <li>Tooltip with data information on hover.</li> <li>Allow hex string color input to ColorAccessor.</li> <li>Versioned documentation website</li> <li>New examples</li> <li>Experimental API to change map height</li> </ul>"},{"location":"CHANGELOG/#fixed_4","title":"Fixed","text":"<ul> <li>Fix handling of 3d coordinates by @kylebarron in developmentseed/lonboard!160</li> <li>Improved validation error messages by @kylebarron in developmentseed/lonboard!161</li> </ul>"},{"location":"CHANGELOG/#new-contributors_2","title":"New Contributors","text":"<ul> <li>@chrisgervang made their first contribution in developmentseed/lonboard!150</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.1.2...v0.2.0</p>"},{"location":"CHANGELOG/#012-2023-10-24","title":"[0.1.2] - 2023-10-24","text":"<ul> <li>Actually update <code>@geoarrow/deck.gl-layers</code> to version 0.2.0, whoops \ud83d\ude05</li> </ul>"},{"location":"CHANGELOG/#011-2023-10-23","title":"[0.1.1] - 2023-10-23","text":"<ul> <li>Updates to <code>@geoarrow/deck.gl-layers</code> version 0.2.0:</li> <li>Fixed MultiPolygon rendering</li> <li>Fixed rendering of polygons with holes.</li> <li>Polygon rendering is roughly 35% faster.</li> </ul>"},{"location":"CHANGELOG/#010-2023-10-17","title":"[0.1.0] - 2023-10-17","text":"<ul> <li>Initial public release.</li> <li>Initial support for <code>ScatterplotLayer</code>, <code>PathLayer</code>, and <code>SolidPolygonLayer</code>.</li> </ul>"},{"location":"alternatives/","title":"Alternatives to Lonboard","text":""},{"location":"alternatives/#lonboard-vs-ipyleaflet","title":"Lonboard vs ipyleaflet","text":"<p>ipyleaflet is a great rendering library for small- and medium-sized datasets. ipyleaflet supports a broad range of data types and formats and gives the user broad control over how to render data.</p> <p>The downside of ipyleaflet is that it doesn't support large datasets as well. It uses GeoJSON to transfer data to the frontend, which is slow to write, slow to read, and large in transit. Additionally, leaflet's primary goal is not to support very large quantities of data.</p>"},{"location":"alternatives/#lonboard-vs-pydeck","title":"Lonboard vs pydeck","text":"<p>Pydeck is a full-featured binding from Python to deck.gl. Pydeck attempts to cover most of the deck.gl API. It's harder to use binary data transport with pydeck, and similarly to ipyleaflet will usually serialize data to GeoJSON.</p> <p>Lonboard does not try to cover deck.gl's full API, but rather has an opinionated approach that nudges users to the fastest rendering for many common use cases.</p>"},{"location":"alternatives/#why-not-contribute-back-to-pydeck","title":"Why not contribute back to pydeck?","text":"<p>Pydeck and lonboard have very different goals.</p> <p>A stated goal of pydeck is to be non-opinionated and to allow users with various data sources (GeoJSON strings, URLs to arbitrary data sources, etc.) to render. It makes sense for \"official\" bindings to be non-opinionated, but lonboard takes an opposite tack. By forcing users to use Arrow, we can get reliably fast performance the very common use case of rendering <code>GeoDataFrame</code>s. A downside here is that an Arrow-based implementation has required dependencies that pydeck wouldn't want. <code>pyarrow</code> on the Python side is 90MB on disk. Arrow JS on the JS side is ~200kb, and the default <code>parquet-wasm</code> build is ~1MB.</p> <p>Pydeck is tightly tied into the deck.gl JSON renderer, which allows describing a map state fully in JSON. It's not clear how this would work with the JavaScript GeoArrow layers.</p> <p>Aside from this, pydeck and lonboard use different widget architectures. Pydeck is built on the historical ipywidget layout, using the widget cookiecutter as inspiration and having a separate Jupyter Widget package published to NPM. Lonboard takes a newer approach (unavailable at the time pydeck was created) that uses Anywidget, vastly simplifying the widget process.</p>"},{"location":"alternatives/#lonboard-vs-keplergl-jupyter","title":"Lonboard vs kepler.gl-jupyter","text":"<p>kepler.gl is a JavaScript application focused on presenting a simple, high-level visualization and analysis toolkit to browser-based users. Just like Lonboard, it's built on top of deck.gl. It also has Python bindings via kepler.gl-jupyter.</p> <p>kepler.gl is a very good tool for data analysis and exploration in the browser. On the contrary, Lonboard targets users who plan to do most analysis in Python, but want the best performance to visualize the maximum amount of data. Lonboard should have better rendering performance, but has no user interface to analyze data in the browser.</p> <p>kepler.gl-jupyter serializes to GeoJSON and kepler.gl uses GeoJSON-like JavaScript objects internally; it therefore has the downside of creating a large text string in Python and serializing that to JavaScript. kepler.gl-jupyter is great for creating standalone static HTML files containing a dataset, but those files tend to be very large, since they contain embedded GeoJSON.</p>"},{"location":"alternatives/#lonboard-vs-datashader","title":"Lonboard vs datashader","text":"<p>Datashader is a truly scalable rendering library. Datashader will re-render your data from scratch when panning around in a map. This allows datashader to aggregate the source data before rendering. Datashader minimizes the amount of data being rendered and thus, in theory, Datashader should perform well for datasets as large as your computer's memory.</p> <p>Lonboard is not scalable in the same sense. It doesn't minimize the amount of data being rendered. If you ask to plot a GeoDataFrame with 3 million points, every single one of those points is transferred to the GPU and drawn to your screen. In contrast to Datashader, Lonboard should perform well for datasets whose geometries fit in your computer's GPU memory, which is usually much smaller than your computer's total memory.</p>"},{"location":"cli/","title":"Command-line Interface","text":"<p>Lonboard includes a command-line interface for quickly viewing local data files.</p> <p>The CLI is accessible either through the <code>lonboard</code> entry point or via <code>python -m lonboard</code> the latter can be useful to ensure that the <code>lonboard</code> instance you're calling is the same as your current <code>python</code> environment.</p> <pre><code>&gt; lonboard --help\nUsage: lonboard [OPTIONS] [FILES]...\n\n  Interactively visualize geospatial data using Lonboard.\n\n  This CLI can be used either to quickly view local files or to create static\n  HTML files.\n\nOptions:\n  -o, --output PATH   The output path for the generated HTML file. If not\n                      provided, will save to a temporary file.\n  --open / --no-open  Whether to open a web browser tab with the generated\n                      map. By default, the web browser is not opened when\n                      --output is provided, but is in other cases.\n  --help              Show this message and exit.\n</code></pre> <p>For example:</p> <pre><code>lonboard data.geojson\n</code></pre>"},{"location":"performance/","title":"Performance","text":"<p>Performance is a critical goal of lonboard. Below are a couple pieces of information you should know to understand lonboard's performance characteristics, as well as some advice for how to get the best performance.</p>"},{"location":"performance/#performance-characteristics","title":"Performance Characteristics","text":"<p>There are two distinct parts to the performance of lonboard: one is the performance of transferring data to the browser and the other is the performance of rendering the data once it's there.</p> <p>In general, these parts are completely distinct. Even if it takes a while to load the data in your browser, the map might be snappy once it loads, and vice versa.</p>"},{"location":"performance/#data-transfer","title":"Data Transfer","text":"<p>Lonboard creates an interactive visualization of your data in your browser. In order to do this, your GeoDataFrame needs to be transferred from your Python environment to your browser.</p> <p>In the case where your Python session is running locally (on the same machine as your browser), this data transfer is extremely fast: less than a second in most cases.</p> <p>However, in the case where your Python session is running on a remote server (such as Google Colab, Binder, or a JupyterHub instance), this data transfer means downloading the data to your local browser. Therefore, when running lonboard from a remote server, your internet speed and the quantity of data you pass into a layer will have large impacts on the data transfer speed.</p> <p>Under the hood, lonboard uses efficient compression (in the form of GeoParquet) to transfer data to the browser, but compression can only do so much; the data still needs to be downloaded.</p>"},{"location":"performance/#rendering-performance","title":"Rendering Performance","text":"<p>Once the data has been transfered from your Python session to your browser, it needs to be rendered.</p> <p>The biggest thing to note is that \u2014 in contrast to projects like datashader \u2014 lonboard does not minimize the amount of data being rendered. If you pass a GeoDataFrame with 10 million coordinates, lonboard will attempt to render all 10 million coordinates at once.</p> <p>The primary determinant of the maximum amount of data you can render with lonboard is your computer's hardware. Via the underlying deck.gl library, lonboard ultimately renders geometries using your computer's Graphics Processing Unit (GPU). If you have a better GPU card, you'll be able to visualize more data.</p> <p>Lonboard is more efficient at rendering than previous libraries, but there will always be some quantity of data beyond which your browser tab is likely to crash while attempting to render. Testing on a recent MacBook Pro M2 computer, lonboard has been able to render a few million points with minimal lag.</p>"},{"location":"performance/#performance-advice","title":"Performance Advice","text":""},{"location":"performance/#use-a-local-python-session","title":"Use a local Python session","text":"<p>Moving from a remote Python environment to a local Python environment is often impractical, but this change will make it much faster to visualize data, especially over slow internet connections.</p>"},{"location":"performance/#mutate-an-existing-map-where-possible","title":"Mutate an existing map where possible","text":"<p>Whenever you create a new map object, all the data will be transfered from your Python environment to your browser. Whenever mutating an existing map object, only the changed data will be sent to the browser.</p> <p>Therefore, unless changing the content of the data being rendered, do not create a new map object. If you're changing the rendering options of an existing map, prefer mutating the existing map object.</p> <p>Bad</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap2 = ScatterplotLayer.from_geopandas(\n    my_geodataframe,\n    get_fill_color=[255, 0, 0]\n)\n</code></pre> <p>Good</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap1.get_fill_color = [255, 0, 0]\n</code></pre> <p>In the first (bad) case, the content of <code>my_geodataframe</code> will be sent twice to the browser. In the second (good) case, the content of <code>my_geodataframe</code> will be sent only once to the browser, and only the tiny array <code>[255, 0, 0]</code> will be transfered on the second call.</p>"},{"location":"performance/#remove-columns-before-rendering","title":"Remove columns before rendering","text":"<p>All columns included in the <code>GeoDataFrame</code> will be transferred to the browser for visualization. (In the future, these other columns will be used to display a tooltip when hovering over/clicking on a geometry.)</p> <p>Especially in the case of a remote Python session, excluding unnecessary attribute columns will make data transfer to the browser faster.</p>"},{"location":"performance/#use-arrow-based-data-types-in-pandas","title":"Use Arrow-based data types in Pandas","text":"<p>As of Pandas 2.0, Pandas supports two backends for data types: either the original numpy-based data types or new data types based on Arrow and the pyarrow library.</p> <p>The first thing that lonboard does when visualizing data is converting from Pandas to an Arrow representation. Any non-geometry attribute columns will be converted to Arrow, so if you're using Arrow-based data types in Pandas already, this step will be \"free\" as no conversion is needed.</p> <p>If you're loading data from a geospatial file format, as of <code>pyogrio</code> version 0.7, you can load data to Arrow-based columns with</p> <pre><code>import pyogrio\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = pyogrio.read_dataframe(\n    \"path/to/file\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>or directly from GeoPandas with <code>geopandas.read_file</code> with <code>engine=\"pyogrio\"</code>:</p> <pre><code>import geopandas as gpd\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = gpd.read_file(\n    \"path/to/file\",\n    engine=\"pyogrio\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>See the pandas guide on data types and the <code>pandas.ArrowDtype</code> class for more information.</p>"},{"location":"performance/#simplify-geometries-before-rendering","title":"Simplify geometries before rendering","text":"<p>Simplifying geometries before rendering reduces the total number of coordinates and can make a visualization snappier. At this point, lonboard does not offer built-in geometry simplification. This is something you would need to do before passing data to lonboard.</p>"},{"location":"api/basemap/","title":"lonboard.basemap","text":""},{"location":"api/basemap/#lonboard.basemap.CartoBasemap","title":"lonboard.basemap.CartoBasemap","text":"<p>             Bases: <code>str</code>, <code>Enum</code></p> <p>Basemap styles provided by Carto.</p> <p>Refer to Carto documentation for information on styles.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatter","title":"DarkMatter  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatter = 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'\n</code></pre> <p>A dark map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatterNoLabels","title":"DarkMatterNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatterNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/dark-matter-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A dark map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Positron","title":"Positron  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Positron = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json'\n</code></pre> <p>A light map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.PositronNoLabels","title":"PositronNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>PositronNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Voyager","title":"Voyager  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Voyager = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json'\n</code></pre> <p>A light, colored map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.VoyagerNoLabels","title":"VoyagerNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>VoyagerNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/voyager-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light, colored map style without labels.</p>"},{"location":"api/colormap/","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap.DiscreteColormap","title":"DiscreteColormap  <code>module-attribute</code>","text":"<pre><code>DiscreteColormap = Dict[Any, RGBColor]\n</code></pre> <p>A type definition for a discrete colormap.</p> <p>For example, for a land cover colormap, you may want to use the following dict:</p> <pre><code>color_map = {\n    \"Open Water\": [70, 107, 159],\n    \"Perennial Snow/Ice\": [209, 222, 248],\n    \"Developed, Open Space\": [222, 197, 197],\n    \"Developed, Low Intensity\": [217, 146, 130],\n    \"Developed, Medium Intensity\": [235, 0, 0],\n    \"Developed, High Intensity\": [171, 0, 0],\n    \"Barren Land\": [179, 172, 159],\n    \"Deciduous Forest\": [104, 171, 95],\n    \"Evergreen Forest\": [28, 95, 44],\n    \"Mixed Forest\": [181, 197, 143],\n    \"Shrub/Scrub\": [204, 184, 121],\n    \"Herbaceous\": [223, 223, 194],\n    \"Hay/Pasture\": [220, 217, 57],\n    \"Cultivated Crops\": [171, 108, 40],\n    \"Woody Wetlands\": [184, 217, 235],\n    \"Emergent Herbaceous Wetlands\": [108, 159, 184],\n}\n</code></pre> <p>This corresponds to the following well-known color palette from the National Land Cover Database.</p> <p> </p> <p>Keep in mind that the type of the key of the <code>color_map</code> dictionary is important. If your data is represented as a string, the keys of the <code>color_map</code> must also be represented as strings. If your data is represented as an integer, the keys of the colormap should be integers.</p>"},{"location":"api/colormap/#lonboard.colormap.RGBColor","title":"RGBColor  <code>module-attribute</code>","text":"<pre><code>RGBColor = Union[Tuple[int, int, int], Tuple[int, int, int, int], Sequence[int]]\n</code></pre> <p>A type definition for an RGB or RGBA color value</p> <p>All values must range between 0 and 255 (inclusive). If only three values are provided, the fourth (alpha) channel will be inferred as 255 (meaning full opacity, no transparency).</p>"},{"location":"api/colormap/#lonboard.colormap.apply_categorical_cmap","title":"apply_categorical_cmap","text":"<pre><code>apply_categorical_cmap(\n    values: Union[NDArray, pd.Series, pa.Array, pa.ChunkedArray],\n    cmap: DiscreteColormap,\n    *,\n    alpha: Optional[int] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of categorical values.</p> <p>If you're working with categorical data in Pandas or GeoPandas, and especially when those categories are strings, you should use the pandas Categorical type. This will use much less memory and be faster to operate on. <code>apply_categorical_cmap</code> will be 2-3x faster (and use less memory itself) when your data is already represented as a categorical data type.</p> <p>The key of the colormap must be the same as the data type of the column of values you pass in. Usually this will be string, when you perform the lookup on a string column of data.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>Union[NDArray, Series, Array, ChunkedArray]</code>)         \u2013          <p>A numpy array, pandas Series, pyarrow Array or pyarrow ChunkedArray of data. The data type of this column must match the keys of the colormap.</p> </li> <li> <code>cmap</code>             (<code>DiscreteColormap</code>)         \u2013          <p>A dictionary mapping keys to colors. See [DiscreteColormap] for more information.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Optional[int]</code>)         \u2013          <p>The default alpha value for entries in the colormap that do not have an alpha value defined. Alpha must be an integer between 0 and 255 (inclusive).</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/colormap/#lonboard.colormap.apply_continuous_cmap","title":"apply_continuous_cmap","text":"<pre><code>apply_continuous_cmap(\n    values: NDArray[np.floating],\n    cmap: Union[Palette, mpl.colors.Colormap],\n    *,\n    alpha: Union[float, int, NDArray[np.floating], None] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of continuous values.</p> <p>If you pass in a <code>Palette</code> object from the <code>palettable</code> package, this will use matplotlib's LinearSegmentedColormap under the hood. As described in Matplotlib's referenced docstring:</p> <p>The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments.</p> <p>This means that input values are linearly combined from the two nearest colormap colors.</p> <p>If you pass in a Matplotlib <code>Colormap</code> object, this will use the lookup method defined in that class.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>NDArray[floating]</code>)         \u2013          <p>A numpy array of floating point values ranging from 0 to 1.</p> </li> <li> <code>cmap</code>             (<code>Union[Palette, Colormap]</code>)         \u2013          <p>Any matplotlib <code>Colormap</code> or <code>Palette</code> object from the <code>palettable</code> package.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Union[float, int, NDArray[floating], None]</code>)         \u2013          <p>Alpha must be a scalar between 0 and 1, a sequence of such floats with shape matching <code>values</code>, or None.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/controls/","title":"lonboard.controls","text":""},{"location":"api/controls/#lonboard.controls.MultiRangeSlider","title":"lonboard.controls.MultiRangeSlider","text":"<p>             Bases: <code>VBox</code></p> <p>A widget for multiple ranged sliders.</p> <p>This is designed to be used with the DataFilterExtension when you want to filter on 2 to 4 columns on the same time.</p> <p>If you have only a single filter, use an ipywidgets FloatRangeSlider directly.</p>"},{"location":"api/controls/#lonboard.controls.MultiRangeSlider--example","title":"Example","text":"<pre><code>from ipywidgets import FloatRangeSlider\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n</code></pre> <p>Then to propagate updates to a rendered layer, call jsdlink to connect the two widgets.</p> <pre><code>from ipywidgets import jsdlink\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>As you change the slider, the <code>filter_range</code> value on the layer class should be updated.</p>"},{"location":"api/map/","title":"Map","text":""},{"location":"api/map/#lonboard.Map","title":"lonboard.Map","text":"<p>The top-level class used to display a map in a Jupyter Widget.</p> <p>Example:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer, SolidPolygonLayer\n\n# A GeoDataFrame with Point geometries\npoint_gdf = gpd.GeoDataFrame()\npoint_layer = ScatterplotLayer.from_geopandas(\n    point_gdf,\n    get_fill_color=[255, 0, 0],\n)\n\n# A GeoDataFrame with Polygon geometries\npolygon_gdf = gpd.GeoDataFrame()\npolygon_layer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\n\nm = Map([point_layer, polygon_layer])\n</code></pre>"},{"location":"api/map/#lonboard.Map.__init__","title":"__init__","text":"<pre><code>__init__(\n    layers: Union[BaseLayer, Sequence[BaseLayer]], **kwargs: Unpack[MapKwargs]\n) -&gt; None\n</code></pre> <p>Create a new Map.</p> <p>Aside from the <code>layers</code> argument, pass keyword arguments for any other attribute defined in this class.</p> <p>Parameters:</p> <ul> <li> <code>layers</code>             (<code>Union[BaseLayer, Sequence[BaseLayer]]</code>)         \u2013          <p>One or more layers to render on this map.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>None</code>         \u2013          <p>A Map object.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map._height","title":"_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>_height = tag(sync=True)\n</code></pre> <p>Height of the map in pixels.</p> <p>This API is not yet stabilized and may change in the future.</p>"},{"location":"api/map/#lonboard.Map.basemap_style","title":"basemap_style  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>basemap_style = tag(sync=True)\n</code></pre> <p>A MapLibre-compatible basemap style.</p> <p>Various styles are provided in <code>lonboard.basemap</code>.</p> <ul> <li>Type: <code>str</code></li> <li>Default   <code>lonboard.basemap.CartoBasemap.PositronNoLabels</code></li> </ul>"},{"location":"api/map/#lonboard.Map.fly_to","title":"fly_to","text":"<pre><code>fly_to(\n    *,\n    longitude: Union[int, float],\n    latitude: Union[int, float],\n    zoom: float,\n    duration: int = 4000,\n    pitch: Union[int, float] = 0,\n    bearing: Union[int, float] = 0,\n    curve: Optional[Union[int, float]] = None,\n    speed: Optional[Union[int, float]] = None,\n    screen_speed: Optional[Union[int, float]] = None\n)\n</code></pre> <p>\"Fly\" the map to a new location.</p> <p>Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The longitude of the new viewport.</p> </li> <li> <code>latitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The latitude of the new viewport.</p> </li> <li> <code>zoom</code>             (<code>float</code>)         \u2013          <p>The zoom of the new viewport.</p> </li> <li> <code>pitch</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The pitch of the new viewport. Defaults to 0.</p> </li> <li> <code>bearing</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The bearing of the new viewport. Defaults to 0.</p> </li> <li> <code>duration</code>             (<code>int</code>, default:                 <code>4000</code> )         \u2013          <p>The number of milliseconds for the viewport transition to take. Defaults to 4000.</p> </li> <li> <code>curve</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The zooming \"curve\" that will occur along the flight path. Default <code>1.414</code>.</p> </li> <li> <code>speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation defined in relation to <code>curve</code>, it linearly affects the duration, higher speed returns smaller durations and vice versa. Default <code>1.2</code>.</p> </li> <li> <code>screen_speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation measured in screenfuls per second. Similar to speed it linearly affects the duration, when specified speed is ignored.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.layers","title":"layers  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>layers = tag(sync=True, **widget_serialization)\n</code></pre> <p>One or more <code>Layer</code> objects to display on this map.</p>"},{"location":"api/map/#lonboard.Map.parameters","title":"parameters  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>parameters = tag(sync=True)\n</code></pre> <p>GPU parameters to pass to deck.gl.</p> <p>This is an advanced API. The vast majority of users should not need to touch this setting.</p> <p>Note</p> <p>The docstring below is copied from upstream deck.gl documentation. Any usage of <code>GL</code> refers to the constants defined in <code>@luma.gl/constants</code> here, which comes from the MDN docs here.</p> <p>In place of any <code>GL</code> constant, you can use the underlying integer it represents. For example, instead of the JS</p> <pre><code>depthFunc: GL.LEQUAL\n</code></pre> <p>referring to the MDN docs, you should use</p> <pre><code>depthFunc: 0x0203\n</code></pre> <p>Note that these parameters do not yet work with integer keys. If you would like to use integer keys, open an issue.</p> <p>Expects an object with GPU parameters. Before each frame is rendered, this object will be passed to luma.gl's <code>setParameters</code> function to reset the GPU context parameters, e.g. to disable depth testing, change blending modes etc. The default parameters set by <code>Deck</code> on initialization are the following:</p> <pre><code>{\n  blend: true,\n  blendFunc: [GL.SRC_ALPHA, GL.ONE_MINUS_SRC_ALPHA, GL.ONE, GL.ONE_MINUS_SRC_ALPHA],\n  polygonOffsetFill: true,\n  depthTest: true,\n  depthFunc: GL.LEQUAL\n}\n</code></pre> <p>Refer to the luma.gl setParameters API for documentation on supported parameters and values.</p> <pre><code>import GL from '@luma.gl/constants';\nnew Deck({\n  // ...\n  parameters: {\n    blendFunc: [GL.ONE, GL.ONE, GL.ONE, GL.ONE],\n    depthTest: false\n  }\n});\n</code></pre> <p>Notes:</p> <ul> <li>Any GPU <code>parameters</code> prop supplied to individual layers will still override the   global <code>parameters</code> when that layer is rendered.</li> </ul>"},{"location":"api/map/#lonboard.Map.picking_radius","title":"picking_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>picking_radius = tag(sync=True)\n</code></pre> <p>Extra pixels around the pointer to include while picking (such as for a tooltip).</p> <p>This is helpful when rendered objects are difficult to target, for example irregularly shaped icons, small moving circles or interaction by touch.</p> <ul> <li>Type: <code>int</code></li> <li>Default: <code>5</code></li> </ul>"},{"location":"api/map/#lonboard.Map.set_view_state","title":"set_view_state","text":"<pre><code>set_view_state(\n    *,\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    zoom: Optional[float] = None,\n    pitch: Optional[float] = None,\n    bearing: Optional[float] = None\n) -&gt; None\n</code></pre> <p>Set the view state of the map.</p> <p>Any parameters that are unset will not be changed.</p> <p>Other Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new longitude to set on the map. Defaults to None.</p> </li> <li> <code>latitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new latitude to set on the map. Defaults to None.</p> </li> <li> <code>zoom</code>             (<code>Optional[float]</code>)         \u2013          <p>the new zoom to set on the map. Defaults to None.</p> </li> <li> <code>pitch</code>             (<code>Optional[float]</code>)         \u2013          <p>the new pitch to set on the map. Defaults to None.</p> </li> <li> <code>bearing</code>             (<code>Optional[float]</code>)         \u2013          <p>the new bearing to set on the map. Defaults to None.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.show_tooltip","title":"show_tooltip  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>show_tooltip = tag(sync=True)\n</code></pre> <p>Whether to render a tooltip on hover on the map.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/map/#lonboard.Map.to_html","title":"to_html","text":"<pre><code>to_html(\n    filename: Union[str, Path, TextIO, IO[str]], title: Optional[str] = None\n) -&gt; None\n</code></pre> <p>Save the current map as a standalone HTML file.</p> <p>Parameters:</p> <ul> <li> <code>filename</code>             (<code>Union[str, Path, TextIO, IO[str]]</code>)         \u2013          <p>where to save the generated HTML file.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>title</code>             (<code>Optional[str]</code>)         \u2013          <p>A title for the exported map. This will show as the browser tab name.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.use_device_pixels","title":"use_device_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>use_device_pixels = tag(sync=True)\n</code></pre> <p>Controls the resolution of the drawing buffer used for rendering.</p> <p>Setting this to <code>false</code> or a number &lt;= 1 will improve performance on high resolution displays.</p> <p>Note: This parameter must be passed to the <code>Map()</code> constructor. It cannot be changed once the map has been created.</p> <p>The available options are:</p> <ul> <li><code>true</code>: Device (physical) pixels resolution is used for rendering, this resolution   is defined by <code>window.devicePixelRatio</code>. On Retina/HD systems this resolution is   usually twice as big as CSS pixels resolution.</li> <li><code>false</code>: CSS pixels resolution (equal to the canvas size) is used for rendering.</li> <li> <p><code>Number</code> (Experimental): Specified Number is used as a custom ratio (drawing   buffer resolution to CSS pixel resolution) to determine drawing buffer size, a   value less than one uses resolution smaller than CSS pixels, gives better   performance but produces blurry images, a value greater than one uses resolution   bigger than CSS pixels resolution (canvas size), produces sharp images but at a   lower performance.</p> </li> <li> <p>Type: <code>float</code>, <code>int</code> or <code>bool</code></p> </li> <li>Default: <code>true</code></li> </ul>"},{"location":"api/map/#lonboard.Map.view_state","title":"view_state  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>view_state = ViewStateTrait()\n</code></pre> <p>The view state of the map.</p> <ul> <li>Type: <code>ViewState</code></li> <li>Default: Automatically inferred from the data passed to the map.</li> </ul> <p>You can initialize the map to a specific view state using this property:</p> <pre><code>Map(\n    layers,\n    view_state={\"longitude\": -74.0060, \"latitude\": 40.7128, \"zoom\": 7}\n)\n</code></pre> <p>Note</p> <p>The properties of the view state are immutable. Use <code>set_view_state</code> to modify a map's view state once it's been initially rendered.</p>"},{"location":"api/map/#lonboard.models.ViewState","title":"lonboard.models.ViewState","text":"<p>             Bases: <code>NamedTuple</code></p>"},{"location":"api/map/#lonboard.models.ViewState.bearing","title":"bearing  <code>instance-attribute</code>","text":"<pre><code>bearing: float\n</code></pre> <p>Bearing angle in degrees. <code>0</code> is north.</p>"},{"location":"api/map/#lonboard.models.ViewState.latitude","title":"latitude  <code>instance-attribute</code>","text":"<pre><code>latitude: float\n</code></pre> <p>Latitude at the map center.</p>"},{"location":"api/map/#lonboard.models.ViewState.longitude","title":"longitude  <code>instance-attribute</code>","text":"<pre><code>longitude: float\n</code></pre> <p>Longitude at the map center</p>"},{"location":"api/map/#lonboard.models.ViewState.pitch","title":"pitch  <code>instance-attribute</code>","text":"<pre><code>pitch: float\n</code></pre> <p>Pitch angle in degrees. <code>0</code> is top-down.</p>"},{"location":"api/map/#lonboard.models.ViewState.zoom","title":"zoom  <code>instance-attribute</code>","text":"<pre><code>zoom: float\n</code></pre> <p>Zoom level.</p>"},{"location":"api/traits/","title":"lonboard.traits","text":""},{"location":"api/traits/#lonboard.traits.ColorAccessor","title":"lonboard.traits.ColorAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl color accessor.</p> <p>Various input is allowed:</p> <ul> <li>A <code>list</code> or <code>tuple</code> with three or four integers, ranging between 0 and 255   (inclusive). This will be used as the color for all objects.</li> <li>A <code>str</code> representing a hex color or \"well known\" color interpretable by   matplotlib.colors.to_rgba.</li> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>3</code> or <code>4</code>, and will correspond to either RGB   or RGBA colors.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>3</code> or <code>4</code> and its child must have type   <code>uint8</code>.</li> <li>Any Arrow fixed size list array from a library that implements the Arrow   PyCapsule   Interface.</li> </ul> <p>You can use helpers in the <code>lonboard.colormap</code> module (i.e. <code>apply_continuous_cmap</code>) to simplify constructing numpy arrays for color values.</p>"},{"location":"api/traits/#lonboard.traits.FloatAccessor","title":"lonboard.traits.FloatAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl float accessor.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects.</li> <li>A numpy <code>ndarray</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index.</li> <li>Any Arrow floating point array from a library that implements the Arrow PyCapsule   Interface.   This includes data structures from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/traits/#lonboard.traits.GetFilterValueAccessor","title":"lonboard.traits.GetFilterValueAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for the <code>get_filter_value</code> accessor added by the <code>DataFilterExtension</code>, which can have between 1 and 4 float values per row.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects. The   <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A one-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must be 1.</li> <li>A two-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the size of the second dimension of the array.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> </ul> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface. - A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The child   array of the fixed size list must be of floating point type. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the list size.</p> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.NormalAccessor","title":"lonboard.traits.NormalAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl \"normal\" accessor</p> <p>This is primarily used in the [lonboard.PointCloudLayer].</p> <p>Acceptable inputs: - A <code>list</code> or <code>tuple</code> with three <code>int</code> or <code>float</code> values. This will be used as the   normal for all objects. - A numpy ndarray with two dimensions and floating point type. The size of the   second dimension must be 3, i.e. its shape must be <code>(N, 3)</code>. - a pyarrow <code>FixedSizeListArray</code> or <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s   where the size of the inner fixed size list 3. The child array must have type   float32. - Any Arrow array that matches the above restrictions from a library that implements   the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.PointAccessor","title":"lonboard.traits.PointAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl point accessor.</p> <p>Various input is allowed:</p> <ul> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>2</code> or <code>3</code>, and will correspond to either XY   or XYZ positions.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>2</code> or <code>3</code> and its child must be of floating   point type.</li> </ul>"},{"location":"api/traits/#lonboard.traits.PyarrowTableTrait","title":"lonboard.traits.PyarrowTableTrait","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a geospatial Arrow-backed table</p> <p>Allowed input includes:</p> <ul> <li>A pyarrow <code>Table</code> or containing a geometry column with GeoArrow metadata.</li> <li>Any GeoArrow table from a library that implements the Arrow PyCapsule   Interface.   This includes the   <code>GeoTable</code>   class from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/viz/","title":"viz","text":"<p>The top-level namespace accessible after <code>import lonboard</code>.</p> <p>Layers are also accessible in the top-level namespace, but are documented in the <code>layers/</code> folder.</p>"},{"location":"api/viz/#lonboard.viz","title":"lonboard.viz","text":"<pre><code>viz(\n    data: Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]],\n    *,\n    scatterplot_kwargs: Optional[ScatterplotLayerKwargs] = None,\n    path_kwargs: Optional[PathLayerKwargs] = None,\n    polygon_kwargs: Optional[PolygonLayerKwargs] = None,\n    map_kwargs: Optional[MapKwargs] = None\n) -&gt; Map\n</code></pre> <p>A high-level function to plot your data easily.</p> <p>The goal of this function is to make it simple to get something showing on a map. For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>This function accepts a variety of geospatial inputs:</p> <ul> <li>geopandas <code>GeoDataFrame</code></li> <li>geopandas <code>GeoSeries</code></li> <li>numpy array of Shapely objects</li> <li>Single Shapely object</li> <li>Any Python class with a <code>__geo_interface__</code> property conforming to the     Geo Interface protocol.</li> <li><code>dict</code> holding GeoJSON-like data.</li> <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li> </ul> <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p> <p>Parameters:</p> <ul> <li> <code>data</code>             (<code>Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]]</code>)         \u2013          <p>a data object of any supported type.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>scatterplot_kwargs</code>             (<code>Optional[ScatterplotLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>ScatterplotLayer</code>s.</p> </li> <li> <code>path_kwargs</code>             (<code>Optional[PathLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PathLayer</code>s.</p> </li> <li> <code>polygon_kwargs</code>             (<code>Optional[PolygonLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PolygonLayer</code>s.</p> </li> <li> <code>map_kwargs</code>             (<code>Optional[MapKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to the generated <code>Map</code>.</p> </li> </ul> <p>For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>Returns:</p> <ul> <li> <code>Map</code>         \u2013          <p>widget visualizing the provided data.</p> </li> </ul>"},{"location":"api/viz/#lonboard._viz.VizDataInput","title":"VizDataInput  <code>module-attribute</code>","text":"<pre><code>VizDataInput = Union[\n    GeoDataFrame,\n    GeoSeries,\n    Table,\n    NDArray[object_],\n    BaseGeometry,\n    ArrowStreamExportable,\n    GeoInterfaceProtocol,\n    Dict[str, Any],\n]\n</code></pre> <p>A type definition for allowed data inputs to the <code>viz</code> function.</p>"},{"location":"api/layer-extensions/","title":"Layer Extensions","text":"<p>Layer extensions are bonus features that you can optionally add to the core deck.gl layers.</p> <p>Layer extensions are in an experimental state. Some things are known to not yet work:</p> <ul> <li>Modifying the extensions on a layer by mutating its <code>extensions</code> list via <code>append</code> or <code>pop</code>. It should work, however, by creating a new list and assigning <code>layer.extensions = new_extensions_list</code>.</li> </ul> <p>If you encounter issues, please create an issue with reproducible steps.</p>"},{"location":"api/layer-extensions/brushing-extension/","title":"BrushingExtension","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension","title":"lonboard.layer_extension.BrushingExtension","text":"<p>Adds GPU-based data brushing functionalities to layers. It allows the layer to show/hide objects based on the current pointer position.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--example","title":"Example","text":"<p>An example is in the County-to-County Migration notebook.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_enabled","title":"<code>brushing_enabled</code>","text":"<p>Enable/disable brushing. If brushing is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_target","title":"<code>brushing_target</code>","text":"<p>The position used to filter each object by. One of the following:</p> <ul> <li><code>\"source\"</code>: Use the primary position for each object. This can mean different   things depending on the layer. It usually refers to the coordinates returned by   <code>getPosition</code> or <code>getSourcePosition</code> accessors.</li> <li><code>\"target\"</code>: Use the secondary position for each object. This may not be available   in some layers. It usually refers to the coordinates returned by   <code>getTargetPosition</code> accessor.</li> <li><code>\"source_target\"</code>: Use both the primary position and secondary position for each   object. Show object if either is in brushing range.</li> <li> <p><code>\"custom\"</code>: Some layers may not describe their data objects with one or two   coordinates, for example <code>PathLayer</code> and <code>PolygonLayer</code>. Use this option with the   <code>get_brushing_target</code> prop to provide a custom position that each object should be   filtered by.</p> </li> <li> <p>Type: <code>str</code>, optional</p> <p>One of: \"source\" | \"target\" | \"source_target\" | \"custom\"</p> </li> <li> <p>Default: <code>\"source\"</code></p> </li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_radius","title":"<code>brushing_radius</code>","text":"<p>The brushing radius centered at the pointer, in meters. If a data object is within this circle, it is rendered; otherwise it is hidden.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10000</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--get_brushing_target","title":"<code>get_brushing_target</code>","text":"<p>An arbitrary position for each object that it will be filtered by.</p> <p>Only effective if <code>brushing_target</code> is set to <code>\"custom\"</code>.</p> <ul> <li>Type: PointAccessor, optional<ul> <li>If a point is provided, it is used as the target for all rows.</li> <li>If an array of points is provided, each value in the array will be used as the   target for the row at the same row index.</li> </ul> </li> <li>Default: <code>None</code>.</li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/","title":"CollisionFilterExtension","text":""},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension","title":"lonboard.layer_extension.CollisionFilterExtension","text":"<p>Allows layers to hide overlapping objects.</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_enabled","title":"<code>collision_enabled</code>","text":"<p>Enable/disable collisions. If collisions are disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_group","title":"<code>collision_group</code>","text":"<p>Collision group this layer belongs to. If it is not set, the 'default' collision group is used</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--get_collision_priority","title":"<code>get_collision_priority</code>","text":"<p>Accessor for collision priority. Must return a number in the range -1000 -&gt; 1000. Features with higher values are shown preferentially.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the priority for all objects.</li> <li>If an array is provided, each value in the array will be used as the priority   for the object at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/","title":"DataFilterExtension","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension","title":"lonboard.layer_extension.DataFilterExtension","text":"<p>Adds GPU-based data filtering functionalities to layers. It allows the layer to show/hide objects based on user-defined properties.</p>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--example","title":"Example","text":"<pre><code>from lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.layer_extension import DataFilterExtension\n\ngdf = gpd.GeoDataFrame(...)\nextension = DataFilterExtension()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[extension],\n    get_filter_value=gdf[\"filter_value\"], # replace with desired column\n    filter_range=[0, 5] # replace with desired filter range\n)\n</code></pre> <p>The <code>DataFilterExtension</code> allows filtering on 1 to 4 attributes at the same time. So if you have four numeric columns of interest, you can filter on the intersection of all of them.</p> <p>For easy visualization, we suggest connecting the <code>DataFilterExtension</code> to an interactive slider from <code>ipywidgets</code>.</p> <pre><code>from ipywidgets import FloatRangeSlider\n\nslider = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"Slider: \"\n)\nslider\n\njsdlink(\n    (slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>If you have 2 to 4 columns, use a <code>MultiRangeSlider</code>, which combines multiple <code>FloatRangeSlider</code> objects in a form that the <code>DataFilterExtension</code> expects.</p> <pre><code>from ipywidgets import FloatRangeSlider, jsdlink\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--important-notes","title":"Important notes","text":"<ul> <li>The DataFilterExtension only supports float32 data, so integer data will be casted   to float32.</li> <li>The DataFilterExtension copies all data referenced by <code>get_filter_value</code> to the   GPU, so it will increase memory pressure on the GPU.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--layer-properties","title":"Layer Properties","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_enabled","title":"<code>filter_enabled</code>","text":"<p>Enable/disable the data filter. If the data filter is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_range","title":"<code>filter_range</code>","text":"<p>The bounds which defines whether an object should be rendered. If an object's filtered value is within the bounds, the object will be rendered; otherwise it will be hidden. This prop can be updated on user input or animation with very little cost.</p> <p>Format:</p> <p>If <code>filter_size</code> is 1, provide a single tuple of <code>(min, max)</code>.</p> <p>If <code>filter_size</code> is 2 to 4, provide a list of tuples: <code>[(min0, max0), (min1, max1), ...]</code> for each filtered property, respectively.</p> <ul> <li>Type: either Tuple[float, float] or List[Tuple[float, float]], optional</li> <li>Default: <code>(-1, 1)</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_soft_range","title":"<code>filter_soft_range</code>","text":"<p>If specified, objects will be faded in/out instead of abruptly shown/hidden.</p> <p>When the filtered value is outside of the bounds defined by <code>filter_soft_range</code> but still within the bounds defined by <code>filter_range</code>, the object will be rendered as \"faded\".</p> <ul> <li>Type: Tuple[float, float], optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_size","title":"<code>filter_transform_size</code>","text":"<p>When an object is \"faded\", manipulate its size so that it appears smaller or thinner. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_color","title":"<code>filter_transform_color</code>","text":"<p>When an object is \"faded\", manipulate its opacity so that it appears more translucent. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--get_filter_value","title":"<code>get_filter_value</code>","text":"<p>Accessor to retrieve the value for each object that it will be filtered by.</p> <ul> <li>Type:   GetFilterValueAccessor<ul> <li>If a scalar value is provided, it is used as the value for all objects.</li> <li>If an array is provided, each value in the array will be used as the value   for the object at the same row index.</li> </ul> </li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension.filter_size","title":"filter_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filter_size = tag(sync=True)\n</code></pre> <p>The size of the filter (number of columns to filter by).</p> <p>The data filter can show/hide data based on 1-4 numeric properties of each object.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default 1.</li> </ul>"},{"location":"api/layers/arc-layer/","title":"ArcLayer","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer","title":"lonboard.experimental.ArcLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render raised arcs joining pairs of source and target coordinates.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_height","title":"get_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_height = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Height color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_color","title":"get_source_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Source color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_position","title":"get_source_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Source position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_color","title":"get_target_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Target color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_position","title":"get_target_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Target position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_tilt","title":"get_tilt  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_tilt = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Use to tilt the arc to the side if you have multiple arcs with the same source and target positions.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The line width of each object, in units specified by <code>widthUnits</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.great_circle","title":"great_circle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>great_circle = tag(sync=True)\n</code></pre> <p>If <code>True</code>, create the arc along the shortest path on the earth surface.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.num_segments","title":"num_segments  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>num_segments = tag(sync=True)\n</code></pre> <p>The number of segments used to draw each arc.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>50</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait()\n</code></pre> <p>A GeoArrow table.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The scaling multiplier for the width of each line.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/base-layer/","title":"Base Classes","text":""},{"location":"api/layers/base-layer/#lonboard.BaseLayer","title":"lonboard.BaseLayer","text":"<p>             Bases: <code>BaseWidget</code></p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer","title":"lonboard.BaseArrowLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>Any Arrow-based layer should subclass from BaseArrowLayer</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/bitmap-layer/","title":"BitmapLayer","text":""},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer","title":"lonboard.BitmapLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The <code>BitmapLayer</code> renders a bitmap (e.g. PNG, JPEG, or WebP) at specified boundaries.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapLayer\n\nlayer = BitmapLayer(\n    image='https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/sf-districts.png',\n    bounds=[-122.5190, 37.7045, -122.355, 37.829]\n)\nm = Map(layer)\nm\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.bounds","title":"bounds  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>bounds = tag(sync=True)\n</code></pre> <p>The bounds of the image.</p> <p>Supported formats:</p> <pre><code>- Coordinates of the bounding box of the bitmap `[left, bottom, right, top]`\n- Coordinates of four corners of the bitmap, should follow the sequence of\n  `[[left, bottom], [left, top], [right, top], [right, bottom]]`.\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.image","title":"image  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>image = tag(sync=True)\n</code></pre> <p>The URL to an image to display.</p> <ul> <li>Type: <code>str</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/","title":"BitmapTileLayer","text":""},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer","title":"lonboard.BitmapTileLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The BitmapTileLayer renders image tiles (e.g. PNG, JPEG, or WebP) in the web mercator tiling system. Only the tiles visible in the current viewport are loaded and rendered.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapTileLayer\n\n# We set `max_requests &lt; 0` because `tile.openstreetmap.org` supports HTTP/2.\nlayer = BitmapTileLayer(\n    data=\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n    tile_size=256,\n    max_requests=-1,\n    min_zoom=0,\n    max_zoom=19,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.data","title":"data  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>data = tag(sync=True)\n</code></pre> <p>Either a URL template or an array of URL templates from which the tile data should be loaded.</p> <p>If the value is a string: a URL template. Substrings {x} {y} and {z}, if present, will be replaced with a tile's actual index when it is requested.</p> <p>If the value is an array: multiple URL templates. Each endpoint must return the same content for the same tile index. This can be used to work around domain sharding, allowing browsers to download more resources simultaneously. Requests made are balanced among the endpoints, based on the tile index.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extent","title":"extent  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extent = tag(sync=True)\n</code></pre> <p>The bounding box of the layer's data, in the form of <code>[min_x, min_y, max_x, max_y]</code>. If provided, the layer will only load and render the tiles that are needed to fill this box.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_cache_size","title":"max_cache_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_cache_size = tag(sync=True)\n</code></pre> <p>The maximum number of tiles that can be cached. The tile cache keeps loaded tiles in memory even if they are no longer visible. It reduces the need to re-download the same data over and over again when the user pan/zooms around the map, providing a smoother experience.</p> <p>If not supplied, the <code>max_cache_size</code> is calculated as 5 times the number of tiles in the current viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_requests","title":"max_requests  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_requests = tag(sync=True)\n</code></pre> <p>The maximum number of concurrent data fetches.</p> <p>If &lt;= 0, no throttling will occur, and <code>get_tile_data</code> may be called an unlimited number of times concurrently regardless of how long that tile is or was visible.</p> <p>If &gt; 0, a maximum of <code>max_requests</code> instances of <code>get_tile_data</code> will be called concurrently. Requests may never be called if the tile wasn't visible long enough to be scheduled and started. Requests may also be aborted (through the signal passed to <code>get_tile_data</code>) if there are more than <code>max_requests</code> ongoing requests and some of those are for tiles that are no longer visible.</p> <p>If <code>get_tile_data</code> makes fetch requests against an HTTP 1 web server, then max_requests should correlate to the browser's maximum number of concurrent fetch requests. For Chrome, the max is 6 per domain. If you use the data prop and specify multiple domains, you can increase this limit. For example, with Chrome and 3 domains specified, you can set max_requests=18.</p> <p>If the web server supports HTTP/2 (Open Chrome dev tools and look for \"h2\" in the Protocol column), then you can make an unlimited number of concurrent requests (and can set max_requests=-1). Note that this will request data for every tile, no matter how long the tile was visible, and may increase server load.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_zoom","title":"max_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_zoom = tag(sync=True)\n</code></pre> <p>The max zoom level of the layer's data. When overzoomed (i.e. <code>zoom &gt; max_zoom</code>), tiles from this level will be displayed.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.min_zoom","title":"min_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>min_zoom = tag(sync=True)\n</code></pre> <p>The min zoom level of the layer's data. When underzoomed (i.e. <code>zoom &lt; min_zoom</code>), the layer will not display any tiles unless <code>extent</code> is defined, to avoid issuing too many tile requests.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.refinement_strategy","title":"refinement_strategy  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>refinement_strategy = tag(sync=True)\n</code></pre> <p>How the tile layer refines the visibility of tiles.</p> <p>When zooming in and out, if the layer only shows tiles from the current zoom level, then the user may observe undesirable flashing while new data is loading. By setting <code>refinement_strategy</code> the layer can attempt to maintain visual continuity by displaying cached data from a different zoom level before data is available.</p> <p>This prop accepts one of the following:</p> <ul> <li><code>\"best-available\"</code>: If a tile in the current viewport is waiting for its data to   load, use cached content from the closest zoom level to fill the empty space. This   approach minimizes the visual flashing due to missing content.</li> <li><code>\"no-overlap\"</code>: Avoid showing overlapping tiles when backfilling with cached   content. This is usually favorable when tiles do not have opaque backgrounds.</li> <li> <p><code>\"never\"</code>: Do not display any tile that is not selected.</p> </li> <li> <p>Type: <code>str</code>, optional</p> </li> <li>Default: <code>\"best-available\"</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tile_size","title":"tile_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tile_size = tag(sync=True)\n</code></pre> <p>The pixel dimension of the tiles, usually a power of 2.</p> <p>Tile size represents the target pixel width and height of each tile when rendered. Smaller tile sizes display the content at higher resolution, while the layer needs to load more tiles to fill the same viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>512</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.zoom_offset","title":"zoom_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>zoom_offset = tag(sync=True)\n</code></pre> <p>This offset changes the zoom level at which the tiles are fetched. Needs to be an integer.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/heatmap-layer/","title":"HeatmapLayer","text":""},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer","title":"lonboard.HeatmapLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>HeatmapLayer</code> visualizes the spatial distribution of data.</p> <p>Example</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, HeatmapLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = HeatmapLayer.from_geopandas(gdf)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, HeatmapLayer\n\n# Example: A GeoParquet file with Point geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = HeatmapLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.aggregation","title":"aggregation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>aggregation = tag(sync=True)\n</code></pre> <p>Defines the type of aggregation operation</p> <p>Valid values are 'SUM', 'MEAN'.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>\"SUM\"</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.debounce_timeout","title":"debounce_timeout  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>debounce_timeout = tag(sync=True)\n</code></pre> <p>Interval in milliseconds during which changes to the viewport don't trigger aggregation.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>500</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.get_weight","title":"get_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_weight = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The weight of each object.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.intensity","title":"intensity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>intensity = tag(sync=True)\n</code></pre> <p>Value that is multiplied with the total weight at a pixel to obtain the final weight.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.radius_pixels","title":"radius_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_pixels = tag(sync=True)\n</code></pre> <p>Radius of the circle in pixels, to which the weight of an object is distributed.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>30</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.threshold","title":"threshold  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>threshold = tag(sync=True)\n</code></pre> <p>Ratio of the fading weight to the max weight, between <code>0</code> and <code>1</code>.</p> <p>For example, <code>0.1</code> affects all pixels with weight under 10% of the max.</p> <p>Ignored when <code>color_domain</code> is specified.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0.05</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.weights_texture_size","title":"weights_texture_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>weights_texture_size = tag(sync=True)\n</code></pre> <p>Specifies the size of weight texture.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>2048</code></li> </ul>"},{"location":"api/layers/path-layer/","title":"PathLayer","text":"<p>Screenshot from North America roads example</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer","title":"lonboard.PathLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PathLayer</code> renders lists of coordinate points as extruded polylines with mitering.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PathLayer\n\n# A GeoDataFrame with LineString or MultiLineString geometries\ngdf = gpd.GeoDataFrame()\nlayer = PathLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PathLayer\n\n# Example: A GeoParquet file with LineString or MultiLineString geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PathLayer(\n    table=table,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, extrude the path in screen space (width always faces the camera). If <code>False</code>, the width always faces up.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.cap_rounded","title":"cap_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>cap_rounded = tag(sync=True)\n</code></pre> <p>Type of caps. If <code>True</code>, draw round caps. Otherwise draw square caps.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of each path, in units specified by <code>width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.joint_rounded","title":"joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>True</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.miter_limit","title":"miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width. Only works if <code>jointRounded</code> is <code>False</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={LINESTRING, MULTILINESTRING})\n</code></pre> <p>A GeoArrow table with a LineString or MultiLineString column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum path width in pixels. This prop can be used to prevent the path from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum path width in pixels. This prop can be used to prevent the path from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The path width multiplier that multiplied to all paths.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/","title":"PointCloudLayer","text":""},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer","title":"lonboard.PointCloudLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PointCloudLayer</code> renders a point cloud with 3D positions, normals and colors.</p> <p>The <code>PointCloudLayer</code> can be more efficient at rendering large quantities of points than the <code>ScatterplotLayer</code>, but has fewer rendering options. In particular, you can have only one point size across all points in your data.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PointCloudLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = PointCloudLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    point_size=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_normal","title":"get_normal  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_normal = NormalAccessor(None, allow_none=True)\n</code></pre> <p>The normal of each object, in <code>[nx, ny, nz]</code>.</p> <ul> <li>Type: NormalAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the normal for all   points.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the normal for the point at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.point_size","title":"point_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>point_size = tag(sync=True)\n</code></pre> <p>Global radius of all points, in units specified by <code>size_units</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(\n    allowed_geometry_types={POINT}, allowed_dimensions={3}\n)\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/","title":"PolygonLayer","text":"<p>Screenshot from Global Boundaries example</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer","title":"lonboard.PolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PolygonLayer</code> renders filled, stroked and/or extruded polygons.</p> <p>Note</p> <p>This layer is essentially a combination of a <code>PathLayer</code> and a <code>SolidPolygonLayer</code>. This has some overhead beyond a <code>SolidPolygonLayer</code>, so if you're looking for the maximum performance with large data, you may want to use a <code>SolidPolygonLayer</code> directly.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = PolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier.</p> <p>The final elevation is calculated by <code>elevationScale * getElevation(d)</code>. <code>elevationScale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons.</p> <p>Based on the elevations provided by the <code>getElevation</code> accessor.</p> <p>If set to <code>false</code>, all polygons will be flat, this generates less geometry and is faster than simply returning 0 from getElevation.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to draw a filled polygon (solid fill).</p> <p>Note that only the area between the outer polygon and any holes will be filled.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each polygon, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_joint_rounded","title":"line_joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>true</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_miter_limit","title":"line_miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width.</p> <p>Only works if <code>line_joint_rounded</code> is false.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the line from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the line from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>The line width multiplier that multiplied to all outlines of <code>Polygon</code> and <code>MultiPolygon</code> features if the <code>stroked</code> attribute is true.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Whether to draw an outline around the polygon (solid fill).</p> <p>Note that both the outer polygon as well the outlines of any holes will be drawn.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/scatterplot-layer/","title":"ScatterplotLayer","text":"<p>Screenshot from Ookla example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer","title":"lonboard.ScatterplotLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>ScatterplotLayer</code> renders circles at given coordinates.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer\n\n# A GeoDataFrame with Point or MultiPoint geometries\ngdf = gpd.GeoDataFrame()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, ScatterplotLayer\n\n# Example: A GeoParquet file with Point or MultiPoint geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = ScatterplotLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.antialiasing","title":"antialiasing  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>antialiasing = tag(sync=True)\n</code></pre> <p>If <code>True</code>, circles are rendered with smoothed edges. If <code>False</code>, circles are rendered with rough edges. Antialiasing can cause artifacts on edges of overlapping circles.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, rendered circles always face the camera. If <code>False</code> circles face up (i.e. are parallel with the ground plane).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Draw the filled area of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The filled color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the filled color for   all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the filled color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The outline color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the outline color   for all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the outline color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each object, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_radius","title":"get_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_radius = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The radius of each object, in units specified by <code>radius_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the radius for all objects.</li> <li>If an array is provided, each value in the array will be used as the radius   for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the stroke from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the stroke from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>A global line width multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_max_pixels","title":"radius_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum radius in pixels. This can be used to prevent the circle from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_min_pixels","title":"radius_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum radius in pixels. This can be used to prevent the circle from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_scale","title":"radius_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_scale = tag(sync=True)\n</code></pre> <p>A global radius multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_units","title":"radius_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_units = tag(sync=True)\n</code></pre> <p>The units of the radius, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Draw the outline of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT, MULTIPOINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/","title":"SolidPolygonLayer","text":""},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer","title":"lonboard.SolidPolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>SolidPolygonLayer</code> renders filled and/or extruded polygons.</p> <p>Note</p> <p>This layer is similar to the <code>PolygonLayer</code> but will not render an outline around polygons. In most cases, you'll want to use the <code>PolygonLayer</code> directly, but for very large datasets not drawing the outline can significantly improve performance, in which case you may want to use this layer.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, SolidPolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = SolidPolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier. The final elevation is calculated by <code>elevation_scale * get_elevation(d)</code>. <code>elevation_scale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons (based on the elevations provided by the <code>get_elevation</code> accessor'). If set to <code>False</code>, all polygons will be flat, this generates less geometry and is faster than simply returning <code>0</code> from <code>get_elevation</code>.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to fill the polygons (based on the color provided by the <code>get_fill_color</code> accessor).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/text-layer/","title":"TextLayer","text":""},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer","title":"lonboard.experimental.TextLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render text labels at given coordinates.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.background_padding","title":"background_padding  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>background_padding = tag(sync=True)\n</code></pre> <p>The padding of the background.</p> <ul> <li>If an array of 2 is supplied, it is interpreted as <code>[padding_x, padding_y]</code> in   pixels.</li> <li>If an array of 4 is supplied, it is interpreted as <code>[padding_left, padding_top,   padding_right, padding_bottom]</code> in pixels.</li> </ul> <p>default [0, 0, 0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>true</code>, the text always faces camera. Otherwise the text faces up (z).</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.character_set","title":"character_set  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>character_set = tag(sync=True)\n</code></pre> <p>Specifies a list of characters to include in the font. If set to 'auto', will be automatically generated from the data set.</p> <p>default (ASCII characters 32-128)</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_family","title":"font_family  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_family = tag(sync=True)\n</code></pre> <p>CSS font family</p> <p>default 'Monaco, monospace'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_settings","title":"font_settings  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_settings = tag(sync=True)\n</code></pre> <p>Advance options for fine tuning the appearance and performance of the generated shared <code>fontAtlas</code>.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_weight","title":"font_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_weight = tag(sync=True)\n</code></pre> <p>CSS font weight</p> <p>default 'normal'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_alignment_baseline","title":"get_alignment_baseline  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_alignment_baseline = tag(sync=True)\n</code></pre> <p>Vertical alignment accessor</p> <p>default 'center'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_angle","title":"get_angle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_angle = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label rotation accessor, in degrees</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_background_color","title":"get_background_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_background_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Background color accessor.</p> <p>default [255, 255, 255, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_color","title":"get_border_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Border color accessor.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_width","title":"get_border_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Border width accessor.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Label color accessor</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_pixel_offset","title":"get_pixel_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_pixel_offset = tag(sync=True)\n</code></pre> <p>Label offset from the anchor position, [x, y] in pixels</p> <p>default [0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_size","title":"get_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_size = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label size accessor</p> <p>default 32</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text","title":"get_text  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text = TextAccessor(None, allow_none=True)\n</code></pre> <p>Label text accessor</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text_anchor","title":"get_text_anchor  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text_anchor = tag(sync=True)\n</code></pre> <p>Horizontal alignment accessor</p> <p>default 'middle'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.line_height","title":"line_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_height = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the line height.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.max_width","title":"max_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_width = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the width limit of a string.</p> <p>If specified, when the text is longer than the width limit, it will be wrapped into multiple lines using the strategy of <code>wordBreak</code>.</p> <p>default -1</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_color","title":"outline_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_color = tag(sync=True)\n</code></pre> <p>Color of outline around the text, in <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_width","title":"outline_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_width = tag(sync=True)\n</code></pre> <p>Width of outline around the text, relative to the text size. Only effective if <code>fontSettings.sdf</code> is <code>true</code>.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_max_pixels","title":"size_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_min_pixels","title":"size_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_scale","title":"size_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_scale = tag(sync=True)\n</code></pre> <p>Text size multiplier.</p> <ul> <li>Type: <code>float</code>.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the size, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. default 'pixels'. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.word_break","title":"word_break  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>word_break = tag(sync=True)\n</code></pre> <p>Available options are <code>break-all</code> and <code>break-word</code>. A valid <code>maxWidth</code> has to be provided to use <code>wordBreak</code>.</p> <p>default 'break-word'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"ecosystem/","title":"Ecosystem","text":"<p>Lonboard aims to both integrate closely in the existing Python geospatial ecosystem and be future proof for the next generation of Python geospatial libraries.</p> <p>The pages in this section outline various integrations. If there's a library you think Lonboard should integrate with or you want to document existing compatibility, create an issue to discuss.</p>"},{"location":"ecosystem/geoarrow/","title":"GeoArrow","text":"<p>GeoArrow is an in-memory data structure for storing vector geospatial data and associated attributes. Lonboard uses GeoArrow internally and is the primary reason why Lonboard is fast.</p> <p>There's a burgeoning ecosystem of Python libraries that use GeoArrow internally. Creating Lonboard <code>Layer</code> objects from GeoArrow tables is the fastest way to visualize data, as no conversions are needed on the Python side.</p>"},{"location":"ecosystem/geoarrow/#geoarrow-rust","title":"geoarrow-rust","text":"<p>geoarrow-rust is a Python library implementing the GeoArrow specification with efficient spatial operations. This library has \"rust\" in the name because it is implemented based on the GeoArrow Rust implementation.</p> <pre><code>from geoarrow.rust.core import GeoTable, read_geojson\nfrom lonboard import Map, PathLayer\n\npath = \"/path/to/file.geojson\"\ngeo_table = read_geojson(path)\n\n# Assuming the GeoJSON contains LineString or MultiLineString data\nlayer = PathLayer(table=geo_table)\nm = Map(layer)\nm\n</code></pre> <p>Refer to the geoarrow-rust documentation for more information.</p>"},{"location":"ecosystem/geopandas/","title":"GeoPandas","text":"<p>GeoPandas extends the Pandas data frame library to allow spatial operations on geospatial data.</p> <p>All relevant Lonboard layer classes have a <code>from_geopandas</code> method for <code>GeoDataFrame</code> input.</p> <p>Some layer types, such as <code>BitmapLayer</code>, don't have a <code>from_geopandas</code> method because the rendering isn't relevant to GeoPandas (i.e. GeoPandas doesn't store image data).</p>"},{"location":"ecosystem/geopandas/#example","title":"Example","text":"<pre><code>import geodatasets\nimport geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# New York City boroughs\ngdf = gpd.read_file(geodatasets.get_path('nybb'))\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"ecosystem/jupyter-widgets/","title":"Jupyter Widgets","text":"<p>Jupyter Widgets are interactive browser controls for Jupyter notebooks. While Lonboard's classes are themselves widgets, Jupyter Widgets' design means that widgets integrate with other widgets seamlessly.</p> <p>For example, the <code>ipywidgets</code> Python library includes many core widgets like sliders, dropdowns, color pickers, and file upload elements. Then you can link widgets together. This means that your widget-based slider or chart can create events that change the display of a Lonboard map based on user input events.</p>"},{"location":"ecosystem/panel/","title":"Panel","text":"<p>Panel is a tool to build interactive web applications and dashboards using Python code.</p> <p>Panel has been reported to work with Lonboard. However, it appears that Panel does not support reactive updates in the same way that Shiny does, so the map will necessarily be recreated from scratch on every update.</p>"},{"location":"ecosystem/panel/#example","title":"Example","text":"<p>This example was written by @MarcSkovMadsen in issue #262.</p> <pre><code>\"\"\"Panel data app based on https://developmentseed.org/lonboard/latest/examples/north-america-roads/\"\"\"\n# pip install panel colorcet ipywidgets_bokeh geopandas palettable lonboard\nimport colorcet as cc\nimport geopandas as gpd\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom palettable.palette import Palette\n\nimport panel as pn\n\nurl = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\npath = \"ne_10m_roads_north_america.zip\"\n\ntry:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=path, engine=\"pyogrio\"\n    )\nexcept:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=url, engine=\"pyogrio\"\n    )\n\nstate_options = sorted(state for state in gdf[\"state\"].unique() if state)\n\n\ndef to_rgb(hex: str) -&gt; list:\n    h = hex.strip(\"#\")\n    return list(int(h[i : i + 2], 16) for i in (0, 2, 4))\n\n\ndef to_palette(cmap) -&gt; Palette:\n    \"\"\"Returns the ColorCet colormap as a palettable Palette\"\"\"\n    colors = [to_rgb(item) for item in cmap]\n    return Palette(name=\"colorcet\", map_type=\"colorcet\", colors=colors)\n\n\ndef create_map(state=\"California\", cmap=cc.fire, alpha=0.8):\n    palette = to_palette(cmap)\n    data = gdf[gdf[\"state\"] == state]\n    layer = PathLayer.from_geopandas(data, width_min_pixels=0.8)\n    normalized_scale_rank = (data[\"scalerank\"] - 3) / 9\n    layer.get_color = apply_continuous_cmap(normalized_scale_rank, palette, alpha=alpha)\n    map_ = Map(layer, _height=650)\n    return map_\n\n\ndescription = \"\"\"# lonboard\n\nA Python library for **fast, interactive geospatial vector data visualization** in Jupyter (and Panel).\n\nBy utilizing new technologies like `GeoArrow` and `GeoParquet` in conjunction with GPU-based map rendering, lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.\"\"\"\n\n\n# THE PANEL APP\npn.extension(\"ipywidgets\")\nstate = pn.widgets.Select(\n    value=\"California\",\n    options=state_options,\n    width=150,\n    name=\"State\",\n    sizing_mode=\"stretch_width\",\n)\ncmap = pn.widgets.ColorMap(\n    value=cc.fire,\n    options=cc.palette,\n    ncols=3,\n    swatch_width=100,\n    name=\"cmap by Colorcet\",\n    sizing_mode=\"stretch_width\",\n)\nalpha = pn.widgets.FloatSlider(\n    value=0.8, start=0, end=1, name=\"Alpha\", min_width=100, sizing_mode=\"stretch_width\"\n)\nlogo = pn.pane.Image(\n    \"https://github.com/developmentseed/lonboard/raw/main/assets/dalle-lonboard.jpg\"\n)\ndef title(state):\n    return f\"# North America Roads: {state}\"\n\nsettings = pn.Column(state, cmap, alpha)\ndescription = pn.Column(pn.pane.Markdown(description, margin=5), logo)\ncomponent = pn.Column(\n    pn.bind(title, state=state),\n    pn.panel(\n        pn.bind(create_map, state=state, cmap=cmap, alpha=alpha.param.value_throttled),\n        sizing_mode=\"stretch_both\",\n    ),\n    sizing_mode=\"stretch_both\",\n)\npn.template.FastListTemplate(\n    logo=\"https://panel.holoviz.org/_static/logo_horizontal_dark_theme.png\",\n    title=\"Works with LonBoard\",\n    main=[component],\n    sidebar=[description, settings],\n).servable()\n</code></pre>"},{"location":"ecosystem/shiny/","title":"Shiny","text":"<p>Shiny is a tool to build interactive web applications and dashboards using Python code. Shiny integrates with Jupyter Widgets, which means that Lonboard is supported out-of-the-box.</p> <p>Pay attention to the \"Efficient updates\" section of the Shiny documentation. This is the most efficient way to create a reactive map. Take care to not recreate the <code>Map</code> widget from scratch on updates, as that will send data from Python to the browser anew.</p>"},{"location":"ecosystem/shiny/#example","title":"Example","text":"<pre><code>import geopandas as gpd\nfrom shiny import reactive\nfrom shiny.express import input, ui\nfrom shinywidgets import render_widget\n\nfrom lonboard import Map, ScatterplotLayer\n\ncolors = {\n    \"Red\": [200, 0, 0],\n    \"Green\": [0, 200, 0],\n    \"Blue\": [0, 0, 200],\n}\n\nui.input_select(\"color_select\", \"Color\", choices=list(colors.keys()))\n\n\n@render_widget\ndef map():\n    gdf = gpd.read_file(gpd.datasets.get_path(\"naturalearth_cities\"))\n    layer = ScatterplotLayer.from_geopandas(gdf, radius_min_pixels=2)\n    return Map(layer)\n\n\n@reactive.effect\ndef set_fill_color():\n    map.widget.layers[0].get_fill_color = colors[input.color_select()]\n</code></pre>"},{"location":"examples/","title":"Examples","text":"<ul> <li>Speedtest data  using <code>ScatterplotLayer</code></li> <li>North America roads  using <code>PathLayer</code></li> <li>Global boundaries  using <code>PolygonLayer</code></li> <li>U.S. County-to-County Migration  using <code>ArcLayer</code> and <code>BrushingExtension</code></li> <li>Scatterplot with GPU data filtering  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code></li> <li>Color picker integration  using <code>SolidPolygonLayer</code></li> <li>JupyterLab Sidecar integration  using <code>ScatterplotLayer</code> and <code>JupyterLab Sidecar</code></li> <li>Motor Vehicle Crashes in NYC  using <code>ScatterplotLayer</code></li> <li>Rivers in Asia  using <code>PathLayer</code></li> </ul>"},{"location":"examples/#third-party-showcase","title":"Third-party showcase","text":"<p>These examples are maintained by external contributors.</p> <ul> <li>American Community Survey exploration  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code> by @jaanli.</li> </ul>"},{"location":"examples/data-filter-extension/","title":"DataFilterExtension","text":"<p>As with the <code>ScatterplotLayer</code> example, this example will use data from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry.</p> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom ipywidgets import FloatRangeSlider, jsdlink\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.controls import MultiRangeSlider\nfrom lonboard.layer_extension import DataFilterExtension\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from ipywidgets import FloatRangeSlider, jsdlink from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap from lonboard.controls import MultiRangeSlider from lonboard.layer_extension import DataFilterExtension In\u00a0[3]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\nlocal_path = Path(\"data-filter-extension.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    non_geom_columns = [col for col in columns if col != \"tile\"]\n    gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")\n    gdf.to_parquet(local_path)\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" local_path = Path(\"data-filter-extension.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     non_geom_columns = [col for col in columns if col != \"tile\"]     gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")     gdf.to_parquet(local_path) <p>Taking a quick look at this data, we see that we have 3.2 million records.</p> In\u00a0[4]: Copied! <pre>gdf\n</pre> gdf Out[4]: avg_d_kbps avg_u_kbps avg_lat_ms devices geometry 0 5983 7886 68 1 POINT (-160.01862 70.63722) 1 3748 5841 78 2 POINT (-160.04059 70.63357) 2 3364 6200 78 2 POINT (-160.04059 70.63175) 3 2381 2328 86 1 POINT (-160.03510 70.63357) 4 3047 5356 75 1 POINT (-160.03510 70.63175) ... ... ... ... ... ... 3231240 19528 3200 68 1 POINT (169.81842 -46.29571) 3231241 15693 10359 56 1 POINT (169.81293 -46.30710) 3231242 26747 9674 58 2 POINT (169.66461 -46.42082) 3231243 67995 13564 63 1 POINT (169.65912 -46.45110) 3231244 1230 1798 55 1 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 5 columns</p> <p>Since we want to cross-filter on three attributes at once, we create a <code>DataFilterExtension</code> instance with <code>filter_size=3</code>.</p> In\u00a0[5]: Copied! <pre>filter_extension = DataFilterExtension(filter_size=3)\n</pre> filter_extension = DataFilterExtension(filter_size=3) <p>Similar to the <code>ScatterplotLayer</code> example notebook, we create a normalized download speed to use for a per-point color and radius.</p> In\u00a0[6]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\nnormalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound)\nfill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10)\nradius = normalized_download_speed * 200\n</pre> min_bound = 5000 max_bound = 50000 normalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound) fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10) radius = normalized_download_speed * 200 <p>Next we prepare the columns that we want to filter on. In this case, we'll filter on the download speed, upload speed, and latency values. These need to be passed in as a single two-dimensional numpy array, so we use <code>numpy.column_stack</code>. Only numeric arrays can be used for GPU-based filtering.</p> <p>We also define initial filter ranges for each of these columns.</p> In\u00a0[7]: Copied! <pre>filter_values = np.column_stack(\n    [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]]\n)\ninitial_filter_range = [\n    [10_000, 50_000],\n    [1000, 10_000],\n    [0, 100],\n]\n</pre> filter_values = np.column_stack(     [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]] ) initial_filter_range = [     [10_000, 50_000],     [1000, 10_000],     [0, 100], ] <p>Next we create a <code>ScatterplotLayer</code> with our desired attributes. Keep in mind that you must pass the <code>DataFilterExtension</code> instance to the <code>extensions</code> parameter in order to activate its functionality on the layer.</p> In\u00a0[8]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[filter_extension],\n    get_fill_color=fill_color,\n    get_radius=radius,\n    get_filter_value=filter_values,\n    filter_range=initial_filter_range,\n    radius_units=\"meters\",\n    radius_min_pixels=0.1,\n)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(     gdf,     extensions=[filter_extension],     get_fill_color=fill_color,     get_radius=radius,     get_filter_value=filter_values,     filter_range=initial_filter_range,     radius_units=\"meters\",     radius_min_pixels=0.1, ) m = Map(layer) m Out[8]: <p>Only the points that are within all of our initial filter ranges are shown, but we'd like something more interactive. Let's connect sliders to the map.</p> <p>Here we create a separate slider for each attribute and then connect them using a <code>MultiRangeSlider</code>. This defines the slider element but it won't be connected to the map until we use <code>ipywidgets.jsdlink</code> later.</p> In\u00a0[9]: Copied! <pre>download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \")\nupload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \")\nlatency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \")\nmulti_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider])\nmulti_slider\n</pre> download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \") upload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \") latency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \") multi_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider]) multi_slider Out[9]: <p>In order to propagate the changes in our sliders to the <code>ScatterplotLayer</code> instance, we use <code>jsdlink</code>. See Linking Widgets in the ipywidgets documentation for more info.</p> In\u00a0[10]: Copied! <pre>_ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\"))\n</pre> _ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\")) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/data-filter-extension/#datafilterextension","title":"DataFilterExtension\u00b6","text":"<p>The <code>DataFilterExtension</code> adds GPU-based data filtering functionalities to layers, allowing the layer to show/hide objects based on user-defined properties.</p> <p>In this demo we'll use the <code>DataFilterExtension</code> to filter millions of points based on three numeric attributes.</p>"},{"location":"examples/data-filter-extension/#imports","title":"Imports\u00b6","text":""},{"location":"examples/data-filter-extension/#fetch-data","title":"Fetch data\u00b6","text":"<p>We use the same data as the <code>ScatterplotLayer</code> example except that here we download extra columns from the dataset to use for filtering.</p> <p>Columns:</p> <ul> <li><code>avg_d_kbps</code>: the average download speed for that data point in kbps</li> <li><code>avg_u_kbps</code>: the average upload speed for that data point in kbps</li> <li><code>avg_lat_ms</code>: the average latency for that data point in milliseconds</li> <li><code>devices</code>: the number of unique devices contributing tests in the tile.</li> <li><code>tile</code>: the WKT string representing a given zoom-16 Web Mercator tile</li> </ul> <p>We cache the downloaded file locally to <code>data-filter-extension.parquet</code> so that the second time running this notebook will be faster.</p>"},{"location":"examples/global-boundaries/","title":"Municipal Boundaries","text":"In\u00a0[1]: Copied! <pre>! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm\n</pre> ! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm <pre>\n[notice] A new release of pip is available: 23.3.2 -&gt; 24.0\n[notice] To update, run: pip install --upgrade pip\n</pre> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport requests\nfrom tqdm import tqdm\n\nfrom lonboard import Map, PolygonLayer\n</pre> from pathlib import Path  import geopandas as gpd import requests from tqdm import tqdm  from lonboard import Map, PolygonLayer <p>The URL for a large data file of CGAZ at ADM1 Districts level:</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\"\nfilepath = \"geoBoundariesCGAZ_ADM1.geojson\"\n</pre> url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\" filepath = \"geoBoundariesCGAZ_ADM1.geojson\" <p>We'll go ahead and fetch the GeoJSON data in that format. If this notebook has already been run once and the file exists locally, we'll skip downloading it again.</p> In\u00a0[4]: Copied! <pre>if not Path(filepath).exists():\n    # Make a GET request to download the file\n    # Streaming, so we can iterate over the response and create a progress bar for this large file\n    response = requests.get(url, stream=True)\n\n    # Sizes in bytes.\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 1024\n\n    with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:\n        with open(filepath, \"wb\") as file:\n            for data in response.iter_content(block_size):\n                progress_bar.update(len(data))\n                file.write(data)\n\n    if total_size != 0 and progress_bar.n != total_size:\n        raise RuntimeError(\"Could not download file\")\n</pre> if not Path(filepath).exists():     # Make a GET request to download the file     # Streaming, so we can iterate over the response and create a progress bar for this large file     response = requests.get(url, stream=True)      # Sizes in bytes.     total_size = int(response.headers.get(\"content-length\", 0))     block_size = 1024      with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:         with open(filepath, \"wb\") as file:             for data in response.iter_content(block_size):                 progress_bar.update(len(data))                 file.write(data)      if total_size != 0 and progress_bar.n != total_size:         raise RuntimeError(\"Could not download file\") In\u00a0[5]: Copied! <pre>gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)\n\n# Keep columns necessary for our visualization\ncols = [\"shapeName\", \"shapeID\", \"geometry\"]\ngdf = gdf[cols]\n</pre> gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)  # Keep columns necessary for our visualization cols = [\"shapeName\", \"shapeID\", \"geometry\"] gdf = gdf[cols] <p>We can take a quick look at the shape of this data:</p> In\u00a0[6]: Copied! <pre>gdf.head(10)\n</pre> gdf.head(10) Out[6]: shapeName shapeID geometry 0 Kandahar 12653393B40111500734429 POLYGON ((65.24153 32.28630, 65.12687 32.03679... 1 Zabul 12653393B56617740339660 POLYGON ((67.60666 31.44378, 67.70162 31.51373... 2 Uruzgan 12653393B46006342616872 POLYGON ((66.27519 32.42550, 66.35907 32.60925... 3 Daykundi 12653393B78791504725813 POLYGON ((66.76157 33.25547, 66.92827 33.59694... 4 Ghanzi 12653393B29313712249365 POLYGON ((68.06945 32.04564, 68.06011 32.45619... 5 Paktika 12653393B93572570471663 MULTIPOLYGON (((68.10797 31.74752, 68.10807 31... 6 Khost 12653393B66876919073955 POLYGON ((69.50619 33.02954, 69.50620 33.02954... 7 Paktia 12653393B16859990709200 POLYGON ((68.76070 33.41986, 68.95811 33.18136... 8 Logar 12653393B769191277822 POLYGON ((68.78347 33.62259, 68.99623 33.62635... 9 Wardak 12653393B94990201402617 POLYGON ((68.68691 33.72585, 68.86023 34.01301... <p>Without doing any simplification, the geometries in this dataset contain nearly 9 million coordinates (You can verify this with <code>shapely.get_num_coordinates(gdf[\"geometry\"]).sum()</code>).</p> <p>On recent computers, Lonboard should be able to visualize the entire dataset, but to ensure this dataset works on all computers, we'll select only rows whose geometries are at least partially in the eastern hemisphere:</p> In\u00a0[7]: Copied! <pre>eastern_hemisphere = gdf.cx[0:180, -90:90]\n</pre> eastern_hemisphere = gdf.cx[0:180, -90:90] In\u00a0[8]: Copied! <pre>layer = PolygonLayer.from_geopandas(\n    eastern_hemisphere,\n    get_line_width=20,  # width in default units (meters)\n    line_width_min_pixels=0.2,  # minimum width when zoomed out\n    get_fill_color=[204, 251, 254],  # light blue\n    get_line_color=[37, 36, 34],  # dark border color\n)\n</pre> layer = PolygonLayer.from_geopandas(     eastern_hemisphere,     get_line_width=20,  # width in default units (meters)     line_width_min_pixels=0.2,  # minimum width when zoomed out     get_fill_color=[204, 251, 254],  # light blue     get_line_color=[37, 36, 34],  # dark border color ) In\u00a0[9]: Copied! <pre>m = Map(layer)\nm\n</pre> m = Map(layer) m Out[9]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/global-boundaries/#municipal-boundaries","title":"Municipal Boundaries\u00b6","text":"<p>This example will use data from GeoBoundaries.org's data set of Comprehensive Global Administrative Zones (CGAZ).</p> <p>CGAZ is a set of global composites for administrative boundaries. There are two important distinctions between our global product and individual country downloads. First, extensive simplification is performed to ensure that file sizes are small enough to be used in most traditional desktop software. Second, disputed areas are removed and replaced with polygons following US Department of State definitions. Attribution: www.geoboundaries.org</p> <p>Note: This notebook incurs downloading a 360MB file. To successfully follow this example, please ensure sufficient storage space. The time to download this data will vary based on internet connection speed. We've added a download progress bar to the data retrieval section of this notebook to allow you to view progress and a time estimate for downloading the visualization data.</p>"},{"location":"examples/global-boundaries/#imports","title":"Imports\u00b6","text":""},{"location":"examples/global-boundaries/#visualize-global-boundaries","title":"Visualize Global Boundaries\u00b6","text":"<p>Let's visualize this dataset.</p> <p>We'll start by loading the data into GeoPandas. It's recommended to use the newer <code>pyogrio</code> library for faster data loading.</p>"},{"location":"examples/global-boundaries/#visualizing-the-data","title":"Visualizing the data\u00b6","text":"<p>We can use a <code>PolygonLayer</code> to customize the rendering of this data.</p>"},{"location":"examples/internet-speeds/","title":"Speedtest Data (ScatterplotLayer)","text":"In\u00a0[1]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>The URL for a single data file for mobile network speeds in the first quarter of 2019:</p> In\u00a0[2]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" <p>The data used in this example is relatively large. In the cell below, we cache the downloading and preparation of the dataset so that it's faster to run this notebook the second time.</p> <p>We fetch two columns \u2014 <code>avg_d_kbps</code> and <code>tile</code> \u2014 from this data file directly from AWS. The <code>pd.read_parquet</code> command will perform a network request for these columns from the data file, so it may take a while on a slow network connection. <code>avg_d_kbps</code> is the average download speed for that data point in kilobits per second. <code>tile</code> is the WKT string representing a given zoom-16 Web Mercator tile.</p> <p>The <code>tile</code> column contains strings representing WKT-formatted geometries. We need to parse those strings into geometries. Then for simplicity we'll convert into their centroids.</p> In\u00a0[3]: Copied! <pre>local_path = Path(\"internet-speeds.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')\n    gdf.to_parquet(local_path)\n</pre> local_path = Path(\"internet-speeds.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')     gdf.to_parquet(local_path) <p>We can take a quick look at this data:</p> In\u00a0[4]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[4]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) <p>To ensure that this demo is snappy on most computers, we'll filter to a bounding box over Europe.</p> <p>If you're on a recent computer, feel free to comment out the next line.</p> In\u00a0[5]: Copied! <pre>gdf = gdf.cx[-11.83:25.5, 34.9:59]\n</pre> gdf = gdf.cx[-11.83:25.5, 34.9:59] <p>Even this filtered data frame still has 800,000 rows, so it's still a lot of data to explore:</p> In\u00a0[6]: Copied! <pre>gdf\n</pre> gdf Out[6]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) ... ... ... 3231240 19528 POINT (169.81842 -46.29571) 3231241 15693 POINT (169.81293 -46.30710) 3231242 26747 POINT (169.66461 -46.42082) 3231243 67995 POINT (169.65912 -46.45110) 3231244 1230 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 2 columns</p> <p>To render point data, first create a <code>ScatterplotLayer</code> and then add it to a <code>Map</code> object:</p> In\u00a0[7]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) m = Map(layer) m Out[7]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\navg_d_kbps: uint32\ngeometry: fixed_size_list&lt;item: double&gt;[2]\u2026</pre> <p>We can look at the documentation for <code>ScatterplotLayer</code> to see what other rendering options it allows. Let's set the fill color to something other than black:</p> In\u00a0[8]: Copied! <pre>layer.get_fill_color = [0, 0, 200, 200]\n</pre> layer.get_fill_color = [0, 0, 200, 200] <p>Blue is pretty, but the map would be more informative if we colored each point by a relevant characteristic. In this case, we have the download speed associated with each location, so let's use that!</p> <p>Here we compute a linear statistic for the download speed. Given a minimum bound of <code>5000</code> and a maximum bound of <code>50,000</code> the normalized speed is linearly scaled to between 0 and 1.</p> In\u00a0[9]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\ndownload_speed = gdf['avg_d_kbps']\nnormalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound)\n</pre> min_bound = 5000 max_bound = 50000 download_speed = gdf['avg_d_kbps'] normalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound) <p><code>normalized_download_speed</code> is now linearly scaled based on the bounds provided above. Keep in mind that the input range of the colormap is 0-1. So any values that are below 0 will receive the left-most color in the colormap, while any values above 1 will receive the right-most color in the colormap.</p> In\u00a0[10]: Copied! <pre>normalized_download_speed\n</pre> normalized_download_speed Out[10]: <pre>0          0.021844\n1         -0.027822\n2         -0.036356\n3         -0.058200\n4         -0.043400\n             ...   \n3231240    0.322844\n3231241    0.237622\n3231242    0.483267\n3231243    1.399889\n3231244   -0.083778\nName: avg_d_kbps, Length: 3231245, dtype: float64</pre> <p>We can use any colormap provided by the <code>palettable</code> package. Let's inspect the <code>BrBG_10</code> diverging colormap below:</p> In\u00a0[11]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap Out[11]: BrBG  underbad over  <p>Now let's apply the colormap on <code>normalized_download_speed</code> using a helper provided by <code>lonboard</code>. We can set it on <code>layer.get_fill_color</code> to update the existing colors.</p> In\u00a0[12]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7)\n</pre> layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7) <p>After running the above cell, you should see the map above update with a different color per point!</p> <p>We can pass an array into any of the \"accessors\" supported by the layer (this is any attribute that starts with <code>get_*</code>).</p> <p>For demonstration purposes, let's also set <code>get_radius</code> to <code>normalized_download_speed</code>.</p> In\u00a0[13]: Copied! <pre>layer.get_radius = normalized_download_speed * 200\nlayer.radius_units = \"meters\"\nlayer.radius_min_pixels = 0.5\n</pre> layer.get_radius = normalized_download_speed * 200 layer.radius_units = \"meters\" layer.radius_min_pixels = 0.5 <p>After running the above cell, you should see the map updated to have a different radius per point!</p>"},{"location":"examples/internet-speeds/#speedtest-data-scatterplotlayer","title":"Speedtest Data (<code>ScatterplotLayer</code>)\u00b6","text":"<p>This example will use data collected from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry. From the AWS page:</p> <p>Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (35MB download).</p>"},{"location":"examples/internet-speeds/#imports","title":"Imports\u00b6","text":""},{"location":"examples/internet-speeds/#fetch-data","title":"Fetch data\u00b6","text":""},{"location":"examples/migration/","title":"U.S. County-to-County Migration","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport requests\nimport shapely\nfrom matplotlib.colors import Normalize\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.experimental import ArcLayer\nfrom lonboard.layer_extension import BrushingExtension\n</pre> import geopandas as gpd import numpy as np import pandas as pd import pyarrow as pa import requests import shapely from matplotlib.colors import Normalize  from lonboard import Map, ScatterplotLayer from lonboard.experimental import ArcLayer from lonboard.layer_extension import BrushingExtension <p>Fetch the data from the version in the <code>deck.gl-data</code> repository.</p> In\u00a0[2]: Copied! <pre>url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\"\nr = requests.get(url)\nsource_data = r.json()\n</pre> url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\" r = requests.get(url) source_data = r.json() <p>The following cell may be a little hard to follow, but what it's doing is taking the raw data, which represents a graph) of the data and normalizing it to a table structure where each row represents one \"arc\" between a source and target county.</p> <p>This is ported from the original JavaScript here.</p> In\u00a0[3]: Copied! <pre>arcs = []\ntargets = []\nsources = []\npairs = {}\n\nfeatures = source_data[\"features\"]\nfor i, county in enumerate(features):\n    flows = county[\"properties\"][\"flows\"]\n    target_centroid = county[\"properties\"][\"centroid\"]\n    total_value = {\n        \"gain\": 0,\n        \"loss\": 0,\n    }\n\n    for to_id, value in flows.items():\n        if value &gt; 0:\n            total_value[\"gain\"] += value\n        else:\n            total_value[\"loss\"] += value\n\n        # If number is too small, ignore it\n        if abs(value) &lt; 50:\n            continue\n\n        pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))\n        source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]\n        gain = np.sign(flows[to_id])\n\n        # add point at arc source\n        sources.append(\n            {\n                \"position\": source_centroid,\n                \"target\": target_centroid,\n                \"name\": features[int(to_id)][\"properties\"][\"name\"],\n                \"radius\": 3,\n                \"gain\": -gain,\n            }\n        )\n        # eliminate duplicate arcs\n        if pair_key in pairs.keys():\n            continue\n\n        pairs[pair_key] = True\n\n        if gain &gt; 0:\n            arcs.append(\n                {\n                    \"target\": target_centroid,\n                    \"source\": source_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n        else:\n            arcs.append(\n                {\n                    \"target\": source_centroid,\n                    \"source\": target_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n\n    # add point at arc target\n    targets.append(\n        {\n            **total_value,\n            \"position\": [target_centroid[0], target_centroid[1], 10],\n            \"net\": total_value[\"gain\"] + total_value[\"loss\"],\n            \"name\": county[\"properties\"][\"name\"],\n        }\n    )\n\n# sort targets by radius large -&gt; small\ntargets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True)\nnormalizer = Normalize(0, abs(targets[0][\"net\"]))\n</pre> arcs = [] targets = [] sources = [] pairs = {}  features = source_data[\"features\"] for i, county in enumerate(features):     flows = county[\"properties\"][\"flows\"]     target_centroid = county[\"properties\"][\"centroid\"]     total_value = {         \"gain\": 0,         \"loss\": 0,     }      for to_id, value in flows.items():         if value &gt; 0:             total_value[\"gain\"] += value         else:             total_value[\"loss\"] += value          # If number is too small, ignore it         if abs(value) &lt; 50:             continue          pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))         source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]         gain = np.sign(flows[to_id])          # add point at arc source         sources.append(             {                 \"position\": source_centroid,                 \"target\": target_centroid,                 \"name\": features[int(to_id)][\"properties\"][\"name\"],                 \"radius\": 3,                 \"gain\": -gain,             }         )         # eliminate duplicate arcs         if pair_key in pairs.keys():             continue          pairs[pair_key] = True          if gain &gt; 0:             arcs.append(                 {                     \"target\": target_centroid,                     \"source\": source_centroid,                     \"value\": flows[to_id],                 }             )         else:             arcs.append(                 {                     \"target\": source_centroid,                     \"source\": target_centroid,                     \"value\": flows[to_id],                 }             )      # add point at arc target     targets.append(         {             **total_value,             \"position\": [target_centroid[0], target_centroid[1], 10],             \"net\": total_value[\"gain\"] + total_value[\"loss\"],             \"name\": county[\"properties\"][\"name\"],         }     )  # sort targets by radius large -&gt; small targets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True) normalizer = Normalize(0, abs(targets[0][\"net\"])) <p>We define some color constants, as well as a color lookup array.</p> <p>A nice trick in numpy is that if you have a two-dimensional array like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184]\n]\n</pre> <p>you can perform a lookup based on the index to transform data from one dimensionality to another. In this case, we'll use <code>0</code> and <code>1</code> \u2014 the two available indexes of the array's first dimension \u2014 to create an array of colors.</p> <p>So when we call <code>COLORS[colors_lookup]</code> that creates an output array of something like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184],\n    [166,   3,   3],\n    [166,   3,   3]\n]\n</pre> <p>equal to the number of rows in our dataset. We can then pass this to any parameter that accepts a ColorAccessor.</p> In\u00a0[4]: Copied! <pre># migrate out\nSOURCE_COLOR = [166, 3, 3]\n# migrate in\nTARGET_COLOR = [35, 181, 184]\n# Combine into a single arr to use as a lookup table\nCOLORS = np.vstack(\n    [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)]\n)\nSOURCE_LOOKUP = 0\nTARGET_LOOKUP = 1\n</pre> # migrate out SOURCE_COLOR = [166, 3, 3] # migrate in TARGET_COLOR = [35, 181, 184] # Combine into a single arr to use as a lookup table COLORS = np.vstack(     [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)] ) SOURCE_LOOKUP = 0 TARGET_LOOKUP = 1 In\u00a0[5]: Copied! <pre>brushing_extension = BrushingExtension()\nbrushing_radius = 200000\n</pre> brushing_extension = BrushingExtension() brushing_radius = 200000 <p>Convert the <code>sources</code> list of dictionaries into a GeoPandas <code>GeoDataFrame</code> to pass into a <code>ScatterplotLayer</code>.</p> In\u00a0[6]: Copied! <pre>source_arr = np.array([source[\"position\"] for source in sources])\nsource_positions = shapely.points(source_arr[:, 0], source_arr[:, 1])\nsource_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],\n    geometry=source_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\nsource_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\nsource_fill_colors = COLORS[source_colors_lookup]\n</pre> source_arr = np.array([source[\"position\"] for source in sources]) source_positions = shapely.points(source_arr[:, 0], source_arr[:, 1]) source_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],     geometry=source_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array source_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) source_fill_colors = COLORS[source_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for source points:</p> In\u00a0[7]: Copied! <pre>source_layer = ScatterplotLayer.from_geopandas(\n    source_gdf,\n    get_fill_color=source_fill_colors,\n    radius_scale=3000,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> source_layer = ScatterplotLayer.from_geopandas(     source_gdf,     get_fill_color=source_fill_colors,     radius_scale=3000,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) In\u00a0[8]: Copied! <pre>targets_arr = np.array([target[\"position\"] for target in targets])\ntarget_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1])\ntarget_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],\n    geometry=target_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\ntarget_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\ntarget_line_colors = COLORS[target_line_colors_lookup]\n</pre> targets_arr = np.array([target[\"position\"] for target in targets]) target_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1]) target_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],     geometry=target_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array target_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) target_line_colors = COLORS[target_line_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for target points:</p> In\u00a0[9]: Copied! <pre>target_ring_layer = ScatterplotLayer.from_geopandas(\n    target_gdf,\n    get_line_color=target_line_colors,\n    radius_scale=4000,\n    pickable=True,\n    stroked=True,\n    filled=False,\n    line_width_min_pixels=2,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> target_ring_layer = ScatterplotLayer.from_geopandas(     target_gdf,     get_line_color=target_line_colors,     radius_scale=4000,     pickable=True,     stroked=True,     filled=False,     line_width_min_pixels=2,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Note: the <code>ArcLayer</code> can't currently be created from a GeoDataFrame because it needs two point columns, not one. This is a large part of why it's still marked under the \"experimental\" module.</p> <p>Here we pass a numpy array for each point column. This is allowed as long as the shape of the array is <code>(N, 2)</code> or <code>(N, 3)</code> (i.e. 2D or 3D coordinates).</p> In\u00a0[10]: Copied! <pre>value = np.array([arc[\"value\"] for arc in arcs])\nget_source_position = np.array([arc[\"source\"] for arc in arcs])\nget_target_position = np.array([arc[\"target\"] for arc in arcs])\ntable = pa.table({\"value\": value})\n\narc_layer = ArcLayer(\n    table=table,\n    get_source_position=get_source_position,\n    get_target_position=get_target_position,\n    get_source_color=SOURCE_COLOR,\n    get_target_color=TARGET_COLOR,\n    get_width=1,\n    opacity=0.4,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> value = np.array([arc[\"value\"] for arc in arcs]) get_source_position = np.array([arc[\"source\"] for arc in arcs]) get_target_position = np.array([arc[\"target\"] for arc in arcs]) table = pa.table({\"value\": value})  arc_layer = ArcLayer(     table=table,     get_source_position=get_source_position,     get_target_position=get_target_position,     get_source_color=SOURCE_COLOR,     get_target_color=TARGET_COLOR,     get_width=1,     opacity=0.4,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Now we can create a map using these three layers we've created.</p> <p>As you hover over the map, it should render only the arcs near your cursor.</p> <p>You can modify <code>brushing_extension.brushing_radius</code> to control how large the brush is around your cursor.</p> In\u00a0[11]: Copied! <pre>map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10)\nmap_\n</pre> map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10) map_ Out[11]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/migration/#us-county-to-county-migration","title":"U.S. County-to-County Migration\u00b6","text":"<p>This notebook is derived from the original deck.gl example in JavaScript, which you can see here.</p> <p>This dataset originally came from the U.S. Census Bureau and represents people moving in and out of each county between 2009-2013.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/migration/#imports","title":"Imports\u00b6","text":""},{"location":"examples/north-america-roads/","title":"North America Roads","text":"In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport palettable.colorbrewer.diverging\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import palettable.colorbrewer.diverging  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap <p>The \"public\" URL for this dataset from the Natural Earth website is</p> <pre><code>https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>That doesn't appear to work directly inside a notebook, so we'll use the URL the above redirects to:</p> <pre><code>https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>It's possible this \"private\" URL will change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\n</pre> url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\" <p>We'll use GeoPandas to fetch this data (45MB) over the internet and load it into a <code>GeoDataFrame</code>. This uses the <code>pyogrio</code> engine, which is faster. Ensure you have <code>pyogrio</code> installed.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>This dataframe has a variety of attributes, plus a geometry column with a <code>LineString</code> type.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.head()\n</pre> gdf.head() <p>To ensure that this demo is snappy on most computers, we'll filter to only the contiguous U.S. If you're on a recent computer, feel free to comment out this line.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"state\"] == \"California\"]\n</pre> gdf = gdf[gdf[\"state\"] == \"California\"] <p>To render <code>LineString</code> data, first create a <code>PathLayer</code> and then add it to a <code>Map</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8) map_ = Map(layers=[layer]) map_ <p>We can look at the documentation for <code>PathLayer</code> to see what other rendering options it allows. Let's set the path color to something other than black:</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = [200, 0, 200]\n</pre> layer.get_color = [200, 0, 200] <p>Ok, so we can see the data! That's great! But let's customize the rendering based on an attribute!</p> <p>The <code>scalerank</code> column tells how important the road is in the road network. Let's see what the distribution of values is for this column:</p> In\u00a0[\u00a0]: Copied! <pre>gdf[\"scalerank\"].value_counts().sort_index()\n</pre> gdf[\"scalerank\"].value_counts().sort_index() <p>Ok, so the values range from <code>3</code> to <code>12</code>. To assign a colormap to this column, we need \"normalized\" values that span between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9\n</pre> normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9 <p>The values of this array now range from 0 to 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank.min(), normalized_scale_rank.max()\n</pre> normalized_scale_rank.min(), normalized_scale_rank.max() <p>Let's select a colormap to apply to this data:</p> In\u00a0[\u00a0]: Copied! <pre>cmap = palettable.colorbrewer.diverging.PuOr_10\n</pre> cmap = palettable.colorbrewer.diverging.PuOr_10 In\u00a0[\u00a0]: Copied! <pre>cmap.mpl_colormap\n</pre> cmap.mpl_colormap <p>Now we'll use <code>apply_continuous_cmap</code> onto this array to generate colors for our data. Just set this new array onto the existing layer, and you'll see the map update with the new colors!</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = apply_continuous_cmap(\n    normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8\n)\n</pre> layer.get_color = apply_continuous_cmap(     normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8 ) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/north-america-roads/#north-america-roads","title":"North America Roads\u00b6","text":"<p>This example uses data from Natural Earth to plot the road network in North America. Refer to the data documentation for more information about this dataset.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/north-america-roads/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>pyogrio</li> </ul>"},{"location":"examples/north-america-roads/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/color-picker/","title":"ColorPicker","text":"<p>Example gif:</p> <p></p> In\u00a0[1]: Copied! <pre>import geodatasets\nimport geopandas as gpd\nimport ipywidgets\n\nfrom lonboard import Map, SolidPolygonLayer\n</pre> import geodatasets import geopandas as gpd import ipywidgets  from lonboard import Map, SolidPolygonLayer <p><code>geodatasets.get_path</code> will download the dataset if it hasn't been downloaded and cached before.</p> In\u00a0[2]: Copied! <pre>gdf = gpd.read_file(geodatasets.get_path(\"nybb\"))\n</pre> gdf = gpd.read_file(geodatasets.get_path(\"nybb\")) <p>Create our basic map:</p> In\u00a0[3]: Copied! <pre>layer = SolidPolygonLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = SolidPolygonLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) map_ <pre>/Users/kyle/github/developmentseed/lonboard/lonboard/_layer.py:99: UserWarning: GeoDataFrame being reprojected to EPSG:4326\n  warnings.warn(\"GeoDataFrame being reprojected to EPSG:4326\")\n</pre> Out[3]: <pre>Map(layers=[SolidPolygonLayer(table=pyarrow.Table\nBoroCode: int64\nBoroName: string\nShape_Leng: double\nShape_Ar\u2026</pre> <p>Create the <code>ColorPicker</code> widget:</p> In\u00a0[4]: Copied! <pre>color_picker = ipywidgets.ColorPicker()\ncolor_picker\n</pre> color_picker = ipywidgets.ColorPicker() color_picker Out[4]: <pre>ColorPicker(value='black')</pre> <p>Note that the \"data\" contained by the <code>color_picker</code> instance is held by the <code>value</code> attribute:</p> In\u00a0[5]: Copied! <pre>color_picker.value\n</pre> color_picker.value Out[5]: <pre>'black'</pre> <p>We'll now link the two widgets together, so that whenever the color picker is updated, <code>ipywidgets</code> will update the value of <code>get_fill_color</code> on the layer.</p> <p>The format of <code>dlink</code> is:</p> <ul> <li>tuple: (source widget, attribute of source widget)</li> <li>tuple: (target widget, attribute of target widget)</li> </ul> <p>So we use the below syntax to link <code>color_picker.value</code> to <code>layer.get_fill_color</code>.</p> <p>We need to use <code>dlink</code>, not <code>link</code>, which creates a one-directional link (from the color picker to the layer) and not a bi-directional link. This is because the color picker and the layer have a different color representation under the hood (<code>ColorPicker</code> stores a hex string; the <code>SolidPolygonLayer</code> works in terms of RGBA color integers).</p> In\u00a0[6]: Copied! <pre>ipywidgets.dlink(\n    (color_picker, 'value'),\n    (layer, 'get_fill_color')\n)\n</pre> ipywidgets.dlink(     (color_picker, 'value'),     (layer, 'get_fill_color') ) Out[6]: <pre>&lt;traitlets.traitlets.directional_link at 0x168822610&gt;</pre> <p>Now click on the color widget and change the color! You need to press Enter to select a specific color.</p> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/color-picker/#colorpicker","title":"ColorPicker\u00b6","text":"<p>Use lonboard with an ipywidgets ColorPicker object.</p>"},{"location":"examples/integrations/color-picker/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li><p>lonboard</p> </li> <li><p>geodatasets</p> <p>Run <code>pip install geodatasets</code> if needed.</p> </li> </ul>"},{"location":"examples/integrations/color-picker/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/sidecar/","title":"Sidecar","text":"<p>The goal of this notebook is to show how using Jupyter Sidecar can help to interactively explore and understand your data.</p> <p>Note that sidecar only works in JupyterLab and will not work in other notebook frontends like Colab.</p> <p></p> In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport ipywidgets\nimport matplotlib as mpl\nimport pandas as pd\nfrom palettable.colorbrewer.diverging import BrBG_10\nfrom sidecar import Sidecar\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import ipywidgets import matplotlib as mpl import pandas as pd from palettable.colorbrewer.diverging import BrBG_10 from sidecar import Sidecar  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>For this example, we'll use a dataset of abandoned cars in Chicago. Since this is a CSV file, we need to pass in the names of the X and Y columns into the \"open options\" to pass to OGR.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\"\ngdf = gpd.read_file(\n    url,\n    engine=\"pyogrio\",\n    use_arrow=True,\n    X_POSSIBLE_NAMES=\"Longitude\",\n    Y_POSSIBLE_NAMES=\"Latitude\",\n    KEEP_GEOM_COLUMNS=\"NO\",\n)\ngdf = gdf.set_crs(\"EPSG:4326\")\n</pre> url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\" gdf = gpd.read_file(     url,     engine=\"pyogrio\",     use_arrow=True,     X_POSSIBLE_NAMES=\"Longitude\",     Y_POSSIBLE_NAMES=\"Latitude\",     KEEP_GEOM_COLUMNS=\"NO\", ) gdf = gdf.set_crs(\"EPSG:4326\") <p>This dataset has some null values for geometries. We'll remove these first:</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf.geometry != None]\n</pre> gdf = gdf[gdf.geometry != None] <p>Let's also remove empty values from another column to make later steps easier (when modifying a single map object with lonboard, you can't change the number of rows).</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"]\n</pre> gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"] <p>We'll now initialize the <code>Sidecar</code> object, which splits the JupyterLab screen in half, creating the output area for our widget.</p> In\u00a0[\u00a0]: Copied! <pre>sc = Sidecar(title='My Map')\n</pre> sc = Sidecar(title='My Map') <p>Now let's create a map layer from our <code>GeoDataFrame</code>.</p> <p>The default map height is 500 pixels, but we'd like to have a taller map because it's vertical. This API may change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) <p>Now we can render this inside the sidecar. You should see the map appear on the right side of your screen.</p> In\u00a0[\u00a0]: Copied! <pre>with sc:\n    display(map_)\n</pre> with sc:     display(map_) <p>It's great that we can see the map, but let's customize the rendering a bit. There's a column in the data titled</p> <p>How Many Days Has the Vehicle Been Reported as Parked?</p> <p>Let's use this column to visualize how long it takes to remove a car in different areas of the city.</p> <p>This is a string column, so first let's convert it to a numeric value.</p> In\u00a0[\u00a0]: Copied! <pre>colname = \"How Many Days Has the Vehicle Been Reported as Parked?\"\ngdf[colname] = pd.to_numeric(gdf[colname])\n</pre> colname = \"How Many Days Has the Vehicle Been Reported as Parked?\" gdf[colname] = pd.to_numeric(gdf[colname]) <p>In order to apply a colormap onto our data, we need to normalize our values to the range of <code>0-1</code>. Matplotlib offers several normalization options. See the tutorial and the <code>matplotlib.colors</code> module.</p> <p>To start, we'll do a simple linear normalization, converting from 0-90 to 0-1. Any values below 0 will be clamped to 0, and any values above 90 will be clamped to 1.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 90)\nnormalized_values = normalizer(gdf[colname])\n</pre> normalizer = mpl.colors.Normalize(0, 90) normalized_values = normalizer(gdf[colname]) <p>These values now range between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_values\n</pre> normalized_values In\u00a0[\u00a0]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap In\u00a0[\u00a0]: Copied! <pre>layer.radius_min_pixels = 0.5\nlayer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10)\n</pre> layer.radius_min_pixels = 0.5 layer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10) <p>Now the circles on the map have colors based on the time they've been parked.</p> <p>But this dataset is skewed with a long tail. Most cars have been sitting way less than 90 days, but a few have been sitting even longer.</p> <p>A logarithmic normalization (aka matplotlib's <code>LogNorm</code>) might be more useful here:</p> In\u00a0[\u00a0]: Copied! <pre>log_normalizer = mpl.colors.LogNorm(3, 90, clip=True)\nlog_values = log_normalizer(gdf[colname])\n</pre> log_normalizer = mpl.colors.LogNorm(3, 90, clip=True) log_values = log_normalizer(gdf[colname]) In\u00a0[\u00a0]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10)\nlayer.get_radius = log_values * 50\nlayer.radius_units = \"meters\"\n</pre> layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10) layer.get_radius = log_values * 50 layer.radius_units = \"meters\" In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/sidecar/#sidecar","title":"Sidecar\u00b6","text":""},{"location":"examples/integrations/sidecar/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>sidecar</li> <li>pyogrio</li> </ul> <p>Run</p> <pre><code>pip install sidecar pyogrio\n</code></pre> <p>if needed. You may need to restart Jupyter and/or refresh your browser tab after installing <code>sidecar</code>.</p>"},{"location":"examples/integrations/sidecar/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/","title":"30 Day Map Challenge: Day 1 (Points)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport shapely\n\nfrom lonboard import Map, ScatterplotLayer\n</pre> import geopandas as gpd import matplotlib as mpl import numpy as np import pandas as pd import shapely  from lonboard import Map, ScatterplotLayer In\u00a0[2]: Copied! <pre>path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\"\n</pre> path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\" <p>Read the data:</p> In\u00a0[3]: Copied! <pre>df = pd.read_csv(path, dtype={\"ZIP CODE\": str})\n</pre> df = pd.read_csv(path, dtype={\"ZIP CODE\": str}) <p>Let's first take a peek at our data:</p> In\u00a0[4]: Copied! <pre>pd.options.display.max_columns = None\ndf.head()\n</pre> pd.options.display.max_columns = None df.head() Out[4]: CRASH DATE CRASH TIME BOROUGH ZIP CODE LATITUDE LONGITUDE LOCATION ON STREET NAME CROSS STREET NAME OFF STREET NAME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED CONTRIBUTING FACTOR VEHICLE 1 CONTRIBUTING FACTOR VEHICLE 2 CONTRIBUTING FACTOR VEHICLE 3 CONTRIBUTING FACTOR VEHICLE 4 CONTRIBUTING FACTOR VEHICLE 5 COLLISION_ID VEHICLE TYPE CODE 1 VEHICLE TYPE CODE 2 VEHICLE TYPE CODE 3 VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5 0 09/11/2021 2:39 NaN NaN NaN NaN NaN WHITESTONE EXPRESSWAY 20 AVENUE NaN 2.0 0.0 0 0 0 0 2 0 Aggressive Driving/Road Rage Unspecified NaN NaN NaN 4455765 Sedan Sedan NaN NaN NaN 1 03/26/2022 11:45 NaN NaN NaN NaN NaN QUEENSBORO BRIDGE UPPER NaN NaN 1.0 0.0 0 0 0 0 1 0 Pavement Slippery NaN NaN NaN NaN 4513547 Sedan NaN NaN NaN NaN 2 06/29/2022 6:55 NaN NaN NaN NaN NaN THROGS NECK BRIDGE NaN NaN 0.0 0.0 0 0 0 0 0 0 Following Too Closely Unspecified NaN NaN NaN 4541903 Sedan Pick-up Truck NaN NaN NaN 3 09/11/2021 9:35 BROOKLYN 11208 40.667202 -73.866500 (40.667202, -73.8665) NaN NaN 1211      LORING AVENUE 0.0 0.0 0 0 0 0 0 0 Unspecified NaN NaN NaN NaN 4456314 Sedan NaN NaN NaN NaN 4 12/14/2021 8:13 BROOKLYN 11233 40.683304 -73.917274 (40.683304, -73.917274) SARATOGA AVENUE DECATUR STREET NaN 0.0 0.0 0 0 0 0 0 0 NaN NaN NaN NaN NaN 4486609 NaN NaN NaN NaN NaN <p>There are a bunch of columns in this dataset:</p> In\u00a0[5]: Copied! <pre>df.columns\n</pre> df.columns Out[5]: <pre>Index(['CRASH DATE', 'CRASH TIME', 'BOROUGH', 'ZIP CODE', 'LATITUDE',\n       'LONGITUDE', 'LOCATION', 'ON STREET NAME', 'CROSS STREET NAME',\n       'OFF STREET NAME', 'NUMBER OF PERSONS INJURED',\n       'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS INJURED',\n       'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST INJURED',\n       'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST INJURED',\n       'NUMBER OF MOTORIST KILLED', 'CONTRIBUTING FACTOR VEHICLE 1',\n       'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3',\n       'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5',\n       'COLLISION_ID', 'VEHICLE TYPE CODE 1', 'VEHICLE TYPE CODE 2',\n       'VEHICLE TYPE CODE 3', 'VEHICLE TYPE CODE 4', 'VEHICLE TYPE CODE 5'],\n      dtype='object')</pre> <p>Let's keep only specific columns that we care about:</p> In\u00a0[6]: Copied! <pre>keep_cols = [\n    \"CRASH DATE\",\n    \"CRASH TIME\",\n    \"LATITUDE\",\n    \"LONGITUDE\",\n    \"NUMBER OF PERSONS INJURED\",\n    \"NUMBER OF PERSONS KILLED\",\n    \"NUMBER OF PEDESTRIANS INJURED\",\n    \"NUMBER OF PEDESTRIANS KILLED\",\n    \"NUMBER OF CYCLIST INJURED\",\n    \"NUMBER OF CYCLIST KILLED\",\n    \"NUMBER OF MOTORIST INJURED\",\n    \"NUMBER OF MOTORIST KILLED\",\n    \"COLLISION_ID\",\n]\ndf = df[keep_cols]\n</pre> keep_cols = [     \"CRASH DATE\",     \"CRASH TIME\",     \"LATITUDE\",     \"LONGITUDE\",     \"NUMBER OF PERSONS INJURED\",     \"NUMBER OF PERSONS KILLED\",     \"NUMBER OF PEDESTRIANS INJURED\",     \"NUMBER OF PEDESTRIANS KILLED\",     \"NUMBER OF CYCLIST INJURED\",     \"NUMBER OF CYCLIST KILLED\",     \"NUMBER OF MOTORIST INJURED\",     \"NUMBER OF MOTORIST KILLED\",     \"COLLISION_ID\", ] df = df[keep_cols] <p>About 11% of the data have missing locations. For this quick demo, I don't have the ability to georeference those rows based on street name, so we'll just remove them. Hopefully there isn't a skew where crashes in some areas are more likely to not be georeferenced.</p> In\u00a0[7]: Copied! <pre>(df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean()\n</pre> (df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean() Out[7]: <pre>0.11342612579376604</pre> <p>Keep only the rows where longitude and latitude are not null.</p> In\u00a0[8]: Copied! <pre>df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())]\n</pre> df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())] <p>This dataset has crash data from the end of 2021, all of 2022, and thus far in 2023. For simplicity and regularity, let's choose only data from 2022.</p> <p>We'll convert the date to a datetime column and then select rows where the date happened in 2022:</p> In\u00a0[9]: Copied! <pre>df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"])\ndf = df[df[\"CRASH DATE\"].dt.year == 2022]\n</pre> df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"]) df = df[df[\"CRASH DATE\"].dt.year == 2022] <p>Let's also convert the time column from a string to a timestamp.</p> In\u00a0[10]: Copied! <pre>df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\")\n</pre> df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\") <p>If we look at the data types in our data, they were inferred as <code>int64</code> and <code>float64</code> data types:</p> In\u00a0[11]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[11]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED               float64\nNUMBER OF PERSONS KILLED                float64\nNUMBER OF PEDESTRIANS INJURED             int64\nNUMBER OF PEDESTRIANS KILLED              int64\nNUMBER OF CYCLIST INJURED                 int64\nNUMBER OF CYCLIST KILLED                  int64\nNUMBER OF MOTORIST INJURED                int64\nNUMBER OF MOTORIST KILLED                 int64\nCOLLISION_ID                              int64\ndtype: object</pre> <p>Knowing that our data holds small values, we can downcast to smaller data types:</p> In\u00a0[12]: Copied! <pre>for numeric_col_name in df.select_dtypes(\"number\").columns:\n    df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\")\n</pre> for numeric_col_name in df.select_dtypes(\"number\").columns:     df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\") <p>Now our data types are much smaller:</p> In\u00a0[13]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[13]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED                 uint8\nNUMBER OF PERSONS KILLED                  uint8\nNUMBER OF PEDESTRIANS INJURED             uint8\nNUMBER OF PEDESTRIANS KILLED              uint8\nNUMBER OF CYCLIST INJURED                 uint8\nNUMBER OF CYCLIST KILLED                  uint8\nNUMBER OF MOTORIST INJURED                uint8\nNUMBER OF MOTORIST KILLED                 uint8\nCOLLISION_ID                             uint32\ndtype: object</pre> <p>Now let's construct a GeoDataFrame from this data.</p> In\u00a0[14]: Copied! <pre>geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\"))\ngdf = gpd.GeoDataFrame(df, geometry=geometry)\n</pre> geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\")) gdf = gpd.GeoDataFrame(df, geometry=geometry) In\u00a0[15]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[15]: CRASH DATE CRASH TIME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED COLLISION_ID geometry 37 2022-07-12 1900-01-01 17:50:00 0 0 0 0 0 0 0 0 4545699 POINT (-73.96049 40.66330) 40 2022-04-24 1900-01-01 16:45:00 1 0 0 0 0 0 1 0 4521660 POINT (-74.13892 40.60768) 41 2022-04-24 1900-01-01 04:49:00 0 0 0 0 0 0 0 0 4521759 POINT (-73.86990 40.85597) 42 2022-04-22 1900-01-01 17:17:00 1 0 1 0 0 0 0 0 4522226 POINT (-73.93960 40.79028) 43 2022-04-24 1900-01-01 01:30:00 0 0 0 0 0 0 0 0 4522015 POINT (-74.01621 40.64299) <p>If we look at the bounding box of our data, we see that some points are way outside of NYC. Let's filter to include only the data in the NYC region:</p> In\u00a0[16]: Copied! <pre>gdf.total_bounds\n</pre> gdf.total_bounds Out[16]: <pre>array([-74.25496 ,   0.      ,   0.      ,  40.912167])</pre> In\u00a0[17]: Copied! <pre>nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696]\n</pre> nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696] In\u00a0[18]: Copied! <pre>gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))]\n</pre> gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))] In\u00a0[19]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap = Map(layers=[layer], _height=800)\nmap\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map = Map(layers=[layer], _height=800) map Out[19]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\nCRASH DATE: timestamp[ns]\nCRASH TIME: timestamp[ns]\nNUMBER OF\u2026</pre> <p>Ok cool, we can see something on the map, but let's make our visualization more interesting.</p> <p>Let's compute the number of non-motorists injured in each collision:</p> In\u00a0[20]: Copied! <pre>num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"]\n</pre> num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"] <p>We can see a quick distribution with <code>value_counts</code></p> In\u00a0[21]: Copied! <pre>num_injured.value_counts()\n</pre> num_injured.value_counts() Out[21]: <pre>0    80613\n1    12057\n2      356\n3       37\n4        3\n5        3\nName: count, dtype: int64</pre> In\u00a0[22]: Copied! <pre>layer.get_radius = np.array(num_injured + 1)\nlayer.radius_scale = 50\nlayer.opacity = 0.05\n</pre> layer.get_radius = np.array(num_injured + 1) layer.radius_scale = 50 layer.opacity = 0.05 <p>Next, let's color each dot by time of day. We'll use a cyclic colormap that starts and ends on the same color, because the time of day is cyclic.</p> In\u00a0[23]: Copied! <pre>colormap = mpl.colormaps[\"twilight\"]\ncolormap\n</pre> colormap = mpl.colormaps[\"twilight\"] colormap Out[23]: twilight  underbad over  <p>Next get the minute of day of the crash:</p> In\u00a0[24]: Copied! <pre>minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute\n</pre> minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute <p>To apply a colormap, we need to normalize the <code>minute_of_day</code> series to <code>0-1</code>. We can do this with matplotlib's <code>Normalize</code>. We set the minimum value to 0 and the maximum value to the number of minutes in a day.</p> In\u00a0[25]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 24 * 60)\nnormalized_minutes = normalizer(minute_of_day)\n</pre> normalizer = mpl.colors.Normalize(0, 24 * 60) normalized_minutes = normalizer(minute_of_day) <p>Now <code>normalized_minutes</code> ranges from 0-1:</p> In\u00a0[26]: Copied! <pre>normalized_minutes\n</pre> normalized_minutes Out[26]: <pre>masked_array(data=[0.74305556, 0.69791667, 0.20069444, ..., 0.54166667,\n                   0.625     , 0.60069444],\n             mask=False,\n       fill_value=1e+20)</pre> <p>Next we can apply the matplotlib colormap on our normalized values. We set <code>bytes=True</code> to ensure the output is of <code>uint8</code> data type.</p> In\u00a0[27]: Copied! <pre>colors = colormap(normalized_minutes, bytes=True)\n</pre> colors = colormap(normalized_minutes, bytes=True) In\u00a0[28]: Copied! <pre>colors\n</pre> colors Out[28]: <pre>array([[175,  81,  81, 255],\n       [156,  57,  79, 255],\n       [109, 144, 191, 255],\n       ...,\n       [ 63,  17,  60, 255],\n       [115,  29,  78, 255],\n       [ 99,  24,  75, 255]], dtype=uint8)</pre> In\u00a0[29]: Copied! <pre>layer.get_fill_color = colors\n</pre> layer.get_fill_color = colors In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/1-points/#30-day-map-challenge-day-1-points","title":"30 Day Map Challenge: Day 1 (Points)\u00b6","text":"<p>This is a notebook for day 1 of 30 Day Map Challenge.</p>"},{"location":"examples/map_challenge/1-points/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/#access-the-data","title":"Access the data\u00b6","text":"<p>This data comes from NYC Open Data, where we use Motor Vehicle Collisions - Crashes. First download this data via <code>Export &gt; CSV</code>, save it somewhere on disk, and update the <code>path</code> variable in the next cell for that location.</p>"},{"location":"examples/map_challenge/6-asia/","title":"30 Day Map Challenge: Day 6 (Asia)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nfrom palettable.colorbrewer.sequential import Blues_8\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd from palettable.colorbrewer.sequential import Blues_8  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap In\u00a0[2]: Copied! <pre>url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\"\n</pre> url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\" In\u00a0[3]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>The <code>Strahler</code> column seems to refer to how important/central the particular river is. It ranges from 1-7.</p> In\u00a0[4]: Copied! <pre>gdf['Strahler'].value_counts()\n</pre> gdf['Strahler'].value_counts() Out[4]: <pre>Strahler\n1    80503\n2    39100\n3    19655\n4     8960\n5     4547\n6     1901\n7      484\nName: count, dtype: int64</pre> <p>We'll use <code>Strahler</code> to plot more central rivers in both a darker blue and with a wider line.</p> In\u00a0[5]: Copied! <pre>layer = PathLayer.from_geopandas(gdf)\nlayer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8)\nlayer.get_width = gdf['Strahler']\nlayer.width_scale = 3000\nlayer.width_min_pixels = 0.5\n\nm = Map(layers=[layer])\nm\n</pre> layer = PathLayer.from_geopandas(gdf) layer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8) layer.get_width = gdf['Strahler'] layer.width_scale = 3000 layer.width_min_pixels = 0.5  m = Map(layers=[layer]) m Out[5]: <pre>Map(layers=[PathLayer(get_color=&lt;pyarrow.lib.FixedSizeListArray object at 0x107cbb220&gt;\n[\n  [\n    8,\n    69,\n  \u2026</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/6-asia/#30-day-map-challenge-day-6-asia","title":"30 Day Map Challenge: Day 6 (Asia)\u00b6","text":"<p>This is a notebook for day 6 of 30 Day Map Challenge.</p> <p>This data comes from the UN's \"Rivers of South and East Asia\".</p>"},{"location":"examples/map_challenge/6-asia/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/6-asia/#fetch-the-data","title":"Fetch the data\u00b6","text":""},{"location":"how-it-works/","title":"How it works?","text":"<p>Lonboard is built on four foundational technologies: deck.gl, GeoArrow, GeoParquet, and anywidget.</p> <ul> <li>deck.gl is a JavaScript geospatial data visualization library. Because deck.gl uses the GPU in your computer to render data, it's capable of performantly rendering very large quantities of data.</li> <li>GeoArrow is a memory format for efficiently representing geospatial vector data. As a memory format, GeoArrow is not compressed and can be used directly.</li> <li>GeoParquet is a file format for efficiently encoding and decoding geospatial vector data. As a file format, GeoParquet contains very efficient compression, and needs to be parsed before it can be used. <sup>1</sup></li> <li>anywidget is a framework for building custom Jupyter widgets that makes the process much easier than before.</li> </ul>"},{"location":"how-it-works/#how-is-it-so-fast","title":"How is it so fast?","text":"<p>Lonboard is so fast because it moves data from Python to JavaScript (in your browser) and then from JavaScript to your Graphics Processing Unit (GPU) more efficiently than ever before.</p> <p>Other Python libraries for interactive maps exist (such as <code>ipyleaflet</code>), and even existing bindings to deck.gl exist (such as <code>pydeck</code>). But those libraries encode data as GeoJSON to copy from Python to the browser. GeoJSON is extremely slow to read and write and results in a very large data file that has to be copied to the browser.</p> <p>With lonboard, the entire pipeline is binary. In Python, GeoPandas to GeoArrow to GeoParquet avoids a text encoding like GeoJSON and results in a compressed binary buffer that can be efficiently copied to the browser. In JavaScript, GeoParquet to GeoArrow offers efficient decoding (in WebAssembly). Then deck.gl is able to interpret the GeoArrow table directly without any parsing (thanks to <code>@geoarrow/deck.gl-layers</code>).</p> <p>GeoPandas is the primary interface for users to add data, allowing lonboard to internally manage the conversion to GeoArrow and its transport to the browser for rendering.</p> <p>Lonboard's goal is to abstract the technical bits of representing and moving data so it can attain its dual goals of performance and ease of use for a vast audience.</p> <ol> <li> <p>lonboard currently doesn't use \"official\" GeoParquet 1.0, because the 1.0 spec requires encoding geometries as Well-Known Binary (WKB) inside of the Parquet file. lonboard uses the highly-efficient GeoArrow encoding inside of GeoParquet (which may become part of the GeoParquet spec in 1.1). This is faster and easier to write when the writer and reader are both using GeoArrow anyways.\u00a0\u21a9</p> </li> </ol>"},{"location":"how-it-works/internals/","title":"Internal Architecture","text":"<p>Last edited April 2024.</p> <p>This document will outline Lonboard's internal architecture, and hopefully should be useful for potential contributors.</p>"},{"location":"how-it-works/internals/#pydeck","title":"Pydeck","text":"<p>Before we consider Lonboard, we should briefly cover how Pydeck manages data transit.</p> <p>Pydeck uses the loaders.gl ecosystem natively supported in deck.gl. This means that pydeck passes the user input directly into the <code>data</code> prop of any given layer. When <code>data</code> is a string, it's interpreted as a URL, passed into loaders.gl machinery to parse the given data format to a list of GeoJSON features. In order to do data-driven styling, users pass in strings like <code>\"@@=properties.valuePerSqm\"</code> to describe the JavaScript callback that should be applied to each GeoJSON feature.</p> <p>In my opinion, this presents performance pitfalls and gives Pydeck a stifling amount of flexibility. The Python API isn't able to do much of anything because the user input could be anything and users end up writing a sort of JavaScript in Python strings.</p>"},{"location":"how-it-works/internals/#lonboard","title":"Lonboard","text":""},{"location":"how-it-works/internals/#goals-and-target-user-persona","title":"Goals and target user persona","text":"<p>While I was never a primary pydeck developer and can't speak to their goals, I can say that Lonboard has a well-formed idea of the target user.</p> <p>When I was learning geospatial data processing in Python, I learned that GeoPandas, Shapely, and others were amazing tools. But I would perform operations and not have a deep understanding of what the output looked like. I wanted some way to quickly get a picture of what my data looked like. This drove me to build <code>keplergl_cli</code>, a single function or CLI command to visualize data in an interactive HTML map. The desire for this simplicity lives on in Lonboard's <code>viz</code> function.</p> <p>This experience informs the target user of Lonboard:</p> <ul> <li>A Python user who does not know or want to know JavaScript.</li> <li>Wants fast exploratory data analysis.</li> <li>Already working with data in Python.</li> <li>A reasonably fast internet connection on a desktop computer.</li> </ul>"},{"location":"how-it-works/internals/#python-classes","title":"Python classes","text":"<p>The <code>Map</code> class is the primary and only widget associated with JavaScript that renders anything. <code>Map</code> subclasses from <code>anywidget.AnyWidget</code>, which dynamically fetches Lonboard's JavaScript ESM bundle. The <code>Map</code> class emulates deck.gl's <code>Deck</code> class. It synchronizes map state and can be passed a sequence of <code>Layer</code> objects.</p> <p><code>Layer</code> classes are designed to map to each underlying deck.gl layer. Most <code>Layer</code> classes subclass from <code>BaseArrowLayer</code>, which stores a <code>pyarrow.Table</code> in the dataclass. This data object gets serialized to Parquet and rendered in a layer provided by <code>@geoarrow/deck.gl-layers</code> as described later on. The primary API for each of these Arrow-based layers is <code>from_geopandas</code>, which converts a <code>GeoDataFrame</code> to a <code>pyarrow.Table</code> with a GeoArrow-represented geometry column. A few layers are not Arrow-based and do not subclass from <code>BaseArrowLayer</code>: primarily <code>BitmapLayer</code> and <code>BitmapTileLayer</code>. These pass user data input directly into a core deck.gl layer.</p> <p>These <code>Layer</code> classes subclass from <code>ipywidgets.Widget</code> but are not associated with any JavaScript of their own. But by being widgets themselves, their data is synced with JavaScript and Python data changes are propagated as events to the core <code>Map</code> object.</p> <p>Each layer has parameters that map to deck.gl accessors and render properties. Each of these has validation on the Python side to catch invalid input early. Accessors are modeled with \"trait\" objects. Scalar accessors are stored as a scalar int/string/float. Function accessors are evaluated in Python and stored as a <code>pyarrow.Array</code>.</p> <p>A variety of layer extensions are supported, which map to deck.gl's upstream layer extensions. These layer extensions dynamically add more parameters to the layers on which they're assigned.</p>"},{"location":"how-it-works/internals/#data-loading","title":"Data loading","text":"<p>While pydeck allows users to pass in URLs to remote files, at the moment this is not possible in Lonboard. With only a few exceptions (e.g. the <code>BitmapLayer</code> and <code>BitmapTileLayer</code>), only data originating in Python is supported in Lonboard. This reduction in scope vastly simplifies Lonboard's internals while not providing much of a hurdle for Lonboard's core audience.</p> <p>Jupyter provides a WebSocket connection between Python and JavaScript. Jupyter widget state from Python classes is serialized over this WebSocket and received in JavaScript. While most widgets synchronize objects through JSON, widgets fully support binary data as well, which Lonboard utilizes.</p>"},{"location":"how-it-works/internals/#simplicity-of-code","title":"Simplicity of code","text":"<p>Supporting only a single data provenance and format makes the internals of Lonboard much more maintainable. Lonboard's JavaScript bindings only need to include a single data parser and can optimize performance for Parquet and Arrow data.</p>"},{"location":"how-it-works/internals/#parquet","title":"Parquet","text":"<p>Parquet was chosen as the underlying data transmission format for its excellent compression, fast read and write speeds, compatibility with GeoArrow and, in turn, compatibility with deck.gl's binary API.</p> <p>I tested with data from the speed test example (3 million points). Saving GeoArrow to Parquet was 135x faster than saving a GeoDataFrame to GeoJSON. The resulting file was 26x smaller. And parsing the Parquet to Arrow on the frontend was 5.6x faster than <code>JSON.parse</code> (including moving the Arrow from WebAssembly to JS).</p> <p>Note that what is sent over the wire is not GeoParquet. GeoParquet 1.0 requires special metadata and WKB-encoded geometries. Using that would require code on the frontend to parse WKB-encoded geometries into GeoArrow. The upcoming GeoParquet 1.1 includes support for GeoArrow but only its \"separated\" coordinates, where <code>x</code> and <code>y</code> coordinate values are in two different buffers (i.e. <code>xxxx</code>, <code>yyyy</code>). deck.gl's binary API currently does not support passing separated coordinates. Therefore Lonboard uses the \"interleaved\" coordinate variant, where <code>x</code> and <code>y</code> coordinates are stored in the same buffer (i.e. <code>xyxyxyxy</code>).</p>"},{"location":"how-it-works/internals/#bootstrapping-geoarrow","title":"Bootstrapping GeoArrow","text":"<p>For performance reasons, Lonboard is very focused on using GeoArrow throughout the pipeline in both Python and JavaScript. At the time when Lonboard was started, there were no user-friendly tools for converting data to GeoArrow in client-side JavaScript. By supporting only data from Python and only Parquet data, we could focus on making data conversions from GeoPandas to GeoArrow in Python efficient and stable. This enables GeoArrow to be \"just an implementation detail\" and out of any primary public APIs while also working to bootstrap a GeoArrow ecosystem in Python and JavaScript.</p>"},{"location":"how-it-works/internals/#authentication","title":"Authentication","text":"<p>Since all data is loaded via Python over the Jupyter-provided WebSocket, Lonboard doesn't need to implement any sort of authentication itself. The user is able to use whatever credentials they're familiar with, say for S3, to access data. Those credentials never need to be passed to the browser.</p> <p>In contrast, having the JavaScript side load data directly from a URL requires some way for users to specify authorization headers or API keys for access to private datasets.</p>"},{"location":"how-it-works/internals/#layer-creation","title":"Layer Creation","text":"<p>With the exception of the <code>BitmapLayer</code>, no direct deck.gl layers are used. deck.gl layers are used exclusively through the <code>@geoarrow/deck.gl-layers</code> glue library. This library connects GeoArrow data to the low-level binary data API of each deck.gl layer. All accessors are passed in directly as binary buffers.</p> <p>This means that in JavaScript the data never leaves an Arrow binary representation. It's parsed from Parquet to Arrow in WebAssembly, the Arrow buffers are copied out of WebAssembly memory, but never parsed to JSON strings or JavaScript objects.</p>"},{"location":"how-it-works/internals/#accessors-and-data-driven-rendering","title":"Accessors and data-driven rendering","text":"<p>Loading data from Python also helps to simplify data-driven rendering.</p>"},{"location":"how-it-works/internals/#no-function-serialization","title":"No function serialization","text":"<p>If we can make the data movement between Python and JavaScript fast enough, we can serialize computed rendering properties rather than the function to generate them. Lonboard serializes data, not functions.</p> <p>Any accessor used to style data is computed in Python, stored as a binary array, serialized to Parquet and sent to the frontend. Other than parsing the input Parquet buffers to Arrow, no data transformations or modifications are done on the JavaScript side.</p> <p>In Pydeck users had to pass something like</p> <pre><code>getRadius=\"@@=properties.valuePerSqm\"\n</code></pre> <p>which would be expanded into</p> <pre><code>getRadius: (object) =&gt; object.properties.valuePerSqm\n</code></pre> <p>on the JavaScript side.</p> <p>I find this syntax to be horrible UX. The user is no longer writing Python, they need to learn a new DSL just for this.</p> <p>RGB Colors for 3 million points take up 9MiB in memory (or 12MiB for RGBA). In the example of speed test data, 12MiB of RGBA data compressed down to 4MiB for transit. Float32 radii values for 3 million points take up 12MiB in memory and 9MiB compressed.</p> <p>Even for users on remote Python environments, this takes no more than a couple seconds to download, and then can render almost instantly. For non-point datasets with a fewer number of geometries, the byte size of accessors are even smaller relative to the geometry buffers.</p>"},{"location":"how-it-works/internals/#update-styling-without-re-sending-data","title":"Update styling without re-sending data","text":"<p>With Lonboard's widget architecture, any accessors other than the geometry are stored separately to the main Arrow table. This means that all accessors and rendering properties can be updated in isolation. The core table does not need to be synced again when a property is updated.</p>"},{"location":"how-it-works/internals/#uses-familiar-objects","title":"Uses familiar objects","text":"<p>Accessors are defined in terms of Numpy arrays, Pandas Series or PyArrow Arrays, which are familiar to Python data scientists.</p>"},{"location":"how-it-works/internals/#full-access-to-python","title":"Full access to Python","text":"<p>Because we serialize data to JavaScript instead of a function in a custom DSL, the user has the full spectrum of Python available to them. They can use one of matplotlib's many normalization helpers before applying a colormap. Or they can use apply some ML model and use its outputs for point radii.</p>"}]}
\ No newline at end of file
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To install Lonboard using conda:</p> <pre><code>conda install -c conda-forge lonboard\n</code></pre> <p>To install from source, refer to the developer documentation.</p>"},{"location":"#get-started","title":"Get Started","text":"<p>For the simplest rendering, pass geospatial data into the top-level <code>viz</code> function.</p> <pre><code>import geopandas as gpd\nfrom lonboard import viz\n\ngdf = gpd.GeoDataFrame(...)\nviz(gdf)\n</code></pre> <p>Under the hood, this delegates to a <code>ScatterplotLayer</code>, <code>PathLayer</code>, or <code>PolygonLayer</code>. Refer to the documentation and examples for more control over rendering.</p>"},{"location":"#documentation","title":"Documentation","text":"<p>Refer to the documentation at developmentseed.org/lonboard.</p>"},{"location":"#why-the-name","title":"Why the name?","text":"<p>This is a new binding to the deck.gl geospatial data visualization library. A \"deck\" is the part of a skateboard you ride on. What's a fast, geospatial skateboard? A lonboard.</p> <p></p>"},{"location":"CHANGELOG/","title":"Changelog","text":""},{"location":"CHANGELOG/#080-2024-04-05","title":"[0.8.0] - 2024-04-05","text":""},{"location":"CHANGELOG/#new","title":"New!","text":"<ul> <li>A new <code>PolygonLayer</code>! This layer renders polygon outlines for easier visibility. @kylebarron in developmentseed/lonboard!330</li> <li>An example using <code>PolygonLayer</code> by @naomatheus in developmentseed/lonboard!351</li> <li>Sync view state between JS and Python by @kylebarron in developmentseed/lonboard!448</li> <li>Support geoarrow array input into <code>viz()</code> by @kylebarron in developmentseed/lonboard!427</li> <li>Internal architecture documentation by @kylebarron in developmentseed/lonboard!450</li> </ul>"},{"location":"CHANGELOG/#fixes","title":"Fixes","text":"<ul> <li>Fix CLI with unset <code>geometry_name</code> by @kylebarron in developmentseed/lonboard!451</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.1...v0.8.0</p>"},{"location":"CHANGELOG/#071-2024-03-22","title":"[0.7.1] - 2024-03-22","text":""},{"location":"CHANGELOG/#fixes_1","title":"Fixes","text":"<ul> <li>Fix CLI with geopackage files by @kylebarron in developmentseed/lonboard!434</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.0...v0.7.1</p>"},{"location":"CHANGELOG/#070-2024-03-21","title":"[0.7.0] - 2024-03-21","text":""},{"location":"CHANGELOG/#new_1","title":"New!","text":"<ul> <li>There's a new command-line interface (CLI)! Use the <code>lonboard</code> command to quickly visualize one or more data files readable by GDAL! For example: <code>lonboard admins.geojson features.gpkg</code>. By @kylebarron in developmentseed/lonboard!379</li> <li>Type hinting for constructors and <code>from_geopandas</code> method. This should make it easier to pass the correct parameters into layers. This has been tested to work in IDEs like VSCode, but unfortunately appears not to work in JupyterLab. By @kylebarron in developmentseed/lonboard!399</li> </ul> <ul> <li>Warn on missing CRS. One of the most common reasons that you might see an empty map is from accidentally visualizing data that is not in EPSG 4326 (longitude-latitude). We now emit a warning for data that doesn't have a CRS defined on the data. By @kylebarron in developmentseed/lonboard!395.</li> <li>Lonboard is now on <code>conda-forge</code>! Install with <code>conda install -c conda-forge lonboard</code>. By @giswqs in developmentseed/lonboard!223</li> <li>Add PointCloudLayer. By @kylebarron in developmentseed/lonboard!396</li> <li>Add fly-to map action to \"fly\" the map to a new location. By @kylebarron in developmentseed/lonboard!408</li> <li>Docs showcase page by @kylebarron in developmentseed/lonboard!401</li> <li>Improve default colors in <code>viz</code>. We now attempt to apply some basic styling onto data passed into <code>viz</code>. This will likely further improve in the future. By @kylebarron in developmentseed/lonboard!389</li> </ul>"},{"location":"CHANGELOG/#fixes_2","title":"Fixes","text":"<ul> <li>Set exported HTML height to 100% by @kylebarron in developmentseed/lonboard!377</li> <li>Raise error on single input to MultiRangeSlider by @kylebarron in developmentseed/lonboard!367</li> <li>Fix pandas <code>to_numeric</code> FutureWarning by @kylebarron in developmentseed/lonboard!368</li> <li>Fix viewing polygons in local html files by @kylebarron in developmentseed/lonboard!387</li> <li>Fix: fix sliced array input for reprojection by @kylebarron in developmentseed/lonboard!391</li> <li>Fix: Don't reproject for epsg:4326 input by @kylebarron in developmentseed/lonboard!392</li> <li>Fix: Fix weighted centroid calculation by @kylebarron in developmentseed/lonboard!393</li> <li>Fix <code>viz()</code> with <code>__geo_interface__</code> input by @kylebarron in developmentseed/lonboard!426</li> <li>Add DataFilterExtension notebook to website by @kylebarron in developmentseed/lonboard!362</li> <li>Allow non-compliant geoarrow CRS metadata by @kylebarron in developmentseed/lonboard!369</li> <li>Automatically parse geoarrow.wkb to native geoarrow by @kylebarron in developmentseed/lonboard!372</li> <li>Parse GeoParquet metadata by @kylebarron in developmentseed/lonboard!407</li> <li>CLI: 'crs' in geoparquet metadata should be optional. by @jwass in developmentseed/lonboard!411</li> </ul>"},{"location":"CHANGELOG/#other-changes","title":"Other changes","text":"<ul> <li>Creating a new user bug report by @emmalu in developmentseed/lonboard!386</li> <li>Update epic template by @emmalu in developmentseed/lonboard!382</li> <li>NormalAccessor by @naomatheus in developmentseed/lonboard!376</li> <li>Conda: Try including <code>manifest.in</code> file for <code>static</code> folder inclusion by @kylebarron in developmentseed/lonboard!421</li> <li>Switch to animated hero image by @kylebarron in developmentseed/lonboard!423</li> <li>Add CRS to GeoDataFrame in notebook examples by @kylebarron in developmentseed/lonboard!419</li> </ul>"},{"location":"CHANGELOG/#new-contributors","title":"New Contributors","text":"<ul> <li>@emmalu made their first contribution in developmentseed/lonboard!382</li> <li>@naomatheus made their first contribution in developmentseed/lonboard!376</li> <li>@jwass made their first contribution in developmentseed/lonboard!411</li> <li>@giswqs made their first contribution in developmentseed/lonboard!223</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.6.0...v0.7.0</p>"},{"location":"CHANGELOG/#060-2024-02-13","title":"[0.6.0] - 2024-02-13","text":""},{"location":"CHANGELOG/#new_2","title":"New!","text":"<ul> <li>DataFilterExtension by @kylebarron in developmentseed/lonboard!278</li> <li>Multi-range sliders for DataFilterExtension by @kylebarron in developmentseed/lonboard!340</li> <li>BitmapLayer and BitmapTileLayer by @kylebarron in developmentseed/lonboard!288</li> <li>Improved GeoArrow interop by @kylebarron in developmentseed/lonboard!308</li> <li>Allow passing a positional <code>layers</code> object into <code>Map</code> by @kylebarron in developmentseed/lonboard!319</li> <li>GeoArrow-based multithreaded coordinate reprojection by @kylebarron in developmentseed/lonboard!337</li> <li>Support <code>pyarrow.Table</code> with <code>geoarrow.pyarrow</code> extension types as geometry columns by @jorisvandenbossche in developmentseed/lonboard!218</li> <li>Add ecosystem/integrations documentation by @kylebarron in developmentseed/lonboard!350</li> </ul>"},{"location":"CHANGELOG/#fixes_3","title":"Fixes","text":"<ul> <li>Add font to index.css to fix static HTML export by @jtmiclat in developmentseed/lonboard!284</li> <li>Fix displaying tooltip for first row by @kylebarron in developmentseed/lonboard!287</li> <li>accept matplotlib colormap input to apply_cmap by @kylebarron in developmentseed/lonboard!289</li> <li>Use preferred OSM tile url by @kylebarron in developmentseed/lonboard!290</li> <li>set max zoom on osm layer by @kylebarron in developmentseed/lonboard!291</li> <li>Update contributor docs by @kylebarron in developmentseed/lonboard!316</li> <li>Check epsg:4326 bounds in layer creation by @kylebarron in developmentseed/lonboard!317</li> <li>add reference for installing from source by @kylebarron in developmentseed/lonboard!318</li> <li>Fix inferring number of rows per chunk by @kylebarron in developmentseed/lonboard!327</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!331</li> <li>Set max number of chunks per layer by @kylebarron in developmentseed/lonboard!332</li> <li>Move accessor length validation to serialization by @kylebarron in developmentseed/lonboard!333</li> <li>Deduplicate serialization for accessors by @kylebarron in developmentseed/lonboard!334</li> <li>Multi-dimensional GPU-based data filtering by @kylebarron in developmentseed/lonboard!335</li> <li>Bump anywidget to 0.9 &amp; simplify Wasm initialization by @kylebarron in developmentseed/lonboard!344</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!348</li> <li>docs fixes by @kylebarron in developmentseed/lonboard!354</li> <li>Add <code>DataFilterExtension</code> example by @kylebarron in developmentseed/lonboard!358</li> <li>fix arc layer default arguments by @kylebarron in developmentseed/lonboard!359</li> </ul>"},{"location":"CHANGELOG/#new-contributors_1","title":"New Contributors","text":"<ul> <li>@jtmiclat made their first contribution in developmentseed/lonboard!284</li> <li>@jorisvandenbossche made their first contribution in developmentseed/lonboard!218</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.5.0...v0.6.0</p>"},{"location":"CHANGELOG/#050-2023-12-01","title":"[0.5.0] - 2023-12-01","text":""},{"location":"CHANGELOG/#new-features","title":"New Features","text":"<ul> <li>Improve map height by @vgeorge in developmentseed/lonboard!220</li> <li>Add method to apply a categorical colormap by @kylebarron in developmentseed/lonboard!251</li> <li>Deduce environment and set map height in colab and vscode by @kylebarron in developmentseed/lonboard!252</li> <li>Add various carto basemaps as options by @kylebarron in developmentseed/lonboard!268</li> <li>Sync the clicked index back to Python by @kylebarron in developmentseed/lonboard!270</li> <li>Set <code>width_min_pixels</code> in PathLayer example by @kylebarron in developmentseed/lonboard!276</li> <li>Bump deck.gl layers for performance benefits by @kylebarron in developmentseed/lonboard!277</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.2...v0.5.0</p>"},{"location":"CHANGELOG/#042-2023-11-13","title":"[0.4.2] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed","title":"Fixed","text":"<ul> <li>Fixed ScatterplotLayer rendering by @kylebarron in developmentseed/lonboard!246</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.1...v0.4.2</p>"},{"location":"CHANGELOG/#041-2023-11-13","title":"[0.4.1] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed_1","title":"Fixed","text":"<ul> <li>Fixed Polygon rendering by @kylebarron in developmentseed/lonboard!243</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.0...v0.4.1</p>"},{"location":"CHANGELOG/#040-2023-11-10","title":"[0.4.0] - 2023-11-10","text":""},{"location":"CHANGELOG/#new-features_1","title":"New Features","text":"<ul> <li>New <code>HeatmapLayer</code></li> <li>New <code>experimental</code> module, with new layers (<code>ArcLayer</code>, <code>TextLayer</code>) and \"layer extensions\" (<code>BrushingExtension</code>, <code>CollisionFilterExtension</code>).</li> <li>New \"migration\" notebook using the experimental <code>ArcLayer</code>.</li> </ul>"},{"location":"CHANGELOG/#fixed_2","title":"Fixed","text":"<ul> <li>Add pandas v2 requirement by @kylebarron in developmentseed/lonboard!229</li> <li>bump anywidget to 0.7.1 by @kylebarron in developmentseed/lonboard!233</li> </ul> <p>This should error when the JS files have not been included when packaging.</p> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.3.0...v0.4.0</p>"},{"location":"CHANGELOG/#030-2023-11-07","title":"[0.3.0] - 2023-11-07","text":""},{"location":"CHANGELOG/#new-features_2","title":"New Features","text":"<ul> <li>Save widget to standalone HTML file by @kylebarron in developmentseed/lonboard!199</li> <li>Support for rendering inside Visual Studio Code</li> <li>Allow customized picking radius by @kylebarron in developmentseed/lonboard!212</li> <li>New example notebooks</li> <li>Automatically downcast data types in <code>from_geopandas</code> by @kylebarron in developmentseed/lonboard!195</li> </ul>"},{"location":"CHANGELOG/#fixed_3","title":"Fixed","text":"<ul> <li>Allow pandas series as accessor to FloatAccessor by @kylebarron in developmentseed/lonboard!208</li> <li>Raise error when creating class with unknown keyword argument by @kylebarron in developmentseed/lonboard!209</li> <li>fix tooltip rendering when not hovering over an object by @kylebarron in developmentseed/lonboard!215</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.2.0...v0.3.0</p>"},{"location":"CHANGELOG/#020-2023-11-01","title":"[0.2.0] - 2023-11-01","text":""},{"location":"CHANGELOG/#breaking-changes","title":"Breaking Changes","text":"<ul> <li>Layers no longer render a map object. Instead, pass one or more layer instances into a <code>lonboard.Map</code> and display that.</li> </ul>"},{"location":"CHANGELOG/#new-features_3","title":"New Features","text":"<ul> <li>Support multiple layers on a single map.</li> <li>Tooltip with data information on hover.</li> <li>Allow hex string color input to ColorAccessor.</li> <li>Versioned documentation website</li> <li>New examples</li> <li>Experimental API to change map height</li> </ul>"},{"location":"CHANGELOG/#fixed_4","title":"Fixed","text":"<ul> <li>Fix handling of 3d coordinates by @kylebarron in developmentseed/lonboard!160</li> <li>Improved validation error messages by @kylebarron in developmentseed/lonboard!161</li> </ul>"},{"location":"CHANGELOG/#new-contributors_2","title":"New Contributors","text":"<ul> <li>@chrisgervang made their first contribution in developmentseed/lonboard!150</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.1.2...v0.2.0</p>"},{"location":"CHANGELOG/#012-2023-10-24","title":"[0.1.2] - 2023-10-24","text":"<ul> <li>Actually update <code>@geoarrow/deck.gl-layers</code> to version 0.2.0, whoops \ud83d\ude05</li> </ul>"},{"location":"CHANGELOG/#011-2023-10-23","title":"[0.1.1] - 2023-10-23","text":"<ul> <li>Updates to <code>@geoarrow/deck.gl-layers</code> version 0.2.0:</li> <li>Fixed MultiPolygon rendering</li> <li>Fixed rendering of polygons with holes.</li> <li>Polygon rendering is roughly 35% faster.</li> </ul>"},{"location":"CHANGELOG/#010-2023-10-17","title":"[0.1.0] - 2023-10-17","text":"<ul> <li>Initial public release.</li> <li>Initial support for <code>ScatterplotLayer</code>, <code>PathLayer</code>, and <code>SolidPolygonLayer</code>.</li> </ul>"},{"location":"alternatives/","title":"Alternatives to Lonboard","text":""},{"location":"alternatives/#lonboard-vs-ipyleaflet","title":"Lonboard vs ipyleaflet","text":"<p>ipyleaflet is a great rendering library for small- and medium-sized datasets. ipyleaflet supports a broad range of data types and formats and gives the user broad control over how to render data.</p> <p>The downside of ipyleaflet is that it doesn't support large datasets as well. It uses GeoJSON to transfer data to the frontend, which is slow to write, slow to read, and large in transit. Additionally, leaflet's primary goal is not to support very large quantities of data.</p>"},{"location":"alternatives/#lonboard-vs-pydeck","title":"Lonboard vs pydeck","text":"<p>Pydeck is a full-featured binding from Python to deck.gl. Pydeck attempts to cover most of the deck.gl API. It's harder to use binary data transport with pydeck, and similarly to ipyleaflet will usually serialize data to GeoJSON.</p> <p>Lonboard does not try to cover deck.gl's full API, but rather has an opinionated approach that nudges users to the fastest rendering for many common use cases.</p>"},{"location":"alternatives/#why-not-contribute-back-to-pydeck","title":"Why not contribute back to pydeck?","text":"<p>Pydeck and lonboard have very different goals.</p> <p>A stated goal of pydeck is to be non-opinionated and to allow users with various data sources (GeoJSON strings, URLs to arbitrary data sources, etc.) to render. It makes sense for \"official\" bindings to be non-opinionated, but lonboard takes an opposite tack. By forcing users to use Arrow, we can get reliably fast performance the very common use case of rendering <code>GeoDataFrame</code>s. A downside here is that an Arrow-based implementation has required dependencies that pydeck wouldn't want. <code>pyarrow</code> on the Python side is 90MB on disk. Arrow JS on the JS side is ~200kb, and the default <code>parquet-wasm</code> build is ~1MB.</p> <p>Pydeck is tightly tied into the deck.gl JSON renderer, which allows describing a map state fully in JSON. It's not clear how this would work with the JavaScript GeoArrow layers.</p> <p>Aside from this, pydeck and lonboard use different widget architectures. Pydeck is built on the historical ipywidget layout, using the widget cookiecutter as inspiration and having a separate Jupyter Widget package published to NPM. Lonboard takes a newer approach (unavailable at the time pydeck was created) that uses Anywidget, vastly simplifying the widget process.</p>"},{"location":"alternatives/#lonboard-vs-keplergl-jupyter","title":"Lonboard vs kepler.gl-jupyter","text":"<p>kepler.gl is a JavaScript application focused on presenting a simple, high-level visualization and analysis toolkit to browser-based users. Just like Lonboard, it's built on top of deck.gl. It also has Python bindings via kepler.gl-jupyter.</p> <p>kepler.gl is a very good tool for data analysis and exploration in the browser. On the contrary, Lonboard targets users who plan to do most analysis in Python, but want the best performance to visualize the maximum amount of data. Lonboard should have better rendering performance, but has no user interface to analyze data in the browser.</p> <p>kepler.gl-jupyter serializes to GeoJSON and kepler.gl uses GeoJSON-like JavaScript objects internally; it therefore has the downside of creating a large text string in Python and serializing that to JavaScript. kepler.gl-jupyter is great for creating standalone static HTML files containing a dataset, but those files tend to be very large, since they contain embedded GeoJSON.</p>"},{"location":"alternatives/#lonboard-vs-datashader","title":"Lonboard vs datashader","text":"<p>Datashader is a truly scalable rendering library. Datashader will re-render your data from scratch when panning around in a map. This allows datashader to aggregate the source data before rendering. Datashader minimizes the amount of data being rendered and thus, in theory, Datashader should perform well for datasets as large as your computer's memory.</p> <p>Lonboard is not scalable in the same sense. It doesn't minimize the amount of data being rendered. If you ask to plot a GeoDataFrame with 3 million points, every single one of those points is transferred to the GPU and drawn to your screen. In contrast to Datashader, Lonboard should perform well for datasets whose geometries fit in your computer's GPU memory, which is usually much smaller than your computer's total memory.</p>"},{"location":"cli/","title":"Command-line Interface","text":"<p>Lonboard includes a command-line interface for quickly viewing local data files.</p> <p>The CLI is accessible either through the <code>lonboard</code> entry point or via <code>python -m lonboard</code> the latter can be useful to ensure that the <code>lonboard</code> instance you're calling is the same as your current <code>python</code> environment.</p> <pre><code>&gt; lonboard --help\nUsage: lonboard [OPTIONS] [FILES]...\n\n  Interactively visualize geospatial data using Lonboard.\n\n  This CLI can be used either to quickly view local files or to create static\n  HTML files.\n\nOptions:\n  -o, --output PATH   The output path for the generated HTML file. If not\n                      provided, will save to a temporary file.\n  --open / --no-open  Whether to open a web browser tab with the generated\n                      map. By default, the web browser is not opened when\n                      --output is provided, but is in other cases.\n  --help              Show this message and exit.\n</code></pre> <p>For example:</p> <pre><code>lonboard data.geojson\n</code></pre>"},{"location":"performance/","title":"Performance","text":"<p>Performance is a critical goal of lonboard. Below are a couple pieces of information you should know to understand lonboard's performance characteristics, as well as some advice for how to get the best performance.</p>"},{"location":"performance/#performance-characteristics","title":"Performance Characteristics","text":"<p>There are two distinct parts to the performance of lonboard: one is the performance of transferring data to the browser and the other is the performance of rendering the data once it's there.</p> <p>In general, these parts are completely distinct. Even if it takes a while to load the data in your browser, the map might be snappy once it loads, and vice versa.</p>"},{"location":"performance/#data-transfer","title":"Data Transfer","text":"<p>Lonboard creates an interactive visualization of your data in your browser. In order to do this, your GeoDataFrame needs to be transferred from your Python environment to your browser.</p> <p>In the case where your Python session is running locally (on the same machine as your browser), this data transfer is extremely fast: less than a second in most cases.</p> <p>However, in the case where your Python session is running on a remote server (such as Google Colab, Binder, or a JupyterHub instance), this data transfer means downloading the data to your local browser. Therefore, when running lonboard from a remote server, your internet speed and the quantity of data you pass into a layer will have large impacts on the data transfer speed.</p> <p>Under the hood, lonboard uses efficient compression (in the form of GeoParquet) to transfer data to the browser, but compression can only do so much; the data still needs to be downloaded.</p>"},{"location":"performance/#rendering-performance","title":"Rendering Performance","text":"<p>Once the data has been transfered from your Python session to your browser, it needs to be rendered.</p> <p>The biggest thing to note is that \u2014 in contrast to projects like datashader \u2014 lonboard does not minimize the amount of data being rendered. If you pass a GeoDataFrame with 10 million coordinates, lonboard will attempt to render all 10 million coordinates at once.</p> <p>The primary determinant of the maximum amount of data you can render with lonboard is your computer's hardware. Via the underlying deck.gl library, lonboard ultimately renders geometries using your computer's Graphics Processing Unit (GPU). If you have a better GPU card, you'll be able to visualize more data.</p> <p>Lonboard is more efficient at rendering than previous libraries, but there will always be some quantity of data beyond which your browser tab is likely to crash while attempting to render. Testing on a recent MacBook Pro M2 computer, lonboard has been able to render a few million points with minimal lag.</p>"},{"location":"performance/#performance-advice","title":"Performance Advice","text":""},{"location":"performance/#use-a-local-python-session","title":"Use a local Python session","text":"<p>Moving from a remote Python environment to a local Python environment is often impractical, but this change will make it much faster to visualize data, especially over slow internet connections.</p>"},{"location":"performance/#mutate-an-existing-map-where-possible","title":"Mutate an existing map where possible","text":"<p>Whenever you create a new map object, all the data will be transfered from your Python environment to your browser. Whenever mutating an existing map object, only the changed data will be sent to the browser.</p> <p>Therefore, unless changing the content of the data being rendered, do not create a new map object. If you're changing the rendering options of an existing map, prefer mutating the existing map object.</p> <p>Bad</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap2 = ScatterplotLayer.from_geopandas(\n    my_geodataframe,\n    get_fill_color=[255, 0, 0]\n)\n</code></pre> <p>Good</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap1.get_fill_color = [255, 0, 0]\n</code></pre> <p>In the first (bad) case, the content of <code>my_geodataframe</code> will be sent twice to the browser. In the second (good) case, the content of <code>my_geodataframe</code> will be sent only once to the browser, and only the tiny array <code>[255, 0, 0]</code> will be transfered on the second call.</p>"},{"location":"performance/#remove-columns-before-rendering","title":"Remove columns before rendering","text":"<p>All columns included in the <code>GeoDataFrame</code> will be transferred to the browser for visualization. (In the future, these other columns will be used to display a tooltip when hovering over/clicking on a geometry.)</p> <p>Especially in the case of a remote Python session, excluding unnecessary attribute columns will make data transfer to the browser faster.</p>"},{"location":"performance/#use-arrow-based-data-types-in-pandas","title":"Use Arrow-based data types in Pandas","text":"<p>As of Pandas 2.0, Pandas supports two backends for data types: either the original numpy-based data types or new data types based on Arrow and the pyarrow library.</p> <p>The first thing that lonboard does when visualizing data is converting from Pandas to an Arrow representation. Any non-geometry attribute columns will be converted to Arrow, so if you're using Arrow-based data types in Pandas already, this step will be \"free\" as no conversion is needed.</p> <p>If you're loading data from a geospatial file format, as of <code>pyogrio</code> version 0.7, you can load data to Arrow-based columns with</p> <pre><code>import pyogrio\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = pyogrio.read_dataframe(\n    \"path/to/file\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>or directly from GeoPandas with <code>geopandas.read_file</code> with <code>engine=\"pyogrio\"</code>:</p> <pre><code>import geopandas as gpd\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = gpd.read_file(\n    \"path/to/file\",\n    engine=\"pyogrio\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>See the pandas guide on data types and the <code>pandas.ArrowDtype</code> class for more information.</p>"},{"location":"performance/#simplify-geometries-before-rendering","title":"Simplify geometries before rendering","text":"<p>Simplifying geometries before rendering reduces the total number of coordinates and can make a visualization snappier. At this point, lonboard does not offer built-in geometry simplification. This is something you would need to do before passing data to lonboard.</p>"},{"location":"api/basemap/","title":"lonboard.basemap","text":""},{"location":"api/basemap/#lonboard.basemap.CartoBasemap","title":"lonboard.basemap.CartoBasemap","text":"<p>             Bases: <code>str</code>, <code>Enum</code></p> <p>Basemap styles provided by Carto.</p> <p>Refer to Carto documentation for information on styles.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatter","title":"DarkMatter  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatter = 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'\n</code></pre> <p>A dark map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatterNoLabels","title":"DarkMatterNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatterNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/dark-matter-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A dark map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Positron","title":"Positron  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Positron = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json'\n</code></pre> <p>A light map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.PositronNoLabels","title":"PositronNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>PositronNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Voyager","title":"Voyager  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Voyager = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json'\n</code></pre> <p>A light, colored map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.VoyagerNoLabels","title":"VoyagerNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>VoyagerNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/voyager-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light, colored map style without labels.</p>"},{"location":"api/colormap/","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap.DiscreteColormap","title":"DiscreteColormap  <code>module-attribute</code>","text":"<pre><code>DiscreteColormap = Dict[Any, RGBColor]\n</code></pre> <p>A type definition for a discrete colormap.</p> <p>For example, for a land cover colormap, you may want to use the following dict:</p> <pre><code>color_map = {\n    \"Open Water\": [70, 107, 159],\n    \"Perennial Snow/Ice\": [209, 222, 248],\n    \"Developed, Open Space\": [222, 197, 197],\n    \"Developed, Low Intensity\": [217, 146, 130],\n    \"Developed, Medium Intensity\": [235, 0, 0],\n    \"Developed, High Intensity\": [171, 0, 0],\n    \"Barren Land\": [179, 172, 159],\n    \"Deciduous Forest\": [104, 171, 95],\n    \"Evergreen Forest\": [28, 95, 44],\n    \"Mixed Forest\": [181, 197, 143],\n    \"Shrub/Scrub\": [204, 184, 121],\n    \"Herbaceous\": [223, 223, 194],\n    \"Hay/Pasture\": [220, 217, 57],\n    \"Cultivated Crops\": [171, 108, 40],\n    \"Woody Wetlands\": [184, 217, 235],\n    \"Emergent Herbaceous Wetlands\": [108, 159, 184],\n}\n</code></pre> <p>This corresponds to the following well-known color palette from the National Land Cover Database.</p> <p> </p> <p>Keep in mind that the type of the key of the <code>color_map</code> dictionary is important. If your data is represented as a string, the keys of the <code>color_map</code> must also be represented as strings. If your data is represented as an integer, the keys of the colormap should be integers.</p>"},{"location":"api/colormap/#lonboard.colormap.RGBColor","title":"RGBColor  <code>module-attribute</code>","text":"<pre><code>RGBColor = Union[Tuple[int, int, int], Tuple[int, int, int, int], Sequence[int]]\n</code></pre> <p>A type definition for an RGB or RGBA color value</p> <p>All values must range between 0 and 255 (inclusive). If only three values are provided, the fourth (alpha) channel will be inferred as 255 (meaning full opacity, no transparency).</p>"},{"location":"api/colormap/#lonboard.colormap.apply_categorical_cmap","title":"apply_categorical_cmap","text":"<pre><code>apply_categorical_cmap(\n    values: Union[NDArray, pd.Series, pa.Array, pa.ChunkedArray],\n    cmap: DiscreteColormap,\n    *,\n    alpha: Optional[int] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of categorical values.</p> <p>If you're working with categorical data in Pandas or GeoPandas, and especially when those categories are strings, you should use the pandas Categorical type. This will use much less memory and be faster to operate on. <code>apply_categorical_cmap</code> will be 2-3x faster (and use less memory itself) when your data is already represented as a categorical data type.</p> <p>The key of the colormap must be the same as the data type of the column of values you pass in. Usually this will be string, when you perform the lookup on a string column of data.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>Union[NDArray, Series, Array, ChunkedArray]</code>)         \u2013          <p>A numpy array, pandas Series, pyarrow Array or pyarrow ChunkedArray of data. The data type of this column must match the keys of the colormap.</p> </li> <li> <code>cmap</code>             (<code>DiscreteColormap</code>)         \u2013          <p>A dictionary mapping keys to colors. See [DiscreteColormap] for more information.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Optional[int]</code>)         \u2013          <p>The default alpha value for entries in the colormap that do not have an alpha value defined. Alpha must be an integer between 0 and 255 (inclusive).</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/colormap/#lonboard.colormap.apply_continuous_cmap","title":"apply_continuous_cmap","text":"<pre><code>apply_continuous_cmap(\n    values: NDArray[np.floating],\n    cmap: Union[Palette, mpl.colors.Colormap],\n    *,\n    alpha: Union[float, int, NDArray[np.floating], None] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of continuous values.</p> <p>If you pass in a <code>Palette</code> object from the <code>palettable</code> package, this will use matplotlib's LinearSegmentedColormap under the hood. As described in Matplotlib's referenced docstring:</p> <p>The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments.</p> <p>This means that input values are linearly combined from the two nearest colormap colors.</p> <p>If you pass in a Matplotlib <code>Colormap</code> object, this will use the lookup method defined in that class.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>NDArray[floating]</code>)         \u2013          <p>A numpy array of floating point values ranging from 0 to 1.</p> </li> <li> <code>cmap</code>             (<code>Union[Palette, Colormap]</code>)         \u2013          <p>Any matplotlib <code>Colormap</code> or <code>Palette</code> object from the <code>palettable</code> package.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Union[float, int, NDArray[floating], None]</code>)         \u2013          <p>Alpha must be a scalar between 0 and 1, a sequence of such floats with shape matching <code>values</code>, or None.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/controls/","title":"lonboard.controls","text":""},{"location":"api/controls/#lonboard.controls.MultiRangeSlider","title":"lonboard.controls.MultiRangeSlider","text":"<p>             Bases: <code>VBox</code></p> <p>A widget for multiple ranged sliders.</p> <p>This is designed to be used with the DataFilterExtension when you want to filter on 2 to 4 columns on the same time.</p> <p>If you have only a single filter, use an ipywidgets FloatRangeSlider directly.</p>"},{"location":"api/controls/#lonboard.controls.MultiRangeSlider--example","title":"Example","text":"<pre><code>from ipywidgets import FloatRangeSlider\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n</code></pre> <p>Then to propagate updates to a rendered layer, call jsdlink to connect the two widgets.</p> <pre><code>from ipywidgets import jsdlink\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>As you change the slider, the <code>filter_range</code> value on the layer class should be updated.</p>"},{"location":"api/map/","title":"Map","text":""},{"location":"api/map/#lonboard.Map","title":"lonboard.Map","text":"<p>The top-level class used to display a map in a Jupyter Widget.</p> <p>Example:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer, SolidPolygonLayer\n\n# A GeoDataFrame with Point geometries\npoint_gdf = gpd.GeoDataFrame()\npoint_layer = ScatterplotLayer.from_geopandas(\n    point_gdf,\n    get_fill_color=[255, 0, 0],\n)\n\n# A GeoDataFrame with Polygon geometries\npolygon_gdf = gpd.GeoDataFrame()\npolygon_layer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\n\nm = Map([point_layer, polygon_layer])\n</code></pre>"},{"location":"api/map/#lonboard.Map.__init__","title":"__init__","text":"<pre><code>__init__(\n    layers: Union[BaseLayer, Sequence[BaseLayer]], **kwargs: Unpack[MapKwargs]\n) -&gt; None\n</code></pre> <p>Create a new Map.</p> <p>Aside from the <code>layers</code> argument, pass keyword arguments for any other attribute defined in this class.</p> <p>Parameters:</p> <ul> <li> <code>layers</code>             (<code>Union[BaseLayer, Sequence[BaseLayer]]</code>)         \u2013          <p>One or more layers to render on this map.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>None</code>         \u2013          <p>A Map object.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map._height","title":"_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>_height = tag(sync=True)\n</code></pre> <p>Height of the map in pixels.</p> <p>This API is not yet stabilized and may change in the future.</p>"},{"location":"api/map/#lonboard.Map.basemap_style","title":"basemap_style  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>basemap_style = tag(sync=True)\n</code></pre> <p>A MapLibre-compatible basemap style.</p> <p>Various styles are provided in <code>lonboard.basemap</code>.</p> <ul> <li>Type: <code>str</code></li> <li>Default   <code>lonboard.basemap.CartoBasemap.PositronNoLabels</code></li> </ul>"},{"location":"api/map/#lonboard.Map.fly_to","title":"fly_to","text":"<pre><code>fly_to(\n    *,\n    longitude: Union[int, float],\n    latitude: Union[int, float],\n    zoom: float,\n    duration: int = 4000,\n    pitch: Union[int, float] = 0,\n    bearing: Union[int, float] = 0,\n    curve: Optional[Union[int, float]] = None,\n    speed: Optional[Union[int, float]] = None,\n    screen_speed: Optional[Union[int, float]] = None\n)\n</code></pre> <p>\"Fly\" the map to a new location.</p> <p>Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The longitude of the new viewport.</p> </li> <li> <code>latitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The latitude of the new viewport.</p> </li> <li> <code>zoom</code>             (<code>float</code>)         \u2013          <p>The zoom of the new viewport.</p> </li> <li> <code>pitch</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The pitch of the new viewport. Defaults to 0.</p> </li> <li> <code>bearing</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The bearing of the new viewport. Defaults to 0.</p> </li> <li> <code>duration</code>             (<code>int</code>, default:                 <code>4000</code> )         \u2013          <p>The number of milliseconds for the viewport transition to take. Defaults to 4000.</p> </li> <li> <code>curve</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The zooming \"curve\" that will occur along the flight path. Default <code>1.414</code>.</p> </li> <li> <code>speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation defined in relation to <code>curve</code>, it linearly affects the duration, higher speed returns smaller durations and vice versa. Default <code>1.2</code>.</p> </li> <li> <code>screen_speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation measured in screenfuls per second. Similar to speed it linearly affects the duration, when specified speed is ignored.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.layers","title":"layers  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>layers = tag(sync=True, **widget_serialization)\n</code></pre> <p>One or more <code>Layer</code> objects to display on this map.</p>"},{"location":"api/map/#lonboard.Map.parameters","title":"parameters  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>parameters = tag(sync=True)\n</code></pre> <p>GPU parameters to pass to deck.gl.</p> <p>This is an advanced API. The vast majority of users should not need to touch this setting.</p> <p>Note</p> <p>The docstring below is copied from upstream deck.gl documentation. Any usage of <code>GL</code> refers to the constants defined in <code>@luma.gl/constants</code> here, which comes from the MDN docs here.</p> <p>In place of any <code>GL</code> constant, you can use the underlying integer it represents. For example, instead of the JS</p> <pre><code>depthFunc: GL.LEQUAL\n</code></pre> <p>referring to the MDN docs, you should use</p> <pre><code>depthFunc: 0x0203\n</code></pre> <p>Note that these parameters do not yet work with integer keys. If you would like to use integer keys, open an issue.</p> <p>Expects an object with GPU parameters. Before each frame is rendered, this object will be passed to luma.gl's <code>setParameters</code> function to reset the GPU context parameters, e.g. to disable depth testing, change blending modes etc. The default parameters set by <code>Deck</code> on initialization are the following:</p> <pre><code>{\n  blend: true,\n  blendFunc: [GL.SRC_ALPHA, GL.ONE_MINUS_SRC_ALPHA, GL.ONE, GL.ONE_MINUS_SRC_ALPHA],\n  polygonOffsetFill: true,\n  depthTest: true,\n  depthFunc: GL.LEQUAL\n}\n</code></pre> <p>Refer to the luma.gl setParameters API for documentation on supported parameters and values.</p> <pre><code>import GL from '@luma.gl/constants';\nnew Deck({\n  // ...\n  parameters: {\n    blendFunc: [GL.ONE, GL.ONE, GL.ONE, GL.ONE],\n    depthTest: false\n  }\n});\n</code></pre> <p>Notes:</p> <ul> <li>Any GPU <code>parameters</code> prop supplied to individual layers will still override the   global <code>parameters</code> when that layer is rendered.</li> </ul>"},{"location":"api/map/#lonboard.Map.picking_radius","title":"picking_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>picking_radius = tag(sync=True)\n</code></pre> <p>Extra pixels around the pointer to include while picking (such as for a tooltip).</p> <p>This is helpful when rendered objects are difficult to target, for example irregularly shaped icons, small moving circles or interaction by touch.</p> <ul> <li>Type: <code>int</code></li> <li>Default: <code>5</code></li> </ul>"},{"location":"api/map/#lonboard.Map.set_view_state","title":"set_view_state","text":"<pre><code>set_view_state(\n    *,\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    zoom: Optional[float] = None,\n    pitch: Optional[float] = None,\n    bearing: Optional[float] = None\n) -&gt; None\n</code></pre> <p>Set the view state of the map.</p> <p>Any parameters that are unset will not be changed.</p> <p>Other Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new longitude to set on the map. Defaults to None.</p> </li> <li> <code>latitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new latitude to set on the map. Defaults to None.</p> </li> <li> <code>zoom</code>             (<code>Optional[float]</code>)         \u2013          <p>the new zoom to set on the map. Defaults to None.</p> </li> <li> <code>pitch</code>             (<code>Optional[float]</code>)         \u2013          <p>the new pitch to set on the map. Defaults to None.</p> </li> <li> <code>bearing</code>             (<code>Optional[float]</code>)         \u2013          <p>the new bearing to set on the map. Defaults to None.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.show_tooltip","title":"show_tooltip  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>show_tooltip = tag(sync=True)\n</code></pre> <p>Whether to render a tooltip on hover on the map.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/map/#lonboard.Map.to_html","title":"to_html","text":"<pre><code>to_html(\n    filename: Union[str, Path, TextIO, IO[str]], title: Optional[str] = None\n) -&gt; None\n</code></pre> <p>Save the current map as a standalone HTML file.</p> <p>Parameters:</p> <ul> <li> <code>filename</code>             (<code>Union[str, Path, TextIO, IO[str]]</code>)         \u2013          <p>where to save the generated HTML file.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>title</code>             (<code>Optional[str]</code>)         \u2013          <p>A title for the exported map. This will show as the browser tab name.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.use_device_pixels","title":"use_device_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>use_device_pixels = tag(sync=True)\n</code></pre> <p>Controls the resolution of the drawing buffer used for rendering.</p> <p>Setting this to <code>false</code> or a number &lt;= 1 will improve performance on high resolution displays.</p> <p>Note: This parameter must be passed to the <code>Map()</code> constructor. It cannot be changed once the map has been created.</p> <p>The available options are:</p> <ul> <li><code>true</code>: Device (physical) pixels resolution is used for rendering, this resolution   is defined by <code>window.devicePixelRatio</code>. On Retina/HD systems this resolution is   usually twice as big as CSS pixels resolution.</li> <li><code>false</code>: CSS pixels resolution (equal to the canvas size) is used for rendering.</li> <li> <p><code>Number</code> (Experimental): Specified Number is used as a custom ratio (drawing   buffer resolution to CSS pixel resolution) to determine drawing buffer size, a   value less than one uses resolution smaller than CSS pixels, gives better   performance but produces blurry images, a value greater than one uses resolution   bigger than CSS pixels resolution (canvas size), produces sharp images but at a   lower performance.</p> </li> <li> <p>Type: <code>float</code>, <code>int</code> or <code>bool</code></p> </li> <li>Default: <code>true</code></li> </ul>"},{"location":"api/map/#lonboard.Map.view_state","title":"view_state  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>view_state = ViewStateTrait()\n</code></pre> <p>The view state of the map.</p> <ul> <li>Type: <code>ViewState</code></li> <li>Default: Automatically inferred from the data passed to the map.</li> </ul> <p>You can initialize the map to a specific view state using this property:</p> <pre><code>Map(\n    layers,\n    view_state={\"longitude\": -74.0060, \"latitude\": 40.7128, \"zoom\": 7}\n)\n</code></pre> <p>Note</p> <p>The properties of the view state are immutable. Use <code>set_view_state</code> to modify a map's view state once it's been initially rendered.</p>"},{"location":"api/map/#lonboard.models.ViewState","title":"lonboard.models.ViewState","text":"<p>             Bases: <code>NamedTuple</code></p>"},{"location":"api/map/#lonboard.models.ViewState.bearing","title":"bearing  <code>instance-attribute</code>","text":"<pre><code>bearing: float\n</code></pre> <p>Bearing angle in degrees. <code>0</code> is north.</p>"},{"location":"api/map/#lonboard.models.ViewState.latitude","title":"latitude  <code>instance-attribute</code>","text":"<pre><code>latitude: float\n</code></pre> <p>Latitude at the map center.</p>"},{"location":"api/map/#lonboard.models.ViewState.longitude","title":"longitude  <code>instance-attribute</code>","text":"<pre><code>longitude: float\n</code></pre> <p>Longitude at the map center</p>"},{"location":"api/map/#lonboard.models.ViewState.pitch","title":"pitch  <code>instance-attribute</code>","text":"<pre><code>pitch: float\n</code></pre> <p>Pitch angle in degrees. <code>0</code> is top-down.</p>"},{"location":"api/map/#lonboard.models.ViewState.zoom","title":"zoom  <code>instance-attribute</code>","text":"<pre><code>zoom: float\n</code></pre> <p>Zoom level.</p>"},{"location":"api/traits/","title":"lonboard.traits","text":""},{"location":"api/traits/#lonboard.traits.ColorAccessor","title":"lonboard.traits.ColorAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl color accessor.</p> <p>Various input is allowed:</p> <ul> <li>A <code>list</code> or <code>tuple</code> with three or four integers, ranging between 0 and 255   (inclusive). This will be used as the color for all objects.</li> <li>A <code>str</code> representing a hex color or \"well known\" color interpretable by   matplotlib.colors.to_rgba.</li> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>3</code> or <code>4</code>, and will correspond to either RGB   or RGBA colors.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>3</code> or <code>4</code> and its child must have type   <code>uint8</code>.</li> <li>Any Arrow fixed size list array from a library that implements the Arrow   PyCapsule   Interface.</li> </ul> <p>You can use helpers in the <code>lonboard.colormap</code> module (i.e. <code>apply_continuous_cmap</code>) to simplify constructing numpy arrays for color values.</p>"},{"location":"api/traits/#lonboard.traits.FloatAccessor","title":"lonboard.traits.FloatAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl float accessor.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects.</li> <li>A numpy <code>ndarray</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index.</li> <li>Any Arrow floating point array from a library that implements the Arrow PyCapsule   Interface.   This includes data structures from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/traits/#lonboard.traits.GetFilterValueAccessor","title":"lonboard.traits.GetFilterValueAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for the <code>get_filter_value</code> accessor added by the <code>DataFilterExtension</code>, which can have between 1 and 4 float values per row.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects. The   <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A one-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must be 1.</li> <li>A two-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the size of the second dimension of the array.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> </ul> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface. - A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The child   array of the fixed size list must be of floating point type. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the list size.</p> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.NormalAccessor","title":"lonboard.traits.NormalAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl \"normal\" accessor</p> <p>This is primarily used in the [lonboard.PointCloudLayer].</p> <p>Acceptable inputs: - A <code>list</code> or <code>tuple</code> with three <code>int</code> or <code>float</code> values. This will be used as the   normal for all objects. - A numpy ndarray with two dimensions and floating point type. The size of the   second dimension must be 3, i.e. its shape must be <code>(N, 3)</code>. - a pyarrow <code>FixedSizeListArray</code> or <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s   where the size of the inner fixed size list 3. The child array must have type   float32. - Any Arrow array that matches the above restrictions from a library that implements   the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.PointAccessor","title":"lonboard.traits.PointAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl point accessor.</p> <p>Various input is allowed:</p> <ul> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>2</code> or <code>3</code>, and will correspond to either XY   or XYZ positions.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>2</code> or <code>3</code> and its child must be of floating   point type.</li> </ul>"},{"location":"api/traits/#lonboard.traits.PyarrowTableTrait","title":"lonboard.traits.PyarrowTableTrait","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a geospatial Arrow-backed table</p> <p>Allowed input includes:</p> <ul> <li>A pyarrow <code>Table</code> or containing a geometry column with GeoArrow metadata.</li> <li>Any GeoArrow table from a library that implements the Arrow PyCapsule   Interface.   This includes the   <code>GeoTable</code>   class from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/viz/","title":"viz","text":"<p>The top-level namespace accessible after <code>import lonboard</code>.</p> <p>Layers are also accessible in the top-level namespace, but are documented in the <code>layers/</code> folder.</p>"},{"location":"api/viz/#lonboard.viz","title":"lonboard.viz","text":"<pre><code>viz(\n    data: Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]],\n    *,\n    scatterplot_kwargs: Optional[ScatterplotLayerKwargs] = None,\n    path_kwargs: Optional[PathLayerKwargs] = None,\n    polygon_kwargs: Optional[PolygonLayerKwargs] = None,\n    map_kwargs: Optional[MapKwargs] = None\n) -&gt; Map\n</code></pre> <p>A high-level function to plot your data easily.</p> <p>The goal of this function is to make it simple to get something showing on a map. For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>This function accepts a variety of geospatial inputs:</p> <ul> <li>geopandas <code>GeoDataFrame</code></li> <li>geopandas <code>GeoSeries</code></li> <li>numpy array of Shapely objects</li> <li>Single Shapely object</li> <li>Any Python class with a <code>__geo_interface__</code> property conforming to the     Geo Interface protocol.</li> <li><code>dict</code> holding GeoJSON-like data.</li> <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li> <li>pyarrow <code>Array</code> marked with a GeoArrow extension type defined by geoarrow-pyarrow</li> </ul> <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p> <p>Parameters:</p> <ul> <li> <code>data</code>             (<code>Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]]</code>)         \u2013          <p>a data object of any supported type.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>scatterplot_kwargs</code>             (<code>Optional[ScatterplotLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>ScatterplotLayer</code>s.</p> </li> <li> <code>path_kwargs</code>             (<code>Optional[PathLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PathLayer</code>s.</p> </li> <li> <code>polygon_kwargs</code>             (<code>Optional[PolygonLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PolygonLayer</code>s.</p> </li> <li> <code>map_kwargs</code>             (<code>Optional[MapKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to the generated <code>Map</code>.</p> </li> </ul> <p>For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>Returns:</p> <ul> <li> <code>Map</code>         \u2013          <p>widget visualizing the provided data.</p> </li> </ul>"},{"location":"api/viz/#lonboard._viz.VizDataInput","title":"VizDataInput  <code>module-attribute</code>","text":"<pre><code>VizDataInput = Union[\n    GeoDataFrame,\n    GeoSeries,\n    Table,\n    NDArray[object_],\n    BaseGeometry,\n    ArrowArrayExportable,\n    ArrowStreamExportable,\n    GeoInterfaceProtocol,\n    Dict[str, Any],\n]\n</code></pre> <p>A type definition for allowed data inputs to the <code>viz</code> function.</p>"},{"location":"api/layer-extensions/","title":"Layer Extensions","text":"<p>Layer extensions are bonus features that you can optionally add to the core deck.gl layers.</p> <p>Layer extensions are in an experimental state. Some things are known to not yet work:</p> <ul> <li>Modifying the extensions on a layer by mutating its <code>extensions</code> list via <code>append</code> or <code>pop</code>. It should work, however, by creating a new list and assigning <code>layer.extensions = new_extensions_list</code>.</li> </ul> <p>If you encounter issues, please create an issue with reproducible steps.</p>"},{"location":"api/layer-extensions/brushing-extension/","title":"BrushingExtension","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension","title":"lonboard.layer_extension.BrushingExtension","text":"<p>Adds GPU-based data brushing functionalities to layers. It allows the layer to show/hide objects based on the current pointer position.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--example","title":"Example","text":"<p>An example is in the County-to-County Migration notebook.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_enabled","title":"<code>brushing_enabled</code>","text":"<p>Enable/disable brushing. If brushing is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_target","title":"<code>brushing_target</code>","text":"<p>The position used to filter each object by. One of the following:</p> <ul> <li><code>\"source\"</code>: Use the primary position for each object. This can mean different   things depending on the layer. It usually refers to the coordinates returned by   <code>getPosition</code> or <code>getSourcePosition</code> accessors.</li> <li><code>\"target\"</code>: Use the secondary position for each object. This may not be available   in some layers. It usually refers to the coordinates returned by   <code>getTargetPosition</code> accessor.</li> <li><code>\"source_target\"</code>: Use both the primary position and secondary position for each   object. Show object if either is in brushing range.</li> <li> <p><code>\"custom\"</code>: Some layers may not describe their data objects with one or two   coordinates, for example <code>PathLayer</code> and <code>PolygonLayer</code>. Use this option with the   <code>get_brushing_target</code> prop to provide a custom position that each object should be   filtered by.</p> </li> <li> <p>Type: <code>str</code>, optional</p> <p>One of: \"source\" | \"target\" | \"source_target\" | \"custom\"</p> </li> <li> <p>Default: <code>\"source\"</code></p> </li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_radius","title":"<code>brushing_radius</code>","text":"<p>The brushing radius centered at the pointer, in meters. If a data object is within this circle, it is rendered; otherwise it is hidden.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10000</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--get_brushing_target","title":"<code>get_brushing_target</code>","text":"<p>An arbitrary position for each object that it will be filtered by.</p> <p>Only effective if <code>brushing_target</code> is set to <code>\"custom\"</code>.</p> <ul> <li>Type: PointAccessor, optional<ul> <li>If a point is provided, it is used as the target for all rows.</li> <li>If an array of points is provided, each value in the array will be used as the   target for the row at the same row index.</li> </ul> </li> <li>Default: <code>None</code>.</li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/","title":"CollisionFilterExtension","text":""},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension","title":"lonboard.layer_extension.CollisionFilterExtension","text":"<p>Allows layers to hide overlapping objects.</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_enabled","title":"<code>collision_enabled</code>","text":"<p>Enable/disable collisions. If collisions are disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_group","title":"<code>collision_group</code>","text":"<p>Collision group this layer belongs to. If it is not set, the 'default' collision group is used</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--get_collision_priority","title":"<code>get_collision_priority</code>","text":"<p>Accessor for collision priority. Must return a number in the range -1000 -&gt; 1000. Features with higher values are shown preferentially.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the priority for all objects.</li> <li>If an array is provided, each value in the array will be used as the priority   for the object at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/","title":"DataFilterExtension","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension","title":"lonboard.layer_extension.DataFilterExtension","text":"<p>Adds GPU-based data filtering functionalities to layers. It allows the layer to show/hide objects based on user-defined properties.</p>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--example","title":"Example","text":"<pre><code>from lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.layer_extension import DataFilterExtension\n\ngdf = gpd.GeoDataFrame(...)\nextension = DataFilterExtension()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[extension],\n    get_filter_value=gdf[\"filter_value\"], # replace with desired column\n    filter_range=[0, 5] # replace with desired filter range\n)\n</code></pre> <p>The <code>DataFilterExtension</code> allows filtering on 1 to 4 attributes at the same time. So if you have four numeric columns of interest, you can filter on the intersection of all of them.</p> <p>For easy visualization, we suggest connecting the <code>DataFilterExtension</code> to an interactive slider from <code>ipywidgets</code>.</p> <pre><code>from ipywidgets import FloatRangeSlider\n\nslider = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"Slider: \"\n)\nslider\n\njsdlink(\n    (slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>If you have 2 to 4 columns, use a <code>MultiRangeSlider</code>, which combines multiple <code>FloatRangeSlider</code> objects in a form that the <code>DataFilterExtension</code> expects.</p> <pre><code>from ipywidgets import FloatRangeSlider, jsdlink\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--important-notes","title":"Important notes","text":"<ul> <li>The DataFilterExtension only supports float32 data, so integer data will be casted   to float32.</li> <li>The DataFilterExtension copies all data referenced by <code>get_filter_value</code> to the   GPU, so it will increase memory pressure on the GPU.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--layer-properties","title":"Layer Properties","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_enabled","title":"<code>filter_enabled</code>","text":"<p>Enable/disable the data filter. If the data filter is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_range","title":"<code>filter_range</code>","text":"<p>The bounds which defines whether an object should be rendered. If an object's filtered value is within the bounds, the object will be rendered; otherwise it will be hidden. This prop can be updated on user input or animation with very little cost.</p> <p>Format:</p> <p>If <code>filter_size</code> is 1, provide a single tuple of <code>(min, max)</code>.</p> <p>If <code>filter_size</code> is 2 to 4, provide a list of tuples: <code>[(min0, max0), (min1, max1), ...]</code> for each filtered property, respectively.</p> <ul> <li>Type: either Tuple[float, float] or List[Tuple[float, float]], optional</li> <li>Default: <code>(-1, 1)</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_soft_range","title":"<code>filter_soft_range</code>","text":"<p>If specified, objects will be faded in/out instead of abruptly shown/hidden.</p> <p>When the filtered value is outside of the bounds defined by <code>filter_soft_range</code> but still within the bounds defined by <code>filter_range</code>, the object will be rendered as \"faded\".</p> <ul> <li>Type: Tuple[float, float], optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_size","title":"<code>filter_transform_size</code>","text":"<p>When an object is \"faded\", manipulate its size so that it appears smaller or thinner. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_color","title":"<code>filter_transform_color</code>","text":"<p>When an object is \"faded\", manipulate its opacity so that it appears more translucent. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--get_filter_value","title":"<code>get_filter_value</code>","text":"<p>Accessor to retrieve the value for each object that it will be filtered by.</p> <ul> <li>Type:   GetFilterValueAccessor<ul> <li>If a scalar value is provided, it is used as the value for all objects.</li> <li>If an array is provided, each value in the array will be used as the value   for the object at the same row index.</li> </ul> </li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension.filter_size","title":"filter_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filter_size = tag(sync=True)\n</code></pre> <p>The size of the filter (number of columns to filter by).</p> <p>The data filter can show/hide data based on 1-4 numeric properties of each object.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default 1.</li> </ul>"},{"location":"api/layers/arc-layer/","title":"ArcLayer","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer","title":"lonboard.experimental.ArcLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render raised arcs joining pairs of source and target coordinates.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_height","title":"get_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_height = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Height color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_color","title":"get_source_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Source color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_position","title":"get_source_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Source position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_color","title":"get_target_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Target color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_position","title":"get_target_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Target position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_tilt","title":"get_tilt  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_tilt = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Use to tilt the arc to the side if you have multiple arcs with the same source and target positions.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The line width of each object, in units specified by <code>widthUnits</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.great_circle","title":"great_circle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>great_circle = tag(sync=True)\n</code></pre> <p>If <code>True</code>, create the arc along the shortest path on the earth surface.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.num_segments","title":"num_segments  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>num_segments = tag(sync=True)\n</code></pre> <p>The number of segments used to draw each arc.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>50</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait()\n</code></pre> <p>A GeoArrow table.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The scaling multiplier for the width of each line.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/base-layer/","title":"Base Classes","text":""},{"location":"api/layers/base-layer/#lonboard.BaseLayer","title":"lonboard.BaseLayer","text":"<p>             Bases: <code>BaseWidget</code></p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer","title":"lonboard.BaseArrowLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>Any Arrow-based layer should subclass from BaseArrowLayer</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/bitmap-layer/","title":"BitmapLayer","text":""},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer","title":"lonboard.BitmapLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The <code>BitmapLayer</code> renders a bitmap (e.g. PNG, JPEG, or WebP) at specified boundaries.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapLayer\n\nlayer = BitmapLayer(\n    image='https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/sf-districts.png',\n    bounds=[-122.5190, 37.7045, -122.355, 37.829]\n)\nm = Map(layer)\nm\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.bounds","title":"bounds  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>bounds = tag(sync=True)\n</code></pre> <p>The bounds of the image.</p> <p>Supported formats:</p> <pre><code>- Coordinates of the bounding box of the bitmap `[left, bottom, right, top]`\n- Coordinates of four corners of the bitmap, should follow the sequence of\n  `[[left, bottom], [left, top], [right, top], [right, bottom]]`.\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.image","title":"image  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>image = tag(sync=True)\n</code></pre> <p>The URL to an image to display.</p> <ul> <li>Type: <code>str</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/","title":"BitmapTileLayer","text":""},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer","title":"lonboard.BitmapTileLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The BitmapTileLayer renders image tiles (e.g. PNG, JPEG, or WebP) in the web mercator tiling system. Only the tiles visible in the current viewport are loaded and rendered.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapTileLayer\n\n# We set `max_requests &lt; 0` because `tile.openstreetmap.org` supports HTTP/2.\nlayer = BitmapTileLayer(\n    data=\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n    tile_size=256,\n    max_requests=-1,\n    min_zoom=0,\n    max_zoom=19,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.data","title":"data  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>data = tag(sync=True)\n</code></pre> <p>Either a URL template or an array of URL templates from which the tile data should be loaded.</p> <p>If the value is a string: a URL template. Substrings {x} {y} and {z}, if present, will be replaced with a tile's actual index when it is requested.</p> <p>If the value is an array: multiple URL templates. Each endpoint must return the same content for the same tile index. This can be used to work around domain sharding, allowing browsers to download more resources simultaneously. Requests made are balanced among the endpoints, based on the tile index.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extent","title":"extent  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extent = tag(sync=True)\n</code></pre> <p>The bounding box of the layer's data, in the form of <code>[min_x, min_y, max_x, max_y]</code>. If provided, the layer will only load and render the tiles that are needed to fill this box.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_cache_size","title":"max_cache_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_cache_size = tag(sync=True)\n</code></pre> <p>The maximum number of tiles that can be cached. The tile cache keeps loaded tiles in memory even if they are no longer visible. It reduces the need to re-download the same data over and over again when the user pan/zooms around the map, providing a smoother experience.</p> <p>If not supplied, the <code>max_cache_size</code> is calculated as 5 times the number of tiles in the current viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_requests","title":"max_requests  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_requests = tag(sync=True)\n</code></pre> <p>The maximum number of concurrent data fetches.</p> <p>If &lt;= 0, no throttling will occur, and <code>get_tile_data</code> may be called an unlimited number of times concurrently regardless of how long that tile is or was visible.</p> <p>If &gt; 0, a maximum of <code>max_requests</code> instances of <code>get_tile_data</code> will be called concurrently. Requests may never be called if the tile wasn't visible long enough to be scheduled and started. Requests may also be aborted (through the signal passed to <code>get_tile_data</code>) if there are more than <code>max_requests</code> ongoing requests and some of those are for tiles that are no longer visible.</p> <p>If <code>get_tile_data</code> makes fetch requests against an HTTP 1 web server, then max_requests should correlate to the browser's maximum number of concurrent fetch requests. For Chrome, the max is 6 per domain. If you use the data prop and specify multiple domains, you can increase this limit. For example, with Chrome and 3 domains specified, you can set max_requests=18.</p> <p>If the web server supports HTTP/2 (Open Chrome dev tools and look for \"h2\" in the Protocol column), then you can make an unlimited number of concurrent requests (and can set max_requests=-1). Note that this will request data for every tile, no matter how long the tile was visible, and may increase server load.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_zoom","title":"max_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_zoom = tag(sync=True)\n</code></pre> <p>The max zoom level of the layer's data. When overzoomed (i.e. <code>zoom &gt; max_zoom</code>), tiles from this level will be displayed.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.min_zoom","title":"min_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>min_zoom = tag(sync=True)\n</code></pre> <p>The min zoom level of the layer's data. When underzoomed (i.e. <code>zoom &lt; min_zoom</code>), the layer will not display any tiles unless <code>extent</code> is defined, to avoid issuing too many tile requests.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.refinement_strategy","title":"refinement_strategy  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>refinement_strategy = tag(sync=True)\n</code></pre> <p>How the tile layer refines the visibility of tiles.</p> <p>When zooming in and out, if the layer only shows tiles from the current zoom level, then the user may observe undesirable flashing while new data is loading. By setting <code>refinement_strategy</code> the layer can attempt to maintain visual continuity by displaying cached data from a different zoom level before data is available.</p> <p>This prop accepts one of the following:</p> <ul> <li><code>\"best-available\"</code>: If a tile in the current viewport is waiting for its data to   load, use cached content from the closest zoom level to fill the empty space. This   approach minimizes the visual flashing due to missing content.</li> <li><code>\"no-overlap\"</code>: Avoid showing overlapping tiles when backfilling with cached   content. This is usually favorable when tiles do not have opaque backgrounds.</li> <li> <p><code>\"never\"</code>: Do not display any tile that is not selected.</p> </li> <li> <p>Type: <code>str</code>, optional</p> </li> <li>Default: <code>\"best-available\"</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tile_size","title":"tile_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tile_size = tag(sync=True)\n</code></pre> <p>The pixel dimension of the tiles, usually a power of 2.</p> <p>Tile size represents the target pixel width and height of each tile when rendered. Smaller tile sizes display the content at higher resolution, while the layer needs to load more tiles to fill the same viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>512</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.zoom_offset","title":"zoom_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>zoom_offset = tag(sync=True)\n</code></pre> <p>This offset changes the zoom level at which the tiles are fetched. Needs to be an integer.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/heatmap-layer/","title":"HeatmapLayer","text":""},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer","title":"lonboard.HeatmapLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>HeatmapLayer</code> visualizes the spatial distribution of data.</p> <p>Example</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, HeatmapLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = HeatmapLayer.from_geopandas(gdf)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, HeatmapLayer\n\n# Example: A GeoParquet file with Point geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = HeatmapLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.aggregation","title":"aggregation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>aggregation = tag(sync=True)\n</code></pre> <p>Defines the type of aggregation operation</p> <p>Valid values are 'SUM', 'MEAN'.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>\"SUM\"</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.debounce_timeout","title":"debounce_timeout  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>debounce_timeout = tag(sync=True)\n</code></pre> <p>Interval in milliseconds during which changes to the viewport don't trigger aggregation.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>500</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.get_weight","title":"get_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_weight = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The weight of each object.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.intensity","title":"intensity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>intensity = tag(sync=True)\n</code></pre> <p>Value that is multiplied with the total weight at a pixel to obtain the final weight.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.radius_pixels","title":"radius_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_pixels = tag(sync=True)\n</code></pre> <p>Radius of the circle in pixels, to which the weight of an object is distributed.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>30</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.threshold","title":"threshold  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>threshold = tag(sync=True)\n</code></pre> <p>Ratio of the fading weight to the max weight, between <code>0</code> and <code>1</code>.</p> <p>For example, <code>0.1</code> affects all pixels with weight under 10% of the max.</p> <p>Ignored when <code>color_domain</code> is specified.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0.05</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.weights_texture_size","title":"weights_texture_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>weights_texture_size = tag(sync=True)\n</code></pre> <p>Specifies the size of weight texture.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>2048</code></li> </ul>"},{"location":"api/layers/path-layer/","title":"PathLayer","text":"<p>Screenshot from North America roads example</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer","title":"lonboard.PathLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PathLayer</code> renders lists of coordinate points as extruded polylines with mitering.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PathLayer\n\n# A GeoDataFrame with LineString or MultiLineString geometries\ngdf = gpd.GeoDataFrame()\nlayer = PathLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PathLayer\n\n# Example: A GeoParquet file with LineString or MultiLineString geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PathLayer(\n    table=table,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, extrude the path in screen space (width always faces the camera). If <code>False</code>, the width always faces up.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.cap_rounded","title":"cap_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>cap_rounded = tag(sync=True)\n</code></pre> <p>Type of caps. If <code>True</code>, draw round caps. Otherwise draw square caps.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of each path, in units specified by <code>width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.joint_rounded","title":"joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>True</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.miter_limit","title":"miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width. Only works if <code>jointRounded</code> is <code>False</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={LINESTRING, MULTILINESTRING})\n</code></pre> <p>A GeoArrow table with a LineString or MultiLineString column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum path width in pixels. This prop can be used to prevent the path from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum path width in pixels. This prop can be used to prevent the path from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The path width multiplier that multiplied to all paths.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/","title":"PointCloudLayer","text":""},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer","title":"lonboard.PointCloudLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PointCloudLayer</code> renders a point cloud with 3D positions, normals and colors.</p> <p>The <code>PointCloudLayer</code> can be more efficient at rendering large quantities of points than the <code>ScatterplotLayer</code>, but has fewer rendering options. In particular, you can have only one point size across all points in your data.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PointCloudLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = PointCloudLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    point_size=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_normal","title":"get_normal  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_normal = NormalAccessor(None, allow_none=True)\n</code></pre> <p>The normal of each object, in <code>[nx, ny, nz]</code>.</p> <ul> <li>Type: NormalAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the normal for all   points.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the normal for the point at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.point_size","title":"point_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>point_size = tag(sync=True)\n</code></pre> <p>Global radius of all points, in units specified by <code>size_units</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(\n    allowed_geometry_types={POINT}, allowed_dimensions={3}\n)\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/","title":"PolygonLayer","text":"<p>Screenshot from Overture Maps example</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer","title":"lonboard.PolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PolygonLayer</code> renders filled, stroked and/or extruded polygons.</p> <p>Note</p> <p>This layer is essentially a combination of a <code>PathLayer</code> and a <code>SolidPolygonLayer</code>. This has some overhead beyond a <code>SolidPolygonLayer</code>, so if you're looking for the maximum performance with large data, you may want to use a <code>SolidPolygonLayer</code> directly.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = PolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier.</p> <p>The final elevation is calculated by <code>elevationScale * getElevation(d)</code>. <code>elevationScale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons.</p> <p>Based on the elevations provided by the <code>getElevation</code> accessor.</p> <p>If set to <code>false</code>, all polygons will be flat, this generates less geometry and is faster than simply returning 0 from getElevation.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to draw a filled polygon (solid fill).</p> <p>Note that only the area between the outer polygon and any holes will be filled.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each polygon, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_joint_rounded","title":"line_joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>true</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_miter_limit","title":"line_miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width.</p> <p>Only works if <code>line_joint_rounded</code> is false.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the line from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the line from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>The line width multiplier that multiplied to all outlines of <code>Polygon</code> and <code>MultiPolygon</code> features if the <code>stroked</code> attribute is true.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Whether to draw an outline around the polygon (solid fill).</p> <p>Note that both the outer polygon as well the outlines of any holes will be drawn.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/scatterplot-layer/","title":"ScatterplotLayer","text":"<p>Screenshot from Ookla example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer","title":"lonboard.ScatterplotLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>ScatterplotLayer</code> renders circles at given coordinates.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer\n\n# A GeoDataFrame with Point or MultiPoint geometries\ngdf = gpd.GeoDataFrame()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, ScatterplotLayer\n\n# Example: A GeoParquet file with Point or MultiPoint geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = ScatterplotLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.antialiasing","title":"antialiasing  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>antialiasing = tag(sync=True)\n</code></pre> <p>If <code>True</code>, circles are rendered with smoothed edges. If <code>False</code>, circles are rendered with rough edges. Antialiasing can cause artifacts on edges of overlapping circles.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, rendered circles always face the camera. If <code>False</code> circles face up (i.e. are parallel with the ground plane).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Draw the filled area of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The filled color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the filled color for   all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the filled color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The outline color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the outline color   for all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the outline color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each object, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_radius","title":"get_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_radius = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The radius of each object, in units specified by <code>radius_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the radius for all objects.</li> <li>If an array is provided, each value in the array will be used as the radius   for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the stroke from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the stroke from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>A global line width multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_max_pixels","title":"radius_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum radius in pixels. This can be used to prevent the circle from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_min_pixels","title":"radius_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum radius in pixels. This can be used to prevent the circle from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_scale","title":"radius_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_scale = tag(sync=True)\n</code></pre> <p>A global radius multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_units","title":"radius_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_units = tag(sync=True)\n</code></pre> <p>The units of the radius, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Draw the outline of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT, MULTIPOINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/","title":"SolidPolygonLayer","text":""},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer","title":"lonboard.SolidPolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>SolidPolygonLayer</code> renders filled and/or extruded polygons.</p> <p>Note</p> <p>This layer is similar to the <code>PolygonLayer</code> but will not render an outline around polygons. In most cases, you'll want to use the <code>PolygonLayer</code> directly, but for very large datasets not drawing the outline can significantly improve performance, in which case you may want to use this layer.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, SolidPolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = SolidPolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier. The final elevation is calculated by <code>elevation_scale * get_elevation(d)</code>. <code>elevation_scale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons (based on the elevations provided by the <code>get_elevation</code> accessor'). If set to <code>False</code>, all polygons will be flat, this generates less geometry and is faster than simply returning <code>0</code> from <code>get_elevation</code>.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to fill the polygons (based on the color provided by the <code>get_fill_color</code> accessor).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/text-layer/","title":"TextLayer","text":""},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer","title":"lonboard.experimental.TextLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render text labels at given coordinates.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.background_padding","title":"background_padding  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>background_padding = tag(sync=True)\n</code></pre> <p>The padding of the background.</p> <ul> <li>If an array of 2 is supplied, it is interpreted as <code>[padding_x, padding_y]</code> in   pixels.</li> <li>If an array of 4 is supplied, it is interpreted as <code>[padding_left, padding_top,   padding_right, padding_bottom]</code> in pixels.</li> </ul> <p>default [0, 0, 0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>true</code>, the text always faces camera. Otherwise the text faces up (z).</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.character_set","title":"character_set  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>character_set = tag(sync=True)\n</code></pre> <p>Specifies a list of characters to include in the font. If set to 'auto', will be automatically generated from the data set.</p> <p>default (ASCII characters 32-128)</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_family","title":"font_family  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_family = tag(sync=True)\n</code></pre> <p>CSS font family</p> <p>default 'Monaco, monospace'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_settings","title":"font_settings  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_settings = tag(sync=True)\n</code></pre> <p>Advance options for fine tuning the appearance and performance of the generated shared <code>fontAtlas</code>.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_weight","title":"font_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_weight = tag(sync=True)\n</code></pre> <p>CSS font weight</p> <p>default 'normal'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_alignment_baseline","title":"get_alignment_baseline  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_alignment_baseline = tag(sync=True)\n</code></pre> <p>Vertical alignment accessor</p> <p>default 'center'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_angle","title":"get_angle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_angle = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label rotation accessor, in degrees</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_background_color","title":"get_background_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_background_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Background color accessor.</p> <p>default [255, 255, 255, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_color","title":"get_border_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Border color accessor.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_width","title":"get_border_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Border width accessor.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Label color accessor</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_pixel_offset","title":"get_pixel_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_pixel_offset = tag(sync=True)\n</code></pre> <p>Label offset from the anchor position, [x, y] in pixels</p> <p>default [0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_size","title":"get_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_size = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label size accessor</p> <p>default 32</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text","title":"get_text  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text = TextAccessor(None, allow_none=True)\n</code></pre> <p>Label text accessor</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text_anchor","title":"get_text_anchor  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text_anchor = tag(sync=True)\n</code></pre> <p>Horizontal alignment accessor</p> <p>default 'middle'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.line_height","title":"line_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_height = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the line height.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.max_width","title":"max_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_width = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the width limit of a string.</p> <p>If specified, when the text is longer than the width limit, it will be wrapped into multiple lines using the strategy of <code>wordBreak</code>.</p> <p>default -1</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_color","title":"outline_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_color = tag(sync=True)\n</code></pre> <p>Color of outline around the text, in <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_width","title":"outline_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_width = tag(sync=True)\n</code></pre> <p>Width of outline around the text, relative to the text size. Only effective if <code>fontSettings.sdf</code> is <code>true</code>.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_max_pixels","title":"size_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_min_pixels","title":"size_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_scale","title":"size_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_scale = tag(sync=True)\n</code></pre> <p>Text size multiplier.</p> <ul> <li>Type: <code>float</code>.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the size, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. default 'pixels'. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.word_break","title":"word_break  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>word_break = tag(sync=True)\n</code></pre> <p>Available options are <code>break-all</code> and <code>break-word</code>. A valid <code>maxWidth</code> has to be provided to use <code>wordBreak</code>.</p> <p>default 'break-word'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"ecosystem/","title":"Ecosystem","text":"<p>Lonboard aims to both integrate closely in the existing Python geospatial ecosystem and be future proof for the next generation of Python geospatial libraries.</p> <p>The pages in this section outline various integrations. If there's a library you think Lonboard should integrate with or you want to document existing compatibility, create an issue to discuss.</p>"},{"location":"ecosystem/geoarrow/","title":"GeoArrow","text":"<p>GeoArrow is an in-memory data structure for storing vector geospatial data and associated attributes. Lonboard uses GeoArrow internally and is the primary reason why Lonboard is fast.</p> <p>There's a burgeoning ecosystem of Python libraries that use GeoArrow internally. Creating Lonboard <code>Layer</code> objects from GeoArrow tables is the fastest way to visualize data, as no conversions are needed on the Python side.</p>"},{"location":"ecosystem/geoarrow/#geoarrow-rust","title":"geoarrow-rust","text":"<p>geoarrow-rust is a Python library implementing the GeoArrow specification with efficient spatial operations. This library has \"rust\" in the name because it is implemented based on the GeoArrow Rust implementation.</p> <pre><code>from geoarrow.rust.core import GeoTable, read_geojson\nfrom lonboard import Map, PathLayer\n\npath = \"/path/to/file.geojson\"\ngeo_table = read_geojson(path)\n\n# Assuming the GeoJSON contains LineString or MultiLineString data\nlayer = PathLayer(table=geo_table)\nm = Map(layer)\nm\n</code></pre> <p>Refer to the geoarrow-rust documentation for more information.</p>"},{"location":"ecosystem/geopandas/","title":"GeoPandas","text":"<p>GeoPandas extends the Pandas data frame library to allow spatial operations on geospatial data.</p> <p>All relevant Lonboard layer classes have a <code>from_geopandas</code> method for <code>GeoDataFrame</code> input.</p> <p>Some layer types, such as <code>BitmapLayer</code>, don't have a <code>from_geopandas</code> method because the rendering isn't relevant to GeoPandas (i.e. GeoPandas doesn't store image data).</p>"},{"location":"ecosystem/geopandas/#example","title":"Example","text":"<pre><code>import geodatasets\nimport geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# New York City boroughs\ngdf = gpd.read_file(geodatasets.get_path('nybb'))\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"ecosystem/jupyter-widgets/","title":"Jupyter Widgets","text":"<p>Jupyter Widgets are interactive browser controls for Jupyter notebooks. While Lonboard's classes are themselves widgets, Jupyter Widgets' design means that widgets integrate with other widgets seamlessly.</p> <p>For example, the <code>ipywidgets</code> Python library includes many core widgets like sliders, dropdowns, color pickers, and file upload elements. Then you can link widgets together. This means that your widget-based slider or chart can create events that change the display of a Lonboard map based on user input events.</p>"},{"location":"ecosystem/panel/","title":"Panel","text":"<p>Panel is a tool to build interactive web applications and dashboards using Python code.</p> <p>Panel has been reported to work with Lonboard. However, it appears that Panel does not support reactive updates in the same way that Shiny does, so the map will necessarily be recreated from scratch on every update.</p>"},{"location":"ecosystem/panel/#example","title":"Example","text":"<p>This example was written by @MarcSkovMadsen in issue #262.</p> <pre><code>\"\"\"Panel data app based on https://developmentseed.org/lonboard/latest/examples/north-america-roads/\"\"\"\n# pip install panel colorcet ipywidgets_bokeh geopandas palettable lonboard\nimport colorcet as cc\nimport geopandas as gpd\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom palettable.palette import Palette\n\nimport panel as pn\n\nurl = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\npath = \"ne_10m_roads_north_america.zip\"\n\ntry:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=path, engine=\"pyogrio\"\n    )\nexcept:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=url, engine=\"pyogrio\"\n    )\n\nstate_options = sorted(state for state in gdf[\"state\"].unique() if state)\n\n\ndef to_rgb(hex: str) -&gt; list:\n    h = hex.strip(\"#\")\n    return list(int(h[i : i + 2], 16) for i in (0, 2, 4))\n\n\ndef to_palette(cmap) -&gt; Palette:\n    \"\"\"Returns the ColorCet colormap as a palettable Palette\"\"\"\n    colors = [to_rgb(item) for item in cmap]\n    return Palette(name=\"colorcet\", map_type=\"colorcet\", colors=colors)\n\n\ndef create_map(state=\"California\", cmap=cc.fire, alpha=0.8):\n    palette = to_palette(cmap)\n    data = gdf[gdf[\"state\"] == state]\n    layer = PathLayer.from_geopandas(data, width_min_pixels=0.8)\n    normalized_scale_rank = (data[\"scalerank\"] - 3) / 9\n    layer.get_color = apply_continuous_cmap(normalized_scale_rank, palette, alpha=alpha)\n    map_ = Map(layer, _height=650)\n    return map_\n\n\ndescription = \"\"\"# lonboard\n\nA Python library for **fast, interactive geospatial vector data visualization** in Jupyter (and Panel).\n\nBy utilizing new technologies like `GeoArrow` and `GeoParquet` in conjunction with GPU-based map rendering, lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.\"\"\"\n\n\n# THE PANEL APP\npn.extension(\"ipywidgets\")\nstate = pn.widgets.Select(\n    value=\"California\",\n    options=state_options,\n    width=150,\n    name=\"State\",\n    sizing_mode=\"stretch_width\",\n)\ncmap = pn.widgets.ColorMap(\n    value=cc.fire,\n    options=cc.palette,\n    ncols=3,\n    swatch_width=100,\n    name=\"cmap by Colorcet\",\n    sizing_mode=\"stretch_width\",\n)\nalpha = pn.widgets.FloatSlider(\n    value=0.8, start=0, end=1, name=\"Alpha\", min_width=100, sizing_mode=\"stretch_width\"\n)\nlogo = pn.pane.Image(\n    \"https://github.com/developmentseed/lonboard/raw/main/assets/dalle-lonboard.jpg\"\n)\ndef title(state):\n    return f\"# North America Roads: {state}\"\n\nsettings = pn.Column(state, cmap, alpha)\ndescription = pn.Column(pn.pane.Markdown(description, margin=5), logo)\ncomponent = pn.Column(\n    pn.bind(title, state=state),\n    pn.panel(\n        pn.bind(create_map, state=state, cmap=cmap, alpha=alpha.param.value_throttled),\n        sizing_mode=\"stretch_both\",\n    ),\n    sizing_mode=\"stretch_both\",\n)\npn.template.FastListTemplate(\n    logo=\"https://panel.holoviz.org/_static/logo_horizontal_dark_theme.png\",\n    title=\"Works with LonBoard\",\n    main=[component],\n    sidebar=[description, settings],\n).servable()\n</code></pre>"},{"location":"ecosystem/shiny/","title":"Shiny","text":"<p>Shiny is a tool to build interactive web applications and dashboards using Python code. Shiny integrates with Jupyter Widgets, which means that Lonboard is supported out-of-the-box.</p> <p>Pay attention to the \"Efficient updates\" section of the Shiny documentation. This is the most efficient way to create a reactive map. Take care to not recreate the <code>Map</code> widget from scratch on updates, as that will send data from Python to the browser anew.</p>"},{"location":"ecosystem/shiny/#example","title":"Example","text":"<pre><code>import geopandas as gpd\nfrom shiny import reactive\nfrom shiny.express import input, ui\nfrom shinywidgets import render_widget\n\nfrom lonboard import Map, ScatterplotLayer\n\ncolors = {\n    \"Red\": [200, 0, 0],\n    \"Green\": [0, 200, 0],\n    \"Blue\": [0, 0, 200],\n}\n\nui.input_select(\"color_select\", \"Color\", choices=list(colors.keys()))\n\n\n@render_widget\ndef map():\n    gdf = gpd.read_file(gpd.datasets.get_path(\"naturalearth_cities\"))\n    layer = ScatterplotLayer.from_geopandas(gdf, radius_min_pixels=2)\n    return Map(layer)\n\n\n@reactive.effect\ndef set_fill_color():\n    map.widget.layers[0].get_fill_color = colors[input.color_select()]\n</code></pre>"},{"location":"examples/","title":"Examples","text":"<ul> <li>Speedtest data  using <code>ScatterplotLayer</code></li> <li>North America roads  using <code>PathLayer</code></li> <li>Global boundaries  using <code>PolygonLayer</code></li> <li>U.S. County-to-County Migration  using <code>ArcLayer</code> and <code>BrushingExtension</code></li> <li>Scatterplot with GPU data filtering  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code></li> <li>Color picker integration  using <code>SolidPolygonLayer</code></li> <li>JupyterLab Sidecar integration  using <code>ScatterplotLayer</code> and <code>JupyterLab Sidecar</code></li> <li>Motor Vehicle Crashes in NYC  using <code>ScatterplotLayer</code></li> <li>Rivers in Asia  using <code>PathLayer</code></li> </ul>"},{"location":"examples/#third-party-showcase","title":"Third-party showcase","text":"<p>These examples are maintained by external contributors.</p> <ul> <li>American Community Survey exploration  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code> by @jaanli.</li> </ul>"},{"location":"examples/data-filter-extension/","title":"DataFilterExtension","text":"<p>As with the <code>ScatterplotLayer</code> example, this example will use data from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry.</p> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom ipywidgets import FloatRangeSlider, jsdlink\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.controls import MultiRangeSlider\nfrom lonboard.layer_extension import DataFilterExtension\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from ipywidgets import FloatRangeSlider, jsdlink from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap from lonboard.controls import MultiRangeSlider from lonboard.layer_extension import DataFilterExtension In\u00a0[3]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\nlocal_path = Path(\"data-filter-extension.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    non_geom_columns = [col for col in columns if col != \"tile\"]\n    gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")\n    gdf.to_parquet(local_path)\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" local_path = Path(\"data-filter-extension.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     non_geom_columns = [col for col in columns if col != \"tile\"]     gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")     gdf.to_parquet(local_path) <p>Taking a quick look at this data, we see that we have 3.2 million records.</p> In\u00a0[4]: Copied! <pre>gdf\n</pre> gdf Out[4]: avg_d_kbps avg_u_kbps avg_lat_ms devices geometry 0 5983 7886 68 1 POINT (-160.01862 70.63722) 1 3748 5841 78 2 POINT (-160.04059 70.63357) 2 3364 6200 78 2 POINT (-160.04059 70.63175) 3 2381 2328 86 1 POINT (-160.03510 70.63357) 4 3047 5356 75 1 POINT (-160.03510 70.63175) ... ... ... ... ... ... 3231240 19528 3200 68 1 POINT (169.81842 -46.29571) 3231241 15693 10359 56 1 POINT (169.81293 -46.30710) 3231242 26747 9674 58 2 POINT (169.66461 -46.42082) 3231243 67995 13564 63 1 POINT (169.65912 -46.45110) 3231244 1230 1798 55 1 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 5 columns</p> <p>Since we want to cross-filter on three attributes at once, we create a <code>DataFilterExtension</code> instance with <code>filter_size=3</code>.</p> In\u00a0[5]: Copied! <pre>filter_extension = DataFilterExtension(filter_size=3)\n</pre> filter_extension = DataFilterExtension(filter_size=3) <p>Similar to the <code>ScatterplotLayer</code> example notebook, we create a normalized download speed to use for a per-point color and radius.</p> In\u00a0[6]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\nnormalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound)\nfill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10)\nradius = normalized_download_speed * 200\n</pre> min_bound = 5000 max_bound = 50000 normalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound) fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10) radius = normalized_download_speed * 200 <p>Next we prepare the columns that we want to filter on. In this case, we'll filter on the download speed, upload speed, and latency values. These need to be passed in as a single two-dimensional numpy array, so we use <code>numpy.column_stack</code>. Only numeric arrays can be used for GPU-based filtering.</p> <p>We also define initial filter ranges for each of these columns.</p> In\u00a0[7]: Copied! <pre>filter_values = np.column_stack(\n    [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]]\n)\ninitial_filter_range = [\n    [10_000, 50_000],\n    [1000, 10_000],\n    [0, 100],\n]\n</pre> filter_values = np.column_stack(     [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]] ) initial_filter_range = [     [10_000, 50_000],     [1000, 10_000],     [0, 100], ] <p>Next we create a <code>ScatterplotLayer</code> with our desired attributes. Keep in mind that you must pass the <code>DataFilterExtension</code> instance to the <code>extensions</code> parameter in order to activate its functionality on the layer.</p> In\u00a0[8]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[filter_extension],\n    get_fill_color=fill_color,\n    get_radius=radius,\n    get_filter_value=filter_values,\n    filter_range=initial_filter_range,\n    radius_units=\"meters\",\n    radius_min_pixels=0.1,\n)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(     gdf,     extensions=[filter_extension],     get_fill_color=fill_color,     get_radius=radius,     get_filter_value=filter_values,     filter_range=initial_filter_range,     radius_units=\"meters\",     radius_min_pixels=0.1, ) m = Map(layer) m Out[8]: <p>Only the points that are within all of our initial filter ranges are shown, but we'd like something more interactive. Let's connect sliders to the map.</p> <p>Here we create a separate slider for each attribute and then connect them using a <code>MultiRangeSlider</code>. This defines the slider element but it won't be connected to the map until we use <code>ipywidgets.jsdlink</code> later.</p> In\u00a0[9]: Copied! <pre>download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \")\nupload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \")\nlatency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \")\nmulti_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider])\nmulti_slider\n</pre> download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \") upload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \") latency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \") multi_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider]) multi_slider Out[9]: <p>In order to propagate the changes in our sliders to the <code>ScatterplotLayer</code> instance, we use <code>jsdlink</code>. See Linking Widgets in the ipywidgets documentation for more info.</p> In\u00a0[10]: Copied! <pre>_ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\"))\n</pre> _ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\")) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/data-filter-extension/#datafilterextension","title":"DataFilterExtension\u00b6","text":"<p>The <code>DataFilterExtension</code> adds GPU-based data filtering functionalities to layers, allowing the layer to show/hide objects based on user-defined properties.</p> <p>In this demo we'll use the <code>DataFilterExtension</code> to filter millions of points based on three numeric attributes.</p>"},{"location":"examples/data-filter-extension/#imports","title":"Imports\u00b6","text":""},{"location":"examples/data-filter-extension/#fetch-data","title":"Fetch data\u00b6","text":"<p>We use the same data as the <code>ScatterplotLayer</code> example except that here we download extra columns from the dataset to use for filtering.</p> <p>Columns:</p> <ul> <li><code>avg_d_kbps</code>: the average download speed for that data point in kbps</li> <li><code>avg_u_kbps</code>: the average upload speed for that data point in kbps</li> <li><code>avg_lat_ms</code>: the average latency for that data point in milliseconds</li> <li><code>devices</code>: the number of unique devices contributing tests in the tile.</li> <li><code>tile</code>: the WKT string representing a given zoom-16 Web Mercator tile</li> </ul> <p>We cache the downloaded file locally to <code>data-filter-extension.parquet</code> so that the second time running this notebook will be faster.</p>"},{"location":"examples/global-boundaries/","title":"Municipal Boundaries","text":"In\u00a0[1]: Copied! <pre>! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm\n</pre> ! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm <pre>\n[notice] A new release of pip is available: 23.3.2 -&gt; 24.0\n[notice] To update, run: pip install --upgrade pip\n</pre> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport requests\nfrom tqdm import tqdm\n\nfrom lonboard import Map, PolygonLayer\n</pre> from pathlib import Path  import geopandas as gpd import requests from tqdm import tqdm  from lonboard import Map, PolygonLayer <p>The URL for a large data file of CGAZ at ADM1 Districts level:</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\"\nfilepath = \"geoBoundariesCGAZ_ADM1.geojson\"\n</pre> url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\" filepath = \"geoBoundariesCGAZ_ADM1.geojson\" <p>We'll go ahead and fetch the GeoJSON data in that format. If this notebook has already been run once and the file exists locally, we'll skip downloading it again.</p> In\u00a0[4]: Copied! <pre>if not Path(filepath).exists():\n    # Make a GET request to download the file\n    # Streaming, so we can iterate over the response and create a progress bar for this large file\n    response = requests.get(url, stream=True)\n\n    # Sizes in bytes.\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 1024\n\n    with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:\n        with open(filepath, \"wb\") as file:\n            for data in response.iter_content(block_size):\n                progress_bar.update(len(data))\n                file.write(data)\n\n    if total_size != 0 and progress_bar.n != total_size:\n        raise RuntimeError(\"Could not download file\")\n</pre> if not Path(filepath).exists():     # Make a GET request to download the file     # Streaming, so we can iterate over the response and create a progress bar for this large file     response = requests.get(url, stream=True)      # Sizes in bytes.     total_size = int(response.headers.get(\"content-length\", 0))     block_size = 1024      with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:         with open(filepath, \"wb\") as file:             for data in response.iter_content(block_size):                 progress_bar.update(len(data))                 file.write(data)      if total_size != 0 and progress_bar.n != total_size:         raise RuntimeError(\"Could not download file\") In\u00a0[5]: Copied! <pre>gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)\n\n# Keep columns necessary for our visualization\ncols = [\"shapeName\", \"shapeID\", \"geometry\"]\ngdf = gdf[cols]\n</pre> gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)  # Keep columns necessary for our visualization cols = [\"shapeName\", \"shapeID\", \"geometry\"] gdf = gdf[cols] <p>We can take a quick look at the shape of this data:</p> In\u00a0[6]: Copied! <pre>gdf.head(10)\n</pre> gdf.head(10) Out[6]: shapeName shapeID geometry 0 Kandahar 12653393B40111500734429 POLYGON ((65.24153 32.28630, 65.12687 32.03679... 1 Zabul 12653393B56617740339660 POLYGON ((67.60666 31.44378, 67.70162 31.51373... 2 Uruzgan 12653393B46006342616872 POLYGON ((66.27519 32.42550, 66.35907 32.60925... 3 Daykundi 12653393B78791504725813 POLYGON ((66.76157 33.25547, 66.92827 33.59694... 4 Ghanzi 12653393B29313712249365 POLYGON ((68.06945 32.04564, 68.06011 32.45619... 5 Paktika 12653393B93572570471663 MULTIPOLYGON (((68.10797 31.74752, 68.10807 31... 6 Khost 12653393B66876919073955 POLYGON ((69.50619 33.02954, 69.50620 33.02954... 7 Paktia 12653393B16859990709200 POLYGON ((68.76070 33.41986, 68.95811 33.18136... 8 Logar 12653393B769191277822 POLYGON ((68.78347 33.62259, 68.99623 33.62635... 9 Wardak 12653393B94990201402617 POLYGON ((68.68691 33.72585, 68.86023 34.01301... <p>Without doing any simplification, the geometries in this dataset contain nearly 9 million coordinates (You can verify this with <code>shapely.get_num_coordinates(gdf[\"geometry\"]).sum()</code>).</p> <p>On recent computers, Lonboard should be able to visualize the entire dataset, but to ensure this dataset works on all computers, we'll select only rows whose geometries are at least partially in the eastern hemisphere:</p> In\u00a0[7]: Copied! <pre>eastern_hemisphere = gdf.cx[0:180, -90:90]\n</pre> eastern_hemisphere = gdf.cx[0:180, -90:90] In\u00a0[8]: Copied! <pre>layer = PolygonLayer.from_geopandas(\n    eastern_hemisphere,\n    get_line_width=20,  # width in default units (meters)\n    line_width_min_pixels=0.2,  # minimum width when zoomed out\n    get_fill_color=[204, 251, 254],  # light blue\n    get_line_color=[37, 36, 34],  # dark border color\n)\n</pre> layer = PolygonLayer.from_geopandas(     eastern_hemisphere,     get_line_width=20,  # width in default units (meters)     line_width_min_pixels=0.2,  # minimum width when zoomed out     get_fill_color=[204, 251, 254],  # light blue     get_line_color=[37, 36, 34],  # dark border color ) In\u00a0[9]: Copied! <pre>m = Map(layer)\nm\n</pre> m = Map(layer) m Out[9]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/global-boundaries/#municipal-boundaries","title":"Municipal Boundaries\u00b6","text":"<p>This example will use data from GeoBoundaries.org's data set of Comprehensive Global Administrative Zones (CGAZ).</p> <p>CGAZ is a set of global composites for administrative boundaries. There are two important distinctions between our global product and individual country downloads. First, extensive simplification is performed to ensure that file sizes are small enough to be used in most traditional desktop software. Second, disputed areas are removed and replaced with polygons following US Department of State definitions. Attribution: www.geoboundaries.org</p> <p>Note: This notebook incurs downloading a 360MB file. To successfully follow this example, please ensure sufficient storage space. The time to download this data will vary based on internet connection speed. We've added a download progress bar to the data retrieval section of this notebook to allow you to view progress and a time estimate for downloading the visualization data.</p>"},{"location":"examples/global-boundaries/#imports","title":"Imports\u00b6","text":""},{"location":"examples/global-boundaries/#visualize-global-boundaries","title":"Visualize Global Boundaries\u00b6","text":"<p>Let's visualize this dataset.</p> <p>We'll start by loading the data into GeoPandas. It's recommended to use the newer <code>pyogrio</code> library for faster data loading.</p>"},{"location":"examples/global-boundaries/#visualizing-the-data","title":"Visualizing the data\u00b6","text":"<p>We can use a <code>PolygonLayer</code> to customize the rendering of this data.</p>"},{"location":"examples/internet-speeds/","title":"Speedtest Data (ScatterplotLayer)","text":"In\u00a0[1]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>The URL for a single data file for mobile network speeds in the first quarter of 2019:</p> In\u00a0[2]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" <p>The data used in this example is relatively large. In the cell below, we cache the downloading and preparation of the dataset so that it's faster to run this notebook the second time.</p> <p>We fetch two columns \u2014 <code>avg_d_kbps</code> and <code>tile</code> \u2014 from this data file directly from AWS. The <code>pd.read_parquet</code> command will perform a network request for these columns from the data file, so it may take a while on a slow network connection. <code>avg_d_kbps</code> is the average download speed for that data point in kilobits per second. <code>tile</code> is the WKT string representing a given zoom-16 Web Mercator tile.</p> <p>The <code>tile</code> column contains strings representing WKT-formatted geometries. We need to parse those strings into geometries. Then for simplicity we'll convert into their centroids.</p> In\u00a0[3]: Copied! <pre>local_path = Path(\"internet-speeds.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')\n    gdf.to_parquet(local_path)\n</pre> local_path = Path(\"internet-speeds.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')     gdf.to_parquet(local_path) <p>We can take a quick look at this data:</p> In\u00a0[4]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[4]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) <p>To ensure that this demo is snappy on most computers, we'll filter to a bounding box over Europe.</p> <p>If you're on a recent computer, feel free to comment out the next line.</p> In\u00a0[5]: Copied! <pre>gdf = gdf.cx[-11.83:25.5, 34.9:59]\n</pre> gdf = gdf.cx[-11.83:25.5, 34.9:59] <p>Even this filtered data frame still has 800,000 rows, so it's still a lot of data to explore:</p> In\u00a0[6]: Copied! <pre>gdf\n</pre> gdf Out[6]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) ... ... ... 3231240 19528 POINT (169.81842 -46.29571) 3231241 15693 POINT (169.81293 -46.30710) 3231242 26747 POINT (169.66461 -46.42082) 3231243 67995 POINT (169.65912 -46.45110) 3231244 1230 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 2 columns</p> <p>To render point data, first create a <code>ScatterplotLayer</code> and then add it to a <code>Map</code> object:</p> In\u00a0[7]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) m = Map(layer) m Out[7]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\navg_d_kbps: uint32\ngeometry: fixed_size_list&lt;item: double&gt;[2]\u2026</pre> <p>We can look at the documentation for <code>ScatterplotLayer</code> to see what other rendering options it allows. Let's set the fill color to something other than black:</p> In\u00a0[8]: Copied! <pre>layer.get_fill_color = [0, 0, 200, 200]\n</pre> layer.get_fill_color = [0, 0, 200, 200] <p>Blue is pretty, but the map would be more informative if we colored each point by a relevant characteristic. In this case, we have the download speed associated with each location, so let's use that!</p> <p>Here we compute a linear statistic for the download speed. Given a minimum bound of <code>5000</code> and a maximum bound of <code>50,000</code> the normalized speed is linearly scaled to between 0 and 1.</p> In\u00a0[9]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\ndownload_speed = gdf['avg_d_kbps']\nnormalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound)\n</pre> min_bound = 5000 max_bound = 50000 download_speed = gdf['avg_d_kbps'] normalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound) <p><code>normalized_download_speed</code> is now linearly scaled based on the bounds provided above. Keep in mind that the input range of the colormap is 0-1. So any values that are below 0 will receive the left-most color in the colormap, while any values above 1 will receive the right-most color in the colormap.</p> In\u00a0[10]: Copied! <pre>normalized_download_speed\n</pre> normalized_download_speed Out[10]: <pre>0          0.021844\n1         -0.027822\n2         -0.036356\n3         -0.058200\n4         -0.043400\n             ...   \n3231240    0.322844\n3231241    0.237622\n3231242    0.483267\n3231243    1.399889\n3231244   -0.083778\nName: avg_d_kbps, Length: 3231245, dtype: float64</pre> <p>We can use any colormap provided by the <code>palettable</code> package. Let's inspect the <code>BrBG_10</code> diverging colormap below:</p> In\u00a0[11]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap Out[11]: BrBG  underbad over  <p>Now let's apply the colormap on <code>normalized_download_speed</code> using a helper provided by <code>lonboard</code>. We can set it on <code>layer.get_fill_color</code> to update the existing colors.</p> In\u00a0[12]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7)\n</pre> layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7) <p>After running the above cell, you should see the map above update with a different color per point!</p> <p>We can pass an array into any of the \"accessors\" supported by the layer (this is any attribute that starts with <code>get_*</code>).</p> <p>For demonstration purposes, let's also set <code>get_radius</code> to <code>normalized_download_speed</code>.</p> In\u00a0[13]: Copied! <pre>layer.get_radius = normalized_download_speed * 200\nlayer.radius_units = \"meters\"\nlayer.radius_min_pixels = 0.5\n</pre> layer.get_radius = normalized_download_speed * 200 layer.radius_units = \"meters\" layer.radius_min_pixels = 0.5 <p>After running the above cell, you should see the map updated to have a different radius per point!</p>"},{"location":"examples/internet-speeds/#speedtest-data-scatterplotlayer","title":"Speedtest Data (<code>ScatterplotLayer</code>)\u00b6","text":"<p>This example will use data collected from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry. From the AWS page:</p> <p>Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (35MB download).</p>"},{"location":"examples/internet-speeds/#imports","title":"Imports\u00b6","text":""},{"location":"examples/internet-speeds/#fetch-data","title":"Fetch data\u00b6","text":""},{"location":"examples/migration/","title":"U.S. County-to-County Migration","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport requests\nimport shapely\nfrom matplotlib.colors import Normalize\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.experimental import ArcLayer\nfrom lonboard.layer_extension import BrushingExtension\n</pre> import geopandas as gpd import numpy as np import pandas as pd import pyarrow as pa import requests import shapely from matplotlib.colors import Normalize  from lonboard import Map, ScatterplotLayer from lonboard.experimental import ArcLayer from lonboard.layer_extension import BrushingExtension <p>Fetch the data from the version in the <code>deck.gl-data</code> repository.</p> In\u00a0[2]: Copied! <pre>url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\"\nr = requests.get(url)\nsource_data = r.json()\n</pre> url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\" r = requests.get(url) source_data = r.json() <p>The following cell may be a little hard to follow, but what it's doing is taking the raw data, which represents a graph) of the data and normalizing it to a table structure where each row represents one \"arc\" between a source and target county.</p> <p>This is ported from the original JavaScript here.</p> In\u00a0[3]: Copied! <pre>arcs = []\ntargets = []\nsources = []\npairs = {}\n\nfeatures = source_data[\"features\"]\nfor i, county in enumerate(features):\n    flows = county[\"properties\"][\"flows\"]\n    target_centroid = county[\"properties\"][\"centroid\"]\n    total_value = {\n        \"gain\": 0,\n        \"loss\": 0,\n    }\n\n    for to_id, value in flows.items():\n        if value &gt; 0:\n            total_value[\"gain\"] += value\n        else:\n            total_value[\"loss\"] += value\n\n        # If number is too small, ignore it\n        if abs(value) &lt; 50:\n            continue\n\n        pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))\n        source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]\n        gain = np.sign(flows[to_id])\n\n        # add point at arc source\n        sources.append(\n            {\n                \"position\": source_centroid,\n                \"target\": target_centroid,\n                \"name\": features[int(to_id)][\"properties\"][\"name\"],\n                \"radius\": 3,\n                \"gain\": -gain,\n            }\n        )\n        # eliminate duplicate arcs\n        if pair_key in pairs.keys():\n            continue\n\n        pairs[pair_key] = True\n\n        if gain &gt; 0:\n            arcs.append(\n                {\n                    \"target\": target_centroid,\n                    \"source\": source_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n        else:\n            arcs.append(\n                {\n                    \"target\": source_centroid,\n                    \"source\": target_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n\n    # add point at arc target\n    targets.append(\n        {\n            **total_value,\n            \"position\": [target_centroid[0], target_centroid[1], 10],\n            \"net\": total_value[\"gain\"] + total_value[\"loss\"],\n            \"name\": county[\"properties\"][\"name\"],\n        }\n    )\n\n# sort targets by radius large -&gt; small\ntargets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True)\nnormalizer = Normalize(0, abs(targets[0][\"net\"]))\n</pre> arcs = [] targets = [] sources = [] pairs = {}  features = source_data[\"features\"] for i, county in enumerate(features):     flows = county[\"properties\"][\"flows\"]     target_centroid = county[\"properties\"][\"centroid\"]     total_value = {         \"gain\": 0,         \"loss\": 0,     }      for to_id, value in flows.items():         if value &gt; 0:             total_value[\"gain\"] += value         else:             total_value[\"loss\"] += value          # If number is too small, ignore it         if abs(value) &lt; 50:             continue          pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))         source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]         gain = np.sign(flows[to_id])          # add point at arc source         sources.append(             {                 \"position\": source_centroid,                 \"target\": target_centroid,                 \"name\": features[int(to_id)][\"properties\"][\"name\"],                 \"radius\": 3,                 \"gain\": -gain,             }         )         # eliminate duplicate arcs         if pair_key in pairs.keys():             continue          pairs[pair_key] = True          if gain &gt; 0:             arcs.append(                 {                     \"target\": target_centroid,                     \"source\": source_centroid,                     \"value\": flows[to_id],                 }             )         else:             arcs.append(                 {                     \"target\": source_centroid,                     \"source\": target_centroid,                     \"value\": flows[to_id],                 }             )      # add point at arc target     targets.append(         {             **total_value,             \"position\": [target_centroid[0], target_centroid[1], 10],             \"net\": total_value[\"gain\"] + total_value[\"loss\"],             \"name\": county[\"properties\"][\"name\"],         }     )  # sort targets by radius large -&gt; small targets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True) normalizer = Normalize(0, abs(targets[0][\"net\"])) <p>We define some color constants, as well as a color lookup array.</p> <p>A nice trick in numpy is that if you have a two-dimensional array like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184]\n]\n</pre> <p>you can perform a lookup based on the index to transform data from one dimensionality to another. In this case, we'll use <code>0</code> and <code>1</code> \u2014 the two available indexes of the array's first dimension \u2014 to create an array of colors.</p> <p>So when we call <code>COLORS[colors_lookup]</code> that creates an output array of something like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184],\n    [166,   3,   3],\n    [166,   3,   3]\n]\n</pre> <p>equal to the number of rows in our dataset. We can then pass this to any parameter that accepts a ColorAccessor.</p> In\u00a0[4]: Copied! <pre># migrate out\nSOURCE_COLOR = [166, 3, 3]\n# migrate in\nTARGET_COLOR = [35, 181, 184]\n# Combine into a single arr to use as a lookup table\nCOLORS = np.vstack(\n    [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)]\n)\nSOURCE_LOOKUP = 0\nTARGET_LOOKUP = 1\n</pre> # migrate out SOURCE_COLOR = [166, 3, 3] # migrate in TARGET_COLOR = [35, 181, 184] # Combine into a single arr to use as a lookup table COLORS = np.vstack(     [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)] ) SOURCE_LOOKUP = 0 TARGET_LOOKUP = 1 In\u00a0[5]: Copied! <pre>brushing_extension = BrushingExtension()\nbrushing_radius = 200000\n</pre> brushing_extension = BrushingExtension() brushing_radius = 200000 <p>Convert the <code>sources</code> list of dictionaries into a GeoPandas <code>GeoDataFrame</code> to pass into a <code>ScatterplotLayer</code>.</p> In\u00a0[6]: Copied! <pre>source_arr = np.array([source[\"position\"] for source in sources])\nsource_positions = shapely.points(source_arr[:, 0], source_arr[:, 1])\nsource_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],\n    geometry=source_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\nsource_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\nsource_fill_colors = COLORS[source_colors_lookup]\n</pre> source_arr = np.array([source[\"position\"] for source in sources]) source_positions = shapely.points(source_arr[:, 0], source_arr[:, 1]) source_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],     geometry=source_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array source_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) source_fill_colors = COLORS[source_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for source points:</p> In\u00a0[7]: Copied! <pre>source_layer = ScatterplotLayer.from_geopandas(\n    source_gdf,\n    get_fill_color=source_fill_colors,\n    radius_scale=3000,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> source_layer = ScatterplotLayer.from_geopandas(     source_gdf,     get_fill_color=source_fill_colors,     radius_scale=3000,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) In\u00a0[8]: Copied! <pre>targets_arr = np.array([target[\"position\"] for target in targets])\ntarget_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1])\ntarget_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],\n    geometry=target_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\ntarget_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\ntarget_line_colors = COLORS[target_line_colors_lookup]\n</pre> targets_arr = np.array([target[\"position\"] for target in targets]) target_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1]) target_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],     geometry=target_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array target_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) target_line_colors = COLORS[target_line_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for target points:</p> In\u00a0[9]: Copied! <pre>target_ring_layer = ScatterplotLayer.from_geopandas(\n    target_gdf,\n    get_line_color=target_line_colors,\n    radius_scale=4000,\n    pickable=True,\n    stroked=True,\n    filled=False,\n    line_width_min_pixels=2,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> target_ring_layer = ScatterplotLayer.from_geopandas(     target_gdf,     get_line_color=target_line_colors,     radius_scale=4000,     pickable=True,     stroked=True,     filled=False,     line_width_min_pixels=2,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Note: the <code>ArcLayer</code> can't currently be created from a GeoDataFrame because it needs two point columns, not one. This is a large part of why it's still marked under the \"experimental\" module.</p> <p>Here we pass a numpy array for each point column. This is allowed as long as the shape of the array is <code>(N, 2)</code> or <code>(N, 3)</code> (i.e. 2D or 3D coordinates).</p> In\u00a0[10]: Copied! <pre>value = np.array([arc[\"value\"] for arc in arcs])\nget_source_position = np.array([arc[\"source\"] for arc in arcs])\nget_target_position = np.array([arc[\"target\"] for arc in arcs])\ntable = pa.table({\"value\": value})\n\narc_layer = ArcLayer(\n    table=table,\n    get_source_position=get_source_position,\n    get_target_position=get_target_position,\n    get_source_color=SOURCE_COLOR,\n    get_target_color=TARGET_COLOR,\n    get_width=1,\n    opacity=0.4,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> value = np.array([arc[\"value\"] for arc in arcs]) get_source_position = np.array([arc[\"source\"] for arc in arcs]) get_target_position = np.array([arc[\"target\"] for arc in arcs]) table = pa.table({\"value\": value})  arc_layer = ArcLayer(     table=table,     get_source_position=get_source_position,     get_target_position=get_target_position,     get_source_color=SOURCE_COLOR,     get_target_color=TARGET_COLOR,     get_width=1,     opacity=0.4,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Now we can create a map using these three layers we've created.</p> <p>As you hover over the map, it should render only the arcs near your cursor.</p> <p>You can modify <code>brushing_extension.brushing_radius</code> to control how large the brush is around your cursor.</p> In\u00a0[11]: Copied! <pre>map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10)\nmap_\n</pre> map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10) map_ Out[11]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/migration/#us-county-to-county-migration","title":"U.S. County-to-County Migration\u00b6","text":"<p>This notebook is derived from the original deck.gl example in JavaScript, which you can see here.</p> <p>This dataset originally came from the U.S. Census Bureau and represents people moving in and out of each county between 2009-2013.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/migration/#imports","title":"Imports\u00b6","text":""},{"location":"examples/north-america-roads/","title":"North America Roads","text":"In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport palettable.colorbrewer.diverging\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import palettable.colorbrewer.diverging  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap <p>The \"public\" URL for this dataset from the Natural Earth website is</p> <pre><code>https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>That doesn't appear to work directly inside a notebook, so we'll use the URL the above redirects to:</p> <pre><code>https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>It's possible this \"private\" URL will change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\n</pre> url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\" <p>We'll use GeoPandas to fetch this data (45MB) over the internet and load it into a <code>GeoDataFrame</code>. This uses the <code>pyogrio</code> engine, which is faster. Ensure you have <code>pyogrio</code> installed.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>This dataframe has a variety of attributes, plus a geometry column with a <code>LineString</code> type.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.head()\n</pre> gdf.head() <p>To ensure that this demo is snappy on most computers, we'll filter to only the contiguous U.S. If you're on a recent computer, feel free to comment out this line.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"state\"] == \"California\"]\n</pre> gdf = gdf[gdf[\"state\"] == \"California\"] <p>To render <code>LineString</code> data, first create a <code>PathLayer</code> and then add it to a <code>Map</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8) map_ = Map(layers=[layer]) map_ <p>We can look at the documentation for <code>PathLayer</code> to see what other rendering options it allows. Let's set the path color to something other than black:</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = [200, 0, 200]\n</pre> layer.get_color = [200, 0, 200] <p>Ok, so we can see the data! That's great! But let's customize the rendering based on an attribute!</p> <p>The <code>scalerank</code> column tells how important the road is in the road network. Let's see what the distribution of values is for this column:</p> In\u00a0[\u00a0]: Copied! <pre>gdf[\"scalerank\"].value_counts().sort_index()\n</pre> gdf[\"scalerank\"].value_counts().sort_index() <p>Ok, so the values range from <code>3</code> to <code>12</code>. To assign a colormap to this column, we need \"normalized\" values that span between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9\n</pre> normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9 <p>The values of this array now range from 0 to 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank.min(), normalized_scale_rank.max()\n</pre> normalized_scale_rank.min(), normalized_scale_rank.max() <p>Let's select a colormap to apply to this data:</p> In\u00a0[\u00a0]: Copied! <pre>cmap = palettable.colorbrewer.diverging.PuOr_10\n</pre> cmap = palettable.colorbrewer.diverging.PuOr_10 In\u00a0[\u00a0]: Copied! <pre>cmap.mpl_colormap\n</pre> cmap.mpl_colormap <p>Now we'll use <code>apply_continuous_cmap</code> onto this array to generate colors for our data. Just set this new array onto the existing layer, and you'll see the map update with the new colors!</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = apply_continuous_cmap(\n    normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8\n)\n</pre> layer.get_color = apply_continuous_cmap(     normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8 ) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/north-america-roads/#north-america-roads","title":"North America Roads\u00b6","text":"<p>This example uses data from Natural Earth to plot the road network in North America. Refer to the data documentation for more information about this dataset.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/north-america-roads/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>pyogrio</li> </ul>"},{"location":"examples/north-america-roads/#imports","title":"Imports\u00b6","text":""},{"location":"examples/overture-maps/","title":"Overture Maps Buildings","text":"In\u00a0[\u00a0]: Copied! <pre>import numpy as np\nimport overturemaps\nfrom matplotlib.colors import LogNorm\nfrom palettable.colorbrewer.sequential import Oranges_9\n\nfrom lonboard import Map, PolygonLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import numpy as np import overturemaps from matplotlib.colors import LogNorm from palettable.colorbrewer.sequential import Oranges_9  from lonboard import Map, PolygonLayer from lonboard.colormap import apply_continuous_cmap <p>Using this bounding box tool we can find a bounding box covering Manhattan. Choose \"CSV\" in the drop down box, then paste the result in the following cell:</p> In\u00a0[\u00a0]: Copied! <pre>bbox = -74.02169, 40.696423, -73.891338, 40.831263\n</pre> bbox = -74.02169, 40.696423, -73.891338, 40.831263 <p>Ensure you pass the bbox into <code>overturemaps.record_batch_reader</code> as in the next line.</p> <p><code>record_batch_reader</code> returns a RecordBatchReader, an iterator of Arrow RecordBatches. We call <code>.read_all()</code> to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.</p> In\u00a0[\u00a0]: Copied! <pre>table = overturemaps.record_batch_reader(\"building\", bbox).read_all()\n\n# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug\ntable = table.combine_chunks()\n</pre> table = overturemaps.record_batch_reader(\"building\", bbox).read_all()  # Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug table = table.combine_chunks() <p>Let's color buildings by their heights. First, convert the \"height\" column to a numpy array, then replace any null values with <code>1</code>.</p> In\u00a0[\u00a0]: Copied! <pre>heights = table[\"height\"].to_numpy()\nheights = np.nan_to_num(heights, nan=1)\n</pre> heights = table[\"height\"].to_numpy() heights = np.nan_to_num(heights, nan=1) <p>Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's <code>LogNorm</code> to normalize these values according to a log scale.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = LogNorm(1, heights.max(), clip=True)\nnormalized_heights = normalizer(heights)\n</pre> normalizer = LogNorm(1, heights.max(), clip=True) normalized_heights = normalizer(heights) <p>Then we can apply a colormap to these heights. For this case, we'll use the <code>Oranges_9</code> colormap.</p> In\u00a0[\u00a0]: Copied! <pre>Oranges_9.mpl_colormap\n</pre> Oranges_9.mpl_colormap <p>Using <code>apply_continuous_cmap</code>, we can apply our values onto this colormap.</p> In\u00a0[\u00a0]: Copied! <pre>colors = apply_continuous_cmap(normalized_heights, Oranges_9)\n</pre> colors = apply_continuous_cmap(normalized_heights, Oranges_9) <p>We create a <code>PolygonLayer</code> with our data, passing in the heights and colors from before.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PolygonLayer(\n    # Select only a few attribute columns from the table\n    table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),\n    extruded=True,\n    get_elevation=heights,\n    get_fill_color=colors,\n)\n</pre> layer = PolygonLayer(     # Select only a few attribute columns from the table     table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),     extruded=True,     get_elevation=heights,     get_fill_color=colors, ) <p>We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this <code>view_state</code> call if you change the input dataset.</p> In\u00a0[\u00a0]: Copied! <pre>view_state = {\n    \"longitude\": -73.98416810282863,\n    \"latitude\": 40.72651721370669,\n    \"zoom\": 12.726630492730596,\n    \"pitch\": 59.80465353190481,\n    \"bearing\": 13.243243243243244,\n}\nm = Map(layer, view_state=view_state)\nm\n</pre> view_state = {     \"longitude\": -73.98416810282863,     \"latitude\": 40.72651721370669,     \"zoom\": 12.726630492730596,     \"pitch\": 59.80465353190481,     \"bearing\": 13.243243243243244, } m = Map(layer, view_state=view_state) m In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/overture-maps/#overture-maps-buildings","title":"Overture Maps Buildings\u00b6","text":"<p>This notebook will give a quick overview of using the new Overture Maps Python library with Lonboard.</p> <p>We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).</p> <p>This uses the latest advancements of the GeoParquet specification. Data is fetched directly from the cloud with no server in the middle!</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (26MB download).</p>"},{"location":"examples/overture-maps/#dependendencies","title":"Dependendencies\u00b6","text":"<p>This notebook requires Lonboard (tested with version 0.8) and <code>overturemaps</code> (tested with version 0.4). To install <code>overturemaps</code>, run:</p> <pre><code>pip install overturemaps\n</code></pre>"},{"location":"examples/overture-maps/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/color-picker/","title":"ColorPicker","text":"<p>Example gif:</p> <p></p> In\u00a0[1]: Copied! <pre>import geodatasets\nimport geopandas as gpd\nimport ipywidgets\n\nfrom lonboard import Map, SolidPolygonLayer\n</pre> import geodatasets import geopandas as gpd import ipywidgets  from lonboard import Map, SolidPolygonLayer <p><code>geodatasets.get_path</code> will download the dataset if it hasn't been downloaded and cached before.</p> In\u00a0[2]: Copied! <pre>gdf = gpd.read_file(geodatasets.get_path(\"nybb\"))\n</pre> gdf = gpd.read_file(geodatasets.get_path(\"nybb\")) <p>Create our basic map:</p> In\u00a0[3]: Copied! <pre>layer = SolidPolygonLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = SolidPolygonLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) map_ <pre>/Users/kyle/github/developmentseed/lonboard/lonboard/_layer.py:99: UserWarning: GeoDataFrame being reprojected to EPSG:4326\n  warnings.warn(\"GeoDataFrame being reprojected to EPSG:4326\")\n</pre> Out[3]: <pre>Map(layers=[SolidPolygonLayer(table=pyarrow.Table\nBoroCode: int64\nBoroName: string\nShape_Leng: double\nShape_Ar\u2026</pre> <p>Create the <code>ColorPicker</code> widget:</p> In\u00a0[4]: Copied! <pre>color_picker = ipywidgets.ColorPicker()\ncolor_picker\n</pre> color_picker = ipywidgets.ColorPicker() color_picker Out[4]: <pre>ColorPicker(value='black')</pre> <p>Note that the \"data\" contained by the <code>color_picker</code> instance is held by the <code>value</code> attribute:</p> In\u00a0[5]: Copied! <pre>color_picker.value\n</pre> color_picker.value Out[5]: <pre>'black'</pre> <p>We'll now link the two widgets together, so that whenever the color picker is updated, <code>ipywidgets</code> will update the value of <code>get_fill_color</code> on the layer.</p> <p>The format of <code>dlink</code> is:</p> <ul> <li>tuple: (source widget, attribute of source widget)</li> <li>tuple: (target widget, attribute of target widget)</li> </ul> <p>So we use the below syntax to link <code>color_picker.value</code> to <code>layer.get_fill_color</code>.</p> <p>We need to use <code>dlink</code>, not <code>link</code>, which creates a one-directional link (from the color picker to the layer) and not a bi-directional link. This is because the color picker and the layer have a different color representation under the hood (<code>ColorPicker</code> stores a hex string; the <code>SolidPolygonLayer</code> works in terms of RGBA color integers).</p> In\u00a0[6]: Copied! <pre>ipywidgets.dlink(\n    (color_picker, 'value'),\n    (layer, 'get_fill_color')\n)\n</pre> ipywidgets.dlink(     (color_picker, 'value'),     (layer, 'get_fill_color') ) Out[6]: <pre>&lt;traitlets.traitlets.directional_link at 0x168822610&gt;</pre> <p>Now click on the color widget and change the color! You need to press Enter to select a specific color.</p> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/color-picker/#colorpicker","title":"ColorPicker\u00b6","text":"<p>Use lonboard with an ipywidgets ColorPicker object.</p>"},{"location":"examples/integrations/color-picker/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li><p>lonboard</p> </li> <li><p>geodatasets</p> <p>Run <code>pip install geodatasets</code> if needed.</p> </li> </ul>"},{"location":"examples/integrations/color-picker/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/sidecar/","title":"Sidecar","text":"<p>The goal of this notebook is to show how using Jupyter Sidecar can help to interactively explore and understand your data.</p> <p>Note that sidecar only works in JupyterLab and will not work in other notebook frontends like Colab.</p> <p></p> In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport ipywidgets\nimport matplotlib as mpl\nimport pandas as pd\nfrom palettable.colorbrewer.diverging import BrBG_10\nfrom sidecar import Sidecar\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import ipywidgets import matplotlib as mpl import pandas as pd from palettable.colorbrewer.diverging import BrBG_10 from sidecar import Sidecar  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>For this example, we'll use a dataset of abandoned cars in Chicago. Since this is a CSV file, we need to pass in the names of the X and Y columns into the \"open options\" to pass to OGR.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\"\ngdf = gpd.read_file(\n    url,\n    engine=\"pyogrio\",\n    use_arrow=True,\n    X_POSSIBLE_NAMES=\"Longitude\",\n    Y_POSSIBLE_NAMES=\"Latitude\",\n    KEEP_GEOM_COLUMNS=\"NO\",\n)\ngdf = gdf.set_crs(\"EPSG:4326\")\n</pre> url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\" gdf = gpd.read_file(     url,     engine=\"pyogrio\",     use_arrow=True,     X_POSSIBLE_NAMES=\"Longitude\",     Y_POSSIBLE_NAMES=\"Latitude\",     KEEP_GEOM_COLUMNS=\"NO\", ) gdf = gdf.set_crs(\"EPSG:4326\") <p>This dataset has some null values for geometries. We'll remove these first:</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf.geometry != None]\n</pre> gdf = gdf[gdf.geometry != None] <p>Let's also remove empty values from another column to make later steps easier (when modifying a single map object with lonboard, you can't change the number of rows).</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"]\n</pre> gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"] <p>We'll now initialize the <code>Sidecar</code> object, which splits the JupyterLab screen in half, creating the output area for our widget.</p> In\u00a0[\u00a0]: Copied! <pre>sc = Sidecar(title='My Map')\n</pre> sc = Sidecar(title='My Map') <p>Now let's create a map layer from our <code>GeoDataFrame</code>.</p> <p>The default map height is 500 pixels, but we'd like to have a taller map because it's vertical. This API may change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) <p>Now we can render this inside the sidecar. You should see the map appear on the right side of your screen.</p> In\u00a0[\u00a0]: Copied! <pre>with sc:\n    display(map_)\n</pre> with sc:     display(map_) <p>It's great that we can see the map, but let's customize the rendering a bit. There's a column in the data titled</p> <p>How Many Days Has the Vehicle Been Reported as Parked?</p> <p>Let's use this column to visualize how long it takes to remove a car in different areas of the city.</p> <p>This is a string column, so first let's convert it to a numeric value.</p> In\u00a0[\u00a0]: Copied! <pre>colname = \"How Many Days Has the Vehicle Been Reported as Parked?\"\ngdf[colname] = pd.to_numeric(gdf[colname])\n</pre> colname = \"How Many Days Has the Vehicle Been Reported as Parked?\" gdf[colname] = pd.to_numeric(gdf[colname]) <p>In order to apply a colormap onto our data, we need to normalize our values to the range of <code>0-1</code>. Matplotlib offers several normalization options. See the tutorial and the <code>matplotlib.colors</code> module.</p> <p>To start, we'll do a simple linear normalization, converting from 0-90 to 0-1. Any values below 0 will be clamped to 0, and any values above 90 will be clamped to 1.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 90)\nnormalized_values = normalizer(gdf[colname])\n</pre> normalizer = mpl.colors.Normalize(0, 90) normalized_values = normalizer(gdf[colname]) <p>These values now range between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_values\n</pre> normalized_values In\u00a0[\u00a0]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap In\u00a0[\u00a0]: Copied! <pre>layer.radius_min_pixels = 0.5\nlayer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10)\n</pre> layer.radius_min_pixels = 0.5 layer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10) <p>Now the circles on the map have colors based on the time they've been parked.</p> <p>But this dataset is skewed with a long tail. Most cars have been sitting way less than 90 days, but a few have been sitting even longer.</p> <p>A logarithmic normalization (aka matplotlib's <code>LogNorm</code>) might be more useful here:</p> In\u00a0[\u00a0]: Copied! <pre>log_normalizer = mpl.colors.LogNorm(3, 90, clip=True)\nlog_values = log_normalizer(gdf[colname])\n</pre> log_normalizer = mpl.colors.LogNorm(3, 90, clip=True) log_values = log_normalizer(gdf[colname]) In\u00a0[\u00a0]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10)\nlayer.get_radius = log_values * 50\nlayer.radius_units = \"meters\"\n</pre> layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10) layer.get_radius = log_values * 50 layer.radius_units = \"meters\" In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/sidecar/#sidecar","title":"Sidecar\u00b6","text":""},{"location":"examples/integrations/sidecar/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>sidecar</li> <li>pyogrio</li> </ul> <p>Run</p> <pre><code>pip install sidecar pyogrio\n</code></pre> <p>if needed. You may need to restart Jupyter and/or refresh your browser tab after installing <code>sidecar</code>.</p>"},{"location":"examples/integrations/sidecar/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/","title":"30 Day Map Challenge: Day 1 (Points)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport shapely\n\nfrom lonboard import Map, ScatterplotLayer\n</pre> import geopandas as gpd import matplotlib as mpl import numpy as np import pandas as pd import shapely  from lonboard import Map, ScatterplotLayer In\u00a0[2]: Copied! <pre>path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\"\n</pre> path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\" <p>Read the data:</p> In\u00a0[3]: Copied! <pre>df = pd.read_csv(path, dtype={\"ZIP CODE\": str})\n</pre> df = pd.read_csv(path, dtype={\"ZIP CODE\": str}) <p>Let's first take a peek at our data:</p> In\u00a0[4]: Copied! <pre>pd.options.display.max_columns = None\ndf.head()\n</pre> pd.options.display.max_columns = None df.head() Out[4]: CRASH DATE CRASH TIME BOROUGH ZIP CODE LATITUDE LONGITUDE LOCATION ON STREET NAME CROSS STREET NAME OFF STREET NAME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED CONTRIBUTING FACTOR VEHICLE 1 CONTRIBUTING FACTOR VEHICLE 2 CONTRIBUTING FACTOR VEHICLE 3 CONTRIBUTING FACTOR VEHICLE 4 CONTRIBUTING FACTOR VEHICLE 5 COLLISION_ID VEHICLE TYPE CODE 1 VEHICLE TYPE CODE 2 VEHICLE TYPE CODE 3 VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5 0 09/11/2021 2:39 NaN NaN NaN NaN NaN WHITESTONE EXPRESSWAY 20 AVENUE NaN 2.0 0.0 0 0 0 0 2 0 Aggressive Driving/Road Rage Unspecified NaN NaN NaN 4455765 Sedan Sedan NaN NaN NaN 1 03/26/2022 11:45 NaN NaN NaN NaN NaN QUEENSBORO BRIDGE UPPER NaN NaN 1.0 0.0 0 0 0 0 1 0 Pavement Slippery NaN NaN NaN NaN 4513547 Sedan NaN NaN NaN NaN 2 06/29/2022 6:55 NaN NaN NaN NaN NaN THROGS NECK BRIDGE NaN NaN 0.0 0.0 0 0 0 0 0 0 Following Too Closely Unspecified NaN NaN NaN 4541903 Sedan Pick-up Truck NaN NaN NaN 3 09/11/2021 9:35 BROOKLYN 11208 40.667202 -73.866500 (40.667202, -73.8665) NaN NaN 1211      LORING AVENUE 0.0 0.0 0 0 0 0 0 0 Unspecified NaN NaN NaN NaN 4456314 Sedan NaN NaN NaN NaN 4 12/14/2021 8:13 BROOKLYN 11233 40.683304 -73.917274 (40.683304, -73.917274) SARATOGA AVENUE DECATUR STREET NaN 0.0 0.0 0 0 0 0 0 0 NaN NaN NaN NaN NaN 4486609 NaN NaN NaN NaN NaN <p>There are a bunch of columns in this dataset:</p> In\u00a0[5]: Copied! <pre>df.columns\n</pre> df.columns Out[5]: <pre>Index(['CRASH DATE', 'CRASH TIME', 'BOROUGH', 'ZIP CODE', 'LATITUDE',\n       'LONGITUDE', 'LOCATION', 'ON STREET NAME', 'CROSS STREET NAME',\n       'OFF STREET NAME', 'NUMBER OF PERSONS INJURED',\n       'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS INJURED',\n       'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST INJURED',\n       'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST INJURED',\n       'NUMBER OF MOTORIST KILLED', 'CONTRIBUTING FACTOR VEHICLE 1',\n       'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3',\n       'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5',\n       'COLLISION_ID', 'VEHICLE TYPE CODE 1', 'VEHICLE TYPE CODE 2',\n       'VEHICLE TYPE CODE 3', 'VEHICLE TYPE CODE 4', 'VEHICLE TYPE CODE 5'],\n      dtype='object')</pre> <p>Let's keep only specific columns that we care about:</p> In\u00a0[6]: Copied! <pre>keep_cols = [\n    \"CRASH DATE\",\n    \"CRASH TIME\",\n    \"LATITUDE\",\n    \"LONGITUDE\",\n    \"NUMBER OF PERSONS INJURED\",\n    \"NUMBER OF PERSONS KILLED\",\n    \"NUMBER OF PEDESTRIANS INJURED\",\n    \"NUMBER OF PEDESTRIANS KILLED\",\n    \"NUMBER OF CYCLIST INJURED\",\n    \"NUMBER OF CYCLIST KILLED\",\n    \"NUMBER OF MOTORIST INJURED\",\n    \"NUMBER OF MOTORIST KILLED\",\n    \"COLLISION_ID\",\n]\ndf = df[keep_cols]\n</pre> keep_cols = [     \"CRASH DATE\",     \"CRASH TIME\",     \"LATITUDE\",     \"LONGITUDE\",     \"NUMBER OF PERSONS INJURED\",     \"NUMBER OF PERSONS KILLED\",     \"NUMBER OF PEDESTRIANS INJURED\",     \"NUMBER OF PEDESTRIANS KILLED\",     \"NUMBER OF CYCLIST INJURED\",     \"NUMBER OF CYCLIST KILLED\",     \"NUMBER OF MOTORIST INJURED\",     \"NUMBER OF MOTORIST KILLED\",     \"COLLISION_ID\", ] df = df[keep_cols] <p>About 11% of the data have missing locations. For this quick demo, I don't have the ability to georeference those rows based on street name, so we'll just remove them. Hopefully there isn't a skew where crashes in some areas are more likely to not be georeferenced.</p> In\u00a0[7]: Copied! <pre>(df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean()\n</pre> (df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean() Out[7]: <pre>0.11342612579376604</pre> <p>Keep only the rows where longitude and latitude are not null.</p> In\u00a0[8]: Copied! <pre>df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())]\n</pre> df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())] <p>This dataset has crash data from the end of 2021, all of 2022, and thus far in 2023. For simplicity and regularity, let's choose only data from 2022.</p> <p>We'll convert the date to a datetime column and then select rows where the date happened in 2022:</p> In\u00a0[9]: Copied! <pre>df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"])\ndf = df[df[\"CRASH DATE\"].dt.year == 2022]\n</pre> df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"]) df = df[df[\"CRASH DATE\"].dt.year == 2022] <p>Let's also convert the time column from a string to a timestamp.</p> In\u00a0[10]: Copied! <pre>df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\")\n</pre> df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\") <p>If we look at the data types in our data, they were inferred as <code>int64</code> and <code>float64</code> data types:</p> In\u00a0[11]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[11]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED               float64\nNUMBER OF PERSONS KILLED                float64\nNUMBER OF PEDESTRIANS INJURED             int64\nNUMBER OF PEDESTRIANS KILLED              int64\nNUMBER OF CYCLIST INJURED                 int64\nNUMBER OF CYCLIST KILLED                  int64\nNUMBER OF MOTORIST INJURED                int64\nNUMBER OF MOTORIST KILLED                 int64\nCOLLISION_ID                              int64\ndtype: object</pre> <p>Knowing that our data holds small values, we can downcast to smaller data types:</p> In\u00a0[12]: Copied! <pre>for numeric_col_name in df.select_dtypes(\"number\").columns:\n    df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\")\n</pre> for numeric_col_name in df.select_dtypes(\"number\").columns:     df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\") <p>Now our data types are much smaller:</p> In\u00a0[13]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[13]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED                 uint8\nNUMBER OF PERSONS KILLED                  uint8\nNUMBER OF PEDESTRIANS INJURED             uint8\nNUMBER OF PEDESTRIANS KILLED              uint8\nNUMBER OF CYCLIST INJURED                 uint8\nNUMBER OF CYCLIST KILLED                  uint8\nNUMBER OF MOTORIST INJURED                uint8\nNUMBER OF MOTORIST KILLED                 uint8\nCOLLISION_ID                             uint32\ndtype: object</pre> <p>Now let's construct a GeoDataFrame from this data.</p> In\u00a0[14]: Copied! <pre>geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\"))\ngdf = gpd.GeoDataFrame(df, geometry=geometry)\n</pre> geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\")) gdf = gpd.GeoDataFrame(df, geometry=geometry) In\u00a0[15]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[15]: CRASH DATE CRASH TIME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED COLLISION_ID geometry 37 2022-07-12 1900-01-01 17:50:00 0 0 0 0 0 0 0 0 4545699 POINT (-73.96049 40.66330) 40 2022-04-24 1900-01-01 16:45:00 1 0 0 0 0 0 1 0 4521660 POINT (-74.13892 40.60768) 41 2022-04-24 1900-01-01 04:49:00 0 0 0 0 0 0 0 0 4521759 POINT (-73.86990 40.85597) 42 2022-04-22 1900-01-01 17:17:00 1 0 1 0 0 0 0 0 4522226 POINT (-73.93960 40.79028) 43 2022-04-24 1900-01-01 01:30:00 0 0 0 0 0 0 0 0 4522015 POINT (-74.01621 40.64299) <p>If we look at the bounding box of our data, we see that some points are way outside of NYC. Let's filter to include only the data in the NYC region:</p> In\u00a0[16]: Copied! <pre>gdf.total_bounds\n</pre> gdf.total_bounds Out[16]: <pre>array([-74.25496 ,   0.      ,   0.      ,  40.912167])</pre> In\u00a0[17]: Copied! <pre>nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696]\n</pre> nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696] In\u00a0[18]: Copied! <pre>gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))]\n</pre> gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))] In\u00a0[19]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap = Map(layers=[layer], _height=800)\nmap\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map = Map(layers=[layer], _height=800) map Out[19]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\nCRASH DATE: timestamp[ns]\nCRASH TIME: timestamp[ns]\nNUMBER OF\u2026</pre> <p>Ok cool, we can see something on the map, but let's make our visualization more interesting.</p> <p>Let's compute the number of non-motorists injured in each collision:</p> In\u00a0[20]: Copied! <pre>num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"]\n</pre> num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"] <p>We can see a quick distribution with <code>value_counts</code></p> In\u00a0[21]: Copied! <pre>num_injured.value_counts()\n</pre> num_injured.value_counts() Out[21]: <pre>0    80613\n1    12057\n2      356\n3       37\n4        3\n5        3\nName: count, dtype: int64</pre> In\u00a0[22]: Copied! <pre>layer.get_radius = np.array(num_injured + 1)\nlayer.radius_scale = 50\nlayer.opacity = 0.05\n</pre> layer.get_radius = np.array(num_injured + 1) layer.radius_scale = 50 layer.opacity = 0.05 <p>Next, let's color each dot by time of day. We'll use a cyclic colormap that starts and ends on the same color, because the time of day is cyclic.</p> In\u00a0[23]: Copied! <pre>colormap = mpl.colormaps[\"twilight\"]\ncolormap\n</pre> colormap = mpl.colormaps[\"twilight\"] colormap Out[23]: twilight  underbad over  <p>Next get the minute of day of the crash:</p> In\u00a0[24]: Copied! <pre>minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute\n</pre> minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute <p>To apply a colormap, we need to normalize the <code>minute_of_day</code> series to <code>0-1</code>. We can do this with matplotlib's <code>Normalize</code>. We set the minimum value to 0 and the maximum value to the number of minutes in a day.</p> In\u00a0[25]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 24 * 60)\nnormalized_minutes = normalizer(minute_of_day)\n</pre> normalizer = mpl.colors.Normalize(0, 24 * 60) normalized_minutes = normalizer(minute_of_day) <p>Now <code>normalized_minutes</code> ranges from 0-1:</p> In\u00a0[26]: Copied! <pre>normalized_minutes\n</pre> normalized_minutes Out[26]: <pre>masked_array(data=[0.74305556, 0.69791667, 0.20069444, ..., 0.54166667,\n                   0.625     , 0.60069444],\n             mask=False,\n       fill_value=1e+20)</pre> <p>Next we can apply the matplotlib colormap on our normalized values. We set <code>bytes=True</code> to ensure the output is of <code>uint8</code> data type.</p> In\u00a0[27]: Copied! <pre>colors = colormap(normalized_minutes, bytes=True)\n</pre> colors = colormap(normalized_minutes, bytes=True) In\u00a0[28]: Copied! <pre>colors\n</pre> colors Out[28]: <pre>array([[175,  81,  81, 255],\n       [156,  57,  79, 255],\n       [109, 144, 191, 255],\n       ...,\n       [ 63,  17,  60, 255],\n       [115,  29,  78, 255],\n       [ 99,  24,  75, 255]], dtype=uint8)</pre> In\u00a0[29]: Copied! <pre>layer.get_fill_color = colors\n</pre> layer.get_fill_color = colors In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/1-points/#30-day-map-challenge-day-1-points","title":"30 Day Map Challenge: Day 1 (Points)\u00b6","text":"<p>This is a notebook for day 1 of 30 Day Map Challenge.</p>"},{"location":"examples/map_challenge/1-points/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/#access-the-data","title":"Access the data\u00b6","text":"<p>This data comes from NYC Open Data, where we use Motor Vehicle Collisions - Crashes. First download this data via <code>Export &gt; CSV</code>, save it somewhere on disk, and update the <code>path</code> variable in the next cell for that location.</p>"},{"location":"examples/map_challenge/6-asia/","title":"30 Day Map Challenge: Day 6 (Asia)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nfrom palettable.colorbrewer.sequential import Blues_8\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd from palettable.colorbrewer.sequential import Blues_8  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap In\u00a0[2]: Copied! <pre>url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\"\n</pre> url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\" In\u00a0[3]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>The <code>Strahler</code> column seems to refer to how important/central the particular river is. It ranges from 1-7.</p> In\u00a0[4]: Copied! <pre>gdf['Strahler'].value_counts()\n</pre> gdf['Strahler'].value_counts() Out[4]: <pre>Strahler\n1    80503\n2    39100\n3    19655\n4     8960\n5     4547\n6     1901\n7      484\nName: count, dtype: int64</pre> <p>We'll use <code>Strahler</code> to plot more central rivers in both a darker blue and with a wider line.</p> In\u00a0[5]: Copied! <pre>layer = PathLayer.from_geopandas(gdf)\nlayer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8)\nlayer.get_width = gdf['Strahler']\nlayer.width_scale = 3000\nlayer.width_min_pixels = 0.5\n\nm = Map(layers=[layer])\nm\n</pre> layer = PathLayer.from_geopandas(gdf) layer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8) layer.get_width = gdf['Strahler'] layer.width_scale = 3000 layer.width_min_pixels = 0.5  m = Map(layers=[layer]) m Out[5]: <pre>Map(layers=[PathLayer(get_color=&lt;pyarrow.lib.FixedSizeListArray object at 0x107cbb220&gt;\n[\n  [\n    8,\n    69,\n  \u2026</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/6-asia/#30-day-map-challenge-day-6-asia","title":"30 Day Map Challenge: Day 6 (Asia)\u00b6","text":"<p>This is a notebook for day 6 of 30 Day Map Challenge.</p> <p>This data comes from the UN's \"Rivers of South and East Asia\".</p>"},{"location":"examples/map_challenge/6-asia/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/6-asia/#fetch-the-data","title":"Fetch the data\u00b6","text":""},{"location":"how-it-works/","title":"How it works?","text":"<p>Lonboard is built on four foundational technologies: deck.gl, GeoArrow, GeoParquet, and anywidget.</p> <ul> <li>deck.gl is a JavaScript geospatial data visualization library. Because deck.gl uses the GPU in your computer to render data, it's capable of performantly rendering very large quantities of data.</li> <li>GeoArrow is a memory format for efficiently representing geospatial vector data. As a memory format, GeoArrow is not compressed and can be used directly.</li> <li>GeoParquet is a file format for efficiently encoding and decoding geospatial vector data. As a file format, GeoParquet contains very efficient compression, and needs to be parsed before it can be used. <sup>1</sup></li> <li>anywidget is a framework for building custom Jupyter widgets that makes the process much easier than before.</li> </ul>"},{"location":"how-it-works/#how-is-it-so-fast","title":"How is it so fast?","text":"<p>Lonboard is so fast because it moves data from Python to JavaScript (in your browser) and then from JavaScript to your Graphics Processing Unit (GPU) more efficiently than ever before.</p> <p>Other Python libraries for interactive maps exist (such as <code>ipyleaflet</code>), and even existing bindings to deck.gl exist (such as <code>pydeck</code>). But those libraries encode data as GeoJSON to copy from Python to the browser. GeoJSON is extremely slow to read and write and results in a very large data file that has to be copied to the browser.</p> <p>With lonboard, the entire pipeline is binary. In Python, GeoPandas to GeoArrow to GeoParquet avoids a text encoding like GeoJSON and results in a compressed binary buffer that can be efficiently copied to the browser. In JavaScript, GeoParquet to GeoArrow offers efficient decoding (in WebAssembly). Then deck.gl is able to interpret the GeoArrow table directly without any parsing (thanks to <code>@geoarrow/deck.gl-layers</code>).</p> <p>GeoPandas is the primary interface for users to add data, allowing lonboard to internally manage the conversion to GeoArrow and its transport to the browser for rendering.</p> <p>Lonboard's goal is to abstract the technical bits of representing and moving data so it can attain its dual goals of performance and ease of use for a vast audience.</p> <ol> <li> <p>lonboard currently doesn't use \"official\" GeoParquet 1.0, because the 1.0 spec requires encoding geometries as Well-Known Binary (WKB) inside of the Parquet file. lonboard uses the highly-efficient GeoArrow encoding inside of GeoParquet (which may become part of the GeoParquet spec in 1.1). This is faster and easier to write when the writer and reader are both using GeoArrow anyways.\u00a0\u21a9</p> </li> </ol>"},{"location":"how-it-works/internals/","title":"Internal Architecture","text":"<p>Last edited April 2024.</p> <p>This document will outline Lonboard's internal architecture, and hopefully should be useful for potential contributors.</p>"},{"location":"how-it-works/internals/#pydeck","title":"Pydeck","text":"<p>Before we consider Lonboard, we should briefly cover how Pydeck manages data transit.</p> <p>Pydeck uses the loaders.gl ecosystem natively supported in deck.gl. This means that pydeck passes the user input directly into the <code>data</code> prop of any given layer. When <code>data</code> is a string, it's interpreted as a URL, passed into loaders.gl machinery to parse the given data format to a list of GeoJSON features. In order to do data-driven styling, users pass in strings like <code>\"@@=properties.valuePerSqm\"</code> to describe the JavaScript callback that should be applied to each GeoJSON feature.</p> <p>In my opinion, this presents performance pitfalls and gives Pydeck a stifling amount of flexibility. The Python API isn't able to do much of anything because the user input could be anything and users end up writing a sort of JavaScript in Python strings.</p>"},{"location":"how-it-works/internals/#lonboard","title":"Lonboard","text":""},{"location":"how-it-works/internals/#goals-and-target-user-persona","title":"Goals and target user persona","text":"<p>While I was never a primary pydeck developer and can't speak to their goals, I can say that Lonboard has a well-formed idea of the target user.</p> <p>When I was learning geospatial data processing in Python, I learned that GeoPandas, Shapely, and others were amazing tools. But I would perform operations and not have a deep understanding of what the output looked like. I wanted some way to quickly get a picture of what my data looked like. This drove me to build <code>keplergl_cli</code>, a single function or CLI command to visualize data in an interactive HTML map. The desire for this simplicity lives on in Lonboard's <code>viz</code> function.</p> <p>This experience informs the target user of Lonboard:</p> <ul> <li>A Python user who does not know or want to know JavaScript.</li> <li>Wants fast exploratory data analysis.</li> <li>Already working with data in Python.</li> <li>A reasonably fast internet connection on a desktop computer.</li> </ul>"},{"location":"how-it-works/internals/#python-classes","title":"Python classes","text":"<p>The <code>Map</code> class is the primary and only widget associated with JavaScript that renders anything. <code>Map</code> subclasses from <code>anywidget.AnyWidget</code>, which dynamically fetches Lonboard's JavaScript ESM bundle. The <code>Map</code> class emulates deck.gl's <code>Deck</code> class. It synchronizes map state and can be passed a sequence of <code>Layer</code> objects.</p> <p><code>Layer</code> classes are designed to map to each underlying deck.gl layer. Most <code>Layer</code> classes subclass from <code>BaseArrowLayer</code>, which stores a <code>pyarrow.Table</code> in the dataclass. This data object gets serialized to Parquet and rendered in a layer provided by <code>@geoarrow/deck.gl-layers</code> as described later on. The primary API for each of these Arrow-based layers is <code>from_geopandas</code>, which converts a <code>GeoDataFrame</code> to a <code>pyarrow.Table</code> with a GeoArrow-represented geometry column. A few layers are not Arrow-based and do not subclass from <code>BaseArrowLayer</code>: primarily <code>BitmapLayer</code> and <code>BitmapTileLayer</code>. These pass user data input directly into a core deck.gl layer.</p> <p>These <code>Layer</code> classes subclass from <code>ipywidgets.Widget</code> but are not associated with any JavaScript of their own. But by being widgets themselves, their data is synced with JavaScript and Python data changes are propagated as events to the core <code>Map</code> object.</p> <p>Each layer has parameters that map to deck.gl accessors and render properties. Each of these has validation on the Python side to catch invalid input early. Accessors are modeled with \"trait\" objects. Scalar accessors are stored as a scalar int/string/float. Function accessors are evaluated in Python and stored as a <code>pyarrow.Array</code>.</p> <p>A variety of layer extensions are supported, which map to deck.gl's upstream layer extensions. These layer extensions dynamically add more parameters to the layers on which they're assigned.</p>"},{"location":"how-it-works/internals/#data-loading","title":"Data loading","text":"<p>While pydeck allows users to pass in URLs to remote files, at the moment this is not possible in Lonboard. With only a few exceptions (e.g. the <code>BitmapLayer</code> and <code>BitmapTileLayer</code>), only data originating in Python is supported in Lonboard. This reduction in scope vastly simplifies Lonboard's internals while not providing much of a hurdle for Lonboard's core audience.</p> <p>Jupyter provides a WebSocket connection between Python and JavaScript. Jupyter widget state from Python classes is serialized over this WebSocket and received in JavaScript. While most widgets synchronize objects through JSON, widgets fully support binary data as well, which Lonboard utilizes.</p>"},{"location":"how-it-works/internals/#simplicity-of-code","title":"Simplicity of code","text":"<p>Supporting only a single data provenance and format makes the internals of Lonboard much more maintainable. Lonboard's JavaScript bindings only need to include a single data parser and can optimize performance for Parquet and Arrow data.</p>"},{"location":"how-it-works/internals/#parquet","title":"Parquet","text":"<p>Parquet was chosen as the underlying data transmission format for its excellent compression, fast read and write speeds, compatibility with GeoArrow and, in turn, compatibility with deck.gl's binary API.</p> <p>I tested with data from the speed test example (3 million points). Saving GeoArrow to Parquet was 135x faster than saving a GeoDataFrame to GeoJSON. The resulting file was 26x smaller. And parsing the Parquet to Arrow on the frontend was 5.6x faster than <code>JSON.parse</code> (including moving the Arrow from WebAssembly to JS).</p> <p>Note that what is sent over the wire is not GeoParquet. GeoParquet 1.0 requires special metadata and WKB-encoded geometries. Using that would require code on the frontend to parse WKB-encoded geometries into GeoArrow. The upcoming GeoParquet 1.1 includes support for GeoArrow but only its \"separated\" coordinates, where <code>x</code> and <code>y</code> coordinate values are in two different buffers (i.e. <code>xxxx</code>, <code>yyyy</code>). deck.gl's binary API currently does not support passing separated coordinates. Therefore Lonboard uses the \"interleaved\" coordinate variant, where <code>x</code> and <code>y</code> coordinates are stored in the same buffer (i.e. <code>xyxyxyxy</code>).</p>"},{"location":"how-it-works/internals/#bootstrapping-geoarrow","title":"Bootstrapping GeoArrow","text":"<p>For performance reasons, Lonboard is very focused on using GeoArrow throughout the pipeline in both Python and JavaScript. At the time when Lonboard was started, there were no user-friendly tools for converting data to GeoArrow in client-side JavaScript. By supporting only data from Python and only Parquet data, we could focus on making data conversions from GeoPandas to GeoArrow in Python efficient and stable. This enables GeoArrow to be \"just an implementation detail\" and out of any primary public APIs while also working to bootstrap a GeoArrow ecosystem in Python and JavaScript.</p>"},{"location":"how-it-works/internals/#authentication","title":"Authentication","text":"<p>Since all data is loaded via Python over the Jupyter-provided WebSocket, Lonboard doesn't need to implement any sort of authentication itself. The user is able to use whatever credentials they're familiar with, say for S3, to access data. Those credentials never need to be passed to the browser.</p> <p>In contrast, having the JavaScript side load data directly from a URL requires some way for users to specify authorization headers or API keys for access to private datasets.</p>"},{"location":"how-it-works/internals/#layer-creation","title":"Layer Creation","text":"<p>With the exception of the <code>BitmapLayer</code>, no direct deck.gl layers are used. deck.gl layers are used exclusively through the <code>@geoarrow/deck.gl-layers</code> glue library. This library connects GeoArrow data to the low-level binary data API of each deck.gl layer. All accessors are passed in directly as binary buffers.</p> <p>This means that in JavaScript the data never leaves an Arrow binary representation. It's parsed from Parquet to Arrow in WebAssembly, the Arrow buffers are copied out of WebAssembly memory, but never parsed to JSON strings or JavaScript objects.</p>"},{"location":"how-it-works/internals/#accessors-and-data-driven-rendering","title":"Accessors and data-driven rendering","text":"<p>Loading data from Python also helps to simplify data-driven rendering.</p>"},{"location":"how-it-works/internals/#no-function-serialization","title":"No function serialization","text":"<p>If we can make the data movement between Python and JavaScript fast enough, we can serialize computed rendering properties rather than the function to generate them. Lonboard serializes data, not functions.</p> <p>Any accessor used to style data is computed in Python, stored as a binary array, serialized to Parquet and sent to the frontend. Other than parsing the input Parquet buffers to Arrow, no data transformations or modifications are done on the JavaScript side.</p> <p>In Pydeck users had to pass something like</p> <pre><code>getRadius=\"@@=properties.valuePerSqm\"\n</code></pre> <p>which would be expanded into</p> <pre><code>getRadius: (object) =&gt; object.properties.valuePerSqm\n</code></pre> <p>on the JavaScript side.</p> <p>I find this syntax to be horrible UX. The user is no longer writing Python, they need to learn a new DSL just for this.</p> <p>RGB Colors for 3 million points take up 9MiB in memory (or 12MiB for RGBA). In the example of speed test data, 12MiB of RGBA data compressed down to 4MiB for transit. Float32 radii values for 3 million points take up 12MiB in memory and 9MiB compressed.</p> <p>Even for users on remote Python environments, this takes no more than a couple seconds to download, and then can render almost instantly. For non-point datasets with a fewer number of geometries, the byte size of accessors are even smaller relative to the geometry buffers.</p>"},{"location":"how-it-works/internals/#update-styling-without-re-sending-data","title":"Update styling without re-sending data","text":"<p>With Lonboard's widget architecture, any accessors other than the geometry are stored separately to the main Arrow table. This means that all accessors and rendering properties can be updated in isolation. The core table does not need to be synced again when a property is updated.</p>"},{"location":"how-it-works/internals/#uses-familiar-objects","title":"Uses familiar objects","text":"<p>Accessors are defined in terms of Numpy arrays, Pandas Series or PyArrow Arrays, which are familiar to Python data scientists.</p>"},{"location":"how-it-works/internals/#full-access-to-python","title":"Full access to Python","text":"<p>Because we serialize data to JavaScript instead of a function in a custom DSL, the user has the full spectrum of Python available to them. They can use one of matplotlib's many normalization helpers before applying a colormap. Or they can use apply some ML model and use its outputs for point radii.</p>"}]}
\ No newline at end of file
diff --git a/latest/sitemap.xml b/latest/sitemap.xml
index 6c2db779..64e4d026 100644
--- a/latest/sitemap.xml
+++ b/latest/sitemap.xml
@@ -2,222 +2,227 @@
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     </url>
     <url>
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     <url>
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     <url>
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-         <lastmod>2024-04-18</lastmod>
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          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/colormap/</loc>
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     <url>
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     </url>
     <url>
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     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/traits/</loc>
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     <url>
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     <url>
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     </url>
     <url>
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     <url>
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     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/internet-speeds/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/migration/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/north-america-roads/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>https://developmentseed.org/lonboard/latest/examples/overture-maps/</loc>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/integrations/color-picker/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/integrations/sidecar/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/map_challenge/1-points/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/map_challenge/6-asia/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/how-it-works/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/how-it-works/internals/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
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diff --git a/v0.8.0/api/layers/polygon-layer/index.html b/v0.8.0/api/layers/polygon-layer/index.html
index 1d227e59..2f93aa01 100644
--- a/v0.8.0/api/layers/polygon-layer/index.html
+++ b/v0.8.0/api/layers/polygon-layer/index.html
@@ -2307,9 +2307,9 @@
 
 
 <h1 id="polygonlayer">PolygonLayer<a class="headerlink" href="#polygonlayer" title="Permanent link">&para;</a></h1>
-<p><img alt="" src="../../../assets/boundaries.png" /></p>
+<p><img alt="" src="../../../assets/overture.jpg" /></p>
 <blockquote>
-<p>Screenshot from <a href="../../examples/global-boundaries">Global Boundaries</a> example</p>
+<p>Screenshot from <a href="../../examples/overture-maps">Overture Maps</a> example</p>
 </blockquote>
 
 
diff --git a/v0.8.0/api/layers/polygon-layer/polygon-layer.md b/v0.8.0/api/layers/polygon-layer/polygon-layer.md
index b7c4dd20..af13025a 100644
--- a/v0.8.0/api/layers/polygon-layer/polygon-layer.md
+++ b/v0.8.0/api/layers/polygon-layer/polygon-layer.md
@@ -1,8 +1,8 @@
 # PolygonLayer
 
-![](../../assets/boundaries.png)
+![](../../assets/overture.jpg)
 
-> Screenshot from [Global Boundaries](../../examples/global-boundaries) example
+> Screenshot from [Overture Maps](../../examples/overture-maps) example
 
 ::: lonboard.PolygonLayer
     options:
diff --git a/v0.8.0/api/viz/index.html b/v0.8.0/api/viz/index.html
index 4fae5f8e..f44b167f 100644
--- a/v0.8.0/api/viz/index.html
+++ b/v0.8.0/api/viz/index.html
@@ -1954,6 +1954,7 @@ <h2 id="lonboard.viz" class="doc doc-heading">
     <a href="https://gist.github.com/sgillies/2217756">Geo Interface protocol</a>.</li>
 <li><code>dict</code> holding GeoJSON-like data.</li>
 <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li>
+<li>pyarrow <code>Array</code> marked with a <a href="https://geoarrow.org/geoarrow-python/main/pyarrow.html#geoarrow.pyarrow.GeometryExtensionType">GeoArrow extension type defined by geoarrow-pyarrow</a></li>
 </ul>
 <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p>
 
@@ -2050,6 +2051,7 @@ <h2 id="lonboard._viz.VizDataInput" class="doc doc-heading">
     <span class="n">Table</span><span class="p">,</span>
     <span class="n">NDArray</span><span class="p">[</span><span class="n">object_</span><span class="p">],</span>
     <span class="n">BaseGeometry</span><span class="p">,</span>
+    <span class="n">ArrowArrayExportable</span><span class="p">,</span>
     <span class="n">ArrowStreamExportable</span><span class="p">,</span>
     <span class="n">GeoInterfaceProtocol</span><span class="p">,</span>
     <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
diff --git a/v0.8.0/assets/images/social/examples/overture-maps.png b/v0.8.0/assets/images/social/examples/overture-maps.png
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+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    Examples
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_2" id="__nav_2_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_2">
+            <span class="md-nav__icon md-icon"></span>
+            Examples
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../internet-speeds/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Speedtest Data (ScatterplotLayer)
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../north-america-roads/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    North America Roads
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../global-boundaries/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Municipal Boundaries
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../migration/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    U.S. County-to-County Migration
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../data-filter-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    DataFilterExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2_7" >
+        
+          
+          <label class="md-nav__link" for="__nav_2_7" id="__nav_2_7_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Integrations
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_2_7_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_2_7">
+            <span class="md-nav__icon md-icon"></span>
+            Integrations
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../integrations/color-picker/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ColorPicker
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../integrations/sidecar/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Sidecar
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2_8" >
+        
+          
+          <label class="md-nav__link" for="__nav_2_8" id="__nav_2_8_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_2_8_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_2_8">
+            <span class="md-nav__icon md-icon"></span>
+            30 Day Map Challenge
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../map_challenge/1-points/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge: Day 1 (Points)
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../map_challenge/6-asia/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    30 Day Map Challenge: Day 6 (Asia)
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
+        
+          
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    API Reference
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
+            <span class="md-nav__icon md-icon"></span>
+            API Reference
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/viz/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    viz
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/map/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Map
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_3" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_3" id="__nav_3_3_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Layers
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_3">
+            <span class="md-nav__icon md-icon"></span>
+            Layers
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/bitmap-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BitmapLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/bitmap-tile-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BitmapTileLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/heatmap-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    HeatmapLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/path-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PathLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/point-cloud-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PointCloudLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/polygon-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PolygonLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/scatterplot-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ScatterplotLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/solid-polygon-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    SolidPolygonLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/base-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Base Classes
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_4" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../api/layer-extensions/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    Layer Extensions
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_3_4" id="__nav_3_4_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_4_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_4">
+            <span class="md-nav__icon md-icon"></span>
+            Layer Extensions
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/brushing-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    BrushingExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/collision-filter-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    CollisionFilterExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layer-extensions/data-filter-extension/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    DataFilterExtension
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/basemap/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.basemap
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/colormap/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.colormap
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/controls/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.controls
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/traits/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    lonboard.traits
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_9" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_9" id="__nav_3_9_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Experimental
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_3_9_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_9">
+            <span class="md-nav__icon md-icon"></span>
+            Experimental
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    
+    
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3_9_1" >
+        
+          
+          <label class="md-nav__link" for="__nav_3_9_1" id="__nav_3_9_1_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Layers
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="3" aria-labelledby="__nav_3_9_1_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3_9_1">
+            <span class="md-nav__icon md-icon"></span>
+            Layers
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/arc-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ArcLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../api/layers/text-layer/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    TextLayer
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../cli/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Command-line Interface
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../ecosystem/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    Ecosystem
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_5" id="__nav_5_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_5">
+            <span class="md-nav__icon md-icon"></span>
+            Ecosystem
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/geoarrow/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    GeoArrow
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/geopandas/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    GeoPandas
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/jupyter-widgets/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Jupyter Widgets
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/panel/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Panel
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../ecosystem/shiny/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Shiny
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../performance/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Performance
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../CHANGELOG/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Changelog
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../alternatives/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Alternatives
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+      
+        
+          
+        
+      
+        
+      
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_9" >
+        
+          
+          
+          <div class="md-nav__link md-nav__container">
+            <a href="../../how-it-works/" class="md-nav__link ">
+              
+  
+  <span class="md-ellipsis">
+    How it works?
+  </span>
+  
+
+            </a>
+            
+              
+              <label class="md-nav__link " for="__nav_9" id="__nav_9_label" tabindex="0">
+                <span class="md-nav__icon md-icon"></span>
+              </label>
+            
+          </div>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_9_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_9">
+            <span class="md-nav__icon md-icon"></span>
+            How it works?
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../how-it-works/internals/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Internal Architecture
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#dependendencies" class="md-nav__link">
+    <span class="md-ellipsis">
+      Dependendencies
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#imports" class="md-nav__link">
+    <span class="md-ellipsis">
+      Imports
+    </span>
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+
+    <a href="/lonboard/latest/examples/overture-maps/overture-maps.ipynb" title="Download Notebook" class="md-content__button md-icon">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 20h14v-2H5m14-9h-4V3H9v6H5l7 7 7-7Z"/></svg>
+    </a>
+
+
+
+                  
+
+  
+  
+
+
+<script>
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
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+  }(this, function () { 'use strict';
+
+    function createNode(text) {
+      const node = document.createElement('pre');
+      node.style.width = '1px';
+      node.style.height = '1px';
+      node.style.position = 'fixed';
+      node.style.top = '5px';
+      node.textContent = text;
+      return node;
+    }
+
+    function copyNode(node) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(node.textContent);
+      }
+
+      const selection = getSelection();
+
+      if (selection == null) {
+        return Promise.reject(new Error());
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+      selection.removeAllRanges();
+      const range = document.createRange();
+      range.selectNodeContents(node);
+      selection.addRange(range);
+      document.execCommand('copy');
+      selection.removeAllRanges();
+      return Promise.resolve();
+    }
+    function copyText(text) {
+      if ('clipboard' in navigator) {
+        // eslint-disable-next-line flowtype/no-flow-fix-me-comments
+        // $FlowFixMe Clipboard is not defined in Flow yet.
+        return navigator.clipboard.writeText(text);
+      }
+
+      const body = document.body;
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+      if (!body) {
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+      const node = createNode(text);
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+      body.removeChild(node);
+      return Promise.resolve();
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+    function copy(button) {
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+      const text = button.getAttribute('value');
+
+      function trigger() {
+        button.dispatchEvent(new CustomEvent('clipboard-copy', {
+          bubbles: true
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+      }
+
+      if (text) {
+        copyText(text).then(trigger);
+      } else if (id) {
+        const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
+        if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
+        const node = root.getElementById(id);
+        if (node) copyTarget(node).then(trigger);
+      }
+    }
+
+    function copyTarget(content) {
+      if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
+        return copyText(content.value);
+      } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
+        return copyText(content.href);
+      } else {
+        return copyNode(content);
+      }
+    }
+
+    function clicked(event) {
+      const button = event.currentTarget;
+
+      if (button instanceof HTMLElement) {
+        copy(button);
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+    }
+
+    function keydown(event) {
+      if (event.key === ' ' || event.key === 'Enter') {
+        const button = event.currentTarget;
+
+        if (button instanceof HTMLElement) {
+          event.preventDefault();
+          copy(button);
+        }
+      }
+    }
+
+    function focused(event) {
+      event.currentTarget.addEventListener('keydown', keydown);
+    }
+
+    function blurred(event) {
+      event.currentTarget.removeEventListener('keydown', keydown);
+    }
+
+    class ClipboardCopyElement extends HTMLElement {
+      constructor() {
+        super();
+        this.addEventListener('click', clicked);
+        this.addEventListener('focus', focused);
+        this.addEventListener('blur', blurred);
+      }
+
+      connectedCallback() {
+        if (!this.hasAttribute('tabindex')) {
+          this.setAttribute('tabindex', '0');
+        }
+
+        if (!this.hasAttribute('role')) {
+          this.setAttribute('role', 'button');
+        }
+      }
+
+      get value() {
+        return this.getAttribute('value') || '';
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+
+      set value(text) {
+        this.setAttribute('value', text);
+      }
+
+    }
+
+    if (!window.customElements.get('clipboard-copy')) {
+      window.ClipboardCopyElement = ClipboardCopyElement;
+      window.customElements.define('clipboard-copy', ClipboardCopyElement);
+    }
+
+    return ClipboardCopyElement;
+
+  }));
+</script>
+<script>
+      document.addEventListener('clipboard-copy', function(event) {
+        const notice = event.target.querySelector('.notice')
+        notice.hidden = false
+        setTimeout(function() {
+          notice.hidden = true
+        }, 1000)
+      })
+</script>
+<script>
+(function() {
+  function addWidgetsRenderer() {
+    var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
+    var scriptElement = document.createElement('script');
+    
+    var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js';
+    
+    var widgetState;
+
+    // Fallback for older version:
+    try {
+      widgetState = mimeElement && JSON.parse(mimeElement.innerHTML);
+
+      if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) {
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+        var widgetRendererSrc = 'https://unpkg.com/@jupyter-js-widgets@*/dist/embed.js';
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+      }
+    } catch(e) {}
+
+    scriptElement.src = widgetRendererSrc;
+    document.body.appendChild(scriptElement);
+  }
+
+  document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
+}());
+</script>
+<style type="text/css">
+    pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
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+.highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
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+.highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
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#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: #ffff00;--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: #eeeeee;--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}.jupyter-wrapper{--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), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--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;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--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, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--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);--jp-inverse-layout-color0: #111111;--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);--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);--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);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .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: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--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 );--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);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--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, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--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: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--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;--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;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .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);--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);--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)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: 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+<h1 id="overture-maps-buildings">Overture Maps Buildings<a class="anchor-link" href="#overture-maps-buildings">¶</a></h1><p><img alt="overture.jpg" 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"/></p>
+<p>This notebook will give a quick overview of using the new <a href="https://github.com/OvertureMaps/overturemaps-py">Overture Maps Python library</a> with Lonboard.</p>
+<p>We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).</p>
+<p>This uses the latest advancements of the <a href="https://geoparquet.org/">GeoParquet specification</a>. Data is fetched <em>directly from the cloud</em> with no server in the middle!</p>
+<p>You can view a <a href="https://notebooksharing.space/view/f16f8f4c7133c4576938b262f17ce617cf112106bc25a24a08b528f8f0d11828#displayOptions=">hosted version of this notebook on Notebook Sharing Space</a> (26MB download).</p>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="dependendencies">Dependendencies<a class="anchor-link" href="#dependendencies">¶</a></h2><p>This notebook requires Lonboard (tested with version 0.8) and <code>overturemaps</code> (tested with version 0.4). To install <code>overturemaps</code>, run:</p>
+<pre><code>pip install overturemaps
+</code></pre>
+</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="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-1">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></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">overturemaps</span>
+<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">LogNorm</span>
+<span class="kn">from</span> <span class="nn">palettable.colorbrewer.sequential</span> <span class="kn">import</span> <span class="n">Oranges_9</span>
+
+<span class="kn">from</span> <span class="nn">lonboard</span> <span class="kn">import</span> <span class="n">Map</span><span class="p">,</span> <span class="n">PolygonLayer</span>
+<span class="kn">from</span> <span class="nn">lonboard.colormap</span> <span class="kn">import</span> <span class="n">apply_continuous_cmap</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-1">import numpy as np
+import overturemaps
+from matplotlib.colors import LogNorm
+from palettable.colorbrewer.sequential import Oranges_9
+
+from lonboard import Map, PolygonLayer
+from lonboard.colormap import apply_continuous_cmap</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Using <a href="https://boundingbox.klokantech.com/">this bounding box tool</a> we can find a bounding box covering Manhattan. Choose "CSV" in the drop down box, then paste the result in the following cell:</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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>
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">bbox</span> <span class="o">=</span> <span class="o">-</span><span class="mf">74.02169</span><span class="p">,</span> <span class="mf">40.696423</span><span class="p">,</span> <span class="o">-</span><span class="mf">73.891338</span><span class="p">,</span> <span class="mf">40.831263</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-2">bbox = -74.02169, 40.696423, -73.891338, 40.831263</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Ensure you pass the bbox into <code>overturemaps.record_batch_reader</code> as in the next line.</p>
+<p><code>record_batch_reader</code> returns a <a href="https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html">RecordBatchReader</a>, an iterator of Arrow RecordBatches. We call <code>.read_all()</code> to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">overturemaps</span><span class="o">.</span><span class="n">record_batch_reader</span><span class="p">(</span><span class="s2">"building"</span><span class="p">,</span> <span class="n">bbox</span><span class="p">)</span><span class="o">.</span><span class="n">read_all</span><span class="p">()</span>
+
+<span class="c1"># Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug</span>
+<span class="n">table</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">combine_chunks</span><span class="p">()</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-3">table = overturemaps.record_batch_reader("building", bbox).read_all()
+
+# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug
+table = table.combine_chunks()</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Let's color buildings by their heights. First, convert the "height" column to a numpy array, then replace any null values with <code>1</code>.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">heights</span> <span class="o">=</span> <span class="n">table</span><span class="p">[</span><span class="s2">"height"</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+<span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan_to_num</span><span class="p">(</span><span class="n">heights</span><span class="p">,</span> <span class="n">nan</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-4">heights = table["height"].to_numpy()
+heights = np.nan_to_num(heights, nan=1)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.LogNorm.html"><code>LogNorm</code></a> to normalize these values according to a log scale.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">normalizer</span> <span class="o">=</span> <span class="n">LogNorm</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">normalized_heights</span> <span class="o">=</span> <span class="n">normalizer</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-5">normalizer = LogNorm(1, heights.max(), clip=True)
+normalized_heights = normalizer(heights)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Then we can apply a colormap to these heights. For this case, we'll use the <a href="https://jiffyclub.github.io/palettable/colorbrewer/sequential/#oranges_9"><code>Oranges_9</code></a> colormap.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-6">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">Oranges_9</span><span class="o">.</span><span class="n">mpl_colormap</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-6">Oranges_9.mpl_colormap</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Using <a href="https://developmentseed.org/lonboard/latest/api/colormap/#lonboard.colormap.apply_continuous_cmap"><code>apply_continuous_cmap</code></a>, we can apply our values onto this colormap.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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">
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+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">colors</span> <span class="o">=</span> <span class="n">apply_continuous_cmap</span><span class="p">(</span><span class="n">normalized_heights</span><span class="p">,</span> <span class="n">Oranges_9</span><span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-7">colors = apply_continuous_cmap(normalized_heights, Oranges_9)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>We create a <code>PolygonLayer</code> with our data, passing in the heights and colors from before.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-8">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">layer</span> <span class="o">=</span> <span class="n">PolygonLayer</span><span class="p">(</span>
+    <span class="c1"># Select only a few attribute columns from the table</span>
+    <span class="n">table</span><span class="o">=</span><span class="n">table</span><span class="o">.</span><span class="n">select</span><span class="p">([</span><span class="s2">"id"</span><span class="p">,</span> <span class="s2">"height"</span><span class="p">,</span> <span class="s2">"geometry"</span><span class="p">,</span> <span class="s2">"names"</span><span class="p">]),</span>
+    <span class="n">extruded</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">get_elevation</span><span class="o">=</span><span class="n">heights</span><span class="p">,</span>
+    <span class="n">get_fill_color</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-8">layer = PolygonLayer(
+    # Select only a few attribute columns from the table
+    table=table.select(["id", "height", "geometry", "names"]),
+    extruded=True,
+    get_elevation=heights,
+    get_fill_color=colors,
+)</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this <code>view_state</code> call if you change the input dataset.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-9">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">view_state</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">"longitude"</span><span class="p">:</span> <span class="o">-</span><span class="mf">73.98416810282863</span><span class="p">,</span>
+    <span class="s2">"latitude"</span><span class="p">:</span> <span class="mf">40.72651721370669</span><span class="p">,</span>
+    <span class="s2">"zoom"</span><span class="p">:</span> <span class="mf">12.726630492730596</span><span class="p">,</span>
+    <span class="s2">"pitch"</span><span class="p">:</span> <span class="mf">59.80465353190481</span><span class="p">,</span>
+    <span class="s2">"bearing"</span><span class="p">:</span> <span class="mf">13.243243243243244</span><span class="p">,</span>
+<span class="p">}</span>
+<span class="n">m</span> <span class="o">=</span> <span class="n">Map</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="n">view_state</span><span class="o">=</span><span class="n">view_state</span><span class="p">)</span>
+<span class="n">m</span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-9">view_state = {
+    "longitude": -73.98416810282863,
+    "latitude": 40.72651721370669,
+    "zoom": 12.726630492730596,
+    "pitch": 59.80465353190481,
+    "bearing": 13.243243243243244,
+}
+m = Map(layer, view_state=view_state)
+m</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<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="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-10">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span>
+</pre></div>
+<div class="clipboard-copy-txt" id="cell-10"></div>
+</div>
+</div>
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\ No newline at end of file
diff --git a/v0.8.0/examples/overture-maps/overture-maps.ipynb b/v0.8.0/examples/overture-maps/overture-maps.ipynb
new file mode 100644
index 00000000..25fb999b
--- /dev/null
+++ b/v0.8.0/examples/overture-maps/overture-maps.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overture Maps Buildings\n",
+    "\n",
+    "![overture.jpg](attachment:2b842dc7-1483-4955-ac16-ad554dd43db8.jpg)\n",
+    "\n",
+    "This notebook will give a quick overview of using the new [Overture Maps Python library](https://github.com/OvertureMaps/overturemaps-py) with Lonboard.\n",
+    "\n",
+    "We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).\n",
+    "\n",
+    "This uses the latest advancements of the [GeoParquet specification](https://geoparquet.org/). Data is fetched _directly from the cloud_ with no server in the middle!\n",
+    "\n",
+    "You can view a [hosted version of this notebook on Notebook Sharing Space](https://notebooksharing.space/view/f16f8f4c7133c4576938b262f17ce617cf112106bc25a24a08b528f8f0d11828#displayOptions=) (26MB download)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Dependendencies\n",
+    "\n",
+    "This notebook requires Lonboard (tested with version 0.8) and `overturemaps` (tested with version 0.4). To install `overturemaps`, run:\n",
+    "\n",
+    "```\n",
+    "pip install overturemaps\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import overturemaps\n",
+    "from matplotlib.colors import LogNorm\n",
+    "from palettable.colorbrewer.sequential import Oranges_9\n",
+    "\n",
+    "from lonboard import Map, PolygonLayer\n",
+    "from lonboard.colormap import apply_continuous_cmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using [this bounding box tool](https://boundingbox.klokantech.com/) we can find a bounding box covering Manhattan. Choose \"CSV\" in the drop down box, then paste the result in the following cell:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = -74.02169, 40.696423, -73.891338, 40.831263"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ensure you pass the bbox into `overturemaps.record_batch_reader` as in the next line.\n",
+    "\n",
+    "`record_batch_reader` returns a [RecordBatchReader](https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html), an iterator of Arrow RecordBatches. We call `.read_all()` to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table = overturemaps.record_batch_reader(\"building\", bbox).read_all()\n",
+    "\n",
+    "# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug\n",
+    "table = table.combine_chunks()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's color buildings by their heights. First, convert the \"height\" column to a numpy array, then replace any null values with `1`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heights = table[\"height\"].to_numpy()\n",
+    "heights = np.nan_to_num(heights, nan=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's [`LogNorm`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.LogNorm.html) to normalize these values according to a log scale.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "normalizer = LogNorm(1, heights.max(), clip=True)\n",
+    "normalized_heights = normalizer(heights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can apply a colormap to these heights. For this case, we'll use the [`Oranges_9`](https://jiffyclub.github.io/palettable/colorbrewer/sequential/#oranges_9) colormap.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Oranges_9.mpl_colormap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using [`apply_continuous_cmap`](https://developmentseed.org/lonboard/latest/api/colormap/#lonboard.colormap.apply_continuous_cmap), we can apply our values onto this colormap.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = apply_continuous_cmap(normalized_heights, Oranges_9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create a `PolygonLayer` with our data, passing in the heights and colors from before.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer = PolygonLayer(\n",
+    "    # Select only a few attribute columns from the table\n",
+    "    table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),\n",
+    "    extruded=True,\n",
+    "    get_elevation=heights,\n",
+    "    get_fill_color=colors,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this `view_state` call if you change the input dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "view_state = {\n",
+    "    \"longitude\": -73.98416810282863,\n",
+    "    \"latitude\": 40.72651721370669,\n",
+    "    \"zoom\": 12.726630492730596,\n",
+    "    \"pitch\": 59.80465353190481,\n",
+    "    \"bearing\": 13.243243243243244,\n",
+    "}\n",
+    "m = Map(layer, view_state=view_state)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/v0.8.0/search/search_index.json b/v0.8.0/search/search_index.json
index 06c09265..8ec36e24 100644
--- a/v0.8.0/search/search_index.json
+++ b/v0.8.0/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Lonboard","text":"<p>A Python library for fast, interactive geospatial vector data visualization in Jupyter.</p> <p>Building on cutting-edge technologies like GeoArrow and GeoParquet in conjunction with GPU-based map rendering, Lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.</p> <p></p> <p>3 million points rendered from a GeoPandas <code>GeoDataFrame</code> in JupyterLab.  Example notebook.</p>"},{"location":"#install","title":"Install","text":"<p>To install Lonboard using pip:</p> <pre><code>pip install lonboard\n</code></pre> <p>Lonboard is on conda-forge and can be installed using conda, mamba, or pixi. To install Lonboard using conda:</p> <pre><code>conda install -c conda-forge lonboard\n</code></pre> <p>To install from source, refer to the developer documentation.</p>"},{"location":"#get-started","title":"Get Started","text":"<p>For the simplest rendering, pass geospatial data into the top-level <code>viz</code> function.</p> <pre><code>import geopandas as gpd\nfrom lonboard import viz\n\ngdf = gpd.GeoDataFrame(...)\nviz(gdf)\n</code></pre> <p>Under the hood, this delegates to a <code>ScatterplotLayer</code>, <code>PathLayer</code>, or <code>PolygonLayer</code>. Refer to the documentation and examples for more control over rendering.</p>"},{"location":"#documentation","title":"Documentation","text":"<p>Refer to the documentation at developmentseed.org/lonboard.</p>"},{"location":"#why-the-name","title":"Why the name?","text":"<p>This is a new binding to the deck.gl geospatial data visualization library. A \"deck\" is the part of a skateboard you ride on. What's a fast, geospatial skateboard? A lonboard.</p> <p></p>"},{"location":"CHANGELOG/","title":"Changelog","text":""},{"location":"CHANGELOG/#080-2024-04-05","title":"[0.8.0] - 2024-04-05","text":""},{"location":"CHANGELOG/#new","title":"New!","text":"<ul> <li>A new <code>PolygonLayer</code>! This layer renders polygon outlines for easier visibility. @kylebarron in developmentseed/lonboard!330</li> <li>An example using <code>PolygonLayer</code> by @naomatheus in developmentseed/lonboard!351</li> <li>Sync view state between JS and Python by @kylebarron in developmentseed/lonboard!448</li> <li>Support geoarrow array input into <code>viz()</code> by @kylebarron in developmentseed/lonboard!427</li> <li>Internal architecture documentation by @kylebarron in developmentseed/lonboard!450</li> </ul>"},{"location":"CHANGELOG/#fixes","title":"Fixes","text":"<ul> <li>Fix CLI with unset <code>geometry_name</code> by @kylebarron in developmentseed/lonboard!451</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.1...v0.8.0</p>"},{"location":"CHANGELOG/#071-2024-03-22","title":"[0.7.1] - 2024-03-22","text":""},{"location":"CHANGELOG/#fixes_1","title":"Fixes","text":"<ul> <li>Fix CLI with geopackage files by @kylebarron in developmentseed/lonboard!434</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.0...v0.7.1</p>"},{"location":"CHANGELOG/#070-2024-03-21","title":"[0.7.0] - 2024-03-21","text":""},{"location":"CHANGELOG/#new_1","title":"New!","text":"<ul> <li>There's a new command-line interface (CLI)! Use the <code>lonboard</code> command to quickly visualize one or more data files readable by GDAL! For example: <code>lonboard admins.geojson features.gpkg</code>. By @kylebarron in developmentseed/lonboard!379</li> <li>Type hinting for constructors and <code>from_geopandas</code> method. This should make it easier to pass the correct parameters into layers. This has been tested to work in IDEs like VSCode, but unfortunately appears not to work in JupyterLab. By @kylebarron in developmentseed/lonboard!399</li> </ul> <ul> <li>Warn on missing CRS. One of the most common reasons that you might see an empty map is from accidentally visualizing data that is not in EPSG 4326 (longitude-latitude). We now emit a warning for data that doesn't have a CRS defined on the data. By @kylebarron in developmentseed/lonboard!395.</li> <li>Lonboard is now on <code>conda-forge</code>! Install with <code>conda install -c conda-forge lonboard</code>. By @giswqs in developmentseed/lonboard!223</li> <li>Add PointCloudLayer. By @kylebarron in developmentseed/lonboard!396</li> <li>Add fly-to map action to \"fly\" the map to a new location. By @kylebarron in developmentseed/lonboard!408</li> <li>Docs showcase page by @kylebarron in developmentseed/lonboard!401</li> <li>Improve default colors in <code>viz</code>. We now attempt to apply some basic styling onto data passed into <code>viz</code>. This will likely further improve in the future. By @kylebarron in developmentseed/lonboard!389</li> </ul>"},{"location":"CHANGELOG/#fixes_2","title":"Fixes","text":"<ul> <li>Set exported HTML height to 100% by @kylebarron in developmentseed/lonboard!377</li> <li>Raise error on single input to MultiRangeSlider by @kylebarron in developmentseed/lonboard!367</li> <li>Fix pandas <code>to_numeric</code> FutureWarning by @kylebarron in developmentseed/lonboard!368</li> <li>Fix viewing polygons in local html files by @kylebarron in developmentseed/lonboard!387</li> <li>Fix: fix sliced array input for reprojection by @kylebarron in developmentseed/lonboard!391</li> <li>Fix: Don't reproject for epsg:4326 input by @kylebarron in developmentseed/lonboard!392</li> <li>Fix: Fix weighted centroid calculation by @kylebarron in developmentseed/lonboard!393</li> <li>Fix <code>viz()</code> with <code>__geo_interface__</code> input by @kylebarron in developmentseed/lonboard!426</li> <li>Add DataFilterExtension notebook to website by @kylebarron in developmentseed/lonboard!362</li> <li>Allow non-compliant geoarrow CRS metadata by @kylebarron in developmentseed/lonboard!369</li> <li>Automatically parse geoarrow.wkb to native geoarrow by @kylebarron in developmentseed/lonboard!372</li> <li>Parse GeoParquet metadata by @kylebarron in developmentseed/lonboard!407</li> <li>CLI: 'crs' in geoparquet metadata should be optional. by @jwass in developmentseed/lonboard!411</li> </ul>"},{"location":"CHANGELOG/#other-changes","title":"Other changes","text":"<ul> <li>Creating a new user bug report by @emmalu in developmentseed/lonboard!386</li> <li>Update epic template by @emmalu in developmentseed/lonboard!382</li> <li>NormalAccessor by @naomatheus in developmentseed/lonboard!376</li> <li>Conda: Try including <code>manifest.in</code> file for <code>static</code> folder inclusion by @kylebarron in developmentseed/lonboard!421</li> <li>Switch to animated hero image by @kylebarron in developmentseed/lonboard!423</li> <li>Add CRS to GeoDataFrame in notebook examples by @kylebarron in developmentseed/lonboard!419</li> </ul>"},{"location":"CHANGELOG/#new-contributors","title":"New Contributors","text":"<ul> <li>@emmalu made their first contribution in developmentseed/lonboard!382</li> <li>@naomatheus made their first contribution in developmentseed/lonboard!376</li> <li>@jwass made their first contribution in developmentseed/lonboard!411</li> <li>@giswqs made their first contribution in developmentseed/lonboard!223</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.6.0...v0.7.0</p>"},{"location":"CHANGELOG/#060-2024-02-13","title":"[0.6.0] - 2024-02-13","text":""},{"location":"CHANGELOG/#new_2","title":"New!","text":"<ul> <li>DataFilterExtension by @kylebarron in developmentseed/lonboard!278</li> <li>Multi-range sliders for DataFilterExtension by @kylebarron in developmentseed/lonboard!340</li> <li>BitmapLayer and BitmapTileLayer by @kylebarron in developmentseed/lonboard!288</li> <li>Improved GeoArrow interop by @kylebarron in developmentseed/lonboard!308</li> <li>Allow passing a positional <code>layers</code> object into <code>Map</code> by @kylebarron in developmentseed/lonboard!319</li> <li>GeoArrow-based multithreaded coordinate reprojection by @kylebarron in developmentseed/lonboard!337</li> <li>Support <code>pyarrow.Table</code> with <code>geoarrow.pyarrow</code> extension types as geometry columns by @jorisvandenbossche in developmentseed/lonboard!218</li> <li>Add ecosystem/integrations documentation by @kylebarron in developmentseed/lonboard!350</li> </ul>"},{"location":"CHANGELOG/#fixes_3","title":"Fixes","text":"<ul> <li>Add font to index.css to fix static HTML export by @jtmiclat in developmentseed/lonboard!284</li> <li>Fix displaying tooltip for first row by @kylebarron in developmentseed/lonboard!287</li> <li>accept matplotlib colormap input to apply_cmap by @kylebarron in developmentseed/lonboard!289</li> <li>Use preferred OSM tile url by @kylebarron in developmentseed/lonboard!290</li> <li>set max zoom on osm layer by @kylebarron in developmentseed/lonboard!291</li> <li>Update contributor docs by @kylebarron in developmentseed/lonboard!316</li> <li>Check epsg:4326 bounds in layer creation by @kylebarron in developmentseed/lonboard!317</li> <li>add reference for installing from source by @kylebarron in developmentseed/lonboard!318</li> <li>Fix inferring number of rows per chunk by @kylebarron in developmentseed/lonboard!327</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!331</li> <li>Set max number of chunks per layer by @kylebarron in developmentseed/lonboard!332</li> <li>Move accessor length validation to serialization by @kylebarron in developmentseed/lonboard!333</li> <li>Deduplicate serialization for accessors by @kylebarron in developmentseed/lonboard!334</li> <li>Multi-dimensional GPU-based data filtering by @kylebarron in developmentseed/lonboard!335</li> <li>Bump anywidget to 0.9 &amp; simplify Wasm initialization by @kylebarron in developmentseed/lonboard!344</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!348</li> <li>docs fixes by @kylebarron in developmentseed/lonboard!354</li> <li>Add <code>DataFilterExtension</code> example by @kylebarron in developmentseed/lonboard!358</li> <li>fix arc layer default arguments by @kylebarron in developmentseed/lonboard!359</li> </ul>"},{"location":"CHANGELOG/#new-contributors_1","title":"New Contributors","text":"<ul> <li>@jtmiclat made their first contribution in developmentseed/lonboard!284</li> <li>@jorisvandenbossche made their first contribution in developmentseed/lonboard!218</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.5.0...v0.6.0</p>"},{"location":"CHANGELOG/#050-2023-12-01","title":"[0.5.0] - 2023-12-01","text":""},{"location":"CHANGELOG/#new-features","title":"New Features","text":"<ul> <li>Improve map height by @vgeorge in developmentseed/lonboard!220</li> <li>Add method to apply a categorical colormap by @kylebarron in developmentseed/lonboard!251</li> <li>Deduce environment and set map height in colab and vscode by @kylebarron in developmentseed/lonboard!252</li> <li>Add various carto basemaps as options by @kylebarron in developmentseed/lonboard!268</li> <li>Sync the clicked index back to Python by @kylebarron in developmentseed/lonboard!270</li> <li>Set <code>width_min_pixels</code> in PathLayer example by @kylebarron in developmentseed/lonboard!276</li> <li>Bump deck.gl layers for performance benefits by @kylebarron in developmentseed/lonboard!277</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.2...v0.5.0</p>"},{"location":"CHANGELOG/#042-2023-11-13","title":"[0.4.2] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed","title":"Fixed","text":"<ul> <li>Fixed ScatterplotLayer rendering by @kylebarron in developmentseed/lonboard!246</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.1...v0.4.2</p>"},{"location":"CHANGELOG/#041-2023-11-13","title":"[0.4.1] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed_1","title":"Fixed","text":"<ul> <li>Fixed Polygon rendering by @kylebarron in developmentseed/lonboard!243</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.0...v0.4.1</p>"},{"location":"CHANGELOG/#040-2023-11-10","title":"[0.4.0] - 2023-11-10","text":""},{"location":"CHANGELOG/#new-features_1","title":"New Features","text":"<ul> <li>New <code>HeatmapLayer</code></li> <li>New <code>experimental</code> module, with new layers (<code>ArcLayer</code>, <code>TextLayer</code>) and \"layer extensions\" (<code>BrushingExtension</code>, <code>CollisionFilterExtension</code>).</li> <li>New \"migration\" notebook using the experimental <code>ArcLayer</code>.</li> </ul>"},{"location":"CHANGELOG/#fixed_2","title":"Fixed","text":"<ul> <li>Add pandas v2 requirement by @kylebarron in developmentseed/lonboard!229</li> <li>bump anywidget to 0.7.1 by @kylebarron in developmentseed/lonboard!233</li> </ul> <p>This should error when the JS files have not been included when packaging.</p> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.3.0...v0.4.0</p>"},{"location":"CHANGELOG/#030-2023-11-07","title":"[0.3.0] - 2023-11-07","text":""},{"location":"CHANGELOG/#new-features_2","title":"New Features","text":"<ul> <li>Save widget to standalone HTML file by @kylebarron in developmentseed/lonboard!199</li> <li>Support for rendering inside Visual Studio Code</li> <li>Allow customized picking radius by @kylebarron in developmentseed/lonboard!212</li> <li>New example notebooks</li> <li>Automatically downcast data types in <code>from_geopandas</code> by @kylebarron in developmentseed/lonboard!195</li> </ul>"},{"location":"CHANGELOG/#fixed_3","title":"Fixed","text":"<ul> <li>Allow pandas series as accessor to FloatAccessor by @kylebarron in developmentseed/lonboard!208</li> <li>Raise error when creating class with unknown keyword argument by @kylebarron in developmentseed/lonboard!209</li> <li>fix tooltip rendering when not hovering over an object by @kylebarron in developmentseed/lonboard!215</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.2.0...v0.3.0</p>"},{"location":"CHANGELOG/#020-2023-11-01","title":"[0.2.0] - 2023-11-01","text":""},{"location":"CHANGELOG/#breaking-changes","title":"Breaking Changes","text":"<ul> <li>Layers no longer render a map object. Instead, pass one or more layer instances into a <code>lonboard.Map</code> and display that.</li> </ul>"},{"location":"CHANGELOG/#new-features_3","title":"New Features","text":"<ul> <li>Support multiple layers on a single map.</li> <li>Tooltip with data information on hover.</li> <li>Allow hex string color input to ColorAccessor.</li> <li>Versioned documentation website</li> <li>New examples</li> <li>Experimental API to change map height</li> </ul>"},{"location":"CHANGELOG/#fixed_4","title":"Fixed","text":"<ul> <li>Fix handling of 3d coordinates by @kylebarron in developmentseed/lonboard!160</li> <li>Improved validation error messages by @kylebarron in developmentseed/lonboard!161</li> </ul>"},{"location":"CHANGELOG/#new-contributors_2","title":"New Contributors","text":"<ul> <li>@chrisgervang made their first contribution in developmentseed/lonboard!150</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.1.2...v0.2.0</p>"},{"location":"CHANGELOG/#012-2023-10-24","title":"[0.1.2] - 2023-10-24","text":"<ul> <li>Actually update <code>@geoarrow/deck.gl-layers</code> to version 0.2.0, whoops \ud83d\ude05</li> </ul>"},{"location":"CHANGELOG/#011-2023-10-23","title":"[0.1.1] - 2023-10-23","text":"<ul> <li>Updates to <code>@geoarrow/deck.gl-layers</code> version 0.2.0:</li> <li>Fixed MultiPolygon rendering</li> <li>Fixed rendering of polygons with holes.</li> <li>Polygon rendering is roughly 35% faster.</li> </ul>"},{"location":"CHANGELOG/#010-2023-10-17","title":"[0.1.0] - 2023-10-17","text":"<ul> <li>Initial public release.</li> <li>Initial support for <code>ScatterplotLayer</code>, <code>PathLayer</code>, and <code>SolidPolygonLayer</code>.</li> </ul>"},{"location":"alternatives/","title":"Alternatives to Lonboard","text":""},{"location":"alternatives/#lonboard-vs-ipyleaflet","title":"Lonboard vs ipyleaflet","text":"<p>ipyleaflet is a great rendering library for small- and medium-sized datasets. ipyleaflet supports a broad range of data types and formats and gives the user broad control over how to render data.</p> <p>The downside of ipyleaflet is that it doesn't support large datasets as well. It uses GeoJSON to transfer data to the frontend, which is slow to write, slow to read, and large in transit. Additionally, leaflet's primary goal is not to support very large quantities of data.</p>"},{"location":"alternatives/#lonboard-vs-pydeck","title":"Lonboard vs pydeck","text":"<p>Pydeck is a full-featured binding from Python to deck.gl. Pydeck attempts to cover most of the deck.gl API. It's harder to use binary data transport with pydeck, and similarly to ipyleaflet will usually serialize data to GeoJSON.</p> <p>Lonboard does not try to cover deck.gl's full API, but rather has an opinionated approach that nudges users to the fastest rendering for many common use cases.</p>"},{"location":"alternatives/#why-not-contribute-back-to-pydeck","title":"Why not contribute back to pydeck?","text":"<p>Pydeck and lonboard have very different goals.</p> <p>A stated goal of pydeck is to be non-opinionated and to allow users with various data sources (GeoJSON strings, URLs to arbitrary data sources, etc.) to render. It makes sense for \"official\" bindings to be non-opinionated, but lonboard takes an opposite tack. By forcing users to use Arrow, we can get reliably fast performance the very common use case of rendering <code>GeoDataFrame</code>s. A downside here is that an Arrow-based implementation has required dependencies that pydeck wouldn't want. <code>pyarrow</code> on the Python side is 90MB on disk. Arrow JS on the JS side is ~200kb, and the default <code>parquet-wasm</code> build is ~1MB.</p> <p>Pydeck is tightly tied into the deck.gl JSON renderer, which allows describing a map state fully in JSON. It's not clear how this would work with the JavaScript GeoArrow layers.</p> <p>Aside from this, pydeck and lonboard use different widget architectures. Pydeck is built on the historical ipywidget layout, using the widget cookiecutter as inspiration and having a separate Jupyter Widget package published to NPM. Lonboard takes a newer approach (unavailable at the time pydeck was created) that uses Anywidget, vastly simplifying the widget process.</p>"},{"location":"alternatives/#lonboard-vs-keplergl-jupyter","title":"Lonboard vs kepler.gl-jupyter","text":"<p>kepler.gl is a JavaScript application focused on presenting a simple, high-level visualization and analysis toolkit to browser-based users. Just like Lonboard, it's built on top of deck.gl. It also has Python bindings via kepler.gl-jupyter.</p> <p>kepler.gl is a very good tool for data analysis and exploration in the browser. On the contrary, Lonboard targets users who plan to do most analysis in Python, but want the best performance to visualize the maximum amount of data. Lonboard should have better rendering performance, but has no user interface to analyze data in the browser.</p> <p>kepler.gl-jupyter serializes to GeoJSON and kepler.gl uses GeoJSON-like JavaScript objects internally; it therefore has the downside of creating a large text string in Python and serializing that to JavaScript. kepler.gl-jupyter is great for creating standalone static HTML files containing a dataset, but those files tend to be very large, since they contain embedded GeoJSON.</p>"},{"location":"alternatives/#lonboard-vs-datashader","title":"Lonboard vs datashader","text":"<p>Datashader is a truly scalable rendering library. Datashader will re-render your data from scratch when panning around in a map. This allows datashader to aggregate the source data before rendering. Datashader minimizes the amount of data being rendered and thus, in theory, Datashader should perform well for datasets as large as your computer's memory.</p> <p>Lonboard is not scalable in the same sense. It doesn't minimize the amount of data being rendered. If you ask to plot a GeoDataFrame with 3 million points, every single one of those points is transferred to the GPU and drawn to your screen. In contrast to Datashader, Lonboard should perform well for datasets whose geometries fit in your computer's GPU memory, which is usually much smaller than your computer's total memory.</p>"},{"location":"cli/","title":"Command-line Interface","text":"<p>Lonboard includes a command-line interface for quickly viewing local data files.</p> <p>The CLI is accessible either through the <code>lonboard</code> entry point or via <code>python -m lonboard</code> the latter can be useful to ensure that the <code>lonboard</code> instance you're calling is the same as your current <code>python</code> environment.</p> <pre><code>&gt; lonboard --help\nUsage: lonboard [OPTIONS] [FILES]...\n\n  Interactively visualize geospatial data using Lonboard.\n\n  This CLI can be used either to quickly view local files or to create static\n  HTML files.\n\nOptions:\n  -o, --output PATH   The output path for the generated HTML file. If not\n                      provided, will save to a temporary file.\n  --open / --no-open  Whether to open a web browser tab with the generated\n                      map. By default, the web browser is not opened when\n                      --output is provided, but is in other cases.\n  --help              Show this message and exit.\n</code></pre> <p>For example:</p> <pre><code>lonboard data.geojson\n</code></pre>"},{"location":"performance/","title":"Performance","text":"<p>Performance is a critical goal of lonboard. Below are a couple pieces of information you should know to understand lonboard's performance characteristics, as well as some advice for how to get the best performance.</p>"},{"location":"performance/#performance-characteristics","title":"Performance Characteristics","text":"<p>There are two distinct parts to the performance of lonboard: one is the performance of transferring data to the browser and the other is the performance of rendering the data once it's there.</p> <p>In general, these parts are completely distinct. Even if it takes a while to load the data in your browser, the map might be snappy once it loads, and vice versa.</p>"},{"location":"performance/#data-transfer","title":"Data Transfer","text":"<p>Lonboard creates an interactive visualization of your data in your browser. In order to do this, your GeoDataFrame needs to be transferred from your Python environment to your browser.</p> <p>In the case where your Python session is running locally (on the same machine as your browser), this data transfer is extremely fast: less than a second in most cases.</p> <p>However, in the case where your Python session is running on a remote server (such as Google Colab, Binder, or a JupyterHub instance), this data transfer means downloading the data to your local browser. Therefore, when running lonboard from a remote server, your internet speed and the quantity of data you pass into a layer will have large impacts on the data transfer speed.</p> <p>Under the hood, lonboard uses efficient compression (in the form of GeoParquet) to transfer data to the browser, but compression can only do so much; the data still needs to be downloaded.</p>"},{"location":"performance/#rendering-performance","title":"Rendering Performance","text":"<p>Once the data has been transfered from your Python session to your browser, it needs to be rendered.</p> <p>The biggest thing to note is that \u2014 in contrast to projects like datashader \u2014 lonboard does not minimize the amount of data being rendered. If you pass a GeoDataFrame with 10 million coordinates, lonboard will attempt to render all 10 million coordinates at once.</p> <p>The primary determinant of the maximum amount of data you can render with lonboard is your computer's hardware. Via the underlying deck.gl library, lonboard ultimately renders geometries using your computer's Graphics Processing Unit (GPU). If you have a better GPU card, you'll be able to visualize more data.</p> <p>Lonboard is more efficient at rendering than previous libraries, but there will always be some quantity of data beyond which your browser tab is likely to crash while attempting to render. Testing on a recent MacBook Pro M2 computer, lonboard has been able to render a few million points with minimal lag.</p>"},{"location":"performance/#performance-advice","title":"Performance Advice","text":""},{"location":"performance/#use-a-local-python-session","title":"Use a local Python session","text":"<p>Moving from a remote Python environment to a local Python environment is often impractical, but this change will make it much faster to visualize data, especially over slow internet connections.</p>"},{"location":"performance/#mutate-an-existing-map-where-possible","title":"Mutate an existing map where possible","text":"<p>Whenever you create a new map object, all the data will be transfered from your Python environment to your browser. Whenever mutating an existing map object, only the changed data will be sent to the browser.</p> <p>Therefore, unless changing the content of the data being rendered, do not create a new map object. If you're changing the rendering options of an existing map, prefer mutating the existing map object.</p> <p>Bad</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap2 = ScatterplotLayer.from_geopandas(\n    my_geodataframe,\n    get_fill_color=[255, 0, 0]\n)\n</code></pre> <p>Good</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap1.get_fill_color = [255, 0, 0]\n</code></pre> <p>In the first (bad) case, the content of <code>my_geodataframe</code> will be sent twice to the browser. In the second (good) case, the content of <code>my_geodataframe</code> will be sent only once to the browser, and only the tiny array <code>[255, 0, 0]</code> will be transfered on the second call.</p>"},{"location":"performance/#remove-columns-before-rendering","title":"Remove columns before rendering","text":"<p>All columns included in the <code>GeoDataFrame</code> will be transferred to the browser for visualization. (In the future, these other columns will be used to display a tooltip when hovering over/clicking on a geometry.)</p> <p>Especially in the case of a remote Python session, excluding unnecessary attribute columns will make data transfer to the browser faster.</p>"},{"location":"performance/#use-arrow-based-data-types-in-pandas","title":"Use Arrow-based data types in Pandas","text":"<p>As of Pandas 2.0, Pandas supports two backends for data types: either the original numpy-based data types or new data types based on Arrow and the pyarrow library.</p> <p>The first thing that lonboard does when visualizing data is converting from Pandas to an Arrow representation. Any non-geometry attribute columns will be converted to Arrow, so if you're using Arrow-based data types in Pandas already, this step will be \"free\" as no conversion is needed.</p> <p>If you're loading data from a geospatial file format, as of <code>pyogrio</code> version 0.7, you can load data to Arrow-based columns with</p> <pre><code>import pyogrio\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = pyogrio.read_dataframe(\n    \"path/to/file\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>or directly from GeoPandas with <code>geopandas.read_file</code> with <code>engine=\"pyogrio\"</code>:</p> <pre><code>import geopandas as gpd\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = gpd.read_file(\n    \"path/to/file\",\n    engine=\"pyogrio\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>See the pandas guide on data types and the <code>pandas.ArrowDtype</code> class for more information.</p>"},{"location":"performance/#simplify-geometries-before-rendering","title":"Simplify geometries before rendering","text":"<p>Simplifying geometries before rendering reduces the total number of coordinates and can make a visualization snappier. At this point, lonboard does not offer built-in geometry simplification. This is something you would need to do before passing data to lonboard.</p>"},{"location":"api/basemap/","title":"lonboard.basemap","text":""},{"location":"api/basemap/#lonboard.basemap.CartoBasemap","title":"lonboard.basemap.CartoBasemap","text":"<p>             Bases: <code>str</code>, <code>Enum</code></p> <p>Basemap styles provided by Carto.</p> <p>Refer to Carto documentation for information on styles.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatter","title":"DarkMatter  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatter = 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'\n</code></pre> <p>A dark map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatterNoLabels","title":"DarkMatterNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatterNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/dark-matter-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A dark map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Positron","title":"Positron  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Positron = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json'\n</code></pre> <p>A light map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.PositronNoLabels","title":"PositronNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>PositronNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Voyager","title":"Voyager  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Voyager = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json'\n</code></pre> <p>A light, colored map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.VoyagerNoLabels","title":"VoyagerNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>VoyagerNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/voyager-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light, colored map style without labels.</p>"},{"location":"api/colormap/","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap.DiscreteColormap","title":"DiscreteColormap  <code>module-attribute</code>","text":"<pre><code>DiscreteColormap = Dict[Any, RGBColor]\n</code></pre> <p>A type definition for a discrete colormap.</p> <p>For example, for a land cover colormap, you may want to use the following dict:</p> <pre><code>color_map = {\n    \"Open Water\": [70, 107, 159],\n    \"Perennial Snow/Ice\": [209, 222, 248],\n    \"Developed, Open Space\": [222, 197, 197],\n    \"Developed, Low Intensity\": [217, 146, 130],\n    \"Developed, Medium Intensity\": [235, 0, 0],\n    \"Developed, High Intensity\": [171, 0, 0],\n    \"Barren Land\": [179, 172, 159],\n    \"Deciduous Forest\": [104, 171, 95],\n    \"Evergreen Forest\": [28, 95, 44],\n    \"Mixed Forest\": [181, 197, 143],\n    \"Shrub/Scrub\": [204, 184, 121],\n    \"Herbaceous\": [223, 223, 194],\n    \"Hay/Pasture\": [220, 217, 57],\n    \"Cultivated Crops\": [171, 108, 40],\n    \"Woody Wetlands\": [184, 217, 235],\n    \"Emergent Herbaceous Wetlands\": [108, 159, 184],\n}\n</code></pre> <p>This corresponds to the following well-known color palette from the National Land Cover Database.</p> <p> </p> <p>Keep in mind that the type of the key of the <code>color_map</code> dictionary is important. If your data is represented as a string, the keys of the <code>color_map</code> must also be represented as strings. If your data is represented as an integer, the keys of the colormap should be integers.</p>"},{"location":"api/colormap/#lonboard.colormap.RGBColor","title":"RGBColor  <code>module-attribute</code>","text":"<pre><code>RGBColor = Union[Tuple[int, int, int], Tuple[int, int, int, int], Sequence[int]]\n</code></pre> <p>A type definition for an RGB or RGBA color value</p> <p>All values must range between 0 and 255 (inclusive). If only three values are provided, the fourth (alpha) channel will be inferred as 255 (meaning full opacity, no transparency).</p>"},{"location":"api/colormap/#lonboard.colormap.apply_categorical_cmap","title":"apply_categorical_cmap","text":"<pre><code>apply_categorical_cmap(\n    values: Union[NDArray, pd.Series, pa.Array, pa.ChunkedArray],\n    cmap: DiscreteColormap,\n    *,\n    alpha: Optional[int] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of categorical values.</p> <p>If you're working with categorical data in Pandas or GeoPandas, and especially when those categories are strings, you should use the pandas Categorical type. This will use much less memory and be faster to operate on. <code>apply_categorical_cmap</code> will be 2-3x faster (and use less memory itself) when your data is already represented as a categorical data type.</p> <p>The key of the colormap must be the same as the data type of the column of values you pass in. Usually this will be string, when you perform the lookup on a string column of data.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>Union[NDArray, Series, Array, ChunkedArray]</code>)         \u2013          <p>A numpy array, pandas Series, pyarrow Array or pyarrow ChunkedArray of data. The data type of this column must match the keys of the colormap.</p> </li> <li> <code>cmap</code>             (<code>DiscreteColormap</code>)         \u2013          <p>A dictionary mapping keys to colors. See [DiscreteColormap] for more information.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Optional[int]</code>)         \u2013          <p>The default alpha value for entries in the colormap that do not have an alpha value defined. Alpha must be an integer between 0 and 255 (inclusive).</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/colormap/#lonboard.colormap.apply_continuous_cmap","title":"apply_continuous_cmap","text":"<pre><code>apply_continuous_cmap(\n    values: NDArray[np.floating],\n    cmap: Union[Palette, mpl.colors.Colormap],\n    *,\n    alpha: Union[float, int, NDArray[np.floating], None] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of continuous values.</p> <p>If you pass in a <code>Palette</code> object from the <code>palettable</code> package, this will use matplotlib's LinearSegmentedColormap under the hood. As described in Matplotlib's referenced docstring:</p> <p>The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments.</p> <p>This means that input values are linearly combined from the two nearest colormap colors.</p> <p>If you pass in a Matplotlib <code>Colormap</code> object, this will use the lookup method defined in that class.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>NDArray[floating]</code>)         \u2013          <p>A numpy array of floating point values ranging from 0 to 1.</p> </li> <li> <code>cmap</code>             (<code>Union[Palette, Colormap]</code>)         \u2013          <p>Any matplotlib <code>Colormap</code> or <code>Palette</code> object from the <code>palettable</code> package.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Union[float, int, NDArray[floating], None]</code>)         \u2013          <p>Alpha must be a scalar between 0 and 1, a sequence of such floats with shape matching <code>values</code>, or None.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/controls/","title":"lonboard.controls","text":""},{"location":"api/controls/#lonboard.controls.MultiRangeSlider","title":"lonboard.controls.MultiRangeSlider","text":"<p>             Bases: <code>VBox</code></p> <p>A widget for multiple ranged sliders.</p> <p>This is designed to be used with the DataFilterExtension when you want to filter on 2 to 4 columns on the same time.</p> <p>If you have only a single filter, use an ipywidgets FloatRangeSlider directly.</p>"},{"location":"api/controls/#lonboard.controls.MultiRangeSlider--example","title":"Example","text":"<pre><code>from ipywidgets import FloatRangeSlider\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n</code></pre> <p>Then to propagate updates to a rendered layer, call jsdlink to connect the two widgets.</p> <pre><code>from ipywidgets import jsdlink\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>As you change the slider, the <code>filter_range</code> value on the layer class should be updated.</p>"},{"location":"api/map/","title":"Map","text":""},{"location":"api/map/#lonboard.Map","title":"lonboard.Map","text":"<p>The top-level class used to display a map in a Jupyter Widget.</p> <p>Example:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer, SolidPolygonLayer\n\n# A GeoDataFrame with Point geometries\npoint_gdf = gpd.GeoDataFrame()\npoint_layer = ScatterplotLayer.from_geopandas(\n    point_gdf,\n    get_fill_color=[255, 0, 0],\n)\n\n# A GeoDataFrame with Polygon geometries\npolygon_gdf = gpd.GeoDataFrame()\npolygon_layer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\n\nm = Map([point_layer, polygon_layer])\n</code></pre>"},{"location":"api/map/#lonboard.Map.__init__","title":"__init__","text":"<pre><code>__init__(\n    layers: Union[BaseLayer, Sequence[BaseLayer]], **kwargs: Unpack[MapKwargs]\n) -&gt; None\n</code></pre> <p>Create a new Map.</p> <p>Aside from the <code>layers</code> argument, pass keyword arguments for any other attribute defined in this class.</p> <p>Parameters:</p> <ul> <li> <code>layers</code>             (<code>Union[BaseLayer, Sequence[BaseLayer]]</code>)         \u2013          <p>One or more layers to render on this map.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>None</code>         \u2013          <p>A Map object.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map._height","title":"_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>_height = tag(sync=True)\n</code></pre> <p>Height of the map in pixels.</p> <p>This API is not yet stabilized and may change in the future.</p>"},{"location":"api/map/#lonboard.Map.basemap_style","title":"basemap_style  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>basemap_style = tag(sync=True)\n</code></pre> <p>A MapLibre-compatible basemap style.</p> <p>Various styles are provided in <code>lonboard.basemap</code>.</p> <ul> <li>Type: <code>str</code></li> <li>Default   <code>lonboard.basemap.CartoBasemap.PositronNoLabels</code></li> </ul>"},{"location":"api/map/#lonboard.Map.fly_to","title":"fly_to","text":"<pre><code>fly_to(\n    *,\n    longitude: Union[int, float],\n    latitude: Union[int, float],\n    zoom: float,\n    duration: int = 4000,\n    pitch: Union[int, float] = 0,\n    bearing: Union[int, float] = 0,\n    curve: Optional[Union[int, float]] = None,\n    speed: Optional[Union[int, float]] = None,\n    screen_speed: Optional[Union[int, float]] = None\n)\n</code></pre> <p>\"Fly\" the map to a new location.</p> <p>Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The longitude of the new viewport.</p> </li> <li> <code>latitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The latitude of the new viewport.</p> </li> <li> <code>zoom</code>             (<code>float</code>)         \u2013          <p>The zoom of the new viewport.</p> </li> <li> <code>pitch</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The pitch of the new viewport. Defaults to 0.</p> </li> <li> <code>bearing</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The bearing of the new viewport. Defaults to 0.</p> </li> <li> <code>duration</code>             (<code>int</code>, default:                 <code>4000</code> )         \u2013          <p>The number of milliseconds for the viewport transition to take. Defaults to 4000.</p> </li> <li> <code>curve</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The zooming \"curve\" that will occur along the flight path. Default <code>1.414</code>.</p> </li> <li> <code>speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation defined in relation to <code>curve</code>, it linearly affects the duration, higher speed returns smaller durations and vice versa. Default <code>1.2</code>.</p> </li> <li> <code>screen_speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation measured in screenfuls per second. Similar to speed it linearly affects the duration, when specified speed is ignored.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.layers","title":"layers  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>layers = tag(sync=True, **widget_serialization)\n</code></pre> <p>One or more <code>Layer</code> objects to display on this map.</p>"},{"location":"api/map/#lonboard.Map.parameters","title":"parameters  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>parameters = tag(sync=True)\n</code></pre> <p>GPU parameters to pass to deck.gl.</p> <p>This is an advanced API. The vast majority of users should not need to touch this setting.</p> <p>Note</p> <p>The docstring below is copied from upstream deck.gl documentation. Any usage of <code>GL</code> refers to the constants defined in <code>@luma.gl/constants</code> here, which comes from the MDN docs here.</p> <p>In place of any <code>GL</code> constant, you can use the underlying integer it represents. For example, instead of the JS</p> <pre><code>depthFunc: GL.LEQUAL\n</code></pre> <p>referring to the MDN docs, you should use</p> <pre><code>depthFunc: 0x0203\n</code></pre> <p>Note that these parameters do not yet work with integer keys. If you would like to use integer keys, open an issue.</p> <p>Expects an object with GPU parameters. Before each frame is rendered, this object will be passed to luma.gl's <code>setParameters</code> function to reset the GPU context parameters, e.g. to disable depth testing, change blending modes etc. The default parameters set by <code>Deck</code> on initialization are the following:</p> <pre><code>{\n  blend: true,\n  blendFunc: [GL.SRC_ALPHA, GL.ONE_MINUS_SRC_ALPHA, GL.ONE, GL.ONE_MINUS_SRC_ALPHA],\n  polygonOffsetFill: true,\n  depthTest: true,\n  depthFunc: GL.LEQUAL\n}\n</code></pre> <p>Refer to the luma.gl setParameters API for documentation on supported parameters and values.</p> <pre><code>import GL from '@luma.gl/constants';\nnew Deck({\n  // ...\n  parameters: {\n    blendFunc: [GL.ONE, GL.ONE, GL.ONE, GL.ONE],\n    depthTest: false\n  }\n});\n</code></pre> <p>Notes:</p> <ul> <li>Any GPU <code>parameters</code> prop supplied to individual layers will still override the   global <code>parameters</code> when that layer is rendered.</li> </ul>"},{"location":"api/map/#lonboard.Map.picking_radius","title":"picking_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>picking_radius = tag(sync=True)\n</code></pre> <p>Extra pixels around the pointer to include while picking (such as for a tooltip).</p> <p>This is helpful when rendered objects are difficult to target, for example irregularly shaped icons, small moving circles or interaction by touch.</p> <ul> <li>Type: <code>int</code></li> <li>Default: <code>5</code></li> </ul>"},{"location":"api/map/#lonboard.Map.set_view_state","title":"set_view_state","text":"<pre><code>set_view_state(\n    *,\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    zoom: Optional[float] = None,\n    pitch: Optional[float] = None,\n    bearing: Optional[float] = None\n) -&gt; None\n</code></pre> <p>Set the view state of the map.</p> <p>Any parameters that are unset will not be changed.</p> <p>Other Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new longitude to set on the map. Defaults to None.</p> </li> <li> <code>latitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new latitude to set on the map. Defaults to None.</p> </li> <li> <code>zoom</code>             (<code>Optional[float]</code>)         \u2013          <p>the new zoom to set on the map. Defaults to None.</p> </li> <li> <code>pitch</code>             (<code>Optional[float]</code>)         \u2013          <p>the new pitch to set on the map. Defaults to None.</p> </li> <li> <code>bearing</code>             (<code>Optional[float]</code>)         \u2013          <p>the new bearing to set on the map. Defaults to None.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.show_tooltip","title":"show_tooltip  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>show_tooltip = tag(sync=True)\n</code></pre> <p>Whether to render a tooltip on hover on the map.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/map/#lonboard.Map.to_html","title":"to_html","text":"<pre><code>to_html(\n    filename: Union[str, Path, TextIO, IO[str]], title: Optional[str] = None\n) -&gt; None\n</code></pre> <p>Save the current map as a standalone HTML file.</p> <p>Parameters:</p> <ul> <li> <code>filename</code>             (<code>Union[str, Path, TextIO, IO[str]]</code>)         \u2013          <p>where to save the generated HTML file.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>title</code>             (<code>Optional[str]</code>)         \u2013          <p>A title for the exported map. This will show as the browser tab name.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.use_device_pixels","title":"use_device_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>use_device_pixels = tag(sync=True)\n</code></pre> <p>Controls the resolution of the drawing buffer used for rendering.</p> <p>Setting this to <code>false</code> or a number &lt;= 1 will improve performance on high resolution displays.</p> <p>Note: This parameter must be passed to the <code>Map()</code> constructor. It cannot be changed once the map has been created.</p> <p>The available options are:</p> <ul> <li><code>true</code>: Device (physical) pixels resolution is used for rendering, this resolution   is defined by <code>window.devicePixelRatio</code>. On Retina/HD systems this resolution is   usually twice as big as CSS pixels resolution.</li> <li><code>false</code>: CSS pixels resolution (equal to the canvas size) is used for rendering.</li> <li> <p><code>Number</code> (Experimental): Specified Number is used as a custom ratio (drawing   buffer resolution to CSS pixel resolution) to determine drawing buffer size, a   value less than one uses resolution smaller than CSS pixels, gives better   performance but produces blurry images, a value greater than one uses resolution   bigger than CSS pixels resolution (canvas size), produces sharp images but at a   lower performance.</p> </li> <li> <p>Type: <code>float</code>, <code>int</code> or <code>bool</code></p> </li> <li>Default: <code>true</code></li> </ul>"},{"location":"api/map/#lonboard.Map.view_state","title":"view_state  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>view_state = ViewStateTrait()\n</code></pre> <p>The view state of the map.</p> <ul> <li>Type: <code>ViewState</code></li> <li>Default: Automatically inferred from the data passed to the map.</li> </ul> <p>You can initialize the map to a specific view state using this property:</p> <pre><code>Map(\n    layers,\n    view_state={\"longitude\": -74.0060, \"latitude\": 40.7128, \"zoom\": 7}\n)\n</code></pre> <p>Note</p> <p>The properties of the view state are immutable. Use <code>set_view_state</code> to modify a map's view state once it's been initially rendered.</p>"},{"location":"api/map/#lonboard.models.ViewState","title":"lonboard.models.ViewState","text":"<p>             Bases: <code>NamedTuple</code></p>"},{"location":"api/map/#lonboard.models.ViewState.bearing","title":"bearing  <code>instance-attribute</code>","text":"<pre><code>bearing: float\n</code></pre> <p>Bearing angle in degrees. <code>0</code> is north.</p>"},{"location":"api/map/#lonboard.models.ViewState.latitude","title":"latitude  <code>instance-attribute</code>","text":"<pre><code>latitude: float\n</code></pre> <p>Latitude at the map center.</p>"},{"location":"api/map/#lonboard.models.ViewState.longitude","title":"longitude  <code>instance-attribute</code>","text":"<pre><code>longitude: float\n</code></pre> <p>Longitude at the map center</p>"},{"location":"api/map/#lonboard.models.ViewState.pitch","title":"pitch  <code>instance-attribute</code>","text":"<pre><code>pitch: float\n</code></pre> <p>Pitch angle in degrees. <code>0</code> is top-down.</p>"},{"location":"api/map/#lonboard.models.ViewState.zoom","title":"zoom  <code>instance-attribute</code>","text":"<pre><code>zoom: float\n</code></pre> <p>Zoom level.</p>"},{"location":"api/traits/","title":"lonboard.traits","text":""},{"location":"api/traits/#lonboard.traits.ColorAccessor","title":"lonboard.traits.ColorAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl color accessor.</p> <p>Various input is allowed:</p> <ul> <li>A <code>list</code> or <code>tuple</code> with three or four integers, ranging between 0 and 255   (inclusive). This will be used as the color for all objects.</li> <li>A <code>str</code> representing a hex color or \"well known\" color interpretable by   matplotlib.colors.to_rgba.</li> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>3</code> or <code>4</code>, and will correspond to either RGB   or RGBA colors.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>3</code> or <code>4</code> and its child must have type   <code>uint8</code>.</li> <li>Any Arrow fixed size list array from a library that implements the Arrow   PyCapsule   Interface.</li> </ul> <p>You can use helpers in the <code>lonboard.colormap</code> module (i.e. <code>apply_continuous_cmap</code>) to simplify constructing numpy arrays for color values.</p>"},{"location":"api/traits/#lonboard.traits.FloatAccessor","title":"lonboard.traits.FloatAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl float accessor.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects.</li> <li>A numpy <code>ndarray</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index.</li> <li>Any Arrow floating point array from a library that implements the Arrow PyCapsule   Interface.   This includes data structures from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/traits/#lonboard.traits.GetFilterValueAccessor","title":"lonboard.traits.GetFilterValueAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for the <code>get_filter_value</code> accessor added by the <code>DataFilterExtension</code>, which can have between 1 and 4 float values per row.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects. The   <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A one-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must be 1.</li> <li>A two-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the size of the second dimension of the array.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> </ul> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface. - A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The child   array of the fixed size list must be of floating point type. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the list size.</p> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.NormalAccessor","title":"lonboard.traits.NormalAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl \"normal\" accessor</p> <p>This is primarily used in the [lonboard.PointCloudLayer].</p> <p>Acceptable inputs: - A <code>list</code> or <code>tuple</code> with three <code>int</code> or <code>float</code> values. This will be used as the   normal for all objects. - A numpy ndarray with two dimensions and floating point type. The size of the   second dimension must be 3, i.e. its shape must be <code>(N, 3)</code>. - a pyarrow <code>FixedSizeListArray</code> or <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s   where the size of the inner fixed size list 3. The child array must have type   float32. - Any Arrow array that matches the above restrictions from a library that implements   the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.PointAccessor","title":"lonboard.traits.PointAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl point accessor.</p> <p>Various input is allowed:</p> <ul> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>2</code> or <code>3</code>, and will correspond to either XY   or XYZ positions.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>2</code> or <code>3</code> and its child must be of floating   point type.</li> </ul>"},{"location":"api/traits/#lonboard.traits.PyarrowTableTrait","title":"lonboard.traits.PyarrowTableTrait","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a geospatial Arrow-backed table</p> <p>Allowed input includes:</p> <ul> <li>A pyarrow <code>Table</code> or containing a geometry column with GeoArrow metadata.</li> <li>Any GeoArrow table from a library that implements the Arrow PyCapsule   Interface.   This includes the   <code>GeoTable</code>   class from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/viz/","title":"viz","text":"<p>The top-level namespace accessible after <code>import lonboard</code>.</p> <p>Layers are also accessible in the top-level namespace, but are documented in the <code>layers/</code> folder.</p>"},{"location":"api/viz/#lonboard.viz","title":"lonboard.viz","text":"<pre><code>viz(\n    data: Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]],\n    *,\n    scatterplot_kwargs: Optional[ScatterplotLayerKwargs] = None,\n    path_kwargs: Optional[PathLayerKwargs] = None,\n    polygon_kwargs: Optional[PolygonLayerKwargs] = None,\n    map_kwargs: Optional[MapKwargs] = None\n) -&gt; Map\n</code></pre> <p>A high-level function to plot your data easily.</p> <p>The goal of this function is to make it simple to get something showing on a map. For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>This function accepts a variety of geospatial inputs:</p> <ul> <li>geopandas <code>GeoDataFrame</code></li> <li>geopandas <code>GeoSeries</code></li> <li>numpy array of Shapely objects</li> <li>Single Shapely object</li> <li>Any Python class with a <code>__geo_interface__</code> property conforming to the     Geo Interface protocol.</li> <li><code>dict</code> holding GeoJSON-like data.</li> <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li> </ul> <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p> <p>Parameters:</p> <ul> <li> <code>data</code>             (<code>Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]]</code>)         \u2013          <p>a data object of any supported type.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>scatterplot_kwargs</code>             (<code>Optional[ScatterplotLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>ScatterplotLayer</code>s.</p> </li> <li> <code>path_kwargs</code>             (<code>Optional[PathLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PathLayer</code>s.</p> </li> <li> <code>polygon_kwargs</code>             (<code>Optional[PolygonLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PolygonLayer</code>s.</p> </li> <li> <code>map_kwargs</code>             (<code>Optional[MapKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to the generated <code>Map</code>.</p> </li> </ul> <p>For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>Returns:</p> <ul> <li> <code>Map</code>         \u2013          <p>widget visualizing the provided data.</p> </li> </ul>"},{"location":"api/viz/#lonboard._viz.VizDataInput","title":"VizDataInput  <code>module-attribute</code>","text":"<pre><code>VizDataInput = Union[\n    GeoDataFrame,\n    GeoSeries,\n    Table,\n    NDArray[object_],\n    BaseGeometry,\n    ArrowStreamExportable,\n    GeoInterfaceProtocol,\n    Dict[str, Any],\n]\n</code></pre> <p>A type definition for allowed data inputs to the <code>viz</code> function.</p>"},{"location":"api/layer-extensions/","title":"Layer Extensions","text":"<p>Layer extensions are bonus features that you can optionally add to the core deck.gl layers.</p> <p>Layer extensions are in an experimental state. Some things are known to not yet work:</p> <ul> <li>Modifying the extensions on a layer by mutating its <code>extensions</code> list via <code>append</code> or <code>pop</code>. It should work, however, by creating a new list and assigning <code>layer.extensions = new_extensions_list</code>.</li> </ul> <p>If you encounter issues, please create an issue with reproducible steps.</p>"},{"location":"api/layer-extensions/brushing-extension/","title":"BrushingExtension","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension","title":"lonboard.layer_extension.BrushingExtension","text":"<p>Adds GPU-based data brushing functionalities to layers. It allows the layer to show/hide objects based on the current pointer position.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--example","title":"Example","text":"<p>An example is in the County-to-County Migration notebook.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_enabled","title":"<code>brushing_enabled</code>","text":"<p>Enable/disable brushing. If brushing is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_target","title":"<code>brushing_target</code>","text":"<p>The position used to filter each object by. One of the following:</p> <ul> <li><code>\"source\"</code>: Use the primary position for each object. This can mean different   things depending on the layer. It usually refers to the coordinates returned by   <code>getPosition</code> or <code>getSourcePosition</code> accessors.</li> <li><code>\"target\"</code>: Use the secondary position for each object. This may not be available   in some layers. It usually refers to the coordinates returned by   <code>getTargetPosition</code> accessor.</li> <li><code>\"source_target\"</code>: Use both the primary position and secondary position for each   object. Show object if either is in brushing range.</li> <li> <p><code>\"custom\"</code>: Some layers may not describe their data objects with one or two   coordinates, for example <code>PathLayer</code> and <code>PolygonLayer</code>. Use this option with the   <code>get_brushing_target</code> prop to provide a custom position that each object should be   filtered by.</p> </li> <li> <p>Type: <code>str</code>, optional</p> <p>One of: \"source\" | \"target\" | \"source_target\" | \"custom\"</p> </li> <li> <p>Default: <code>\"source\"</code></p> </li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_radius","title":"<code>brushing_radius</code>","text":"<p>The brushing radius centered at the pointer, in meters. If a data object is within this circle, it is rendered; otherwise it is hidden.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10000</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--get_brushing_target","title":"<code>get_brushing_target</code>","text":"<p>An arbitrary position for each object that it will be filtered by.</p> <p>Only effective if <code>brushing_target</code> is set to <code>\"custom\"</code>.</p> <ul> <li>Type: PointAccessor, optional<ul> <li>If a point is provided, it is used as the target for all rows.</li> <li>If an array of points is provided, each value in the array will be used as the   target for the row at the same row index.</li> </ul> </li> <li>Default: <code>None</code>.</li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/","title":"CollisionFilterExtension","text":""},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension","title":"lonboard.layer_extension.CollisionFilterExtension","text":"<p>Allows layers to hide overlapping objects.</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_enabled","title":"<code>collision_enabled</code>","text":"<p>Enable/disable collisions. If collisions are disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_group","title":"<code>collision_group</code>","text":"<p>Collision group this layer belongs to. If it is not set, the 'default' collision group is used</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--get_collision_priority","title":"<code>get_collision_priority</code>","text":"<p>Accessor for collision priority. Must return a number in the range -1000 -&gt; 1000. Features with higher values are shown preferentially.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the priority for all objects.</li> <li>If an array is provided, each value in the array will be used as the priority   for the object at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/","title":"DataFilterExtension","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension","title":"lonboard.layer_extension.DataFilterExtension","text":"<p>Adds GPU-based data filtering functionalities to layers. It allows the layer to show/hide objects based on user-defined properties.</p>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--example","title":"Example","text":"<pre><code>from lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.layer_extension import DataFilterExtension\n\ngdf = gpd.GeoDataFrame(...)\nextension = DataFilterExtension()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[extension],\n    get_filter_value=gdf[\"filter_value\"], # replace with desired column\n    filter_range=[0, 5] # replace with desired filter range\n)\n</code></pre> <p>The <code>DataFilterExtension</code> allows filtering on 1 to 4 attributes at the same time. So if you have four numeric columns of interest, you can filter on the intersection of all of them.</p> <p>For easy visualization, we suggest connecting the <code>DataFilterExtension</code> to an interactive slider from <code>ipywidgets</code>.</p> <pre><code>from ipywidgets import FloatRangeSlider\n\nslider = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"Slider: \"\n)\nslider\n\njsdlink(\n    (slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>If you have 2 to 4 columns, use a <code>MultiRangeSlider</code>, which combines multiple <code>FloatRangeSlider</code> objects in a form that the <code>DataFilterExtension</code> expects.</p> <pre><code>from ipywidgets import FloatRangeSlider, jsdlink\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--important-notes","title":"Important notes","text":"<ul> <li>The DataFilterExtension only supports float32 data, so integer data will be casted   to float32.</li> <li>The DataFilterExtension copies all data referenced by <code>get_filter_value</code> to the   GPU, so it will increase memory pressure on the GPU.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--layer-properties","title":"Layer Properties","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_enabled","title":"<code>filter_enabled</code>","text":"<p>Enable/disable the data filter. If the data filter is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_range","title":"<code>filter_range</code>","text":"<p>The bounds which defines whether an object should be rendered. If an object's filtered value is within the bounds, the object will be rendered; otherwise it will be hidden. This prop can be updated on user input or animation with very little cost.</p> <p>Format:</p> <p>If <code>filter_size</code> is 1, provide a single tuple of <code>(min, max)</code>.</p> <p>If <code>filter_size</code> is 2 to 4, provide a list of tuples: <code>[(min0, max0), (min1, max1), ...]</code> for each filtered property, respectively.</p> <ul> <li>Type: either Tuple[float, float] or List[Tuple[float, float]], optional</li> <li>Default: <code>(-1, 1)</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_soft_range","title":"<code>filter_soft_range</code>","text":"<p>If specified, objects will be faded in/out instead of abruptly shown/hidden.</p> <p>When the filtered value is outside of the bounds defined by <code>filter_soft_range</code> but still within the bounds defined by <code>filter_range</code>, the object will be rendered as \"faded\".</p> <ul> <li>Type: Tuple[float, float], optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_size","title":"<code>filter_transform_size</code>","text":"<p>When an object is \"faded\", manipulate its size so that it appears smaller or thinner. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_color","title":"<code>filter_transform_color</code>","text":"<p>When an object is \"faded\", manipulate its opacity so that it appears more translucent. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--get_filter_value","title":"<code>get_filter_value</code>","text":"<p>Accessor to retrieve the value for each object that it will be filtered by.</p> <ul> <li>Type:   GetFilterValueAccessor<ul> <li>If a scalar value is provided, it is used as the value for all objects.</li> <li>If an array is provided, each value in the array will be used as the value   for the object at the same row index.</li> </ul> </li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension.filter_size","title":"filter_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filter_size = tag(sync=True)\n</code></pre> <p>The size of the filter (number of columns to filter by).</p> <p>The data filter can show/hide data based on 1-4 numeric properties of each object.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default 1.</li> </ul>"},{"location":"api/layers/arc-layer/","title":"ArcLayer","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer","title":"lonboard.experimental.ArcLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render raised arcs joining pairs of source and target coordinates.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_height","title":"get_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_height = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Height color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_color","title":"get_source_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Source color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_position","title":"get_source_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Source position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_color","title":"get_target_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Target color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_position","title":"get_target_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Target position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_tilt","title":"get_tilt  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_tilt = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Use to tilt the arc to the side if you have multiple arcs with the same source and target positions.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The line width of each object, in units specified by <code>widthUnits</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.great_circle","title":"great_circle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>great_circle = tag(sync=True)\n</code></pre> <p>If <code>True</code>, create the arc along the shortest path on the earth surface.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.num_segments","title":"num_segments  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>num_segments = tag(sync=True)\n</code></pre> <p>The number of segments used to draw each arc.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>50</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait()\n</code></pre> <p>A GeoArrow table.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The scaling multiplier for the width of each line.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/base-layer/","title":"Base Classes","text":""},{"location":"api/layers/base-layer/#lonboard.BaseLayer","title":"lonboard.BaseLayer","text":"<p>             Bases: <code>BaseWidget</code></p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer","title":"lonboard.BaseArrowLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>Any Arrow-based layer should subclass from BaseArrowLayer</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/bitmap-layer/","title":"BitmapLayer","text":""},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer","title":"lonboard.BitmapLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The <code>BitmapLayer</code> renders a bitmap (e.g. PNG, JPEG, or WebP) at specified boundaries.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapLayer\n\nlayer = BitmapLayer(\n    image='https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/sf-districts.png',\n    bounds=[-122.5190, 37.7045, -122.355, 37.829]\n)\nm = Map(layer)\nm\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.bounds","title":"bounds  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>bounds = tag(sync=True)\n</code></pre> <p>The bounds of the image.</p> <p>Supported formats:</p> <pre><code>- Coordinates of the bounding box of the bitmap `[left, bottom, right, top]`\n- Coordinates of four corners of the bitmap, should follow the sequence of\n  `[[left, bottom], [left, top], [right, top], [right, bottom]]`.\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.image","title":"image  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>image = tag(sync=True)\n</code></pre> <p>The URL to an image to display.</p> <ul> <li>Type: <code>str</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/","title":"BitmapTileLayer","text":""},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer","title":"lonboard.BitmapTileLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The BitmapTileLayer renders image tiles (e.g. PNG, JPEG, or WebP) in the web mercator tiling system. Only the tiles visible in the current viewport are loaded and rendered.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapTileLayer\n\n# We set `max_requests &lt; 0` because `tile.openstreetmap.org` supports HTTP/2.\nlayer = BitmapTileLayer(\n    data=\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n    tile_size=256,\n    max_requests=-1,\n    min_zoom=0,\n    max_zoom=19,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.data","title":"data  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>data = tag(sync=True)\n</code></pre> <p>Either a URL template or an array of URL templates from which the tile data should be loaded.</p> <p>If the value is a string: a URL template. Substrings {x} {y} and {z}, if present, will be replaced with a tile's actual index when it is requested.</p> <p>If the value is an array: multiple URL templates. Each endpoint must return the same content for the same tile index. This can be used to work around domain sharding, allowing browsers to download more resources simultaneously. Requests made are balanced among the endpoints, based on the tile index.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extent","title":"extent  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extent = tag(sync=True)\n</code></pre> <p>The bounding box of the layer's data, in the form of <code>[min_x, min_y, max_x, max_y]</code>. If provided, the layer will only load and render the tiles that are needed to fill this box.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_cache_size","title":"max_cache_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_cache_size = tag(sync=True)\n</code></pre> <p>The maximum number of tiles that can be cached. The tile cache keeps loaded tiles in memory even if they are no longer visible. It reduces the need to re-download the same data over and over again when the user pan/zooms around the map, providing a smoother experience.</p> <p>If not supplied, the <code>max_cache_size</code> is calculated as 5 times the number of tiles in the current viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_requests","title":"max_requests  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_requests = tag(sync=True)\n</code></pre> <p>The maximum number of concurrent data fetches.</p> <p>If &lt;= 0, no throttling will occur, and <code>get_tile_data</code> may be called an unlimited number of times concurrently regardless of how long that tile is or was visible.</p> <p>If &gt; 0, a maximum of <code>max_requests</code> instances of <code>get_tile_data</code> will be called concurrently. Requests may never be called if the tile wasn't visible long enough to be scheduled and started. Requests may also be aborted (through the signal passed to <code>get_tile_data</code>) if there are more than <code>max_requests</code> ongoing requests and some of those are for tiles that are no longer visible.</p> <p>If <code>get_tile_data</code> makes fetch requests against an HTTP 1 web server, then max_requests should correlate to the browser's maximum number of concurrent fetch requests. For Chrome, the max is 6 per domain. If you use the data prop and specify multiple domains, you can increase this limit. For example, with Chrome and 3 domains specified, you can set max_requests=18.</p> <p>If the web server supports HTTP/2 (Open Chrome dev tools and look for \"h2\" in the Protocol column), then you can make an unlimited number of concurrent requests (and can set max_requests=-1). Note that this will request data for every tile, no matter how long the tile was visible, and may increase server load.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_zoom","title":"max_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_zoom = tag(sync=True)\n</code></pre> <p>The max zoom level of the layer's data. When overzoomed (i.e. <code>zoom &gt; max_zoom</code>), tiles from this level will be displayed.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.min_zoom","title":"min_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>min_zoom = tag(sync=True)\n</code></pre> <p>The min zoom level of the layer's data. When underzoomed (i.e. <code>zoom &lt; min_zoom</code>), the layer will not display any tiles unless <code>extent</code> is defined, to avoid issuing too many tile requests.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.refinement_strategy","title":"refinement_strategy  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>refinement_strategy = tag(sync=True)\n</code></pre> <p>How the tile layer refines the visibility of tiles.</p> <p>When zooming in and out, if the layer only shows tiles from the current zoom level, then the user may observe undesirable flashing while new data is loading. By setting <code>refinement_strategy</code> the layer can attempt to maintain visual continuity by displaying cached data from a different zoom level before data is available.</p> <p>This prop accepts one of the following:</p> <ul> <li><code>\"best-available\"</code>: If a tile in the current viewport is waiting for its data to   load, use cached content from the closest zoom level to fill the empty space. This   approach minimizes the visual flashing due to missing content.</li> <li><code>\"no-overlap\"</code>: Avoid showing overlapping tiles when backfilling with cached   content. This is usually favorable when tiles do not have opaque backgrounds.</li> <li> <p><code>\"never\"</code>: Do not display any tile that is not selected.</p> </li> <li> <p>Type: <code>str</code>, optional</p> </li> <li>Default: <code>\"best-available\"</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tile_size","title":"tile_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tile_size = tag(sync=True)\n</code></pre> <p>The pixel dimension of the tiles, usually a power of 2.</p> <p>Tile size represents the target pixel width and height of each tile when rendered. Smaller tile sizes display the content at higher resolution, while the layer needs to load more tiles to fill the same viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>512</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.zoom_offset","title":"zoom_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>zoom_offset = tag(sync=True)\n</code></pre> <p>This offset changes the zoom level at which the tiles are fetched. Needs to be an integer.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/heatmap-layer/","title":"HeatmapLayer","text":""},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer","title":"lonboard.HeatmapLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>HeatmapLayer</code> visualizes the spatial distribution of data.</p> <p>Example</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, HeatmapLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = HeatmapLayer.from_geopandas(gdf)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, HeatmapLayer\n\n# Example: A GeoParquet file with Point geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = HeatmapLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.aggregation","title":"aggregation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>aggregation = tag(sync=True)\n</code></pre> <p>Defines the type of aggregation operation</p> <p>Valid values are 'SUM', 'MEAN'.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>\"SUM\"</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.debounce_timeout","title":"debounce_timeout  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>debounce_timeout = tag(sync=True)\n</code></pre> <p>Interval in milliseconds during which changes to the viewport don't trigger aggregation.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>500</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.get_weight","title":"get_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_weight = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The weight of each object.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.intensity","title":"intensity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>intensity = tag(sync=True)\n</code></pre> <p>Value that is multiplied with the total weight at a pixel to obtain the final weight.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.radius_pixels","title":"radius_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_pixels = tag(sync=True)\n</code></pre> <p>Radius of the circle in pixels, to which the weight of an object is distributed.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>30</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.threshold","title":"threshold  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>threshold = tag(sync=True)\n</code></pre> <p>Ratio of the fading weight to the max weight, between <code>0</code> and <code>1</code>.</p> <p>For example, <code>0.1</code> affects all pixels with weight under 10% of the max.</p> <p>Ignored when <code>color_domain</code> is specified.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0.05</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.weights_texture_size","title":"weights_texture_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>weights_texture_size = tag(sync=True)\n</code></pre> <p>Specifies the size of weight texture.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>2048</code></li> </ul>"},{"location":"api/layers/path-layer/","title":"PathLayer","text":"<p>Screenshot from North America roads example</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer","title":"lonboard.PathLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PathLayer</code> renders lists of coordinate points as extruded polylines with mitering.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PathLayer\n\n# A GeoDataFrame with LineString or MultiLineString geometries\ngdf = gpd.GeoDataFrame()\nlayer = PathLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PathLayer\n\n# Example: A GeoParquet file with LineString or MultiLineString geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PathLayer(\n    table=table,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, extrude the path in screen space (width always faces the camera). If <code>False</code>, the width always faces up.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.cap_rounded","title":"cap_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>cap_rounded = tag(sync=True)\n</code></pre> <p>Type of caps. If <code>True</code>, draw round caps. Otherwise draw square caps.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of each path, in units specified by <code>width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.joint_rounded","title":"joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>True</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.miter_limit","title":"miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width. Only works if <code>jointRounded</code> is <code>False</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={LINESTRING, MULTILINESTRING})\n</code></pre> <p>A GeoArrow table with a LineString or MultiLineString column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum path width in pixels. This prop can be used to prevent the path from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum path width in pixels. This prop can be used to prevent the path from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The path width multiplier that multiplied to all paths.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/","title":"PointCloudLayer","text":""},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer","title":"lonboard.PointCloudLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PointCloudLayer</code> renders a point cloud with 3D positions, normals and colors.</p> <p>The <code>PointCloudLayer</code> can be more efficient at rendering large quantities of points than the <code>ScatterplotLayer</code>, but has fewer rendering options. In particular, you can have only one point size across all points in your data.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PointCloudLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = PointCloudLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    point_size=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_normal","title":"get_normal  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_normal = NormalAccessor(None, allow_none=True)\n</code></pre> <p>The normal of each object, in <code>[nx, ny, nz]</code>.</p> <ul> <li>Type: NormalAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the normal for all   points.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the normal for the point at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.point_size","title":"point_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>point_size = tag(sync=True)\n</code></pre> <p>Global radius of all points, in units specified by <code>size_units</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(\n    allowed_geometry_types={POINT}, allowed_dimensions={3}\n)\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/","title":"PolygonLayer","text":"<p>Screenshot from Global Boundaries example</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer","title":"lonboard.PolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PolygonLayer</code> renders filled, stroked and/or extruded polygons.</p> <p>Note</p> <p>This layer is essentially a combination of a <code>PathLayer</code> and a <code>SolidPolygonLayer</code>. This has some overhead beyond a <code>SolidPolygonLayer</code>, so if you're looking for the maximum performance with large data, you may want to use a <code>SolidPolygonLayer</code> directly.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = PolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier.</p> <p>The final elevation is calculated by <code>elevationScale * getElevation(d)</code>. <code>elevationScale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons.</p> <p>Based on the elevations provided by the <code>getElevation</code> accessor.</p> <p>If set to <code>false</code>, all polygons will be flat, this generates less geometry and is faster than simply returning 0 from getElevation.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to draw a filled polygon (solid fill).</p> <p>Note that only the area between the outer polygon and any holes will be filled.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each polygon, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_joint_rounded","title":"line_joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>true</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_miter_limit","title":"line_miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width.</p> <p>Only works if <code>line_joint_rounded</code> is false.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the line from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the line from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>The line width multiplier that multiplied to all outlines of <code>Polygon</code> and <code>MultiPolygon</code> features if the <code>stroked</code> attribute is true.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Whether to draw an outline around the polygon (solid fill).</p> <p>Note that both the outer polygon as well the outlines of any holes will be drawn.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/scatterplot-layer/","title":"ScatterplotLayer","text":"<p>Screenshot from Ookla example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer","title":"lonboard.ScatterplotLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>ScatterplotLayer</code> renders circles at given coordinates.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer\n\n# A GeoDataFrame with Point or MultiPoint geometries\ngdf = gpd.GeoDataFrame()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, ScatterplotLayer\n\n# Example: A GeoParquet file with Point or MultiPoint geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = ScatterplotLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.antialiasing","title":"antialiasing  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>antialiasing = tag(sync=True)\n</code></pre> <p>If <code>True</code>, circles are rendered with smoothed edges. If <code>False</code>, circles are rendered with rough edges. Antialiasing can cause artifacts on edges of overlapping circles.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, rendered circles always face the camera. If <code>False</code> circles face up (i.e. are parallel with the ground plane).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Draw the filled area of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The filled color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the filled color for   all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the filled color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The outline color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the outline color   for all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the outline color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each object, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_radius","title":"get_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_radius = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The radius of each object, in units specified by <code>radius_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the radius for all objects.</li> <li>If an array is provided, each value in the array will be used as the radius   for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the stroke from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the stroke from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>A global line width multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_max_pixels","title":"radius_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum radius in pixels. This can be used to prevent the circle from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_min_pixels","title":"radius_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum radius in pixels. This can be used to prevent the circle from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_scale","title":"radius_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_scale = tag(sync=True)\n</code></pre> <p>A global radius multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_units","title":"radius_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_units = tag(sync=True)\n</code></pre> <p>The units of the radius, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Draw the outline of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT, MULTIPOINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/","title":"SolidPolygonLayer","text":""},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer","title":"lonboard.SolidPolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>SolidPolygonLayer</code> renders filled and/or extruded polygons.</p> <p>Note</p> <p>This layer is similar to the <code>PolygonLayer</code> but will not render an outline around polygons. In most cases, you'll want to use the <code>PolygonLayer</code> directly, but for very large datasets not drawing the outline can significantly improve performance, in which case you may want to use this layer.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, SolidPolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = SolidPolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier. The final elevation is calculated by <code>elevation_scale * get_elevation(d)</code>. <code>elevation_scale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons (based on the elevations provided by the <code>get_elevation</code> accessor'). If set to <code>False</code>, all polygons will be flat, this generates less geometry and is faster than simply returning <code>0</code> from <code>get_elevation</code>.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to fill the polygons (based on the color provided by the <code>get_fill_color</code> accessor).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/text-layer/","title":"TextLayer","text":""},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer","title":"lonboard.experimental.TextLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render text labels at given coordinates.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.background_padding","title":"background_padding  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>background_padding = tag(sync=True)\n</code></pre> <p>The padding of the background.</p> <ul> <li>If an array of 2 is supplied, it is interpreted as <code>[padding_x, padding_y]</code> in   pixels.</li> <li>If an array of 4 is supplied, it is interpreted as <code>[padding_left, padding_top,   padding_right, padding_bottom]</code> in pixels.</li> </ul> <p>default [0, 0, 0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>true</code>, the text always faces camera. Otherwise the text faces up (z).</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.character_set","title":"character_set  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>character_set = tag(sync=True)\n</code></pre> <p>Specifies a list of characters to include in the font. If set to 'auto', will be automatically generated from the data set.</p> <p>default (ASCII characters 32-128)</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_family","title":"font_family  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_family = tag(sync=True)\n</code></pre> <p>CSS font family</p> <p>default 'Monaco, monospace'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_settings","title":"font_settings  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_settings = tag(sync=True)\n</code></pre> <p>Advance options for fine tuning the appearance and performance of the generated shared <code>fontAtlas</code>.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_weight","title":"font_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_weight = tag(sync=True)\n</code></pre> <p>CSS font weight</p> <p>default 'normal'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_alignment_baseline","title":"get_alignment_baseline  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_alignment_baseline = tag(sync=True)\n</code></pre> <p>Vertical alignment accessor</p> <p>default 'center'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_angle","title":"get_angle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_angle = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label rotation accessor, in degrees</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_background_color","title":"get_background_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_background_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Background color accessor.</p> <p>default [255, 255, 255, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_color","title":"get_border_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Border color accessor.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_width","title":"get_border_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Border width accessor.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Label color accessor</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_pixel_offset","title":"get_pixel_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_pixel_offset = tag(sync=True)\n</code></pre> <p>Label offset from the anchor position, [x, y] in pixels</p> <p>default [0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_size","title":"get_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_size = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label size accessor</p> <p>default 32</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text","title":"get_text  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text = TextAccessor(None, allow_none=True)\n</code></pre> <p>Label text accessor</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text_anchor","title":"get_text_anchor  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text_anchor = tag(sync=True)\n</code></pre> <p>Horizontal alignment accessor</p> <p>default 'middle'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.line_height","title":"line_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_height = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the line height.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.max_width","title":"max_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_width = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the width limit of a string.</p> <p>If specified, when the text is longer than the width limit, it will be wrapped into multiple lines using the strategy of <code>wordBreak</code>.</p> <p>default -1</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_color","title":"outline_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_color = tag(sync=True)\n</code></pre> <p>Color of outline around the text, in <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_width","title":"outline_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_width = tag(sync=True)\n</code></pre> <p>Width of outline around the text, relative to the text size. Only effective if <code>fontSettings.sdf</code> is <code>true</code>.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_max_pixels","title":"size_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_min_pixels","title":"size_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_scale","title":"size_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_scale = tag(sync=True)\n</code></pre> <p>Text size multiplier.</p> <ul> <li>Type: <code>float</code>.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the size, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. default 'pixels'. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.word_break","title":"word_break  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>word_break = tag(sync=True)\n</code></pre> <p>Available options are <code>break-all</code> and <code>break-word</code>. A valid <code>maxWidth</code> has to be provided to use <code>wordBreak</code>.</p> <p>default 'break-word'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"ecosystem/","title":"Ecosystem","text":"<p>Lonboard aims to both integrate closely in the existing Python geospatial ecosystem and be future proof for the next generation of Python geospatial libraries.</p> <p>The pages in this section outline various integrations. If there's a library you think Lonboard should integrate with or you want to document existing compatibility, create an issue to discuss.</p>"},{"location":"ecosystem/geoarrow/","title":"GeoArrow","text":"<p>GeoArrow is an in-memory data structure for storing vector geospatial data and associated attributes. Lonboard uses GeoArrow internally and is the primary reason why Lonboard is fast.</p> <p>There's a burgeoning ecosystem of Python libraries that use GeoArrow internally. Creating Lonboard <code>Layer</code> objects from GeoArrow tables is the fastest way to visualize data, as no conversions are needed on the Python side.</p>"},{"location":"ecosystem/geoarrow/#geoarrow-rust","title":"geoarrow-rust","text":"<p>geoarrow-rust is a Python library implementing the GeoArrow specification with efficient spatial operations. This library has \"rust\" in the name because it is implemented based on the GeoArrow Rust implementation.</p> <pre><code>from geoarrow.rust.core import GeoTable, read_geojson\nfrom lonboard import Map, PathLayer\n\npath = \"/path/to/file.geojson\"\ngeo_table = read_geojson(path)\n\n# Assuming the GeoJSON contains LineString or MultiLineString data\nlayer = PathLayer(table=geo_table)\nm = Map(layer)\nm\n</code></pre> <p>Refer to the geoarrow-rust documentation for more information.</p>"},{"location":"ecosystem/geopandas/","title":"GeoPandas","text":"<p>GeoPandas extends the Pandas data frame library to allow spatial operations on geospatial data.</p> <p>All relevant Lonboard layer classes have a <code>from_geopandas</code> method for <code>GeoDataFrame</code> input.</p> <p>Some layer types, such as <code>BitmapLayer</code>, don't have a <code>from_geopandas</code> method because the rendering isn't relevant to GeoPandas (i.e. GeoPandas doesn't store image data).</p>"},{"location":"ecosystem/geopandas/#example","title":"Example","text":"<pre><code>import geodatasets\nimport geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# New York City boroughs\ngdf = gpd.read_file(geodatasets.get_path('nybb'))\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"ecosystem/jupyter-widgets/","title":"Jupyter Widgets","text":"<p>Jupyter Widgets are interactive browser controls for Jupyter notebooks. While Lonboard's classes are themselves widgets, Jupyter Widgets' design means that widgets integrate with other widgets seamlessly.</p> <p>For example, the <code>ipywidgets</code> Python library includes many core widgets like sliders, dropdowns, color pickers, and file upload elements. Then you can link widgets together. This means that your widget-based slider or chart can create events that change the display of a Lonboard map based on user input events.</p>"},{"location":"ecosystem/panel/","title":"Panel","text":"<p>Panel is a tool to build interactive web applications and dashboards using Python code.</p> <p>Panel has been reported to work with Lonboard. However, it appears that Panel does not support reactive updates in the same way that Shiny does, so the map will necessarily be recreated from scratch on every update.</p>"},{"location":"ecosystem/panel/#example","title":"Example","text":"<p>This example was written by @MarcSkovMadsen in issue #262.</p> <pre><code>\"\"\"Panel data app based on https://developmentseed.org/lonboard/latest/examples/north-america-roads/\"\"\"\n# pip install panel colorcet ipywidgets_bokeh geopandas palettable lonboard\nimport colorcet as cc\nimport geopandas as gpd\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom palettable.palette import Palette\n\nimport panel as pn\n\nurl = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\npath = \"ne_10m_roads_north_america.zip\"\n\ntry:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=path, engine=\"pyogrio\"\n    )\nexcept:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=url, engine=\"pyogrio\"\n    )\n\nstate_options = sorted(state for state in gdf[\"state\"].unique() if state)\n\n\ndef to_rgb(hex: str) -&gt; list:\n    h = hex.strip(\"#\")\n    return list(int(h[i : i + 2], 16) for i in (0, 2, 4))\n\n\ndef to_palette(cmap) -&gt; Palette:\n    \"\"\"Returns the ColorCet colormap as a palettable Palette\"\"\"\n    colors = [to_rgb(item) for item in cmap]\n    return Palette(name=\"colorcet\", map_type=\"colorcet\", colors=colors)\n\n\ndef create_map(state=\"California\", cmap=cc.fire, alpha=0.8):\n    palette = to_palette(cmap)\n    data = gdf[gdf[\"state\"] == state]\n    layer = PathLayer.from_geopandas(data, width_min_pixels=0.8)\n    normalized_scale_rank = (data[\"scalerank\"] - 3) / 9\n    layer.get_color = apply_continuous_cmap(normalized_scale_rank, palette, alpha=alpha)\n    map_ = Map(layer, _height=650)\n    return map_\n\n\ndescription = \"\"\"# lonboard\n\nA Python library for **fast, interactive geospatial vector data visualization** in Jupyter (and Panel).\n\nBy utilizing new technologies like `GeoArrow` and `GeoParquet` in conjunction with GPU-based map rendering, lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.\"\"\"\n\n\n# THE PANEL APP\npn.extension(\"ipywidgets\")\nstate = pn.widgets.Select(\n    value=\"California\",\n    options=state_options,\n    width=150,\n    name=\"State\",\n    sizing_mode=\"stretch_width\",\n)\ncmap = pn.widgets.ColorMap(\n    value=cc.fire,\n    options=cc.palette,\n    ncols=3,\n    swatch_width=100,\n    name=\"cmap by Colorcet\",\n    sizing_mode=\"stretch_width\",\n)\nalpha = pn.widgets.FloatSlider(\n    value=0.8, start=0, end=1, name=\"Alpha\", min_width=100, sizing_mode=\"stretch_width\"\n)\nlogo = pn.pane.Image(\n    \"https://github.com/developmentseed/lonboard/raw/main/assets/dalle-lonboard.jpg\"\n)\ndef title(state):\n    return f\"# North America Roads: {state}\"\n\nsettings = pn.Column(state, cmap, alpha)\ndescription = pn.Column(pn.pane.Markdown(description, margin=5), logo)\ncomponent = pn.Column(\n    pn.bind(title, state=state),\n    pn.panel(\n        pn.bind(create_map, state=state, cmap=cmap, alpha=alpha.param.value_throttled),\n        sizing_mode=\"stretch_both\",\n    ),\n    sizing_mode=\"stretch_both\",\n)\npn.template.FastListTemplate(\n    logo=\"https://panel.holoviz.org/_static/logo_horizontal_dark_theme.png\",\n    title=\"Works with LonBoard\",\n    main=[component],\n    sidebar=[description, settings],\n).servable()\n</code></pre>"},{"location":"ecosystem/shiny/","title":"Shiny","text":"<p>Shiny is a tool to build interactive web applications and dashboards using Python code. Shiny integrates with Jupyter Widgets, which means that Lonboard is supported out-of-the-box.</p> <p>Pay attention to the \"Efficient updates\" section of the Shiny documentation. This is the most efficient way to create a reactive map. Take care to not recreate the <code>Map</code> widget from scratch on updates, as that will send data from Python to the browser anew.</p>"},{"location":"ecosystem/shiny/#example","title":"Example","text":"<pre><code>import geopandas as gpd\nfrom shiny import reactive\nfrom shiny.express import input, ui\nfrom shinywidgets import render_widget\n\nfrom lonboard import Map, ScatterplotLayer\n\ncolors = {\n    \"Red\": [200, 0, 0],\n    \"Green\": [0, 200, 0],\n    \"Blue\": [0, 0, 200],\n}\n\nui.input_select(\"color_select\", \"Color\", choices=list(colors.keys()))\n\n\n@render_widget\ndef map():\n    gdf = gpd.read_file(gpd.datasets.get_path(\"naturalearth_cities\"))\n    layer = ScatterplotLayer.from_geopandas(gdf, radius_min_pixels=2)\n    return Map(layer)\n\n\n@reactive.effect\ndef set_fill_color():\n    map.widget.layers[0].get_fill_color = colors[input.color_select()]\n</code></pre>"},{"location":"examples/","title":"Examples","text":"<ul> <li>Speedtest data  using <code>ScatterplotLayer</code></li> <li>North America roads  using <code>PathLayer</code></li> <li>Global boundaries  using <code>PolygonLayer</code></li> <li>U.S. County-to-County Migration  using <code>ArcLayer</code> and <code>BrushingExtension</code></li> <li>Scatterplot with GPU data filtering  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code></li> <li>Color picker integration  using <code>SolidPolygonLayer</code></li> <li>JupyterLab Sidecar integration  using <code>ScatterplotLayer</code> and <code>JupyterLab Sidecar</code></li> <li>Motor Vehicle Crashes in NYC  using <code>ScatterplotLayer</code></li> <li>Rivers in Asia  using <code>PathLayer</code></li> </ul>"},{"location":"examples/#third-party-showcase","title":"Third-party showcase","text":"<p>These examples are maintained by external contributors.</p> <ul> <li>American Community Survey exploration  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code> by @jaanli.</li> </ul>"},{"location":"examples/data-filter-extension/","title":"DataFilterExtension","text":"<p>As with the <code>ScatterplotLayer</code> example, this example will use data from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry.</p> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom ipywidgets import FloatRangeSlider, jsdlink\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.controls import MultiRangeSlider\nfrom lonboard.layer_extension import DataFilterExtension\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from ipywidgets import FloatRangeSlider, jsdlink from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap from lonboard.controls import MultiRangeSlider from lonboard.layer_extension import DataFilterExtension In\u00a0[3]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\nlocal_path = Path(\"data-filter-extension.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    non_geom_columns = [col for col in columns if col != \"tile\"]\n    gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")\n    gdf.to_parquet(local_path)\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" local_path = Path(\"data-filter-extension.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     non_geom_columns = [col for col in columns if col != \"tile\"]     gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")     gdf.to_parquet(local_path) <p>Taking a quick look at this data, we see that we have 3.2 million records.</p> In\u00a0[4]: Copied! <pre>gdf\n</pre> gdf Out[4]: avg_d_kbps avg_u_kbps avg_lat_ms devices geometry 0 5983 7886 68 1 POINT (-160.01862 70.63722) 1 3748 5841 78 2 POINT (-160.04059 70.63357) 2 3364 6200 78 2 POINT (-160.04059 70.63175) 3 2381 2328 86 1 POINT (-160.03510 70.63357) 4 3047 5356 75 1 POINT (-160.03510 70.63175) ... ... ... ... ... ... 3231240 19528 3200 68 1 POINT (169.81842 -46.29571) 3231241 15693 10359 56 1 POINT (169.81293 -46.30710) 3231242 26747 9674 58 2 POINT (169.66461 -46.42082) 3231243 67995 13564 63 1 POINT (169.65912 -46.45110) 3231244 1230 1798 55 1 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 5 columns</p> <p>Since we want to cross-filter on three attributes at once, we create a <code>DataFilterExtension</code> instance with <code>filter_size=3</code>.</p> In\u00a0[5]: Copied! <pre>filter_extension = DataFilterExtension(filter_size=3)\n</pre> filter_extension = DataFilterExtension(filter_size=3) <p>Similar to the <code>ScatterplotLayer</code> example notebook, we create a normalized download speed to use for a per-point color and radius.</p> In\u00a0[6]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\nnormalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound)\nfill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10)\nradius = normalized_download_speed * 200\n</pre> min_bound = 5000 max_bound = 50000 normalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound) fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10) radius = normalized_download_speed * 200 <p>Next we prepare the columns that we want to filter on. In this case, we'll filter on the download speed, upload speed, and latency values. These need to be passed in as a single two-dimensional numpy array, so we use <code>numpy.column_stack</code>. Only numeric arrays can be used for GPU-based filtering.</p> <p>We also define initial filter ranges for each of these columns.</p> In\u00a0[7]: Copied! <pre>filter_values = np.column_stack(\n    [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]]\n)\ninitial_filter_range = [\n    [10_000, 50_000],\n    [1000, 10_000],\n    [0, 100],\n]\n</pre> filter_values = np.column_stack(     [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]] ) initial_filter_range = [     [10_000, 50_000],     [1000, 10_000],     [0, 100], ] <p>Next we create a <code>ScatterplotLayer</code> with our desired attributes. Keep in mind that you must pass the <code>DataFilterExtension</code> instance to the <code>extensions</code> parameter in order to activate its functionality on the layer.</p> In\u00a0[8]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[filter_extension],\n    get_fill_color=fill_color,\n    get_radius=radius,\n    get_filter_value=filter_values,\n    filter_range=initial_filter_range,\n    radius_units=\"meters\",\n    radius_min_pixels=0.1,\n)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(     gdf,     extensions=[filter_extension],     get_fill_color=fill_color,     get_radius=radius,     get_filter_value=filter_values,     filter_range=initial_filter_range,     radius_units=\"meters\",     radius_min_pixels=0.1, ) m = Map(layer) m Out[8]: <p>Only the points that are within all of our initial filter ranges are shown, but we'd like something more interactive. Let's connect sliders to the map.</p> <p>Here we create a separate slider for each attribute and then connect them using a <code>MultiRangeSlider</code>. This defines the slider element but it won't be connected to the map until we use <code>ipywidgets.jsdlink</code> later.</p> In\u00a0[9]: Copied! <pre>download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \")\nupload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \")\nlatency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \")\nmulti_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider])\nmulti_slider\n</pre> download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \") upload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \") latency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \") multi_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider]) multi_slider Out[9]: <p>In order to propagate the changes in our sliders to the <code>ScatterplotLayer</code> instance, we use <code>jsdlink</code>. See Linking Widgets in the ipywidgets documentation for more info.</p> In\u00a0[10]: Copied! <pre>_ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\"))\n</pre> _ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\")) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/data-filter-extension/#datafilterextension","title":"DataFilterExtension\u00b6","text":"<p>The <code>DataFilterExtension</code> adds GPU-based data filtering functionalities to layers, allowing the layer to show/hide objects based on user-defined properties.</p> <p>In this demo we'll use the <code>DataFilterExtension</code> to filter millions of points based on three numeric attributes.</p>"},{"location":"examples/data-filter-extension/#imports","title":"Imports\u00b6","text":""},{"location":"examples/data-filter-extension/#fetch-data","title":"Fetch data\u00b6","text":"<p>We use the same data as the <code>ScatterplotLayer</code> example except that here we download extra columns from the dataset to use for filtering.</p> <p>Columns:</p> <ul> <li><code>avg_d_kbps</code>: the average download speed for that data point in kbps</li> <li><code>avg_u_kbps</code>: the average upload speed for that data point in kbps</li> <li><code>avg_lat_ms</code>: the average latency for that data point in milliseconds</li> <li><code>devices</code>: the number of unique devices contributing tests in the tile.</li> <li><code>tile</code>: the WKT string representing a given zoom-16 Web Mercator tile</li> </ul> <p>We cache the downloaded file locally to <code>data-filter-extension.parquet</code> so that the second time running this notebook will be faster.</p>"},{"location":"examples/global-boundaries/","title":"Municipal Boundaries","text":"In\u00a0[1]: Copied! <pre>! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm\n</pre> ! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm <pre>\n[notice] A new release of pip is available: 23.3.2 -&gt; 24.0\n[notice] To update, run: pip install --upgrade pip\n</pre> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport requests\nfrom tqdm import tqdm\n\nfrom lonboard import Map, PolygonLayer\n</pre> from pathlib import Path  import geopandas as gpd import requests from tqdm import tqdm  from lonboard import Map, PolygonLayer <p>The URL for a large data file of CGAZ at ADM1 Districts level:</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\"\nfilepath = \"geoBoundariesCGAZ_ADM1.geojson\"\n</pre> url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\" filepath = \"geoBoundariesCGAZ_ADM1.geojson\" <p>We'll go ahead and fetch the GeoJSON data in that format. If this notebook has already been run once and the file exists locally, we'll skip downloading it again.</p> In\u00a0[4]: Copied! <pre>if not Path(filepath).exists():\n    # Make a GET request to download the file\n    # Streaming, so we can iterate over the response and create a progress bar for this large file\n    response = requests.get(url, stream=True)\n\n    # Sizes in bytes.\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 1024\n\n    with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:\n        with open(filepath, \"wb\") as file:\n            for data in response.iter_content(block_size):\n                progress_bar.update(len(data))\n                file.write(data)\n\n    if total_size != 0 and progress_bar.n != total_size:\n        raise RuntimeError(\"Could not download file\")\n</pre> if not Path(filepath).exists():     # Make a GET request to download the file     # Streaming, so we can iterate over the response and create a progress bar for this large file     response = requests.get(url, stream=True)      # Sizes in bytes.     total_size = int(response.headers.get(\"content-length\", 0))     block_size = 1024      with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:         with open(filepath, \"wb\") as file:             for data in response.iter_content(block_size):                 progress_bar.update(len(data))                 file.write(data)      if total_size != 0 and progress_bar.n != total_size:         raise RuntimeError(\"Could not download file\") In\u00a0[5]: Copied! <pre>gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)\n\n# Keep columns necessary for our visualization\ncols = [\"shapeName\", \"shapeID\", \"geometry\"]\ngdf = gdf[cols]\n</pre> gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)  # Keep columns necessary for our visualization cols = [\"shapeName\", \"shapeID\", \"geometry\"] gdf = gdf[cols] <p>We can take a quick look at the shape of this data:</p> In\u00a0[6]: Copied! <pre>gdf.head(10)\n</pre> gdf.head(10) Out[6]: shapeName shapeID geometry 0 Kandahar 12653393B40111500734429 POLYGON ((65.24153 32.28630, 65.12687 32.03679... 1 Zabul 12653393B56617740339660 POLYGON ((67.60666 31.44378, 67.70162 31.51373... 2 Uruzgan 12653393B46006342616872 POLYGON ((66.27519 32.42550, 66.35907 32.60925... 3 Daykundi 12653393B78791504725813 POLYGON ((66.76157 33.25547, 66.92827 33.59694... 4 Ghanzi 12653393B29313712249365 POLYGON ((68.06945 32.04564, 68.06011 32.45619... 5 Paktika 12653393B93572570471663 MULTIPOLYGON (((68.10797 31.74752, 68.10807 31... 6 Khost 12653393B66876919073955 POLYGON ((69.50619 33.02954, 69.50620 33.02954... 7 Paktia 12653393B16859990709200 POLYGON ((68.76070 33.41986, 68.95811 33.18136... 8 Logar 12653393B769191277822 POLYGON ((68.78347 33.62259, 68.99623 33.62635... 9 Wardak 12653393B94990201402617 POLYGON ((68.68691 33.72585, 68.86023 34.01301... <p>Without doing any simplification, the geometries in this dataset contain nearly 9 million coordinates (You can verify this with <code>shapely.get_num_coordinates(gdf[\"geometry\"]).sum()</code>).</p> <p>On recent computers, Lonboard should be able to visualize the entire dataset, but to ensure this dataset works on all computers, we'll select only rows whose geometries are at least partially in the eastern hemisphere:</p> In\u00a0[7]: Copied! <pre>eastern_hemisphere = gdf.cx[0:180, -90:90]\n</pre> eastern_hemisphere = gdf.cx[0:180, -90:90] In\u00a0[8]: Copied! <pre>layer = PolygonLayer.from_geopandas(\n    eastern_hemisphere,\n    get_line_width=20,  # width in default units (meters)\n    line_width_min_pixels=0.2,  # minimum width when zoomed out\n    get_fill_color=[204, 251, 254],  # light blue\n    get_line_color=[37, 36, 34],  # dark border color\n)\n</pre> layer = PolygonLayer.from_geopandas(     eastern_hemisphere,     get_line_width=20,  # width in default units (meters)     line_width_min_pixels=0.2,  # minimum width when zoomed out     get_fill_color=[204, 251, 254],  # light blue     get_line_color=[37, 36, 34],  # dark border color ) In\u00a0[9]: Copied! <pre>m = Map(layer)\nm\n</pre> m = Map(layer) m Out[9]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/global-boundaries/#municipal-boundaries","title":"Municipal Boundaries\u00b6","text":"<p>This example will use data from GeoBoundaries.org's data set of Comprehensive Global Administrative Zones (CGAZ).</p> <p>CGAZ is a set of global composites for administrative boundaries. There are two important distinctions between our global product and individual country downloads. First, extensive simplification is performed to ensure that file sizes are small enough to be used in most traditional desktop software. Second, disputed areas are removed and replaced with polygons following US Department of State definitions. Attribution: www.geoboundaries.org</p> <p>Note: This notebook incurs downloading a 360MB file. To successfully follow this example, please ensure sufficient storage space. The time to download this data will vary based on internet connection speed. We've added a download progress bar to the data retrieval section of this notebook to allow you to view progress and a time estimate for downloading the visualization data.</p>"},{"location":"examples/global-boundaries/#imports","title":"Imports\u00b6","text":""},{"location":"examples/global-boundaries/#visualize-global-boundaries","title":"Visualize Global Boundaries\u00b6","text":"<p>Let's visualize this dataset.</p> <p>We'll start by loading the data into GeoPandas. It's recommended to use the newer <code>pyogrio</code> library for faster data loading.</p>"},{"location":"examples/global-boundaries/#visualizing-the-data","title":"Visualizing the data\u00b6","text":"<p>We can use a <code>PolygonLayer</code> to customize the rendering of this data.</p>"},{"location":"examples/internet-speeds/","title":"Speedtest Data (ScatterplotLayer)","text":"In\u00a0[1]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>The URL for a single data file for mobile network speeds in the first quarter of 2019:</p> In\u00a0[2]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" <p>The data used in this example is relatively large. In the cell below, we cache the downloading and preparation of the dataset so that it's faster to run this notebook the second time.</p> <p>We fetch two columns \u2014 <code>avg_d_kbps</code> and <code>tile</code> \u2014 from this data file directly from AWS. The <code>pd.read_parquet</code> command will perform a network request for these columns from the data file, so it may take a while on a slow network connection. <code>avg_d_kbps</code> is the average download speed for that data point in kilobits per second. <code>tile</code> is the WKT string representing a given zoom-16 Web Mercator tile.</p> <p>The <code>tile</code> column contains strings representing WKT-formatted geometries. We need to parse those strings into geometries. Then for simplicity we'll convert into their centroids.</p> In\u00a0[3]: Copied! <pre>local_path = Path(\"internet-speeds.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')\n    gdf.to_parquet(local_path)\n</pre> local_path = Path(\"internet-speeds.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')     gdf.to_parquet(local_path) <p>We can take a quick look at this data:</p> In\u00a0[4]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[4]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) <p>To ensure that this demo is snappy on most computers, we'll filter to a bounding box over Europe.</p> <p>If you're on a recent computer, feel free to comment out the next line.</p> In\u00a0[5]: Copied! <pre>gdf = gdf.cx[-11.83:25.5, 34.9:59]\n</pre> gdf = gdf.cx[-11.83:25.5, 34.9:59] <p>Even this filtered data frame still has 800,000 rows, so it's still a lot of data to explore:</p> In\u00a0[6]: Copied! <pre>gdf\n</pre> gdf Out[6]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) ... ... ... 3231240 19528 POINT (169.81842 -46.29571) 3231241 15693 POINT (169.81293 -46.30710) 3231242 26747 POINT (169.66461 -46.42082) 3231243 67995 POINT (169.65912 -46.45110) 3231244 1230 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 2 columns</p> <p>To render point data, first create a <code>ScatterplotLayer</code> and then add it to a <code>Map</code> object:</p> In\u00a0[7]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) m = Map(layer) m Out[7]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\navg_d_kbps: uint32\ngeometry: fixed_size_list&lt;item: double&gt;[2]\u2026</pre> <p>We can look at the documentation for <code>ScatterplotLayer</code> to see what other rendering options it allows. Let's set the fill color to something other than black:</p> In\u00a0[8]: Copied! <pre>layer.get_fill_color = [0, 0, 200, 200]\n</pre> layer.get_fill_color = [0, 0, 200, 200] <p>Blue is pretty, but the map would be more informative if we colored each point by a relevant characteristic. In this case, we have the download speed associated with each location, so let's use that!</p> <p>Here we compute a linear statistic for the download speed. Given a minimum bound of <code>5000</code> and a maximum bound of <code>50,000</code> the normalized speed is linearly scaled to between 0 and 1.</p> In\u00a0[9]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\ndownload_speed = gdf['avg_d_kbps']\nnormalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound)\n</pre> min_bound = 5000 max_bound = 50000 download_speed = gdf['avg_d_kbps'] normalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound) <p><code>normalized_download_speed</code> is now linearly scaled based on the bounds provided above. Keep in mind that the input range of the colormap is 0-1. So any values that are below 0 will receive the left-most color in the colormap, while any values above 1 will receive the right-most color in the colormap.</p> In\u00a0[10]: Copied! <pre>normalized_download_speed\n</pre> normalized_download_speed Out[10]: <pre>0          0.021844\n1         -0.027822\n2         -0.036356\n3         -0.058200\n4         -0.043400\n             ...   \n3231240    0.322844\n3231241    0.237622\n3231242    0.483267\n3231243    1.399889\n3231244   -0.083778\nName: avg_d_kbps, Length: 3231245, dtype: float64</pre> <p>We can use any colormap provided by the <code>palettable</code> package. Let's inspect the <code>BrBG_10</code> diverging colormap below:</p> In\u00a0[11]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap Out[11]: BrBG  underbad over  <p>Now let's apply the colormap on <code>normalized_download_speed</code> using a helper provided by <code>lonboard</code>. We can set it on <code>layer.get_fill_color</code> to update the existing colors.</p> In\u00a0[12]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7)\n</pre> layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7) <p>After running the above cell, you should see the map above update with a different color per point!</p> <p>We can pass an array into any of the \"accessors\" supported by the layer (this is any attribute that starts with <code>get_*</code>).</p> <p>For demonstration purposes, let's also set <code>get_radius</code> to <code>normalized_download_speed</code>.</p> In\u00a0[13]: Copied! <pre>layer.get_radius = normalized_download_speed * 200\nlayer.radius_units = \"meters\"\nlayer.radius_min_pixels = 0.5\n</pre> layer.get_radius = normalized_download_speed * 200 layer.radius_units = \"meters\" layer.radius_min_pixels = 0.5 <p>After running the above cell, you should see the map updated to have a different radius per point!</p>"},{"location":"examples/internet-speeds/#speedtest-data-scatterplotlayer","title":"Speedtest Data (<code>ScatterplotLayer</code>)\u00b6","text":"<p>This example will use data collected from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry. From the AWS page:</p> <p>Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (35MB download).</p>"},{"location":"examples/internet-speeds/#imports","title":"Imports\u00b6","text":""},{"location":"examples/internet-speeds/#fetch-data","title":"Fetch data\u00b6","text":""},{"location":"examples/migration/","title":"U.S. County-to-County Migration","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport requests\nimport shapely\nfrom matplotlib.colors import Normalize\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.experimental import ArcLayer\nfrom lonboard.layer_extension import BrushingExtension\n</pre> import geopandas as gpd import numpy as np import pandas as pd import pyarrow as pa import requests import shapely from matplotlib.colors import Normalize  from lonboard import Map, ScatterplotLayer from lonboard.experimental import ArcLayer from lonboard.layer_extension import BrushingExtension <p>Fetch the data from the version in the <code>deck.gl-data</code> repository.</p> In\u00a0[2]: Copied! <pre>url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\"\nr = requests.get(url)\nsource_data = r.json()\n</pre> url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\" r = requests.get(url) source_data = r.json() <p>The following cell may be a little hard to follow, but what it's doing is taking the raw data, which represents a graph) of the data and normalizing it to a table structure where each row represents one \"arc\" between a source and target county.</p> <p>This is ported from the original JavaScript here.</p> In\u00a0[3]: Copied! <pre>arcs = []\ntargets = []\nsources = []\npairs = {}\n\nfeatures = source_data[\"features\"]\nfor i, county in enumerate(features):\n    flows = county[\"properties\"][\"flows\"]\n    target_centroid = county[\"properties\"][\"centroid\"]\n    total_value = {\n        \"gain\": 0,\n        \"loss\": 0,\n    }\n\n    for to_id, value in flows.items():\n        if value &gt; 0:\n            total_value[\"gain\"] += value\n        else:\n            total_value[\"loss\"] += value\n\n        # If number is too small, ignore it\n        if abs(value) &lt; 50:\n            continue\n\n        pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))\n        source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]\n        gain = np.sign(flows[to_id])\n\n        # add point at arc source\n        sources.append(\n            {\n                \"position\": source_centroid,\n                \"target\": target_centroid,\n                \"name\": features[int(to_id)][\"properties\"][\"name\"],\n                \"radius\": 3,\n                \"gain\": -gain,\n            }\n        )\n        # eliminate duplicate arcs\n        if pair_key in pairs.keys():\n            continue\n\n        pairs[pair_key] = True\n\n        if gain &gt; 0:\n            arcs.append(\n                {\n                    \"target\": target_centroid,\n                    \"source\": source_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n        else:\n            arcs.append(\n                {\n                    \"target\": source_centroid,\n                    \"source\": target_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n\n    # add point at arc target\n    targets.append(\n        {\n            **total_value,\n            \"position\": [target_centroid[0], target_centroid[1], 10],\n            \"net\": total_value[\"gain\"] + total_value[\"loss\"],\n            \"name\": county[\"properties\"][\"name\"],\n        }\n    )\n\n# sort targets by radius large -&gt; small\ntargets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True)\nnormalizer = Normalize(0, abs(targets[0][\"net\"]))\n</pre> arcs = [] targets = [] sources = [] pairs = {}  features = source_data[\"features\"] for i, county in enumerate(features):     flows = county[\"properties\"][\"flows\"]     target_centroid = county[\"properties\"][\"centroid\"]     total_value = {         \"gain\": 0,         \"loss\": 0,     }      for to_id, value in flows.items():         if value &gt; 0:             total_value[\"gain\"] += value         else:             total_value[\"loss\"] += value          # If number is too small, ignore it         if abs(value) &lt; 50:             continue          pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))         source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]         gain = np.sign(flows[to_id])          # add point at arc source         sources.append(             {                 \"position\": source_centroid,                 \"target\": target_centroid,                 \"name\": features[int(to_id)][\"properties\"][\"name\"],                 \"radius\": 3,                 \"gain\": -gain,             }         )         # eliminate duplicate arcs         if pair_key in pairs.keys():             continue          pairs[pair_key] = True          if gain &gt; 0:             arcs.append(                 {                     \"target\": target_centroid,                     \"source\": source_centroid,                     \"value\": flows[to_id],                 }             )         else:             arcs.append(                 {                     \"target\": source_centroid,                     \"source\": target_centroid,                     \"value\": flows[to_id],                 }             )      # add point at arc target     targets.append(         {             **total_value,             \"position\": [target_centroid[0], target_centroid[1], 10],             \"net\": total_value[\"gain\"] + total_value[\"loss\"],             \"name\": county[\"properties\"][\"name\"],         }     )  # sort targets by radius large -&gt; small targets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True) normalizer = Normalize(0, abs(targets[0][\"net\"])) <p>We define some color constants, as well as a color lookup array.</p> <p>A nice trick in numpy is that if you have a two-dimensional array like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184]\n]\n</pre> <p>you can perform a lookup based on the index to transform data from one dimensionality to another. In this case, we'll use <code>0</code> and <code>1</code> \u2014 the two available indexes of the array's first dimension \u2014 to create an array of colors.</p> <p>So when we call <code>COLORS[colors_lookup]</code> that creates an output array of something like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184],\n    [166,   3,   3],\n    [166,   3,   3]\n]\n</pre> <p>equal to the number of rows in our dataset. We can then pass this to any parameter that accepts a ColorAccessor.</p> In\u00a0[4]: Copied! <pre># migrate out\nSOURCE_COLOR = [166, 3, 3]\n# migrate in\nTARGET_COLOR = [35, 181, 184]\n# Combine into a single arr to use as a lookup table\nCOLORS = np.vstack(\n    [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)]\n)\nSOURCE_LOOKUP = 0\nTARGET_LOOKUP = 1\n</pre> # migrate out SOURCE_COLOR = [166, 3, 3] # migrate in TARGET_COLOR = [35, 181, 184] # Combine into a single arr to use as a lookup table COLORS = np.vstack(     [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)] ) SOURCE_LOOKUP = 0 TARGET_LOOKUP = 1 In\u00a0[5]: Copied! <pre>brushing_extension = BrushingExtension()\nbrushing_radius = 200000\n</pre> brushing_extension = BrushingExtension() brushing_radius = 200000 <p>Convert the <code>sources</code> list of dictionaries into a GeoPandas <code>GeoDataFrame</code> to pass into a <code>ScatterplotLayer</code>.</p> In\u00a0[6]: Copied! <pre>source_arr = np.array([source[\"position\"] for source in sources])\nsource_positions = shapely.points(source_arr[:, 0], source_arr[:, 1])\nsource_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],\n    geometry=source_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\nsource_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\nsource_fill_colors = COLORS[source_colors_lookup]\n</pre> source_arr = np.array([source[\"position\"] for source in sources]) source_positions = shapely.points(source_arr[:, 0], source_arr[:, 1]) source_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],     geometry=source_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array source_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) source_fill_colors = COLORS[source_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for source points:</p> In\u00a0[7]: Copied! <pre>source_layer = ScatterplotLayer.from_geopandas(\n    source_gdf,\n    get_fill_color=source_fill_colors,\n    radius_scale=3000,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> source_layer = ScatterplotLayer.from_geopandas(     source_gdf,     get_fill_color=source_fill_colors,     radius_scale=3000,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) In\u00a0[8]: Copied! <pre>targets_arr = np.array([target[\"position\"] for target in targets])\ntarget_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1])\ntarget_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],\n    geometry=target_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\ntarget_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\ntarget_line_colors = COLORS[target_line_colors_lookup]\n</pre> targets_arr = np.array([target[\"position\"] for target in targets]) target_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1]) target_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],     geometry=target_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array target_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) target_line_colors = COLORS[target_line_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for target points:</p> In\u00a0[9]: Copied! <pre>target_ring_layer = ScatterplotLayer.from_geopandas(\n    target_gdf,\n    get_line_color=target_line_colors,\n    radius_scale=4000,\n    pickable=True,\n    stroked=True,\n    filled=False,\n    line_width_min_pixels=2,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> target_ring_layer = ScatterplotLayer.from_geopandas(     target_gdf,     get_line_color=target_line_colors,     radius_scale=4000,     pickable=True,     stroked=True,     filled=False,     line_width_min_pixels=2,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Note: the <code>ArcLayer</code> can't currently be created from a GeoDataFrame because it needs two point columns, not one. This is a large part of why it's still marked under the \"experimental\" module.</p> <p>Here we pass a numpy array for each point column. This is allowed as long as the shape of the array is <code>(N, 2)</code> or <code>(N, 3)</code> (i.e. 2D or 3D coordinates).</p> In\u00a0[10]: Copied! <pre>value = np.array([arc[\"value\"] for arc in arcs])\nget_source_position = np.array([arc[\"source\"] for arc in arcs])\nget_target_position = np.array([arc[\"target\"] for arc in arcs])\ntable = pa.table({\"value\": value})\n\narc_layer = ArcLayer(\n    table=table,\n    get_source_position=get_source_position,\n    get_target_position=get_target_position,\n    get_source_color=SOURCE_COLOR,\n    get_target_color=TARGET_COLOR,\n    get_width=1,\n    opacity=0.4,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> value = np.array([arc[\"value\"] for arc in arcs]) get_source_position = np.array([arc[\"source\"] for arc in arcs]) get_target_position = np.array([arc[\"target\"] for arc in arcs]) table = pa.table({\"value\": value})  arc_layer = ArcLayer(     table=table,     get_source_position=get_source_position,     get_target_position=get_target_position,     get_source_color=SOURCE_COLOR,     get_target_color=TARGET_COLOR,     get_width=1,     opacity=0.4,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Now we can create a map using these three layers we've created.</p> <p>As you hover over the map, it should render only the arcs near your cursor.</p> <p>You can modify <code>brushing_extension.brushing_radius</code> to control how large the brush is around your cursor.</p> In\u00a0[11]: Copied! <pre>map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10)\nmap_\n</pre> map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10) map_ Out[11]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/migration/#us-county-to-county-migration","title":"U.S. County-to-County Migration\u00b6","text":"<p>This notebook is derived from the original deck.gl example in JavaScript, which you can see here.</p> <p>This dataset originally came from the U.S. Census Bureau and represents people moving in and out of each county between 2009-2013.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/migration/#imports","title":"Imports\u00b6","text":""},{"location":"examples/north-america-roads/","title":"North America Roads","text":"In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport palettable.colorbrewer.diverging\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import palettable.colorbrewer.diverging  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap <p>The \"public\" URL for this dataset from the Natural Earth website is</p> <pre><code>https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>That doesn't appear to work directly inside a notebook, so we'll use the URL the above redirects to:</p> <pre><code>https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>It's possible this \"private\" URL will change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\n</pre> url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\" <p>We'll use GeoPandas to fetch this data (45MB) over the internet and load it into a <code>GeoDataFrame</code>. This uses the <code>pyogrio</code> engine, which is faster. Ensure you have <code>pyogrio</code> installed.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>This dataframe has a variety of attributes, plus a geometry column with a <code>LineString</code> type.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.head()\n</pre> gdf.head() <p>To ensure that this demo is snappy on most computers, we'll filter to only the contiguous U.S. If you're on a recent computer, feel free to comment out this line.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"state\"] == \"California\"]\n</pre> gdf = gdf[gdf[\"state\"] == \"California\"] <p>To render <code>LineString</code> data, first create a <code>PathLayer</code> and then add it to a <code>Map</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8) map_ = Map(layers=[layer]) map_ <p>We can look at the documentation for <code>PathLayer</code> to see what other rendering options it allows. Let's set the path color to something other than black:</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = [200, 0, 200]\n</pre> layer.get_color = [200, 0, 200] <p>Ok, so we can see the data! That's great! But let's customize the rendering based on an attribute!</p> <p>The <code>scalerank</code> column tells how important the road is in the road network. Let's see what the distribution of values is for this column:</p> In\u00a0[\u00a0]: Copied! <pre>gdf[\"scalerank\"].value_counts().sort_index()\n</pre> gdf[\"scalerank\"].value_counts().sort_index() <p>Ok, so the values range from <code>3</code> to <code>12</code>. To assign a colormap to this column, we need \"normalized\" values that span between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9\n</pre> normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9 <p>The values of this array now range from 0 to 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank.min(), normalized_scale_rank.max()\n</pre> normalized_scale_rank.min(), normalized_scale_rank.max() <p>Let's select a colormap to apply to this data:</p> In\u00a0[\u00a0]: Copied! <pre>cmap = palettable.colorbrewer.diverging.PuOr_10\n</pre> cmap = palettable.colorbrewer.diverging.PuOr_10 In\u00a0[\u00a0]: Copied! <pre>cmap.mpl_colormap\n</pre> cmap.mpl_colormap <p>Now we'll use <code>apply_continuous_cmap</code> onto this array to generate colors for our data. Just set this new array onto the existing layer, and you'll see the map update with the new colors!</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = apply_continuous_cmap(\n    normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8\n)\n</pre> layer.get_color = apply_continuous_cmap(     normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8 ) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/north-america-roads/#north-america-roads","title":"North America Roads\u00b6","text":"<p>This example uses data from Natural Earth to plot the road network in North America. Refer to the data documentation for more information about this dataset.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/north-america-roads/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>pyogrio</li> </ul>"},{"location":"examples/north-america-roads/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/color-picker/","title":"ColorPicker","text":"<p>Example gif:</p> <p></p> In\u00a0[1]: Copied! <pre>import geodatasets\nimport geopandas as gpd\nimport ipywidgets\n\nfrom lonboard import Map, SolidPolygonLayer\n</pre> import geodatasets import geopandas as gpd import ipywidgets  from lonboard import Map, SolidPolygonLayer <p><code>geodatasets.get_path</code> will download the dataset if it hasn't been downloaded and cached before.</p> In\u00a0[2]: Copied! <pre>gdf = gpd.read_file(geodatasets.get_path(\"nybb\"))\n</pre> gdf = gpd.read_file(geodatasets.get_path(\"nybb\")) <p>Create our basic map:</p> In\u00a0[3]: Copied! <pre>layer = SolidPolygonLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = SolidPolygonLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) map_ <pre>/Users/kyle/github/developmentseed/lonboard/lonboard/_layer.py:99: UserWarning: GeoDataFrame being reprojected to EPSG:4326\n  warnings.warn(\"GeoDataFrame being reprojected to EPSG:4326\")\n</pre> Out[3]: <pre>Map(layers=[SolidPolygonLayer(table=pyarrow.Table\nBoroCode: int64\nBoroName: string\nShape_Leng: double\nShape_Ar\u2026</pre> <p>Create the <code>ColorPicker</code> widget:</p> In\u00a0[4]: Copied! <pre>color_picker = ipywidgets.ColorPicker()\ncolor_picker\n</pre> color_picker = ipywidgets.ColorPicker() color_picker Out[4]: <pre>ColorPicker(value='black')</pre> <p>Note that the \"data\" contained by the <code>color_picker</code> instance is held by the <code>value</code> attribute:</p> In\u00a0[5]: Copied! <pre>color_picker.value\n</pre> color_picker.value Out[5]: <pre>'black'</pre> <p>We'll now link the two widgets together, so that whenever the color picker is updated, <code>ipywidgets</code> will update the value of <code>get_fill_color</code> on the layer.</p> <p>The format of <code>dlink</code> is:</p> <ul> <li>tuple: (source widget, attribute of source widget)</li> <li>tuple: (target widget, attribute of target widget)</li> </ul> <p>So we use the below syntax to link <code>color_picker.value</code> to <code>layer.get_fill_color</code>.</p> <p>We need to use <code>dlink</code>, not <code>link</code>, which creates a one-directional link (from the color picker to the layer) and not a bi-directional link. This is because the color picker and the layer have a different color representation under the hood (<code>ColorPicker</code> stores a hex string; the <code>SolidPolygonLayer</code> works in terms of RGBA color integers).</p> In\u00a0[6]: Copied! <pre>ipywidgets.dlink(\n    (color_picker, 'value'),\n    (layer, 'get_fill_color')\n)\n</pre> ipywidgets.dlink(     (color_picker, 'value'),     (layer, 'get_fill_color') ) Out[6]: <pre>&lt;traitlets.traitlets.directional_link at 0x168822610&gt;</pre> <p>Now click on the color widget and change the color! You need to press Enter to select a specific color.</p> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/color-picker/#colorpicker","title":"ColorPicker\u00b6","text":"<p>Use lonboard with an ipywidgets ColorPicker object.</p>"},{"location":"examples/integrations/color-picker/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li><p>lonboard</p> </li> <li><p>geodatasets</p> <p>Run <code>pip install geodatasets</code> if needed.</p> </li> </ul>"},{"location":"examples/integrations/color-picker/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/sidecar/","title":"Sidecar","text":"<p>The goal of this notebook is to show how using Jupyter Sidecar can help to interactively explore and understand your data.</p> <p>Note that sidecar only works in JupyterLab and will not work in other notebook frontends like Colab.</p> <p></p> In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport ipywidgets\nimport matplotlib as mpl\nimport pandas as pd\nfrom palettable.colorbrewer.diverging import BrBG_10\nfrom sidecar import Sidecar\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import ipywidgets import matplotlib as mpl import pandas as pd from palettable.colorbrewer.diverging import BrBG_10 from sidecar import Sidecar  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>For this example, we'll use a dataset of abandoned cars in Chicago. Since this is a CSV file, we need to pass in the names of the X and Y columns into the \"open options\" to pass to OGR.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\"\ngdf = gpd.read_file(\n    url,\n    engine=\"pyogrio\",\n    use_arrow=True,\n    X_POSSIBLE_NAMES=\"Longitude\",\n    Y_POSSIBLE_NAMES=\"Latitude\",\n    KEEP_GEOM_COLUMNS=\"NO\",\n)\ngdf = gdf.set_crs(\"EPSG:4326\")\n</pre> url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\" gdf = gpd.read_file(     url,     engine=\"pyogrio\",     use_arrow=True,     X_POSSIBLE_NAMES=\"Longitude\",     Y_POSSIBLE_NAMES=\"Latitude\",     KEEP_GEOM_COLUMNS=\"NO\", ) gdf = gdf.set_crs(\"EPSG:4326\") <p>This dataset has some null values for geometries. We'll remove these first:</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf.geometry != None]\n</pre> gdf = gdf[gdf.geometry != None] <p>Let's also remove empty values from another column to make later steps easier (when modifying a single map object with lonboard, you can't change the number of rows).</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"]\n</pre> gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"] <p>We'll now initialize the <code>Sidecar</code> object, which splits the JupyterLab screen in half, creating the output area for our widget.</p> In\u00a0[\u00a0]: Copied! <pre>sc = Sidecar(title='My Map')\n</pre> sc = Sidecar(title='My Map') <p>Now let's create a map layer from our <code>GeoDataFrame</code>.</p> <p>The default map height is 500 pixels, but we'd like to have a taller map because it's vertical. This API may change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) <p>Now we can render this inside the sidecar. You should see the map appear on the right side of your screen.</p> In\u00a0[\u00a0]: Copied! <pre>with sc:\n    display(map_)\n</pre> with sc:     display(map_) <p>It's great that we can see the map, but let's customize the rendering a bit. There's a column in the data titled</p> <p>How Many Days Has the Vehicle Been Reported as Parked?</p> <p>Let's use this column to visualize how long it takes to remove a car in different areas of the city.</p> <p>This is a string column, so first let's convert it to a numeric value.</p> In\u00a0[\u00a0]: Copied! <pre>colname = \"How Many Days Has the Vehicle Been Reported as Parked?\"\ngdf[colname] = pd.to_numeric(gdf[colname])\n</pre> colname = \"How Many Days Has the Vehicle Been Reported as Parked?\" gdf[colname] = pd.to_numeric(gdf[colname]) <p>In order to apply a colormap onto our data, we need to normalize our values to the range of <code>0-1</code>. Matplotlib offers several normalization options. See the tutorial and the <code>matplotlib.colors</code> module.</p> <p>To start, we'll do a simple linear normalization, converting from 0-90 to 0-1. Any values below 0 will be clamped to 0, and any values above 90 will be clamped to 1.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 90)\nnormalized_values = normalizer(gdf[colname])\n</pre> normalizer = mpl.colors.Normalize(0, 90) normalized_values = normalizer(gdf[colname]) <p>These values now range between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_values\n</pre> normalized_values In\u00a0[\u00a0]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap In\u00a0[\u00a0]: Copied! <pre>layer.radius_min_pixels = 0.5\nlayer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10)\n</pre> layer.radius_min_pixels = 0.5 layer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10) <p>Now the circles on the map have colors based on the time they've been parked.</p> <p>But this dataset is skewed with a long tail. Most cars have been sitting way less than 90 days, but a few have been sitting even longer.</p> <p>A logarithmic normalization (aka matplotlib's <code>LogNorm</code>) might be more useful here:</p> In\u00a0[\u00a0]: Copied! <pre>log_normalizer = mpl.colors.LogNorm(3, 90, clip=True)\nlog_values = log_normalizer(gdf[colname])\n</pre> log_normalizer = mpl.colors.LogNorm(3, 90, clip=True) log_values = log_normalizer(gdf[colname]) In\u00a0[\u00a0]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10)\nlayer.get_radius = log_values * 50\nlayer.radius_units = \"meters\"\n</pre> layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10) layer.get_radius = log_values * 50 layer.radius_units = \"meters\" In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/sidecar/#sidecar","title":"Sidecar\u00b6","text":""},{"location":"examples/integrations/sidecar/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>sidecar</li> <li>pyogrio</li> </ul> <p>Run</p> <pre><code>pip install sidecar pyogrio\n</code></pre> <p>if needed. You may need to restart Jupyter and/or refresh your browser tab after installing <code>sidecar</code>.</p>"},{"location":"examples/integrations/sidecar/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/","title":"30 Day Map Challenge: Day 1 (Points)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport shapely\n\nfrom lonboard import Map, ScatterplotLayer\n</pre> import geopandas as gpd import matplotlib as mpl import numpy as np import pandas as pd import shapely  from lonboard import Map, ScatterplotLayer In\u00a0[2]: Copied! <pre>path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\"\n</pre> path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\" <p>Read the data:</p> In\u00a0[3]: Copied! <pre>df = pd.read_csv(path, dtype={\"ZIP CODE\": str})\n</pre> df = pd.read_csv(path, dtype={\"ZIP CODE\": str}) <p>Let's first take a peek at our data:</p> In\u00a0[4]: Copied! <pre>pd.options.display.max_columns = None\ndf.head()\n</pre> pd.options.display.max_columns = None df.head() Out[4]: CRASH DATE CRASH TIME BOROUGH ZIP CODE LATITUDE LONGITUDE LOCATION ON STREET NAME CROSS STREET NAME OFF STREET NAME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED CONTRIBUTING FACTOR VEHICLE 1 CONTRIBUTING FACTOR VEHICLE 2 CONTRIBUTING FACTOR VEHICLE 3 CONTRIBUTING FACTOR VEHICLE 4 CONTRIBUTING FACTOR VEHICLE 5 COLLISION_ID VEHICLE TYPE CODE 1 VEHICLE TYPE CODE 2 VEHICLE TYPE CODE 3 VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5 0 09/11/2021 2:39 NaN NaN NaN NaN NaN WHITESTONE EXPRESSWAY 20 AVENUE NaN 2.0 0.0 0 0 0 0 2 0 Aggressive Driving/Road Rage Unspecified NaN NaN NaN 4455765 Sedan Sedan NaN NaN NaN 1 03/26/2022 11:45 NaN NaN NaN NaN NaN QUEENSBORO BRIDGE UPPER NaN NaN 1.0 0.0 0 0 0 0 1 0 Pavement Slippery NaN NaN NaN NaN 4513547 Sedan NaN NaN NaN NaN 2 06/29/2022 6:55 NaN NaN NaN NaN NaN THROGS NECK BRIDGE NaN NaN 0.0 0.0 0 0 0 0 0 0 Following Too Closely Unspecified NaN NaN NaN 4541903 Sedan Pick-up Truck NaN NaN NaN 3 09/11/2021 9:35 BROOKLYN 11208 40.667202 -73.866500 (40.667202, -73.8665) NaN NaN 1211      LORING AVENUE 0.0 0.0 0 0 0 0 0 0 Unspecified NaN NaN NaN NaN 4456314 Sedan NaN NaN NaN NaN 4 12/14/2021 8:13 BROOKLYN 11233 40.683304 -73.917274 (40.683304, -73.917274) SARATOGA AVENUE DECATUR STREET NaN 0.0 0.0 0 0 0 0 0 0 NaN NaN NaN NaN NaN 4486609 NaN NaN NaN NaN NaN <p>There are a bunch of columns in this dataset:</p> In\u00a0[5]: Copied! <pre>df.columns\n</pre> df.columns Out[5]: <pre>Index(['CRASH DATE', 'CRASH TIME', 'BOROUGH', 'ZIP CODE', 'LATITUDE',\n       'LONGITUDE', 'LOCATION', 'ON STREET NAME', 'CROSS STREET NAME',\n       'OFF STREET NAME', 'NUMBER OF PERSONS INJURED',\n       'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS INJURED',\n       'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST INJURED',\n       'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST INJURED',\n       'NUMBER OF MOTORIST KILLED', 'CONTRIBUTING FACTOR VEHICLE 1',\n       'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3',\n       'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5',\n       'COLLISION_ID', 'VEHICLE TYPE CODE 1', 'VEHICLE TYPE CODE 2',\n       'VEHICLE TYPE CODE 3', 'VEHICLE TYPE CODE 4', 'VEHICLE TYPE CODE 5'],\n      dtype='object')</pre> <p>Let's keep only specific columns that we care about:</p> In\u00a0[6]: Copied! <pre>keep_cols = [\n    \"CRASH DATE\",\n    \"CRASH TIME\",\n    \"LATITUDE\",\n    \"LONGITUDE\",\n    \"NUMBER OF PERSONS INJURED\",\n    \"NUMBER OF PERSONS KILLED\",\n    \"NUMBER OF PEDESTRIANS INJURED\",\n    \"NUMBER OF PEDESTRIANS KILLED\",\n    \"NUMBER OF CYCLIST INJURED\",\n    \"NUMBER OF CYCLIST KILLED\",\n    \"NUMBER OF MOTORIST INJURED\",\n    \"NUMBER OF MOTORIST KILLED\",\n    \"COLLISION_ID\",\n]\ndf = df[keep_cols]\n</pre> keep_cols = [     \"CRASH DATE\",     \"CRASH TIME\",     \"LATITUDE\",     \"LONGITUDE\",     \"NUMBER OF PERSONS INJURED\",     \"NUMBER OF PERSONS KILLED\",     \"NUMBER OF PEDESTRIANS INJURED\",     \"NUMBER OF PEDESTRIANS KILLED\",     \"NUMBER OF CYCLIST INJURED\",     \"NUMBER OF CYCLIST KILLED\",     \"NUMBER OF MOTORIST INJURED\",     \"NUMBER OF MOTORIST KILLED\",     \"COLLISION_ID\", ] df = df[keep_cols] <p>About 11% of the data have missing locations. For this quick demo, I don't have the ability to georeference those rows based on street name, so we'll just remove them. Hopefully there isn't a skew where crashes in some areas are more likely to not be georeferenced.</p> In\u00a0[7]: Copied! <pre>(df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean()\n</pre> (df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean() Out[7]: <pre>0.11342612579376604</pre> <p>Keep only the rows where longitude and latitude are not null.</p> In\u00a0[8]: Copied! <pre>df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())]\n</pre> df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())] <p>This dataset has crash data from the end of 2021, all of 2022, and thus far in 2023. For simplicity and regularity, let's choose only data from 2022.</p> <p>We'll convert the date to a datetime column and then select rows where the date happened in 2022:</p> In\u00a0[9]: Copied! <pre>df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"])\ndf = df[df[\"CRASH DATE\"].dt.year == 2022]\n</pre> df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"]) df = df[df[\"CRASH DATE\"].dt.year == 2022] <p>Let's also convert the time column from a string to a timestamp.</p> In\u00a0[10]: Copied! <pre>df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\")\n</pre> df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\") <p>If we look at the data types in our data, they were inferred as <code>int64</code> and <code>float64</code> data types:</p> In\u00a0[11]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[11]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED               float64\nNUMBER OF PERSONS KILLED                float64\nNUMBER OF PEDESTRIANS INJURED             int64\nNUMBER OF PEDESTRIANS KILLED              int64\nNUMBER OF CYCLIST INJURED                 int64\nNUMBER OF CYCLIST KILLED                  int64\nNUMBER OF MOTORIST INJURED                int64\nNUMBER OF MOTORIST KILLED                 int64\nCOLLISION_ID                              int64\ndtype: object</pre> <p>Knowing that our data holds small values, we can downcast to smaller data types:</p> In\u00a0[12]: Copied! <pre>for numeric_col_name in df.select_dtypes(\"number\").columns:\n    df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\")\n</pre> for numeric_col_name in df.select_dtypes(\"number\").columns:     df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\") <p>Now our data types are much smaller:</p> In\u00a0[13]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[13]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED                 uint8\nNUMBER OF PERSONS KILLED                  uint8\nNUMBER OF PEDESTRIANS INJURED             uint8\nNUMBER OF PEDESTRIANS KILLED              uint8\nNUMBER OF CYCLIST INJURED                 uint8\nNUMBER OF CYCLIST KILLED                  uint8\nNUMBER OF MOTORIST INJURED                uint8\nNUMBER OF MOTORIST KILLED                 uint8\nCOLLISION_ID                             uint32\ndtype: object</pre> <p>Now let's construct a GeoDataFrame from this data.</p> In\u00a0[14]: Copied! <pre>geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\"))\ngdf = gpd.GeoDataFrame(df, geometry=geometry)\n</pre> geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\")) gdf = gpd.GeoDataFrame(df, geometry=geometry) In\u00a0[15]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[15]: CRASH DATE CRASH TIME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED COLLISION_ID geometry 37 2022-07-12 1900-01-01 17:50:00 0 0 0 0 0 0 0 0 4545699 POINT (-73.96049 40.66330) 40 2022-04-24 1900-01-01 16:45:00 1 0 0 0 0 0 1 0 4521660 POINT (-74.13892 40.60768) 41 2022-04-24 1900-01-01 04:49:00 0 0 0 0 0 0 0 0 4521759 POINT (-73.86990 40.85597) 42 2022-04-22 1900-01-01 17:17:00 1 0 1 0 0 0 0 0 4522226 POINT (-73.93960 40.79028) 43 2022-04-24 1900-01-01 01:30:00 0 0 0 0 0 0 0 0 4522015 POINT (-74.01621 40.64299) <p>If we look at the bounding box of our data, we see that some points are way outside of NYC. Let's filter to include only the data in the NYC region:</p> In\u00a0[16]: Copied! <pre>gdf.total_bounds\n</pre> gdf.total_bounds Out[16]: <pre>array([-74.25496 ,   0.      ,   0.      ,  40.912167])</pre> In\u00a0[17]: Copied! <pre>nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696]\n</pre> nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696] In\u00a0[18]: Copied! <pre>gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))]\n</pre> gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))] In\u00a0[19]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap = Map(layers=[layer], _height=800)\nmap\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map = Map(layers=[layer], _height=800) map Out[19]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\nCRASH DATE: timestamp[ns]\nCRASH TIME: timestamp[ns]\nNUMBER OF\u2026</pre> <p>Ok cool, we can see something on the map, but let's make our visualization more interesting.</p> <p>Let's compute the number of non-motorists injured in each collision:</p> In\u00a0[20]: Copied! <pre>num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"]\n</pre> num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"] <p>We can see a quick distribution with <code>value_counts</code></p> In\u00a0[21]: Copied! <pre>num_injured.value_counts()\n</pre> num_injured.value_counts() Out[21]: <pre>0    80613\n1    12057\n2      356\n3       37\n4        3\n5        3\nName: count, dtype: int64</pre> In\u00a0[22]: Copied! <pre>layer.get_radius = np.array(num_injured + 1)\nlayer.radius_scale = 50\nlayer.opacity = 0.05\n</pre> layer.get_radius = np.array(num_injured + 1) layer.radius_scale = 50 layer.opacity = 0.05 <p>Next, let's color each dot by time of day. We'll use a cyclic colormap that starts and ends on the same color, because the time of day is cyclic.</p> In\u00a0[23]: Copied! <pre>colormap = mpl.colormaps[\"twilight\"]\ncolormap\n</pre> colormap = mpl.colormaps[\"twilight\"] colormap Out[23]: twilight  underbad over  <p>Next get the minute of day of the crash:</p> In\u00a0[24]: Copied! <pre>minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute\n</pre> minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute <p>To apply a colormap, we need to normalize the <code>minute_of_day</code> series to <code>0-1</code>. We can do this with matplotlib's <code>Normalize</code>. We set the minimum value to 0 and the maximum value to the number of minutes in a day.</p> In\u00a0[25]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 24 * 60)\nnormalized_minutes = normalizer(minute_of_day)\n</pre> normalizer = mpl.colors.Normalize(0, 24 * 60) normalized_minutes = normalizer(minute_of_day) <p>Now <code>normalized_minutes</code> ranges from 0-1:</p> In\u00a0[26]: Copied! <pre>normalized_minutes\n</pre> normalized_minutes Out[26]: <pre>masked_array(data=[0.74305556, 0.69791667, 0.20069444, ..., 0.54166667,\n                   0.625     , 0.60069444],\n             mask=False,\n       fill_value=1e+20)</pre> <p>Next we can apply the matplotlib colormap on our normalized values. We set <code>bytes=True</code> to ensure the output is of <code>uint8</code> data type.</p> In\u00a0[27]: Copied! <pre>colors = colormap(normalized_minutes, bytes=True)\n</pre> colors = colormap(normalized_minutes, bytes=True) In\u00a0[28]: Copied! <pre>colors\n</pre> colors Out[28]: <pre>array([[175,  81,  81, 255],\n       [156,  57,  79, 255],\n       [109, 144, 191, 255],\n       ...,\n       [ 63,  17,  60, 255],\n       [115,  29,  78, 255],\n       [ 99,  24,  75, 255]], dtype=uint8)</pre> In\u00a0[29]: Copied! <pre>layer.get_fill_color = colors\n</pre> layer.get_fill_color = colors In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/1-points/#30-day-map-challenge-day-1-points","title":"30 Day Map Challenge: Day 1 (Points)\u00b6","text":"<p>This is a notebook for day 1 of 30 Day Map Challenge.</p>"},{"location":"examples/map_challenge/1-points/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/#access-the-data","title":"Access the data\u00b6","text":"<p>This data comes from NYC Open Data, where we use Motor Vehicle Collisions - Crashes. First download this data via <code>Export &gt; CSV</code>, save it somewhere on disk, and update the <code>path</code> variable in the next cell for that location.</p>"},{"location":"examples/map_challenge/6-asia/","title":"30 Day Map Challenge: Day 6 (Asia)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nfrom palettable.colorbrewer.sequential import Blues_8\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd from palettable.colorbrewer.sequential import Blues_8  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap In\u00a0[2]: Copied! <pre>url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\"\n</pre> url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\" In\u00a0[3]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>The <code>Strahler</code> column seems to refer to how important/central the particular river is. It ranges from 1-7.</p> In\u00a0[4]: Copied! <pre>gdf['Strahler'].value_counts()\n</pre> gdf['Strahler'].value_counts() Out[4]: <pre>Strahler\n1    80503\n2    39100\n3    19655\n4     8960\n5     4547\n6     1901\n7      484\nName: count, dtype: int64</pre> <p>We'll use <code>Strahler</code> to plot more central rivers in both a darker blue and with a wider line.</p> In\u00a0[5]: Copied! <pre>layer = PathLayer.from_geopandas(gdf)\nlayer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8)\nlayer.get_width = gdf['Strahler']\nlayer.width_scale = 3000\nlayer.width_min_pixels = 0.5\n\nm = Map(layers=[layer])\nm\n</pre> layer = PathLayer.from_geopandas(gdf) layer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8) layer.get_width = gdf['Strahler'] layer.width_scale = 3000 layer.width_min_pixels = 0.5  m = Map(layers=[layer]) m Out[5]: <pre>Map(layers=[PathLayer(get_color=&lt;pyarrow.lib.FixedSizeListArray object at 0x107cbb220&gt;\n[\n  [\n    8,\n    69,\n  \u2026</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/6-asia/#30-day-map-challenge-day-6-asia","title":"30 Day Map Challenge: Day 6 (Asia)\u00b6","text":"<p>This is a notebook for day 6 of 30 Day Map Challenge.</p> <p>This data comes from the UN's \"Rivers of South and East Asia\".</p>"},{"location":"examples/map_challenge/6-asia/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/6-asia/#fetch-the-data","title":"Fetch the data\u00b6","text":""},{"location":"how-it-works/","title":"How it works?","text":"<p>Lonboard is built on four foundational technologies: deck.gl, GeoArrow, GeoParquet, and anywidget.</p> <ul> <li>deck.gl is a JavaScript geospatial data visualization library. Because deck.gl uses the GPU in your computer to render data, it's capable of performantly rendering very large quantities of data.</li> <li>GeoArrow is a memory format for efficiently representing geospatial vector data. As a memory format, GeoArrow is not compressed and can be used directly.</li> <li>GeoParquet is a file format for efficiently encoding and decoding geospatial vector data. As a file format, GeoParquet contains very efficient compression, and needs to be parsed before it can be used. <sup>1</sup></li> <li>anywidget is a framework for building custom Jupyter widgets that makes the process much easier than before.</li> </ul>"},{"location":"how-it-works/#how-is-it-so-fast","title":"How is it so fast?","text":"<p>Lonboard is so fast because it moves data from Python to JavaScript (in your browser) and then from JavaScript to your Graphics Processing Unit (GPU) more efficiently than ever before.</p> <p>Other Python libraries for interactive maps exist (such as <code>ipyleaflet</code>), and even existing bindings to deck.gl exist (such as <code>pydeck</code>). But those libraries encode data as GeoJSON to copy from Python to the browser. GeoJSON is extremely slow to read and write and results in a very large data file that has to be copied to the browser.</p> <p>With lonboard, the entire pipeline is binary. In Python, GeoPandas to GeoArrow to GeoParquet avoids a text encoding like GeoJSON and results in a compressed binary buffer that can be efficiently copied to the browser. In JavaScript, GeoParquet to GeoArrow offers efficient decoding (in WebAssembly). Then deck.gl is able to interpret the GeoArrow table directly without any parsing (thanks to <code>@geoarrow/deck.gl-layers</code>).</p> <p>GeoPandas is the primary interface for users to add data, allowing lonboard to internally manage the conversion to GeoArrow and its transport to the browser for rendering.</p> <p>Lonboard's goal is to abstract the technical bits of representing and moving data so it can attain its dual goals of performance and ease of use for a vast audience.</p> <ol> <li> <p>lonboard currently doesn't use \"official\" GeoParquet 1.0, because the 1.0 spec requires encoding geometries as Well-Known Binary (WKB) inside of the Parquet file. lonboard uses the highly-efficient GeoArrow encoding inside of GeoParquet (which may become part of the GeoParquet spec in 1.1). This is faster and easier to write when the writer and reader are both using GeoArrow anyways.\u00a0\u21a9</p> </li> </ol>"},{"location":"how-it-works/internals/","title":"Internal Architecture","text":"<p>Last edited April 2024.</p> <p>This document will outline Lonboard's internal architecture, and hopefully should be useful for potential contributors.</p>"},{"location":"how-it-works/internals/#pydeck","title":"Pydeck","text":"<p>Before we consider Lonboard, we should briefly cover how Pydeck manages data transit.</p> <p>Pydeck uses the loaders.gl ecosystem natively supported in deck.gl. This means that pydeck passes the user input directly into the <code>data</code> prop of any given layer. When <code>data</code> is a string, it's interpreted as a URL, passed into loaders.gl machinery to parse the given data format to a list of GeoJSON features. In order to do data-driven styling, users pass in strings like <code>\"@@=properties.valuePerSqm\"</code> to describe the JavaScript callback that should be applied to each GeoJSON feature.</p> <p>In my opinion, this presents performance pitfalls and gives Pydeck a stifling amount of flexibility. The Python API isn't able to do much of anything because the user input could be anything and users end up writing a sort of JavaScript in Python strings.</p>"},{"location":"how-it-works/internals/#lonboard","title":"Lonboard","text":""},{"location":"how-it-works/internals/#goals-and-target-user-persona","title":"Goals and target user persona","text":"<p>While I was never a primary pydeck developer and can't speak to their goals, I can say that Lonboard has a well-formed idea of the target user.</p> <p>When I was learning geospatial data processing in Python, I learned that GeoPandas, Shapely, and others were amazing tools. But I would perform operations and not have a deep understanding of what the output looked like. I wanted some way to quickly get a picture of what my data looked like. This drove me to build <code>keplergl_cli</code>, a single function or CLI command to visualize data in an interactive HTML map. The desire for this simplicity lives on in Lonboard's <code>viz</code> function.</p> <p>This experience informs the target user of Lonboard:</p> <ul> <li>A Python user who does not know or want to know JavaScript.</li> <li>Wants fast exploratory data analysis.</li> <li>Already working with data in Python.</li> <li>A reasonably fast internet connection on a desktop computer.</li> </ul>"},{"location":"how-it-works/internals/#python-classes","title":"Python classes","text":"<p>The <code>Map</code> class is the primary and only widget associated with JavaScript that renders anything. <code>Map</code> subclasses from <code>anywidget.AnyWidget</code>, which dynamically fetches Lonboard's JavaScript ESM bundle. The <code>Map</code> class emulates deck.gl's <code>Deck</code> class. It synchronizes map state and can be passed a sequence of <code>Layer</code> objects.</p> <p><code>Layer</code> classes are designed to map to each underlying deck.gl layer. Most <code>Layer</code> classes subclass from <code>BaseArrowLayer</code>, which stores a <code>pyarrow.Table</code> in the dataclass. This data object gets serialized to Parquet and rendered in a layer provided by <code>@geoarrow/deck.gl-layers</code> as described later on. The primary API for each of these Arrow-based layers is <code>from_geopandas</code>, which converts a <code>GeoDataFrame</code> to a <code>pyarrow.Table</code> with a GeoArrow-represented geometry column. A few layers are not Arrow-based and do not subclass from <code>BaseArrowLayer</code>: primarily <code>BitmapLayer</code> and <code>BitmapTileLayer</code>. These pass user data input directly into a core deck.gl layer.</p> <p>These <code>Layer</code> classes subclass from <code>ipywidgets.Widget</code> but are not associated with any JavaScript of their own. But by being widgets themselves, their data is synced with JavaScript and Python data changes are propagated as events to the core <code>Map</code> object.</p> <p>Each layer has parameters that map to deck.gl accessors and render properties. Each of these has validation on the Python side to catch invalid input early. Accessors are modeled with \"trait\" objects. Scalar accessors are stored as a scalar int/string/float. Function accessors are evaluated in Python and stored as a <code>pyarrow.Array</code>.</p> <p>A variety of layer extensions are supported, which map to deck.gl's upstream layer extensions. These layer extensions dynamically add more parameters to the layers on which they're assigned.</p>"},{"location":"how-it-works/internals/#data-loading","title":"Data loading","text":"<p>While pydeck allows users to pass in URLs to remote files, at the moment this is not possible in Lonboard. With only a few exceptions (e.g. the <code>BitmapLayer</code> and <code>BitmapTileLayer</code>), only data originating in Python is supported in Lonboard. This reduction in scope vastly simplifies Lonboard's internals while not providing much of a hurdle for Lonboard's core audience.</p> <p>Jupyter provides a WebSocket connection between Python and JavaScript. Jupyter widget state from Python classes is serialized over this WebSocket and received in JavaScript. While most widgets synchronize objects through JSON, widgets fully support binary data as well, which Lonboard utilizes.</p>"},{"location":"how-it-works/internals/#simplicity-of-code","title":"Simplicity of code","text":"<p>Supporting only a single data provenance and format makes the internals of Lonboard much more maintainable. Lonboard's JavaScript bindings only need to include a single data parser and can optimize performance for Parquet and Arrow data.</p>"},{"location":"how-it-works/internals/#parquet","title":"Parquet","text":"<p>Parquet was chosen as the underlying data transmission format for its excellent compression, fast read and write speeds, compatibility with GeoArrow and, in turn, compatibility with deck.gl's binary API.</p> <p>I tested with data from the speed test example (3 million points). Saving GeoArrow to Parquet was 135x faster than saving a GeoDataFrame to GeoJSON. The resulting file was 26x smaller. And parsing the Parquet to Arrow on the frontend was 5.6x faster than <code>JSON.parse</code> (including moving the Arrow from WebAssembly to JS).</p> <p>Note that what is sent over the wire is not GeoParquet. GeoParquet 1.0 requires special metadata and WKB-encoded geometries. Using that would require code on the frontend to parse WKB-encoded geometries into GeoArrow. The upcoming GeoParquet 1.1 includes support for GeoArrow but only its \"separated\" coordinates, where <code>x</code> and <code>y</code> coordinate values are in two different buffers (i.e. <code>xxxx</code>, <code>yyyy</code>). deck.gl's binary API currently does not support passing separated coordinates. Therefore Lonboard uses the \"interleaved\" coordinate variant, where <code>x</code> and <code>y</code> coordinates are stored in the same buffer (i.e. <code>xyxyxyxy</code>).</p>"},{"location":"how-it-works/internals/#bootstrapping-geoarrow","title":"Bootstrapping GeoArrow","text":"<p>For performance reasons, Lonboard is very focused on using GeoArrow throughout the pipeline in both Python and JavaScript. At the time when Lonboard was started, there were no user-friendly tools for converting data to GeoArrow in client-side JavaScript. By supporting only data from Python and only Parquet data, we could focus on making data conversions from GeoPandas to GeoArrow in Python efficient and stable. This enables GeoArrow to be \"just an implementation detail\" and out of any primary public APIs while also working to bootstrap a GeoArrow ecosystem in Python and JavaScript.</p>"},{"location":"how-it-works/internals/#authentication","title":"Authentication","text":"<p>Since all data is loaded via Python over the Jupyter-provided WebSocket, Lonboard doesn't need to implement any sort of authentication itself. The user is able to use whatever credentials they're familiar with, say for S3, to access data. Those credentials never need to be passed to the browser.</p> <p>In contrast, having the JavaScript side load data directly from a URL requires some way for users to specify authorization headers or API keys for access to private datasets.</p>"},{"location":"how-it-works/internals/#layer-creation","title":"Layer Creation","text":"<p>With the exception of the <code>BitmapLayer</code>, no direct deck.gl layers are used. deck.gl layers are used exclusively through the <code>@geoarrow/deck.gl-layers</code> glue library. This library connects GeoArrow data to the low-level binary data API of each deck.gl layer. All accessors are passed in directly as binary buffers.</p> <p>This means that in JavaScript the data never leaves an Arrow binary representation. It's parsed from Parquet to Arrow in WebAssembly, the Arrow buffers are copied out of WebAssembly memory, but never parsed to JSON strings or JavaScript objects.</p>"},{"location":"how-it-works/internals/#accessors-and-data-driven-rendering","title":"Accessors and data-driven rendering","text":"<p>Loading data from Python also helps to simplify data-driven rendering.</p>"},{"location":"how-it-works/internals/#no-function-serialization","title":"No function serialization","text":"<p>If we can make the data movement between Python and JavaScript fast enough, we can serialize computed rendering properties rather than the function to generate them. Lonboard serializes data, not functions.</p> <p>Any accessor used to style data is computed in Python, stored as a binary array, serialized to Parquet and sent to the frontend. Other than parsing the input Parquet buffers to Arrow, no data transformations or modifications are done on the JavaScript side.</p> <p>In Pydeck users had to pass something like</p> <pre><code>getRadius=\"@@=properties.valuePerSqm\"\n</code></pre> <p>which would be expanded into</p> <pre><code>getRadius: (object) =&gt; object.properties.valuePerSqm\n</code></pre> <p>on the JavaScript side.</p> <p>I find this syntax to be horrible UX. The user is no longer writing Python, they need to learn a new DSL just for this.</p> <p>RGB Colors for 3 million points take up 9MiB in memory (or 12MiB for RGBA). In the example of speed test data, 12MiB of RGBA data compressed down to 4MiB for transit. Float32 radii values for 3 million points take up 12MiB in memory and 9MiB compressed.</p> <p>Even for users on remote Python environments, this takes no more than a couple seconds to download, and then can render almost instantly. For non-point datasets with a fewer number of geometries, the byte size of accessors are even smaller relative to the geometry buffers.</p>"},{"location":"how-it-works/internals/#update-styling-without-re-sending-data","title":"Update styling without re-sending data","text":"<p>With Lonboard's widget architecture, any accessors other than the geometry are stored separately to the main Arrow table. This means that all accessors and rendering properties can be updated in isolation. The core table does not need to be synced again when a property is updated.</p>"},{"location":"how-it-works/internals/#uses-familiar-objects","title":"Uses familiar objects","text":"<p>Accessors are defined in terms of Numpy arrays, Pandas Series or PyArrow Arrays, which are familiar to Python data scientists.</p>"},{"location":"how-it-works/internals/#full-access-to-python","title":"Full access to Python","text":"<p>Because we serialize data to JavaScript instead of a function in a custom DSL, the user has the full spectrum of Python available to them. They can use one of matplotlib's many normalization helpers before applying a colormap. Or they can use apply some ML model and use its outputs for point radii.</p>"}]}
\ No newline at end of file
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To install Lonboard using conda:</p> <pre><code>conda install -c conda-forge lonboard\n</code></pre> <p>To install from source, refer to the developer documentation.</p>"},{"location":"#get-started","title":"Get Started","text":"<p>For the simplest rendering, pass geospatial data into the top-level <code>viz</code> function.</p> <pre><code>import geopandas as gpd\nfrom lonboard import viz\n\ngdf = gpd.GeoDataFrame(...)\nviz(gdf)\n</code></pre> <p>Under the hood, this delegates to a <code>ScatterplotLayer</code>, <code>PathLayer</code>, or <code>PolygonLayer</code>. Refer to the documentation and examples for more control over rendering.</p>"},{"location":"#documentation","title":"Documentation","text":"<p>Refer to the documentation at developmentseed.org/lonboard.</p>"},{"location":"#why-the-name","title":"Why the name?","text":"<p>This is a new binding to the deck.gl geospatial data visualization library. A \"deck\" is the part of a skateboard you ride on. What's a fast, geospatial skateboard? A lonboard.</p> <p></p>"},{"location":"CHANGELOG/","title":"Changelog","text":""},{"location":"CHANGELOG/#080-2024-04-05","title":"[0.8.0] - 2024-04-05","text":""},{"location":"CHANGELOG/#new","title":"New!","text":"<ul> <li>A new <code>PolygonLayer</code>! This layer renders polygon outlines for easier visibility. @kylebarron in developmentseed/lonboard!330</li> <li>An example using <code>PolygonLayer</code> by @naomatheus in developmentseed/lonboard!351</li> <li>Sync view state between JS and Python by @kylebarron in developmentseed/lonboard!448</li> <li>Support geoarrow array input into <code>viz()</code> by @kylebarron in developmentseed/lonboard!427</li> <li>Internal architecture documentation by @kylebarron in developmentseed/lonboard!450</li> </ul>"},{"location":"CHANGELOG/#fixes","title":"Fixes","text":"<ul> <li>Fix CLI with unset <code>geometry_name</code> by @kylebarron in developmentseed/lonboard!451</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.1...v0.8.0</p>"},{"location":"CHANGELOG/#071-2024-03-22","title":"[0.7.1] - 2024-03-22","text":""},{"location":"CHANGELOG/#fixes_1","title":"Fixes","text":"<ul> <li>Fix CLI with geopackage files by @kylebarron in developmentseed/lonboard!434</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.7.0...v0.7.1</p>"},{"location":"CHANGELOG/#070-2024-03-21","title":"[0.7.0] - 2024-03-21","text":""},{"location":"CHANGELOG/#new_1","title":"New!","text":"<ul> <li>There's a new command-line interface (CLI)! Use the <code>lonboard</code> command to quickly visualize one or more data files readable by GDAL! For example: <code>lonboard admins.geojson features.gpkg</code>. By @kylebarron in developmentseed/lonboard!379</li> <li>Type hinting for constructors and <code>from_geopandas</code> method. This should make it easier to pass the correct parameters into layers. This has been tested to work in IDEs like VSCode, but unfortunately appears not to work in JupyterLab. By @kylebarron in developmentseed/lonboard!399</li> </ul> <ul> <li>Warn on missing CRS. One of the most common reasons that you might see an empty map is from accidentally visualizing data that is not in EPSG 4326 (longitude-latitude). We now emit a warning for data that doesn't have a CRS defined on the data. By @kylebarron in developmentseed/lonboard!395.</li> <li>Lonboard is now on <code>conda-forge</code>! Install with <code>conda install -c conda-forge lonboard</code>. By @giswqs in developmentseed/lonboard!223</li> <li>Add PointCloudLayer. By @kylebarron in developmentseed/lonboard!396</li> <li>Add fly-to map action to \"fly\" the map to a new location. By @kylebarron in developmentseed/lonboard!408</li> <li>Docs showcase page by @kylebarron in developmentseed/lonboard!401</li> <li>Improve default colors in <code>viz</code>. We now attempt to apply some basic styling onto data passed into <code>viz</code>. This will likely further improve in the future. By @kylebarron in developmentseed/lonboard!389</li> </ul>"},{"location":"CHANGELOG/#fixes_2","title":"Fixes","text":"<ul> <li>Set exported HTML height to 100% by @kylebarron in developmentseed/lonboard!377</li> <li>Raise error on single input to MultiRangeSlider by @kylebarron in developmentseed/lonboard!367</li> <li>Fix pandas <code>to_numeric</code> FutureWarning by @kylebarron in developmentseed/lonboard!368</li> <li>Fix viewing polygons in local html files by @kylebarron in developmentseed/lonboard!387</li> <li>Fix: fix sliced array input for reprojection by @kylebarron in developmentseed/lonboard!391</li> <li>Fix: Don't reproject for epsg:4326 input by @kylebarron in developmentseed/lonboard!392</li> <li>Fix: Fix weighted centroid calculation by @kylebarron in developmentseed/lonboard!393</li> <li>Fix <code>viz()</code> with <code>__geo_interface__</code> input by @kylebarron in developmentseed/lonboard!426</li> <li>Add DataFilterExtension notebook to website by @kylebarron in developmentseed/lonboard!362</li> <li>Allow non-compliant geoarrow CRS metadata by @kylebarron in developmentseed/lonboard!369</li> <li>Automatically parse geoarrow.wkb to native geoarrow by @kylebarron in developmentseed/lonboard!372</li> <li>Parse GeoParquet metadata by @kylebarron in developmentseed/lonboard!407</li> <li>CLI: 'crs' in geoparquet metadata should be optional. by @jwass in developmentseed/lonboard!411</li> </ul>"},{"location":"CHANGELOG/#other-changes","title":"Other changes","text":"<ul> <li>Creating a new user bug report by @emmalu in developmentseed/lonboard!386</li> <li>Update epic template by @emmalu in developmentseed/lonboard!382</li> <li>NormalAccessor by @naomatheus in developmentseed/lonboard!376</li> <li>Conda: Try including <code>manifest.in</code> file for <code>static</code> folder inclusion by @kylebarron in developmentseed/lonboard!421</li> <li>Switch to animated hero image by @kylebarron in developmentseed/lonboard!423</li> <li>Add CRS to GeoDataFrame in notebook examples by @kylebarron in developmentseed/lonboard!419</li> </ul>"},{"location":"CHANGELOG/#new-contributors","title":"New Contributors","text":"<ul> <li>@emmalu made their first contribution in developmentseed/lonboard!382</li> <li>@naomatheus made their first contribution in developmentseed/lonboard!376</li> <li>@jwass made their first contribution in developmentseed/lonboard!411</li> <li>@giswqs made their first contribution in developmentseed/lonboard!223</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.6.0...v0.7.0</p>"},{"location":"CHANGELOG/#060-2024-02-13","title":"[0.6.0] - 2024-02-13","text":""},{"location":"CHANGELOG/#new_2","title":"New!","text":"<ul> <li>DataFilterExtension by @kylebarron in developmentseed/lonboard!278</li> <li>Multi-range sliders for DataFilterExtension by @kylebarron in developmentseed/lonboard!340</li> <li>BitmapLayer and BitmapTileLayer by @kylebarron in developmentseed/lonboard!288</li> <li>Improved GeoArrow interop by @kylebarron in developmentseed/lonboard!308</li> <li>Allow passing a positional <code>layers</code> object into <code>Map</code> by @kylebarron in developmentseed/lonboard!319</li> <li>GeoArrow-based multithreaded coordinate reprojection by @kylebarron in developmentseed/lonboard!337</li> <li>Support <code>pyarrow.Table</code> with <code>geoarrow.pyarrow</code> extension types as geometry columns by @jorisvandenbossche in developmentseed/lonboard!218</li> <li>Add ecosystem/integrations documentation by @kylebarron in developmentseed/lonboard!350</li> </ul>"},{"location":"CHANGELOG/#fixes_3","title":"Fixes","text":"<ul> <li>Add font to index.css to fix static HTML export by @jtmiclat in developmentseed/lonboard!284</li> <li>Fix displaying tooltip for first row by @kylebarron in developmentseed/lonboard!287</li> <li>accept matplotlib colormap input to apply_cmap by @kylebarron in developmentseed/lonboard!289</li> <li>Use preferred OSM tile url by @kylebarron in developmentseed/lonboard!290</li> <li>set max zoom on osm layer by @kylebarron in developmentseed/lonboard!291</li> <li>Update contributor docs by @kylebarron in developmentseed/lonboard!316</li> <li>Check epsg:4326 bounds in layer creation by @kylebarron in developmentseed/lonboard!317</li> <li>add reference for installing from source by @kylebarron in developmentseed/lonboard!318</li> <li>Fix inferring number of rows per chunk by @kylebarron in developmentseed/lonboard!327</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!331</li> <li>Set max number of chunks per layer by @kylebarron in developmentseed/lonboard!332</li> <li>Move accessor length validation to serialization by @kylebarron in developmentseed/lonboard!333</li> <li>Deduplicate serialization for accessors by @kylebarron in developmentseed/lonboard!334</li> <li>Multi-dimensional GPU-based data filtering by @kylebarron in developmentseed/lonboard!335</li> <li>Bump anywidget to 0.9 &amp; simplify Wasm initialization by @kylebarron in developmentseed/lonboard!344</li> <li>Fix null checks by @kylebarron in developmentseed/lonboard!348</li> <li>docs fixes by @kylebarron in developmentseed/lonboard!354</li> <li>Add <code>DataFilterExtension</code> example by @kylebarron in developmentseed/lonboard!358</li> <li>fix arc layer default arguments by @kylebarron in developmentseed/lonboard!359</li> </ul>"},{"location":"CHANGELOG/#new-contributors_1","title":"New Contributors","text":"<ul> <li>@jtmiclat made their first contribution in developmentseed/lonboard!284</li> <li>@jorisvandenbossche made their first contribution in developmentseed/lonboard!218</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.5.0...v0.6.0</p>"},{"location":"CHANGELOG/#050-2023-12-01","title":"[0.5.0] - 2023-12-01","text":""},{"location":"CHANGELOG/#new-features","title":"New Features","text":"<ul> <li>Improve map height by @vgeorge in developmentseed/lonboard!220</li> <li>Add method to apply a categorical colormap by @kylebarron in developmentseed/lonboard!251</li> <li>Deduce environment and set map height in colab and vscode by @kylebarron in developmentseed/lonboard!252</li> <li>Add various carto basemaps as options by @kylebarron in developmentseed/lonboard!268</li> <li>Sync the clicked index back to Python by @kylebarron in developmentseed/lonboard!270</li> <li>Set <code>width_min_pixels</code> in PathLayer example by @kylebarron in developmentseed/lonboard!276</li> <li>Bump deck.gl layers for performance benefits by @kylebarron in developmentseed/lonboard!277</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.2...v0.5.0</p>"},{"location":"CHANGELOG/#042-2023-11-13","title":"[0.4.2] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed","title":"Fixed","text":"<ul> <li>Fixed ScatterplotLayer rendering by @kylebarron in developmentseed/lonboard!246</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.1...v0.4.2</p>"},{"location":"CHANGELOG/#041-2023-11-13","title":"[0.4.1] - 2023-11-13","text":""},{"location":"CHANGELOG/#fixed_1","title":"Fixed","text":"<ul> <li>Fixed Polygon rendering by @kylebarron in developmentseed/lonboard!243</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.4.0...v0.4.1</p>"},{"location":"CHANGELOG/#040-2023-11-10","title":"[0.4.0] - 2023-11-10","text":""},{"location":"CHANGELOG/#new-features_1","title":"New Features","text":"<ul> <li>New <code>HeatmapLayer</code></li> <li>New <code>experimental</code> module, with new layers (<code>ArcLayer</code>, <code>TextLayer</code>) and \"layer extensions\" (<code>BrushingExtension</code>, <code>CollisionFilterExtension</code>).</li> <li>New \"migration\" notebook using the experimental <code>ArcLayer</code>.</li> </ul>"},{"location":"CHANGELOG/#fixed_2","title":"Fixed","text":"<ul> <li>Add pandas v2 requirement by @kylebarron in developmentseed/lonboard!229</li> <li>bump anywidget to 0.7.1 by @kylebarron in developmentseed/lonboard!233</li> </ul> <p>This should error when the JS files have not been included when packaging.</p> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.3.0...v0.4.0</p>"},{"location":"CHANGELOG/#030-2023-11-07","title":"[0.3.0] - 2023-11-07","text":""},{"location":"CHANGELOG/#new-features_2","title":"New Features","text":"<ul> <li>Save widget to standalone HTML file by @kylebarron in developmentseed/lonboard!199</li> <li>Support for rendering inside Visual Studio Code</li> <li>Allow customized picking radius by @kylebarron in developmentseed/lonboard!212</li> <li>New example notebooks</li> <li>Automatically downcast data types in <code>from_geopandas</code> by @kylebarron in developmentseed/lonboard!195</li> </ul>"},{"location":"CHANGELOG/#fixed_3","title":"Fixed","text":"<ul> <li>Allow pandas series as accessor to FloatAccessor by @kylebarron in developmentseed/lonboard!208</li> <li>Raise error when creating class with unknown keyword argument by @kylebarron in developmentseed/lonboard!209</li> <li>fix tooltip rendering when not hovering over an object by @kylebarron in developmentseed/lonboard!215</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.2.0...v0.3.0</p>"},{"location":"CHANGELOG/#020-2023-11-01","title":"[0.2.0] - 2023-11-01","text":""},{"location":"CHANGELOG/#breaking-changes","title":"Breaking Changes","text":"<ul> <li>Layers no longer render a map object. Instead, pass one or more layer instances into a <code>lonboard.Map</code> and display that.</li> </ul>"},{"location":"CHANGELOG/#new-features_3","title":"New Features","text":"<ul> <li>Support multiple layers on a single map.</li> <li>Tooltip with data information on hover.</li> <li>Allow hex string color input to ColorAccessor.</li> <li>Versioned documentation website</li> <li>New examples</li> <li>Experimental API to change map height</li> </ul>"},{"location":"CHANGELOG/#fixed_4","title":"Fixed","text":"<ul> <li>Fix handling of 3d coordinates by @kylebarron in developmentseed/lonboard!160</li> <li>Improved validation error messages by @kylebarron in developmentseed/lonboard!161</li> </ul>"},{"location":"CHANGELOG/#new-contributors_2","title":"New Contributors","text":"<ul> <li>@chrisgervang made their first contribution in developmentseed/lonboard!150</li> </ul> <p>Full Changelog: github.com/developmentseed/lonboard/compare/v0.1.2...v0.2.0</p>"},{"location":"CHANGELOG/#012-2023-10-24","title":"[0.1.2] - 2023-10-24","text":"<ul> <li>Actually update <code>@geoarrow/deck.gl-layers</code> to version 0.2.0, whoops \ud83d\ude05</li> </ul>"},{"location":"CHANGELOG/#011-2023-10-23","title":"[0.1.1] - 2023-10-23","text":"<ul> <li>Updates to <code>@geoarrow/deck.gl-layers</code> version 0.2.0:</li> <li>Fixed MultiPolygon rendering</li> <li>Fixed rendering of polygons with holes.</li> <li>Polygon rendering is roughly 35% faster.</li> </ul>"},{"location":"CHANGELOG/#010-2023-10-17","title":"[0.1.0] - 2023-10-17","text":"<ul> <li>Initial public release.</li> <li>Initial support for <code>ScatterplotLayer</code>, <code>PathLayer</code>, and <code>SolidPolygonLayer</code>.</li> </ul>"},{"location":"alternatives/","title":"Alternatives to Lonboard","text":""},{"location":"alternatives/#lonboard-vs-ipyleaflet","title":"Lonboard vs ipyleaflet","text":"<p>ipyleaflet is a great rendering library for small- and medium-sized datasets. ipyleaflet supports a broad range of data types and formats and gives the user broad control over how to render data.</p> <p>The downside of ipyleaflet is that it doesn't support large datasets as well. It uses GeoJSON to transfer data to the frontend, which is slow to write, slow to read, and large in transit. Additionally, leaflet's primary goal is not to support very large quantities of data.</p>"},{"location":"alternatives/#lonboard-vs-pydeck","title":"Lonboard vs pydeck","text":"<p>Pydeck is a full-featured binding from Python to deck.gl. Pydeck attempts to cover most of the deck.gl API. It's harder to use binary data transport with pydeck, and similarly to ipyleaflet will usually serialize data to GeoJSON.</p> <p>Lonboard does not try to cover deck.gl's full API, but rather has an opinionated approach that nudges users to the fastest rendering for many common use cases.</p>"},{"location":"alternatives/#why-not-contribute-back-to-pydeck","title":"Why not contribute back to pydeck?","text":"<p>Pydeck and lonboard have very different goals.</p> <p>A stated goal of pydeck is to be non-opinionated and to allow users with various data sources (GeoJSON strings, URLs to arbitrary data sources, etc.) to render. It makes sense for \"official\" bindings to be non-opinionated, but lonboard takes an opposite tack. By forcing users to use Arrow, we can get reliably fast performance the very common use case of rendering <code>GeoDataFrame</code>s. A downside here is that an Arrow-based implementation has required dependencies that pydeck wouldn't want. <code>pyarrow</code> on the Python side is 90MB on disk. Arrow JS on the JS side is ~200kb, and the default <code>parquet-wasm</code> build is ~1MB.</p> <p>Pydeck is tightly tied into the deck.gl JSON renderer, which allows describing a map state fully in JSON. It's not clear how this would work with the JavaScript GeoArrow layers.</p> <p>Aside from this, pydeck and lonboard use different widget architectures. Pydeck is built on the historical ipywidget layout, using the widget cookiecutter as inspiration and having a separate Jupyter Widget package published to NPM. Lonboard takes a newer approach (unavailable at the time pydeck was created) that uses Anywidget, vastly simplifying the widget process.</p>"},{"location":"alternatives/#lonboard-vs-keplergl-jupyter","title":"Lonboard vs kepler.gl-jupyter","text":"<p>kepler.gl is a JavaScript application focused on presenting a simple, high-level visualization and analysis toolkit to browser-based users. Just like Lonboard, it's built on top of deck.gl. It also has Python bindings via kepler.gl-jupyter.</p> <p>kepler.gl is a very good tool for data analysis and exploration in the browser. On the contrary, Lonboard targets users who plan to do most analysis in Python, but want the best performance to visualize the maximum amount of data. Lonboard should have better rendering performance, but has no user interface to analyze data in the browser.</p> <p>kepler.gl-jupyter serializes to GeoJSON and kepler.gl uses GeoJSON-like JavaScript objects internally; it therefore has the downside of creating a large text string in Python and serializing that to JavaScript. kepler.gl-jupyter is great for creating standalone static HTML files containing a dataset, but those files tend to be very large, since they contain embedded GeoJSON.</p>"},{"location":"alternatives/#lonboard-vs-datashader","title":"Lonboard vs datashader","text":"<p>Datashader is a truly scalable rendering library. Datashader will re-render your data from scratch when panning around in a map. This allows datashader to aggregate the source data before rendering. Datashader minimizes the amount of data being rendered and thus, in theory, Datashader should perform well for datasets as large as your computer's memory.</p> <p>Lonboard is not scalable in the same sense. It doesn't minimize the amount of data being rendered. If you ask to plot a GeoDataFrame with 3 million points, every single one of those points is transferred to the GPU and drawn to your screen. In contrast to Datashader, Lonboard should perform well for datasets whose geometries fit in your computer's GPU memory, which is usually much smaller than your computer's total memory.</p>"},{"location":"cli/","title":"Command-line Interface","text":"<p>Lonboard includes a command-line interface for quickly viewing local data files.</p> <p>The CLI is accessible either through the <code>lonboard</code> entry point or via <code>python -m lonboard</code> the latter can be useful to ensure that the <code>lonboard</code> instance you're calling is the same as your current <code>python</code> environment.</p> <pre><code>&gt; lonboard --help\nUsage: lonboard [OPTIONS] [FILES]...\n\n  Interactively visualize geospatial data using Lonboard.\n\n  This CLI can be used either to quickly view local files or to create static\n  HTML files.\n\nOptions:\n  -o, --output PATH   The output path for the generated HTML file. If not\n                      provided, will save to a temporary file.\n  --open / --no-open  Whether to open a web browser tab with the generated\n                      map. By default, the web browser is not opened when\n                      --output is provided, but is in other cases.\n  --help              Show this message and exit.\n</code></pre> <p>For example:</p> <pre><code>lonboard data.geojson\n</code></pre>"},{"location":"performance/","title":"Performance","text":"<p>Performance is a critical goal of lonboard. Below are a couple pieces of information you should know to understand lonboard's performance characteristics, as well as some advice for how to get the best performance.</p>"},{"location":"performance/#performance-characteristics","title":"Performance Characteristics","text":"<p>There are two distinct parts to the performance of lonboard: one is the performance of transferring data to the browser and the other is the performance of rendering the data once it's there.</p> <p>In general, these parts are completely distinct. Even if it takes a while to load the data in your browser, the map might be snappy once it loads, and vice versa.</p>"},{"location":"performance/#data-transfer","title":"Data Transfer","text":"<p>Lonboard creates an interactive visualization of your data in your browser. In order to do this, your GeoDataFrame needs to be transferred from your Python environment to your browser.</p> <p>In the case where your Python session is running locally (on the same machine as your browser), this data transfer is extremely fast: less than a second in most cases.</p> <p>However, in the case where your Python session is running on a remote server (such as Google Colab, Binder, or a JupyterHub instance), this data transfer means downloading the data to your local browser. Therefore, when running lonboard from a remote server, your internet speed and the quantity of data you pass into a layer will have large impacts on the data transfer speed.</p> <p>Under the hood, lonboard uses efficient compression (in the form of GeoParquet) to transfer data to the browser, but compression can only do so much; the data still needs to be downloaded.</p>"},{"location":"performance/#rendering-performance","title":"Rendering Performance","text":"<p>Once the data has been transfered from your Python session to your browser, it needs to be rendered.</p> <p>The biggest thing to note is that \u2014 in contrast to projects like datashader \u2014 lonboard does not minimize the amount of data being rendered. If you pass a GeoDataFrame with 10 million coordinates, lonboard will attempt to render all 10 million coordinates at once.</p> <p>The primary determinant of the maximum amount of data you can render with lonboard is your computer's hardware. Via the underlying deck.gl library, lonboard ultimately renders geometries using your computer's Graphics Processing Unit (GPU). If you have a better GPU card, you'll be able to visualize more data.</p> <p>Lonboard is more efficient at rendering than previous libraries, but there will always be some quantity of data beyond which your browser tab is likely to crash while attempting to render. Testing on a recent MacBook Pro M2 computer, lonboard has been able to render a few million points with minimal lag.</p>"},{"location":"performance/#performance-advice","title":"Performance Advice","text":""},{"location":"performance/#use-a-local-python-session","title":"Use a local Python session","text":"<p>Moving from a remote Python environment to a local Python environment is often impractical, but this change will make it much faster to visualize data, especially over slow internet connections.</p>"},{"location":"performance/#mutate-an-existing-map-where-possible","title":"Mutate an existing map where possible","text":"<p>Whenever you create a new map object, all the data will be transfered from your Python environment to your browser. Whenever mutating an existing map object, only the changed data will be sent to the browser.</p> <p>Therefore, unless changing the content of the data being rendered, do not create a new map object. If you're changing the rendering options of an existing map, prefer mutating the existing map object.</p> <p>Bad</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap2 = ScatterplotLayer.from_geopandas(\n    my_geodataframe,\n    get_fill_color=[255, 0, 0]\n)\n</code></pre> <p>Good</p> <pre><code>map1 = ScatterplotLayer.from_geopandas(my_geodataframe)\n\n# Next cell\nmap1.get_fill_color = [255, 0, 0]\n</code></pre> <p>In the first (bad) case, the content of <code>my_geodataframe</code> will be sent twice to the browser. In the second (good) case, the content of <code>my_geodataframe</code> will be sent only once to the browser, and only the tiny array <code>[255, 0, 0]</code> will be transfered on the second call.</p>"},{"location":"performance/#remove-columns-before-rendering","title":"Remove columns before rendering","text":"<p>All columns included in the <code>GeoDataFrame</code> will be transferred to the browser for visualization. (In the future, these other columns will be used to display a tooltip when hovering over/clicking on a geometry.)</p> <p>Especially in the case of a remote Python session, excluding unnecessary attribute columns will make data transfer to the browser faster.</p>"},{"location":"performance/#use-arrow-based-data-types-in-pandas","title":"Use Arrow-based data types in Pandas","text":"<p>As of Pandas 2.0, Pandas supports two backends for data types: either the original numpy-based data types or new data types based on Arrow and the pyarrow library.</p> <p>The first thing that lonboard does when visualizing data is converting from Pandas to an Arrow representation. Any non-geometry attribute columns will be converted to Arrow, so if you're using Arrow-based data types in Pandas already, this step will be \"free\" as no conversion is needed.</p> <p>If you're loading data from a geospatial file format, as of <code>pyogrio</code> version 0.7, you can load data to Arrow-based columns with</p> <pre><code>import pyogrio\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = pyogrio.read_dataframe(\n    \"path/to/file\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>or directly from GeoPandas with <code>geopandas.read_file</code> with <code>engine=\"pyogrio\"</code>:</p> <pre><code>import geopandas as gpd\nimport pandas as pd\n\narrow_to_pandas_kwargs = {\n    'types_mapper': lambda pa_dtype: pd.ArrowDtype(pa_dtype)\n}\ngdf = gpd.read_file(\n    \"path/to/file\",\n    engine=\"pyogrio\",\n    use_arrow=True,\n    arrow_to_pandas_kwargs=arrow_to_pandas_kwargs\n)\n</code></pre> <p>See the pandas guide on data types and the <code>pandas.ArrowDtype</code> class for more information.</p>"},{"location":"performance/#simplify-geometries-before-rendering","title":"Simplify geometries before rendering","text":"<p>Simplifying geometries before rendering reduces the total number of coordinates and can make a visualization snappier. At this point, lonboard does not offer built-in geometry simplification. This is something you would need to do before passing data to lonboard.</p>"},{"location":"api/basemap/","title":"lonboard.basemap","text":""},{"location":"api/basemap/#lonboard.basemap.CartoBasemap","title":"lonboard.basemap.CartoBasemap","text":"<p>             Bases: <code>str</code>, <code>Enum</code></p> <p>Basemap styles provided by Carto.</p> <p>Refer to Carto documentation for information on styles.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatter","title":"DarkMatter  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatter = 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'\n</code></pre> <p>A dark map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.DarkMatterNoLabels","title":"DarkMatterNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>DarkMatterNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/dark-matter-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A dark map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Positron","title":"Positron  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Positron = 'https://basemaps.cartocdn.com/gl/positron-gl-style/style.json'\n</code></pre> <p>A light map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.PositronNoLabels","title":"PositronNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>PositronNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light map style without labels.</p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.Voyager","title":"Voyager  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>Voyager = 'https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json'\n</code></pre> <p>A light, colored map style with labels.</p> <p></p>"},{"location":"api/basemap/#lonboard.basemap.CartoBasemap.VoyagerNoLabels","title":"VoyagerNoLabels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>VoyagerNoLabels = (\n    \"https://basemaps.cartocdn.com/gl/voyager-nolabels-gl-style/style.json\"\n)\n</code></pre> <p>A light, colored map style without labels.</p>"},{"location":"api/colormap/","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap","title":"lonboard.colormap","text":""},{"location":"api/colormap/#lonboard.colormap.DiscreteColormap","title":"DiscreteColormap  <code>module-attribute</code>","text":"<pre><code>DiscreteColormap = Dict[Any, RGBColor]\n</code></pre> <p>A type definition for a discrete colormap.</p> <p>For example, for a land cover colormap, you may want to use the following dict:</p> <pre><code>color_map = {\n    \"Open Water\": [70, 107, 159],\n    \"Perennial Snow/Ice\": [209, 222, 248],\n    \"Developed, Open Space\": [222, 197, 197],\n    \"Developed, Low Intensity\": [217, 146, 130],\n    \"Developed, Medium Intensity\": [235, 0, 0],\n    \"Developed, High Intensity\": [171, 0, 0],\n    \"Barren Land\": [179, 172, 159],\n    \"Deciduous Forest\": [104, 171, 95],\n    \"Evergreen Forest\": [28, 95, 44],\n    \"Mixed Forest\": [181, 197, 143],\n    \"Shrub/Scrub\": [204, 184, 121],\n    \"Herbaceous\": [223, 223, 194],\n    \"Hay/Pasture\": [220, 217, 57],\n    \"Cultivated Crops\": [171, 108, 40],\n    \"Woody Wetlands\": [184, 217, 235],\n    \"Emergent Herbaceous Wetlands\": [108, 159, 184],\n}\n</code></pre> <p>This corresponds to the following well-known color palette from the National Land Cover Database.</p> <p> </p> <p>Keep in mind that the type of the key of the <code>color_map</code> dictionary is important. If your data is represented as a string, the keys of the <code>color_map</code> must also be represented as strings. If your data is represented as an integer, the keys of the colormap should be integers.</p>"},{"location":"api/colormap/#lonboard.colormap.RGBColor","title":"RGBColor  <code>module-attribute</code>","text":"<pre><code>RGBColor = Union[Tuple[int, int, int], Tuple[int, int, int, int], Sequence[int]]\n</code></pre> <p>A type definition for an RGB or RGBA color value</p> <p>All values must range between 0 and 255 (inclusive). If only three values are provided, the fourth (alpha) channel will be inferred as 255 (meaning full opacity, no transparency).</p>"},{"location":"api/colormap/#lonboard.colormap.apply_categorical_cmap","title":"apply_categorical_cmap","text":"<pre><code>apply_categorical_cmap(\n    values: Union[NDArray, pd.Series, pa.Array, pa.ChunkedArray],\n    cmap: DiscreteColormap,\n    *,\n    alpha: Optional[int] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of categorical values.</p> <p>If you're working with categorical data in Pandas or GeoPandas, and especially when those categories are strings, you should use the pandas Categorical type. This will use much less memory and be faster to operate on. <code>apply_categorical_cmap</code> will be 2-3x faster (and use less memory itself) when your data is already represented as a categorical data type.</p> <p>The key of the colormap must be the same as the data type of the column of values you pass in. Usually this will be string, when you perform the lookup on a string column of data.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>Union[NDArray, Series, Array, ChunkedArray]</code>)         \u2013          <p>A numpy array, pandas Series, pyarrow Array or pyarrow ChunkedArray of data. The data type of this column must match the keys of the colormap.</p> </li> <li> <code>cmap</code>             (<code>DiscreteColormap</code>)         \u2013          <p>A dictionary mapping keys to colors. See [DiscreteColormap] for more information.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Optional[int]</code>)         \u2013          <p>The default alpha value for entries in the colormap that do not have an alpha value defined. Alpha must be an integer between 0 and 255 (inclusive).</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/colormap/#lonboard.colormap.apply_continuous_cmap","title":"apply_continuous_cmap","text":"<pre><code>apply_continuous_cmap(\n    values: NDArray[np.floating],\n    cmap: Union[Palette, mpl.colors.Colormap],\n    *,\n    alpha: Union[float, int, NDArray[np.floating], None] = None\n) -&gt; NDArray[np.uint8]\n</code></pre> <p>Apply a colormap to a column of continuous values.</p> <p>If you pass in a <code>Palette</code> object from the <code>palettable</code> package, this will use matplotlib's LinearSegmentedColormap under the hood. As described in Matplotlib's referenced docstring:</p> <p>The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments.</p> <p>This means that input values are linearly combined from the two nearest colormap colors.</p> <p>If you pass in a Matplotlib <code>Colormap</code> object, this will use the lookup method defined in that class.</p> <p>Parameters:</p> <ul> <li> <code>values</code>             (<code>NDArray[floating]</code>)         \u2013          <p>A numpy array of floating point values ranging from 0 to 1.</p> </li> <li> <code>cmap</code>             (<code>Union[Palette, Colormap]</code>)         \u2013          <p>Any matplotlib <code>Colormap</code> or <code>Palette</code> object from the <code>palettable</code> package.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>alpha</code>             (<code>Union[float, int, NDArray[floating], None]</code>)         \u2013          <p>Alpha must be a scalar between 0 and 1, a sequence of such floats with shape matching <code>values</code>, or None.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>NDArray[uint8]</code>         \u2013          <p>A two dimensional numpy array with data type np.uint8. The second dimension will have a length of either <code>3</code> if <code>alpha</code> is <code>None</code>, or <code>4</code> is each color has an alpha value.</p> </li> </ul>"},{"location":"api/controls/","title":"lonboard.controls","text":""},{"location":"api/controls/#lonboard.controls.MultiRangeSlider","title":"lonboard.controls.MultiRangeSlider","text":"<p>             Bases: <code>VBox</code></p> <p>A widget for multiple ranged sliders.</p> <p>This is designed to be used with the DataFilterExtension when you want to filter on 2 to 4 columns on the same time.</p> <p>If you have only a single filter, use an ipywidgets FloatRangeSlider directly.</p>"},{"location":"api/controls/#lonboard.controls.MultiRangeSlider--example","title":"Example","text":"<pre><code>from ipywidgets import FloatRangeSlider\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n</code></pre> <p>Then to propagate updates to a rendered layer, call jsdlink to connect the two widgets.</p> <pre><code>from ipywidgets import jsdlink\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>As you change the slider, the <code>filter_range</code> value on the layer class should be updated.</p>"},{"location":"api/map/","title":"Map","text":""},{"location":"api/map/#lonboard.Map","title":"lonboard.Map","text":"<p>The top-level class used to display a map in a Jupyter Widget.</p> <p>Example:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer, SolidPolygonLayer\n\n# A GeoDataFrame with Point geometries\npoint_gdf = gpd.GeoDataFrame()\npoint_layer = ScatterplotLayer.from_geopandas(\n    point_gdf,\n    get_fill_color=[255, 0, 0],\n)\n\n# A GeoDataFrame with Polygon geometries\npolygon_gdf = gpd.GeoDataFrame()\npolygon_layer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\n\nm = Map([point_layer, polygon_layer])\n</code></pre>"},{"location":"api/map/#lonboard.Map.__init__","title":"__init__","text":"<pre><code>__init__(\n    layers: Union[BaseLayer, Sequence[BaseLayer]], **kwargs: Unpack[MapKwargs]\n) -&gt; None\n</code></pre> <p>Create a new Map.</p> <p>Aside from the <code>layers</code> argument, pass keyword arguments for any other attribute defined in this class.</p> <p>Parameters:</p> <ul> <li> <code>layers</code>             (<code>Union[BaseLayer, Sequence[BaseLayer]]</code>)         \u2013          <p>One or more layers to render on this map.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>None</code>         \u2013          <p>A Map object.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map._height","title":"_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>_height = tag(sync=True)\n</code></pre> <p>Height of the map in pixels.</p> <p>This API is not yet stabilized and may change in the future.</p>"},{"location":"api/map/#lonboard.Map.basemap_style","title":"basemap_style  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>basemap_style = tag(sync=True)\n</code></pre> <p>A MapLibre-compatible basemap style.</p> <p>Various styles are provided in <code>lonboard.basemap</code>.</p> <ul> <li>Type: <code>str</code></li> <li>Default   <code>lonboard.basemap.CartoBasemap.PositronNoLabels</code></li> </ul>"},{"location":"api/map/#lonboard.Map.fly_to","title":"fly_to","text":"<pre><code>fly_to(\n    *,\n    longitude: Union[int, float],\n    latitude: Union[int, float],\n    zoom: float,\n    duration: int = 4000,\n    pitch: Union[int, float] = 0,\n    bearing: Union[int, float] = 0,\n    curve: Optional[Union[int, float]] = None,\n    speed: Optional[Union[int, float]] = None,\n    screen_speed: Optional[Union[int, float]] = None\n)\n</code></pre> <p>\"Fly\" the map to a new location.</p> <p>Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The longitude of the new viewport.</p> </li> <li> <code>latitude</code>             (<code>Union[int, float]</code>)         \u2013          <p>The latitude of the new viewport.</p> </li> <li> <code>zoom</code>             (<code>float</code>)         \u2013          <p>The zoom of the new viewport.</p> </li> <li> <code>pitch</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The pitch of the new viewport. Defaults to 0.</p> </li> <li> <code>bearing</code>             (<code>Union[int, float]</code>, default:                 <code>0</code> )         \u2013          <p>The bearing of the new viewport. Defaults to 0.</p> </li> <li> <code>duration</code>             (<code>int</code>, default:                 <code>4000</code> )         \u2013          <p>The number of milliseconds for the viewport transition to take. Defaults to 4000.</p> </li> <li> <code>curve</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The zooming \"curve\" that will occur along the flight path. Default <code>1.414</code>.</p> </li> <li> <code>speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation defined in relation to <code>curve</code>, it linearly affects the duration, higher speed returns smaller durations and vice versa. Default <code>1.2</code>.</p> </li> <li> <code>screen_speed</code>             (<code>Optional[Union[int, float]]</code>, default:                 <code>None</code> )         \u2013          <p>The average speed of the animation measured in screenfuls per second. Similar to speed it linearly affects the duration, when specified speed is ignored.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.layers","title":"layers  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>layers = tag(sync=True, **widget_serialization)\n</code></pre> <p>One or more <code>Layer</code> objects to display on this map.</p>"},{"location":"api/map/#lonboard.Map.parameters","title":"parameters  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>parameters = tag(sync=True)\n</code></pre> <p>GPU parameters to pass to deck.gl.</p> <p>This is an advanced API. The vast majority of users should not need to touch this setting.</p> <p>Note</p> <p>The docstring below is copied from upstream deck.gl documentation. Any usage of <code>GL</code> refers to the constants defined in <code>@luma.gl/constants</code> here, which comes from the MDN docs here.</p> <p>In place of any <code>GL</code> constant, you can use the underlying integer it represents. For example, instead of the JS</p> <pre><code>depthFunc: GL.LEQUAL\n</code></pre> <p>referring to the MDN docs, you should use</p> <pre><code>depthFunc: 0x0203\n</code></pre> <p>Note that these parameters do not yet work with integer keys. If you would like to use integer keys, open an issue.</p> <p>Expects an object with GPU parameters. Before each frame is rendered, this object will be passed to luma.gl's <code>setParameters</code> function to reset the GPU context parameters, e.g. to disable depth testing, change blending modes etc. The default parameters set by <code>Deck</code> on initialization are the following:</p> <pre><code>{\n  blend: true,\n  blendFunc: [GL.SRC_ALPHA, GL.ONE_MINUS_SRC_ALPHA, GL.ONE, GL.ONE_MINUS_SRC_ALPHA],\n  polygonOffsetFill: true,\n  depthTest: true,\n  depthFunc: GL.LEQUAL\n}\n</code></pre> <p>Refer to the luma.gl setParameters API for documentation on supported parameters and values.</p> <pre><code>import GL from '@luma.gl/constants';\nnew Deck({\n  // ...\n  parameters: {\n    blendFunc: [GL.ONE, GL.ONE, GL.ONE, GL.ONE],\n    depthTest: false\n  }\n});\n</code></pre> <p>Notes:</p> <ul> <li>Any GPU <code>parameters</code> prop supplied to individual layers will still override the   global <code>parameters</code> when that layer is rendered.</li> </ul>"},{"location":"api/map/#lonboard.Map.picking_radius","title":"picking_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>picking_radius = tag(sync=True)\n</code></pre> <p>Extra pixels around the pointer to include while picking (such as for a tooltip).</p> <p>This is helpful when rendered objects are difficult to target, for example irregularly shaped icons, small moving circles or interaction by touch.</p> <ul> <li>Type: <code>int</code></li> <li>Default: <code>5</code></li> </ul>"},{"location":"api/map/#lonboard.Map.set_view_state","title":"set_view_state","text":"<pre><code>set_view_state(\n    *,\n    longitude: Optional[float] = None,\n    latitude: Optional[float] = None,\n    zoom: Optional[float] = None,\n    pitch: Optional[float] = None,\n    bearing: Optional[float] = None\n) -&gt; None\n</code></pre> <p>Set the view state of the map.</p> <p>Any parameters that are unset will not be changed.</p> <p>Other Parameters:</p> <ul> <li> <code>longitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new longitude to set on the map. Defaults to None.</p> </li> <li> <code>latitude</code>             (<code>Optional[float]</code>)         \u2013          <p>the new latitude to set on the map. Defaults to None.</p> </li> <li> <code>zoom</code>             (<code>Optional[float]</code>)         \u2013          <p>the new zoom to set on the map. Defaults to None.</p> </li> <li> <code>pitch</code>             (<code>Optional[float]</code>)         \u2013          <p>the new pitch to set on the map. Defaults to None.</p> </li> <li> <code>bearing</code>             (<code>Optional[float]</code>)         \u2013          <p>the new bearing to set on the map. Defaults to None.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.show_tooltip","title":"show_tooltip  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>show_tooltip = tag(sync=True)\n</code></pre> <p>Whether to render a tooltip on hover on the map.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/map/#lonboard.Map.to_html","title":"to_html","text":"<pre><code>to_html(\n    filename: Union[str, Path, TextIO, IO[str]], title: Optional[str] = None\n) -&gt; None\n</code></pre> <p>Save the current map as a standalone HTML file.</p> <p>Parameters:</p> <ul> <li> <code>filename</code>             (<code>Union[str, Path, TextIO, IO[str]]</code>)         \u2013          <p>where to save the generated HTML file.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>title</code>             (<code>Optional[str]</code>)         \u2013          <p>A title for the exported map. This will show as the browser tab name.</p> </li> </ul>"},{"location":"api/map/#lonboard.Map.use_device_pixels","title":"use_device_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>use_device_pixels = tag(sync=True)\n</code></pre> <p>Controls the resolution of the drawing buffer used for rendering.</p> <p>Setting this to <code>false</code> or a number &lt;= 1 will improve performance on high resolution displays.</p> <p>Note: This parameter must be passed to the <code>Map()</code> constructor. It cannot be changed once the map has been created.</p> <p>The available options are:</p> <ul> <li><code>true</code>: Device (physical) pixels resolution is used for rendering, this resolution   is defined by <code>window.devicePixelRatio</code>. On Retina/HD systems this resolution is   usually twice as big as CSS pixels resolution.</li> <li><code>false</code>: CSS pixels resolution (equal to the canvas size) is used for rendering.</li> <li> <p><code>Number</code> (Experimental): Specified Number is used as a custom ratio (drawing   buffer resolution to CSS pixel resolution) to determine drawing buffer size, a   value less than one uses resolution smaller than CSS pixels, gives better   performance but produces blurry images, a value greater than one uses resolution   bigger than CSS pixels resolution (canvas size), produces sharp images but at a   lower performance.</p> </li> <li> <p>Type: <code>float</code>, <code>int</code> or <code>bool</code></p> </li> <li>Default: <code>true</code></li> </ul>"},{"location":"api/map/#lonboard.Map.view_state","title":"view_state  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>view_state = ViewStateTrait()\n</code></pre> <p>The view state of the map.</p> <ul> <li>Type: <code>ViewState</code></li> <li>Default: Automatically inferred from the data passed to the map.</li> </ul> <p>You can initialize the map to a specific view state using this property:</p> <pre><code>Map(\n    layers,\n    view_state={\"longitude\": -74.0060, \"latitude\": 40.7128, \"zoom\": 7}\n)\n</code></pre> <p>Note</p> <p>The properties of the view state are immutable. Use <code>set_view_state</code> to modify a map's view state once it's been initially rendered.</p>"},{"location":"api/map/#lonboard.models.ViewState","title":"lonboard.models.ViewState","text":"<p>             Bases: <code>NamedTuple</code></p>"},{"location":"api/map/#lonboard.models.ViewState.bearing","title":"bearing  <code>instance-attribute</code>","text":"<pre><code>bearing: float\n</code></pre> <p>Bearing angle in degrees. <code>0</code> is north.</p>"},{"location":"api/map/#lonboard.models.ViewState.latitude","title":"latitude  <code>instance-attribute</code>","text":"<pre><code>latitude: float\n</code></pre> <p>Latitude at the map center.</p>"},{"location":"api/map/#lonboard.models.ViewState.longitude","title":"longitude  <code>instance-attribute</code>","text":"<pre><code>longitude: float\n</code></pre> <p>Longitude at the map center</p>"},{"location":"api/map/#lonboard.models.ViewState.pitch","title":"pitch  <code>instance-attribute</code>","text":"<pre><code>pitch: float\n</code></pre> <p>Pitch angle in degrees. <code>0</code> is top-down.</p>"},{"location":"api/map/#lonboard.models.ViewState.zoom","title":"zoom  <code>instance-attribute</code>","text":"<pre><code>zoom: float\n</code></pre> <p>Zoom level.</p>"},{"location":"api/traits/","title":"lonboard.traits","text":""},{"location":"api/traits/#lonboard.traits.ColorAccessor","title":"lonboard.traits.ColorAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl color accessor.</p> <p>Various input is allowed:</p> <ul> <li>A <code>list</code> or <code>tuple</code> with three or four integers, ranging between 0 and 255   (inclusive). This will be used as the color for all objects.</li> <li>A <code>str</code> representing a hex color or \"well known\" color interpretable by   matplotlib.colors.to_rgba.</li> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>3</code> or <code>4</code>, and will correspond to either RGB   or RGBA colors.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>3</code> or <code>4</code> and its child must have type   <code>uint8</code>.</li> <li>Any Arrow fixed size list array from a library that implements the Arrow   PyCapsule   Interface.</li> </ul> <p>You can use helpers in the <code>lonboard.colormap</code> module (i.e. <code>apply_continuous_cmap</code>) to simplify constructing numpy arrays for color values.</p>"},{"location":"api/traits/#lonboard.traits.FloatAccessor","title":"lonboard.traits.FloatAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a deck.gl float accessor.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects.</li> <li>A numpy <code>ndarray</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index.</li> <li>Any Arrow floating point array from a library that implements the Arrow PyCapsule   Interface.   This includes data structures from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/traits/#lonboard.traits.GetFilterValueAccessor","title":"lonboard.traits.GetFilterValueAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for the <code>get_filter_value</code> accessor added by the <code>DataFilterExtension</code>, which can have between 1 and 4 float values per row.</p> <p>Various input is allowed:</p> <ul> <li>An <code>int</code> or <code>float</code>. This will be used as the value for all objects. The   <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A one-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must be 1.</li> <li>A two-dimensional numpy <code>ndarray</code> with a numeric data type. This will be casted to   an array of data type <code>np.float32</code>. Each value in the array will   be used as the value for the object at the same row index. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the size of the second dimension of the array.</li> <li>A pandas <code>Series</code> with a numeric data type. This will be casted to an array of   data type <code>np.float32</code>. Each value in the array will be used as   the value for the object at the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> <li>A pyarrow <code>FloatArray</code>, <code>DoubleArray</code>   or <code>ChunkedArray</code> containing either a <code>FloatArray</code> or   <code>DoubleArray</code>. Each value in the array will be used as the value for the object at   the same row index. The <code>filter_size</code> of the   <code>DataFilterExtension</code> instance   must be 1.</li> </ul> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface. - A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The child   array of the fixed size list must be of floating point type. The <code>filter_size</code> of   the <code>DataFilterExtension</code> instance   must match the list size.</p> <p>Alternatively, you can pass any corresponding Arrow data structure from a library   that implements the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.NormalAccessor","title":"lonboard.traits.NormalAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl \"normal\" accessor</p> <p>This is primarily used in the [lonboard.PointCloudLayer].</p> <p>Acceptable inputs: - A <code>list</code> or <code>tuple</code> with three <code>int</code> or <code>float</code> values. This will be used as the   normal for all objects. - A numpy ndarray with two dimensions and floating point type. The size of the   second dimension must be 3, i.e. its shape must be <code>(N, 3)</code>. - a pyarrow <code>FixedSizeListArray</code> or <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s   where the size of the inner fixed size list 3. The child array must have type   float32. - Any Arrow array that matches the above restrictions from a library that implements   the Arrow PyCapsule   Interface.</p>"},{"location":"api/traits/#lonboard.traits.PointAccessor","title":"lonboard.traits.PointAccessor","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A representation of a deck.gl point accessor.</p> <p>Various input is allowed:</p> <ul> <li>A numpy <code>ndarray</code> with two dimensions and data type <code>np.uint8</code>. The   size of the second dimension must be <code>2</code> or <code>3</code>, and will correspond to either XY   or XYZ positions.</li> <li>A pyarrow <code>FixedSizeListArray</code> or   <code>ChunkedArray</code> containing <code>FixedSizeListArray</code>s. The inner   size of the fixed size list must be <code>2</code> or <code>3</code> and its child must be of floating   point type.</li> </ul>"},{"location":"api/traits/#lonboard.traits.PyarrowTableTrait","title":"lonboard.traits.PyarrowTableTrait","text":"<p>             Bases: <code>FixedErrorTraitType</code></p> <p>A trait to validate input for a geospatial Arrow-backed table</p> <p>Allowed input includes:</p> <ul> <li>A pyarrow <code>Table</code> or containing a geometry column with GeoArrow metadata.</li> <li>Any GeoArrow table from a library that implements the Arrow PyCapsule   Interface.   This includes the   <code>GeoTable</code>   class from   <code>geoarrow-rust</code>.</li> </ul>"},{"location":"api/viz/","title":"viz","text":"<p>The top-level namespace accessible after <code>import lonboard</code>.</p> <p>Layers are also accessible in the top-level namespace, but are documented in the <code>layers/</code> folder.</p>"},{"location":"api/viz/#lonboard.viz","title":"lonboard.viz","text":"<pre><code>viz(\n    data: Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]],\n    *,\n    scatterplot_kwargs: Optional[ScatterplotLayerKwargs] = None,\n    path_kwargs: Optional[PathLayerKwargs] = None,\n    polygon_kwargs: Optional[PolygonLayerKwargs] = None,\n    map_kwargs: Optional[MapKwargs] = None\n) -&gt; Map\n</code></pre> <p>A high-level function to plot your data easily.</p> <p>The goal of this function is to make it simple to get something showing on a map. For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>This function accepts a variety of geospatial inputs:</p> <ul> <li>geopandas <code>GeoDataFrame</code></li> <li>geopandas <code>GeoSeries</code></li> <li>numpy array of Shapely objects</li> <li>Single Shapely object</li> <li>Any Python class with a <code>__geo_interface__</code> property conforming to the     Geo Interface protocol.</li> <li><code>dict</code> holding GeoJSON-like data.</li> <li>pyarrow <code>Table</code> with a geometry column marked with a GeoArrow extension type</li> <li>pyarrow <code>Array</code> marked with a GeoArrow extension type defined by geoarrow-pyarrow</li> </ul> <p>Alternatively, you can pass a <code>list</code> or <code>tuple</code> of any of the above inputs.</p> <p>Parameters:</p> <ul> <li> <code>data</code>             (<code>Union[VizDataInput, List[VizDataInput], Tuple[VizDataInput, ...]]</code>)         \u2013          <p>a data object of any supported type.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>scatterplot_kwargs</code>             (<code>Optional[ScatterplotLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>ScatterplotLayer</code>s.</p> </li> <li> <code>path_kwargs</code>             (<code>Optional[PathLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PathLayer</code>s.</p> </li> <li> <code>polygon_kwargs</code>             (<code>Optional[PolygonLayerKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to all generated <code>PolygonLayer</code>s.</p> </li> <li> <code>map_kwargs</code>             (<code>Optional[MapKwargs]</code>)         \u2013          <p>a <code>dict</code> of parameters to pass down to the generated <code>Map</code>.</p> </li> </ul> <p>For more control over rendering, construct <code>Map</code> and <code>Layer</code> objects directly.</p> <p>Returns:</p> <ul> <li> <code>Map</code>         \u2013          <p>widget visualizing the provided data.</p> </li> </ul>"},{"location":"api/viz/#lonboard._viz.VizDataInput","title":"VizDataInput  <code>module-attribute</code>","text":"<pre><code>VizDataInput = Union[\n    GeoDataFrame,\n    GeoSeries,\n    Table,\n    NDArray[object_],\n    BaseGeometry,\n    ArrowArrayExportable,\n    ArrowStreamExportable,\n    GeoInterfaceProtocol,\n    Dict[str, Any],\n]\n</code></pre> <p>A type definition for allowed data inputs to the <code>viz</code> function.</p>"},{"location":"api/layer-extensions/","title":"Layer Extensions","text":"<p>Layer extensions are bonus features that you can optionally add to the core deck.gl layers.</p> <p>Layer extensions are in an experimental state. Some things are known to not yet work:</p> <ul> <li>Modifying the extensions on a layer by mutating its <code>extensions</code> list via <code>append</code> or <code>pop</code>. It should work, however, by creating a new list and assigning <code>layer.extensions = new_extensions_list</code>.</li> </ul> <p>If you encounter issues, please create an issue with reproducible steps.</p>"},{"location":"api/layer-extensions/brushing-extension/","title":"BrushingExtension","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension","title":"lonboard.layer_extension.BrushingExtension","text":"<p>Adds GPU-based data brushing functionalities to layers. It allows the layer to show/hide objects based on the current pointer position.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--example","title":"Example","text":"<p>An example is in the County-to-County Migration notebook.</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_enabled","title":"<code>brushing_enabled</code>","text":"<p>Enable/disable brushing. If brushing is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_target","title":"<code>brushing_target</code>","text":"<p>The position used to filter each object by. One of the following:</p> <ul> <li><code>\"source\"</code>: Use the primary position for each object. This can mean different   things depending on the layer. It usually refers to the coordinates returned by   <code>getPosition</code> or <code>getSourcePosition</code> accessors.</li> <li><code>\"target\"</code>: Use the secondary position for each object. This may not be available   in some layers. It usually refers to the coordinates returned by   <code>getTargetPosition</code> accessor.</li> <li><code>\"source_target\"</code>: Use both the primary position and secondary position for each   object. Show object if either is in brushing range.</li> <li> <p><code>\"custom\"</code>: Some layers may not describe their data objects with one or two   coordinates, for example <code>PathLayer</code> and <code>PolygonLayer</code>. Use this option with the   <code>get_brushing_target</code> prop to provide a custom position that each object should be   filtered by.</p> </li> <li> <p>Type: <code>str</code>, optional</p> <p>One of: \"source\" | \"target\" | \"source_target\" | \"custom\"</p> </li> <li> <p>Default: <code>\"source\"</code></p> </li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--brushing_radius","title":"<code>brushing_radius</code>","text":"<p>The brushing radius centered at the pointer, in meters. If a data object is within this circle, it is rendered; otherwise it is hidden.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10000</code></li> </ul>"},{"location":"api/layer-extensions/brushing-extension/#lonboard.layer_extension.BrushingExtension--get_brushing_target","title":"<code>get_brushing_target</code>","text":"<p>An arbitrary position for each object that it will be filtered by.</p> <p>Only effective if <code>brushing_target</code> is set to <code>\"custom\"</code>.</p> <ul> <li>Type: PointAccessor, optional<ul> <li>If a point is provided, it is used as the target for all rows.</li> <li>If an array of points is provided, each value in the array will be used as the   target for the row at the same row index.</li> </ul> </li> <li>Default: <code>None</code>.</li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/","title":"CollisionFilterExtension","text":""},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension","title":"lonboard.layer_extension.CollisionFilterExtension","text":"<p>Allows layers to hide overlapping objects.</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--layer-properties","title":"Layer Properties","text":"<p>This extension dynamically enables the following properties onto the layer(s) where it is included:</p>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_enabled","title":"<code>collision_enabled</code>","text":"<p>Enable/disable collisions. If collisions are disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--collision_group","title":"<code>collision_group</code>","text":"<p>Collision group this layer belongs to. If it is not set, the 'default' collision group is used</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/collision-filter-extension/#lonboard.layer_extension.CollisionFilterExtension--get_collision_priority","title":"<code>get_collision_priority</code>","text":"<p>Accessor for collision priority. Must return a number in the range -1000 -&gt; 1000. Features with higher values are shown preferentially.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the priority for all objects.</li> <li>If an array is provided, each value in the array will be used as the priority   for the object at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/","title":"DataFilterExtension","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension","title":"lonboard.layer_extension.DataFilterExtension","text":"<p>Adds GPU-based data filtering functionalities to layers. It allows the layer to show/hide objects based on user-defined properties.</p>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--example","title":"Example","text":"<pre><code>from lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.layer_extension import DataFilterExtension\n\ngdf = gpd.GeoDataFrame(...)\nextension = DataFilterExtension()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[extension],\n    get_filter_value=gdf[\"filter_value\"], # replace with desired column\n    filter_range=[0, 5] # replace with desired filter range\n)\n</code></pre> <p>The <code>DataFilterExtension</code> allows filtering on 1 to 4 attributes at the same time. So if you have four numeric columns of interest, you can filter on the intersection of all of them.</p> <p>For easy visualization, we suggest connecting the <code>DataFilterExtension</code> to an interactive slider from <code>ipywidgets</code>.</p> <pre><code>from ipywidgets import FloatRangeSlider\n\nslider = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"Slider: \"\n)\nslider\n\njsdlink(\n    (slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre> <p>If you have 2 to 4 columns, use a <code>MultiRangeSlider</code>, which combines multiple <code>FloatRangeSlider</code> objects in a form that the <code>DataFilterExtension</code> expects.</p> <pre><code>from ipywidgets import FloatRangeSlider, jsdlink\n\nslider1 = FloatRangeSlider(\n    value=(2, 5),\n    min=0,\n    max=10,\n    step=0.1,\n    description=\"First slider: \"\n)\nslider2 = FloatRangeSlider(\n    value=(30, 40),\n    min=0,\n    max=50,\n    step=1,\n    description=\"Second slider: \"\n)\nmulti_slider = MultiRangeSlider([slider1, slider2])\nmulti_slider\n\njsdlink(\n    (multi_slider, \"value\"),\n    (layer, \"filter_range\")\n)\n</code></pre>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--important-notes","title":"Important notes","text":"<ul> <li>The DataFilterExtension only supports float32 data, so integer data will be casted   to float32.</li> <li>The DataFilterExtension copies all data referenced by <code>get_filter_value</code> to the   GPU, so it will increase memory pressure on the GPU.</li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--layer-properties","title":"Layer Properties","text":""},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_enabled","title":"<code>filter_enabled</code>","text":"<p>Enable/disable the data filter. If the data filter is disabled, all objects are rendered.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_range","title":"<code>filter_range</code>","text":"<p>The bounds which defines whether an object should be rendered. If an object's filtered value is within the bounds, the object will be rendered; otherwise it will be hidden. This prop can be updated on user input or animation with very little cost.</p> <p>Format:</p> <p>If <code>filter_size</code> is 1, provide a single tuple of <code>(min, max)</code>.</p> <p>If <code>filter_size</code> is 2 to 4, provide a list of tuples: <code>[(min0, max0), (min1, max1), ...]</code> for each filtered property, respectively.</p> <ul> <li>Type: either Tuple[float, float] or List[Tuple[float, float]], optional</li> <li>Default: <code>(-1, 1)</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_soft_range","title":"<code>filter_soft_range</code>","text":"<p>If specified, objects will be faded in/out instead of abruptly shown/hidden.</p> <p>When the filtered value is outside of the bounds defined by <code>filter_soft_range</code> but still within the bounds defined by <code>filter_range</code>, the object will be rendered as \"faded\".</p> <ul> <li>Type: Tuple[float, float], optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_size","title":"<code>filter_transform_size</code>","text":"<p>When an object is \"faded\", manipulate its size so that it appears smaller or thinner. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--filter_transform_color","title":"<code>filter_transform_color</code>","text":"<p>When an object is \"faded\", manipulate its opacity so that it appears more translucent. Only works if <code>filter_soft_range</code> is specified.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension--get_filter_value","title":"<code>get_filter_value</code>","text":"<p>Accessor to retrieve the value for each object that it will be filtered by.</p> <ul> <li>Type:   GetFilterValueAccessor<ul> <li>If a scalar value is provided, it is used as the value for all objects.</li> <li>If an array is provided, each value in the array will be used as the value   for the object at the same row index.</li> </ul> </li> </ul>"},{"location":"api/layer-extensions/data-filter-extension/#lonboard.layer_extension.DataFilterExtension.filter_size","title":"filter_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filter_size = tag(sync=True)\n</code></pre> <p>The size of the filter (number of columns to filter by).</p> <p>The data filter can show/hide data based on 1-4 numeric properties of each object.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default 1.</li> </ul>"},{"location":"api/layers/arc-layer/","title":"ArcLayer","text":"<p>Screen recording from U.S. County-to-County Migration example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer","title":"lonboard.experimental.ArcLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render raised arcs joining pairs of source and target coordinates.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_height","title":"get_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_height = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Height color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_color","title":"get_source_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Source color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_source_position","title":"get_source_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_source_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Source position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_color","title":"get_target_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Target color of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_target_position","title":"get_target_position  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_target_position = PointAccessor(None, allow_none=True)\n</code></pre> <p>Target position of each object</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_tilt","title":"get_tilt  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_tilt = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Use to tilt the arc to the side if you have multiple arcs with the same source and target positions.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>0</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The line width of each object, in units specified by <code>widthUnits</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.great_circle","title":"great_circle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>great_circle = tag(sync=True)\n</code></pre> <p>If <code>True</code>, create the arc along the shortest path on the earth surface.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.num_segments","title":"num_segments  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>num_segments = tag(sync=True)\n</code></pre> <p>The number of segments used to draw each arc.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>50</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait()\n</code></pre> <p>A GeoArrow table.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The scaling multiplier for the width of each line.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/arc-layer/#lonboard.experimental.ArcLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/base-layer/","title":"Base Classes","text":""},{"location":"api/layers/base-layer/#lonboard.BaseLayer","title":"lonboard.BaseLayer","text":"<p>             Bases: <code>BaseWidget</code></p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer","title":"lonboard.BaseArrowLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>Any Arrow-based layer should subclass from BaseArrowLayer</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/base-layer/#lonboard.BaseArrowLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"api/layers/bitmap-layer/","title":"BitmapLayer","text":""},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer","title":"lonboard.BitmapLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The <code>BitmapLayer</code> renders a bitmap (e.g. PNG, JPEG, or WebP) at specified boundaries.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapLayer\n\nlayer = BitmapLayer(\n    image='https://raw.githubusercontent.com/visgl/deck.gl-data/master/website/sf-districts.png',\n    bounds=[-122.5190, 37.7045, -122.355, 37.829]\n)\nm = Map(layer)\nm\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.bounds","title":"bounds  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>bounds = tag(sync=True)\n</code></pre> <p>The bounds of the image.</p> <p>Supported formats:</p> <pre><code>- Coordinates of the bounding box of the bitmap `[left, bottom, right, top]`\n- Coordinates of four corners of the bitmap, should follow the sequence of\n  `[[left, bottom], [left, top], [right, top], [right, bottom]]`.\n</code></pre>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.image","title":"image  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>image = tag(sync=True)\n</code></pre> <p>The URL to an image to display.</p> <ul> <li>Type: <code>str</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-layer/#lonboard.BitmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/","title":"BitmapTileLayer","text":""},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer","title":"lonboard.BitmapTileLayer","text":"<p>             Bases: <code>BaseLayer</code></p> <p>The BitmapTileLayer renders image tiles (e.g. PNG, JPEG, or WebP) in the web mercator tiling system. Only the tiles visible in the current viewport are loaded and rendered.</p> <p>Example:</p> <pre><code>from lonboard import Map, BitmapTileLayer\n\n# We set `max_requests &lt; 0` because `tile.openstreetmap.org` supports HTTP/2.\nlayer = BitmapTileLayer(\n    data=\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n    tile_size=256,\n    max_requests=-1,\n    min_zoom=0,\n    max_zoom=19,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.data","title":"data  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>data = tag(sync=True)\n</code></pre> <p>Either a URL template or an array of URL templates from which the tile data should be loaded.</p> <p>If the value is a string: a URL template. Substrings {x} {y} and {z}, if present, will be replaced with a tile's actual index when it is requested.</p> <p>If the value is an array: multiple URL templates. Each endpoint must return the same content for the same tile index. This can be used to work around domain sharding, allowing browsers to download more resources simultaneously. Requests made are balanced among the endpoints, based on the tile index.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.desaturate","title":"desaturate  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>desaturate = tag(sync=True)\n</code></pre> <p>The desaturation of the bitmap. Between <code>[0, 1]</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.extent","title":"extent  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extent = tag(sync=True)\n</code></pre> <p>The bounding box of the layer's data, in the form of <code>[min_x, min_y, max_x, max_y]</code>. If provided, the layer will only load and render the tiles that are needed to fill this box.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_cache_size","title":"max_cache_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_cache_size = tag(sync=True)\n</code></pre> <p>The maximum number of tiles that can be cached. The tile cache keeps loaded tiles in memory even if they are no longer visible. It reduces the need to re-download the same data over and over again when the user pan/zooms around the map, providing a smoother experience.</p> <p>If not supplied, the <code>max_cache_size</code> is calculated as 5 times the number of tiles in the current viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_requests","title":"max_requests  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_requests = tag(sync=True)\n</code></pre> <p>The maximum number of concurrent data fetches.</p> <p>If &lt;= 0, no throttling will occur, and <code>get_tile_data</code> may be called an unlimited number of times concurrently regardless of how long that tile is or was visible.</p> <p>If &gt; 0, a maximum of <code>max_requests</code> instances of <code>get_tile_data</code> will be called concurrently. Requests may never be called if the tile wasn't visible long enough to be scheduled and started. Requests may also be aborted (through the signal passed to <code>get_tile_data</code>) if there are more than <code>max_requests</code> ongoing requests and some of those are for tiles that are no longer visible.</p> <p>If <code>get_tile_data</code> makes fetch requests against an HTTP 1 web server, then max_requests should correlate to the browser's maximum number of concurrent fetch requests. For Chrome, the max is 6 per domain. If you use the data prop and specify multiple domains, you can increase this limit. For example, with Chrome and 3 domains specified, you can set max_requests=18.</p> <p>If the web server supports HTTP/2 (Open Chrome dev tools and look for \"h2\" in the Protocol column), then you can make an unlimited number of concurrent requests (and can set max_requests=-1). Note that this will request data for every tile, no matter how long the tile was visible, and may increase server load.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.max_zoom","title":"max_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_zoom = tag(sync=True)\n</code></pre> <p>The max zoom level of the layer's data. When overzoomed (i.e. <code>zoom &gt; max_zoom</code>), tiles from this level will be displayed.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.min_zoom","title":"min_zoom  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>min_zoom = tag(sync=True)\n</code></pre> <p>The min zoom level of the layer's data. When underzoomed (i.e. <code>zoom &lt; min_zoom</code>), the layer will not display any tiles unless <code>extent</code> is defined, to avoid issuing too many tile requests.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.refinement_strategy","title":"refinement_strategy  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>refinement_strategy = tag(sync=True)\n</code></pre> <p>How the tile layer refines the visibility of tiles.</p> <p>When zooming in and out, if the layer only shows tiles from the current zoom level, then the user may observe undesirable flashing while new data is loading. By setting <code>refinement_strategy</code> the layer can attempt to maintain visual continuity by displaying cached data from a different zoom level before data is available.</p> <p>This prop accepts one of the following:</p> <ul> <li><code>\"best-available\"</code>: If a tile in the current viewport is waiting for its data to   load, use cached content from the closest zoom level to fill the empty space. This   approach minimizes the visual flashing due to missing content.</li> <li><code>\"no-overlap\"</code>: Avoid showing overlapping tiles when backfilling with cached   content. This is usually favorable when tiles do not have opaque backgrounds.</li> <li> <p><code>\"never\"</code>: Do not display any tile that is not selected.</p> </li> <li> <p>Type: <code>str</code>, optional</p> </li> <li>Default: <code>\"best-available\"</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tile_size","title":"tile_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tile_size = tag(sync=True)\n</code></pre> <p>The pixel dimension of the tiles, usually a power of 2.</p> <p>Tile size represents the target pixel width and height of each tile when rendered. Smaller tile sizes display the content at higher resolution, while the layer needs to load more tiles to fill the same viewport.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>512</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.tint_color","title":"tint_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>tint_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to tint the bitmap by, in <code>[r, g, b]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[255, 255, 255]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.transparent_color","title":"transparent_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>transparent_color = List(\n    Float(), default_value=None, allow_none=True, minlen=3, maxlen=4\n)\n</code></pre> <p>The color to use for transparent pixels, in <code>[r, g, b, a]</code>.</p> <ul> <li>Type: <code>List[float]</code>, optional</li> <li>Default: <code>[0, 0, 0, 0]</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/bitmap-tile-layer/#lonboard.BitmapTileLayer.zoom_offset","title":"zoom_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>zoom_offset = tag(sync=True)\n</code></pre> <p>This offset changes the zoom level at which the tiles are fetched. Needs to be an integer.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/heatmap-layer/","title":"HeatmapLayer","text":""},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer","title":"lonboard.HeatmapLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>HeatmapLayer</code> visualizes the spatial distribution of data.</p> <p>Example</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, HeatmapLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = HeatmapLayer.from_geopandas(gdf)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, HeatmapLayer\n\n# Example: A GeoParquet file with Point geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = HeatmapLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.aggregation","title":"aggregation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>aggregation = tag(sync=True)\n</code></pre> <p>Defines the type of aggregation operation</p> <p>Valid values are 'SUM', 'MEAN'.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>\"SUM\"</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.debounce_timeout","title":"debounce_timeout  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>debounce_timeout = tag(sync=True)\n</code></pre> <p>Interval in milliseconds during which changes to the viewport don't trigger aggregation.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>500</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.get_weight","title":"get_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_weight = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The weight of each object.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.intensity","title":"intensity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>intensity = tag(sync=True)\n</code></pre> <p>Value that is multiplied with the total weight at a pixel to obtain the final weight.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.radius_pixels","title":"radius_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_pixels = tag(sync=True)\n</code></pre> <p>Radius of the circle in pixels, to which the weight of an object is distributed.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>30</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.threshold","title":"threshold  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>threshold = tag(sync=True)\n</code></pre> <p>Ratio of the fading weight to the max weight, between <code>0</code> and <code>1</code>.</p> <p>For example, <code>0.1</code> affects all pixels with weight under 10% of the max.</p> <p>Ignored when <code>color_domain</code> is specified.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0.05</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/heatmap-layer/#lonboard.HeatmapLayer.weights_texture_size","title":"weights_texture_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>weights_texture_size = tag(sync=True)\n</code></pre> <p>Specifies the size of weight texture.</p> <ul> <li>Type: <code>int</code>, optional</li> <li>Default: <code>2048</code></li> </ul>"},{"location":"api/layers/path-layer/","title":"PathLayer","text":"<p>Screenshot from North America roads example</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer","title":"lonboard.PathLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PathLayer</code> renders lists of coordinate points as extruded polylines with mitering.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PathLayer\n\n# A GeoDataFrame with LineString or MultiLineString geometries\ngdf = gpd.GeoDataFrame()\nlayer = PathLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PathLayer\n\n# Example: A GeoParquet file with LineString or MultiLineString geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PathLayer(\n    table=table,\n    get_color=[255, 0, 0],\n    width_min_pixels=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, extrude the path in screen space (width always faces the camera). If <code>False</code>, the width always faces up.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.cap_rounded","title":"cap_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>cap_rounded = tag(sync=True)\n</code></pre> <p>Type of caps. If <code>True</code>, draw round caps. Otherwise draw square caps.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.get_width","title":"get_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of each path, in units specified by <code>width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all paths.</li> <li>If an array is provided, each value in the array will be used as the width for   the path at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.joint_rounded","title":"joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>True</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.miter_limit","title":"miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width. Only works if <code>jointRounded</code> is <code>False</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={LINESTRING, MULTILINESTRING})\n</code></pre> <p>A GeoArrow table with a LineString or MultiLineString column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_max_pixels","title":"width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum path width in pixels. This prop can be used to prevent the path from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_min_pixels","title":"width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum path width in pixels. This prop can be used to prevent the path from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_scale","title":"width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_scale = tag(sync=True)\n</code></pre> <p>The path width multiplier that multiplied to all paths.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/path-layer/#lonboard.PathLayer.width_units","title":"width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/","title":"PointCloudLayer","text":""},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer","title":"lonboard.PointCloudLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PointCloudLayer</code> renders a point cloud with 3D positions, normals and colors.</p> <p>The <code>PointCloudLayer</code> can be more efficient at rendering large quantities of points than the <code>ScatterplotLayer</code>, but has fewer rendering options. In particular, you can have only one point size across all points in your data.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PointCloudLayer\n\n# A GeoDataFrame with Point geometries\ngdf = gpd.GeoDataFrame()\nlayer = PointCloudLayer.from_geopandas(\n    gdf,\n    get_color=[255, 0, 0],\n    point_size=2,\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The color of each path in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the color for all   paths.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the color for the path at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.get_normal","title":"get_normal  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_normal = NormalAccessor(None, allow_none=True)\n</code></pre> <p>The normal of each object, in <code>[nx, ny, nz]</code>.</p> <ul> <li>Type: NormalAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the normal for all   points.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the normal for the point at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.point_size","title":"point_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>point_size = tag(sync=True)\n</code></pre> <p>Global radius of all points, in units specified by <code>size_units</code>.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>10</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(\n    allowed_geometry_types={POINT}, allowed_dimensions={3}\n)\n</code></pre> <p>A GeoArrow table with a Point column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/point-cloud-layer/#lonboard.PointCloudLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/","title":"PolygonLayer","text":"<p>Screenshot from Overture Maps example</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer","title":"lonboard.PolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>PolygonLayer</code> renders filled, stroked and/or extruded polygons.</p> <p>Note</p> <p>This layer is essentially a combination of a <code>PathLayer</code> and a <code>SolidPolygonLayer</code>. This has some overhead beyond a <code>SolidPolygonLayer</code>, so if you're looking for the maximum performance with large data, you may want to use a <code>SolidPolygonLayer</code> directly.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, PolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = PolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, PolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = PolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n    get_line_color=[0, 100, 100, 150],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier.</p> <p>The final elevation is calculated by <code>elevationScale * getElevation(d)</code>. <code>elevationScale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons.</p> <p>Based on the elevations provided by the <code>getElevation</code> accessor.</p> <p>If set to <code>false</code>, all polygons will be flat, this generates less geometry and is faster than simply returning 0 from getElevation.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to draw a filled polygon (solid fill).</p> <p>Note that only the area between the outer polygon and any holes will be filled.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each polygon, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_joint_rounded","title":"line_joint_rounded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_joint_rounded = tag(sync=True)\n</code></pre> <p>Type of joint. If <code>true</code>, draw round joints. Otherwise draw miter joints.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_miter_limit","title":"line_miter_limit  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_miter_limit = tag(sync=True)\n</code></pre> <p>The maximum extent of a joint in ratio to the stroke width.</p> <p>Only works if <code>line_joint_rounded</code> is false.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>4</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the line from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the line from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>The line width multiplier that multiplied to all outlines of <code>Polygon</code> and <code>MultiPolygon</code> features if the <code>stroked</code> attribute is true.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Whether to draw an outline around the polygon (solid fill).</p> <p>Note that both the outer polygon as well the outlines of any holes will be drawn.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/polygon-layer/#lonboard.PolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/scatterplot-layer/","title":"ScatterplotLayer","text":"<p>Screenshot from Ookla example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer","title":"lonboard.ScatterplotLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>ScatterplotLayer</code> renders circles at given coordinates.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, ScatterplotLayer\n\n# A GeoDataFrame with Point or MultiPoint geometries\ngdf = gpd.GeoDataFrame()\nlayer = ScatterplotLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, ScatterplotLayer\n\n# Example: A GeoParquet file with Point or MultiPoint geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = ScatterplotLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.antialiasing","title":"antialiasing  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>antialiasing = tag(sync=True)\n</code></pre> <p>If <code>True</code>, circles are rendered with smoothed edges. If <code>False</code>, circles are rendered with rough edges. Antialiasing can cause artifacts on edges of overlapping circles.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>True</code>, rendered circles always face the camera. If <code>False</code> circles face up (i.e. are parallel with the ground plane).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Draw the filled area of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The filled color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the filled color for   all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the filled color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The outline color of each object in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the outline color   for all objects.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the outline color for the object at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_line_width","title":"get_line_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The width of the outline of each object, in units specified by <code>line_width_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the outline width for all objects.</li> <li>If an array is provided, each value in the array will be used as the outline   width for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.get_radius","title":"get_radius  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_radius = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The radius of each object, in units specified by <code>radius_units</code> (default <code>'meters'</code>).</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the radius for all objects.</li> <li>If an array is provided, each value in the array will be used as the radius   for the object at the same row index.</li> </ul> </li> <li>Default: <code>1</code>.</li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_max_pixels","title":"line_width_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum line width in pixels. This can be used to prevent the stroke from getting too thick when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_min_pixels","title":"line_width_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum line width in pixels. This can be used to prevent the stroke from getting too thin when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_scale","title":"line_width_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_scale = tag(sync=True)\n</code></pre> <p>A global line width multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.line_width_units","title":"line_width_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_width_units = tag(sync=True)\n</code></pre> <p>The units of the line width, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_max_pixels","title":"radius_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum radius in pixels. This can be used to prevent the circle from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_min_pixels","title":"radius_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum radius in pixels. This can be used to prevent the circle from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_scale","title":"radius_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_scale = tag(sync=True)\n</code></pre> <p>A global radius multiplier for all points.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.radius_units","title":"radius_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>radius_units = tag(sync=True)\n</code></pre> <p>The units of the radius, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'meters'</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.stroked","title":"stroked  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>stroked = tag(sync=True)\n</code></pre> <p>Draw the outline of points.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT, MULTIPOINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/scatterplot-layer/#lonboard.ScatterplotLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/","title":"SolidPolygonLayer","text":""},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer","title":"lonboard.SolidPolygonLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>The <code>SolidPolygonLayer</code> renders filled and/or extruded polygons.</p> <p>Note</p> <p>This layer is similar to the <code>PolygonLayer</code> but will not render an outline around polygons. In most cases, you'll want to use the <code>PolygonLayer</code> directly, but for very large datasets not drawing the outline can significantly improve performance, in which case you may want to use this layer.</p> <p>Example:</p> <p>From GeoPandas:</p> <pre><code>import geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# A GeoDataFrame with Polygon or MultiPolygon geometries\ngdf = gpd.GeoDataFrame()\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre> <p>From geoarrow-rust:</p> <pre><code>from geoarrow.rust.core import read_parquet\nfrom lonboard import Map, SolidPolygonLayer\n\n# Example: A GeoParquet file with Polygon or MultiPolygon geometries\ntable = read_parquet(\"path/to/file.parquet\")\nlayer = SolidPolygonLayer(\n    table=table,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.elevation_scale","title":"elevation_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>elevation_scale = tag(sync=True)\n</code></pre> <p>Elevation multiplier. The final elevation is calculated by <code>elevation_scale * get_elevation(d)</code>. <code>elevation_scale</code> is a handy property to scale all elevation without updating the data.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.extruded","title":"extruded  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extruded = tag(sync=True)\n</code></pre> <p>Whether to extrude the polygons (based on the elevations provided by the <code>get_elevation</code> accessor'). If set to <code>False</code>, all polygons will be flat, this generates less geometry and is faster than simply returning <code>0</code> from <code>get_elevation</code>.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.filled","title":"filled  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>filled = tag(sync=True)\n</code></pre> <p>Whether to fill the polygons (based on the color provided by the <code>get_fill_color</code> accessor).</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_elevation","title":"get_elevation  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_elevation = FloatAccessor(None, allow_none=True)\n</code></pre> <p>The elevation to extrude each polygon with, in meters.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: FloatAccessor, optional<ul> <li>If a number is provided, it is used as the width for all polygons.</li> <li>If an array is provided, each value in the array will be used as the width for   the polygon at the same row index.</li> </ul> </li> <li>Default: <code>1000</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_fill_color","title":"get_fill_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_fill_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The fill color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the fill color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the fill color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.get_line_color","title":"get_line_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_line_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>The line color of each polygon in the format of <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>Only applies if <code>extruded=True</code>.</p> <ul> <li>Type: ColorAccessor, optional<ul> <li>If a single <code>list</code> or <code>tuple</code> is provided, it is used as the line color for   all polygons.</li> <li>If a numpy or pyarrow array is provided, each value in the array will be used   as the line color for the polygon at the same row index.</li> </ul> </li> <li>Default: <code>[0, 0, 0, 255]</code>.</li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POLYGON, MULTIPOLYGON})\n</code></pre> <p>A GeoArrow table with a Polygon or MultiPolygon column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/solid-polygon-layer/#lonboard.SolidPolygonLayer.wireframe","title":"wireframe  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>wireframe = tag(sync=True)\n</code></pre> <p>Whether to generate a line wireframe of the polygon. The outline will have \"horizontal\" lines closing the top and bottom polygons and a vertical line (a \"strut\") for each vertex on the polygon.</p> <ul> <li>Type: <code>bool</code>, optional</li> <li>Default: <code>False</code></li> </ul> <p>Remarks:</p> <ul> <li>These lines are rendered with <code>GL.LINE</code> and will thus always be 1 pixel wide.</li> <li>Wireframe and solid extrusions are exclusive, you'll need to create two layers   with the same data if you want a combined rendering effect.</li> </ul>"},{"location":"api/layers/text-layer/","title":"TextLayer","text":""},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer","title":"lonboard.experimental.TextLayer","text":"<p>             Bases: <code>BaseArrowLayer</code></p> <p>Render text labels at given coordinates.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.auto_highlight","title":"auto_highlight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>auto_highlight = tag(sync=True)\n</code></pre> <p>When true, the current object pointed to by the mouse pointer (when hovered over) is highlighted with <code>highlightColor</code>.</p> <p>Requires <code>pickable</code> to be <code>True</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>False</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.background_padding","title":"background_padding  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>background_padding = tag(sync=True)\n</code></pre> <p>The padding of the background.</p> <ul> <li>If an array of 2 is supplied, it is interpreted as <code>[padding_x, padding_y]</code> in   pixels.</li> <li>If an array of 4 is supplied, it is interpreted as <code>[padding_left, padding_top,   padding_right, padding_bottom]</code> in pixels.</li> </ul> <p>default [0, 0, 0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.billboard","title":"billboard  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>billboard = tag(sync=True)\n</code></pre> <p>If <code>true</code>, the text always faces camera. Otherwise the text faces up (z).</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.character_set","title":"character_set  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>character_set = tag(sync=True)\n</code></pre> <p>Specifies a list of characters to include in the font. If set to 'auto', will be automatically generated from the data set.</p> <p>default (ASCII characters 32-128)</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.extensions","title":"extensions  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>extensions = tag(sync=True, **widget_serialization)\n</code></pre> <p>A list of layer extension objects to add additional features to a layer.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_family","title":"font_family  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_family = tag(sync=True)\n</code></pre> <p>CSS font family</p> <p>default 'Monaco, monospace'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_settings","title":"font_settings  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_settings = tag(sync=True)\n</code></pre> <p>Advance options for fine tuning the appearance and performance of the generated shared <code>fontAtlas</code>.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.font_weight","title":"font_weight  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>font_weight = tag(sync=True)\n</code></pre> <p>CSS font weight</p> <p>default 'normal'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_alignment_baseline","title":"get_alignment_baseline  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_alignment_baseline = tag(sync=True)\n</code></pre> <p>Vertical alignment accessor</p> <p>default 'center'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_angle","title":"get_angle  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_angle = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label rotation accessor, in degrees</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_background_color","title":"get_background_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_background_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Background color accessor.</p> <p>default [255, 255, 255, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_color","title":"get_border_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Border color accessor.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_border_width","title":"get_border_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_border_width = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Border width accessor.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_color","title":"get_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_color = ColorAccessor(None, allow_none=True)\n</code></pre> <p>Label color accessor</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_pixel_offset","title":"get_pixel_offset  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_pixel_offset = tag(sync=True)\n</code></pre> <p>Label offset from the anchor position, [x, y] in pixels</p> <p>default [0, 0]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_size","title":"get_size  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_size = FloatAccessor(None, allow_none=True)\n</code></pre> <p>Label size accessor</p> <p>default 32</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text","title":"get_text  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text = TextAccessor(None, allow_none=True)\n</code></pre> <p>Label text accessor</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.get_text_anchor","title":"get_text_anchor  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>get_text_anchor = tag(sync=True)\n</code></pre> <p>Horizontal alignment accessor</p> <p>default 'middle'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.line_height","title":"line_height  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>line_height = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the line height.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.max_width","title":"max_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>max_width = tag(sync=True)\n</code></pre> <p>A unitless number that will be multiplied with the current text size to set the width limit of a string.</p> <p>If specified, when the text is longer than the width limit, it will be wrapped into multiple lines using the strategy of <code>wordBreak</code>.</p> <p>default -1</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.opacity","title":"opacity  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>opacity = tag(sync=True)\n</code></pre> <p>The opacity of the layer.</p> <ul> <li>Type: <code>float</code>. Must range between 0 and 1.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_color","title":"outline_color  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_color = tag(sync=True)\n</code></pre> <p>Color of outline around the text, in <code>[r, g, b, [a]]</code>. Each channel is a number between 0-255 and <code>a</code> is 255 if not supplied.</p> <p>default [0, 0, 0, 255]</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.outline_width","title":"outline_width  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>outline_width = tag(sync=True)\n</code></pre> <p>Width of outline around the text, relative to the text size. Only effective if <code>fontSettings.sdf</code> is <code>true</code>.</p> <p>default 0</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.pickable","title":"pickable  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>pickable = tag(sync=True)\n</code></pre> <p>Whether the layer responds to mouse pointer picking events.</p> <p>This must be set to <code>True</code> for tooltips and other interactive elements to be available. This can also be used to only allow picking on specific layers within a map instance.</p> <p>Note that picking has some performance overhead in rendering. To get the absolute best rendering performance with large data (at the cost of removing interactivity), set this to <code>False</code>.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.selected_index","title":"selected_index  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>selected_index = tag(sync=True)\n</code></pre> <p>The positional index of the most-recently clicked on row of data.</p> <p>You can use this to access the full row of data from a GeoDataFrame</p> <pre><code>gdf.iloc[layer.selected_index]\n</code></pre> <p>Setting a value here from Python will do nothing. This attribute only exists to be updated from JavaScript on a map click. Note that <code>pickable</code> must be <code>True</code> (the default) on this layer for the JavaScript <code>onClick</code> handler to work; if <code>pickable</code> is set to <code>False</code>, <code>selected_index</code> will never update.</p> <p>Note that you can use <code>observe</code> to call a function whenever a new value is received from JavaScript. Refer here for an example.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_max_pixels","title":"size_max_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_max_pixels = tag(sync=True)\n</code></pre> <p>The maximum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too big when zoomed in.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>None</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_min_pixels","title":"size_min_pixels  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_min_pixels = tag(sync=True)\n</code></pre> <p>The minimum size in pixels. When using non-pixel <code>sizeUnits</code>, this prop can be used to prevent the icon from getting too small when zoomed out.</p> <ul> <li>Type: <code>float</code>, optional</li> <li>Default: <code>0</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_scale","title":"size_scale  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_scale = tag(sync=True)\n</code></pre> <p>Text size multiplier.</p> <ul> <li>Type: <code>float</code>.</li> <li>Default: <code>1</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.size_units","title":"size_units  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>size_units = tag(sync=True)\n</code></pre> <p>The units of the size, one of <code>'meters'</code>, <code>'common'</code>, and <code>'pixels'</code>. default 'pixels'. See unit system.</p> <ul> <li>Type: <code>str</code>, optional</li> <li>Default: <code>'pixels'</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.table","title":"table  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>table = PyarrowTableTrait(allowed_geometry_types={POINT})\n</code></pre> <p>A GeoArrow table with a Point or MultiPoint column.</p> <p>This is the fastest way to plot data from an existing GeoArrow source, such as geoarrow-rust or geoarrow-pyarrow.</p> <p>If you have a GeoPandas <code>GeoDataFrame</code>, use <code>from_geopandas</code> instead.</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.visible","title":"visible  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>visible = tag(sync=True)\n</code></pre> <p>Whether the layer is visible.</p> <p>Under most circumstances, using the <code>visible</code> attribute to control the visibility of layers is recommended over removing/adding the layer from the <code>Map.layers</code> list.</p> <p>In particular, toggling the <code>visible</code> attribute will persist the layer on the JavaScript side, while removing/adding the layer from the <code>Map.layers</code> list will re-download and re-render from scratch.</p> <ul> <li>Type: <code>bool</code></li> <li>Default: <code>True</code></li> </ul>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.word_break","title":"word_break  <code>class-attribute</code> <code>instance-attribute</code>","text":"<pre><code>word_break = tag(sync=True)\n</code></pre> <p>Available options are <code>break-all</code> and <code>break-word</code>. A valid <code>maxWidth</code> has to be provided to use <code>wordBreak</code>.</p> <p>default 'break-word'</p>"},{"location":"api/layers/text-layer/#lonboard.experimental.TextLayer.from_geopandas","title":"from_geopandas  <code>classmethod</code>","text":"<pre><code>from_geopandas(\n    gdf: gpd.GeoDataFrame,\n    *,\n    auto_downcast: bool = True,\n    **kwargs: Unpack[BaseLayerKwargs]\n) -&gt; Self\n</code></pre> <p>Construct a Layer from a geopandas GeoDataFrame.</p> <p>The GeoDataFrame will be reprojected to EPSG:4326 if it is not already in that coordinate system.</p> <p>Parameters:</p> <ul> <li> <code>gdf</code>             (<code>GeoDataFrame</code>)         \u2013          <p>The GeoDataFrame to set on the layer.</p> </li> </ul> <p>Other Parameters:</p> <ul> <li> <code>auto_downcast</code>             (<code>bool</code>)         \u2013          <p>If <code>True</code>, automatically downcast to smaller-size data types if possible without loss of precision. This calls pandas.DataFrame.convert_dtypes and pandas.to_numeric under the hood.</p> </li> </ul> <p>Returns:</p> <ul> <li> <code>Self</code>         \u2013          <p>A Layer with the initialized data.</p> </li> </ul>"},{"location":"ecosystem/","title":"Ecosystem","text":"<p>Lonboard aims to both integrate closely in the existing Python geospatial ecosystem and be future proof for the next generation of Python geospatial libraries.</p> <p>The pages in this section outline various integrations. If there's a library you think Lonboard should integrate with or you want to document existing compatibility, create an issue to discuss.</p>"},{"location":"ecosystem/geoarrow/","title":"GeoArrow","text":"<p>GeoArrow is an in-memory data structure for storing vector geospatial data and associated attributes. Lonboard uses GeoArrow internally and is the primary reason why Lonboard is fast.</p> <p>There's a burgeoning ecosystem of Python libraries that use GeoArrow internally. Creating Lonboard <code>Layer</code> objects from GeoArrow tables is the fastest way to visualize data, as no conversions are needed on the Python side.</p>"},{"location":"ecosystem/geoarrow/#geoarrow-rust","title":"geoarrow-rust","text":"<p>geoarrow-rust is a Python library implementing the GeoArrow specification with efficient spatial operations. This library has \"rust\" in the name because it is implemented based on the GeoArrow Rust implementation.</p> <pre><code>from geoarrow.rust.core import GeoTable, read_geojson\nfrom lonboard import Map, PathLayer\n\npath = \"/path/to/file.geojson\"\ngeo_table = read_geojson(path)\n\n# Assuming the GeoJSON contains LineString or MultiLineString data\nlayer = PathLayer(table=geo_table)\nm = Map(layer)\nm\n</code></pre> <p>Refer to the geoarrow-rust documentation for more information.</p>"},{"location":"ecosystem/geopandas/","title":"GeoPandas","text":"<p>GeoPandas extends the Pandas data frame library to allow spatial operations on geospatial data.</p> <p>All relevant Lonboard layer classes have a <code>from_geopandas</code> method for <code>GeoDataFrame</code> input.</p> <p>Some layer types, such as <code>BitmapLayer</code>, don't have a <code>from_geopandas</code> method because the rendering isn't relevant to GeoPandas (i.e. GeoPandas doesn't store image data).</p>"},{"location":"ecosystem/geopandas/#example","title":"Example","text":"<pre><code>import geodatasets\nimport geopandas as gpd\nfrom lonboard import Map, SolidPolygonLayer\n\n# New York City boroughs\ngdf = gpd.read_file(geodatasets.get_path('nybb'))\nlayer = SolidPolygonLayer.from_geopandas(\n    gdf,\n    get_fill_color=[255, 0, 0],\n)\nm = Map(layer)\n</code></pre>"},{"location":"ecosystem/jupyter-widgets/","title":"Jupyter Widgets","text":"<p>Jupyter Widgets are interactive browser controls for Jupyter notebooks. While Lonboard's classes are themselves widgets, Jupyter Widgets' design means that widgets integrate with other widgets seamlessly.</p> <p>For example, the <code>ipywidgets</code> Python library includes many core widgets like sliders, dropdowns, color pickers, and file upload elements. Then you can link widgets together. This means that your widget-based slider or chart can create events that change the display of a Lonboard map based on user input events.</p>"},{"location":"ecosystem/panel/","title":"Panel","text":"<p>Panel is a tool to build interactive web applications and dashboards using Python code.</p> <p>Panel has been reported to work with Lonboard. However, it appears that Panel does not support reactive updates in the same way that Shiny does, so the map will necessarily be recreated from scratch on every update.</p>"},{"location":"ecosystem/panel/#example","title":"Example","text":"<p>This example was written by @MarcSkovMadsen in issue #262.</p> <pre><code>\"\"\"Panel data app based on https://developmentseed.org/lonboard/latest/examples/north-america-roads/\"\"\"\n# pip install panel colorcet ipywidgets_bokeh geopandas palettable lonboard\nimport colorcet as cc\nimport geopandas as gpd\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom palettable.palette import Palette\n\nimport panel as pn\n\nurl = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\npath = \"ne_10m_roads_north_america.zip\"\n\ntry:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=path, engine=\"pyogrio\"\n    )\nexcept:\n    gdf = pn.state.as_cached(\n        \"ne_10m_roads_north_america\", gpd.read_file, filename=url, engine=\"pyogrio\"\n    )\n\nstate_options = sorted(state for state in gdf[\"state\"].unique() if state)\n\n\ndef to_rgb(hex: str) -&gt; list:\n    h = hex.strip(\"#\")\n    return list(int(h[i : i + 2], 16) for i in (0, 2, 4))\n\n\ndef to_palette(cmap) -&gt; Palette:\n    \"\"\"Returns the ColorCet colormap as a palettable Palette\"\"\"\n    colors = [to_rgb(item) for item in cmap]\n    return Palette(name=\"colorcet\", map_type=\"colorcet\", colors=colors)\n\n\ndef create_map(state=\"California\", cmap=cc.fire, alpha=0.8):\n    palette = to_palette(cmap)\n    data = gdf[gdf[\"state\"] == state]\n    layer = PathLayer.from_geopandas(data, width_min_pixels=0.8)\n    normalized_scale_rank = (data[\"scalerank\"] - 3) / 9\n    layer.get_color = apply_continuous_cmap(normalized_scale_rank, palette, alpha=alpha)\n    map_ = Map(layer, _height=650)\n    return map_\n\n\ndescription = \"\"\"# lonboard\n\nA Python library for **fast, interactive geospatial vector data visualization** in Jupyter (and Panel).\n\nBy utilizing new technologies like `GeoArrow` and `GeoParquet` in conjunction with GPU-based map rendering, lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.\"\"\"\n\n\n# THE PANEL APP\npn.extension(\"ipywidgets\")\nstate = pn.widgets.Select(\n    value=\"California\",\n    options=state_options,\n    width=150,\n    name=\"State\",\n    sizing_mode=\"stretch_width\",\n)\ncmap = pn.widgets.ColorMap(\n    value=cc.fire,\n    options=cc.palette,\n    ncols=3,\n    swatch_width=100,\n    name=\"cmap by Colorcet\",\n    sizing_mode=\"stretch_width\",\n)\nalpha = pn.widgets.FloatSlider(\n    value=0.8, start=0, end=1, name=\"Alpha\", min_width=100, sizing_mode=\"stretch_width\"\n)\nlogo = pn.pane.Image(\n    \"https://github.com/developmentseed/lonboard/raw/main/assets/dalle-lonboard.jpg\"\n)\ndef title(state):\n    return f\"# North America Roads: {state}\"\n\nsettings = pn.Column(state, cmap, alpha)\ndescription = pn.Column(pn.pane.Markdown(description, margin=5), logo)\ncomponent = pn.Column(\n    pn.bind(title, state=state),\n    pn.panel(\n        pn.bind(create_map, state=state, cmap=cmap, alpha=alpha.param.value_throttled),\n        sizing_mode=\"stretch_both\",\n    ),\n    sizing_mode=\"stretch_both\",\n)\npn.template.FastListTemplate(\n    logo=\"https://panel.holoviz.org/_static/logo_horizontal_dark_theme.png\",\n    title=\"Works with LonBoard\",\n    main=[component],\n    sidebar=[description, settings],\n).servable()\n</code></pre>"},{"location":"ecosystem/shiny/","title":"Shiny","text":"<p>Shiny is a tool to build interactive web applications and dashboards using Python code. Shiny integrates with Jupyter Widgets, which means that Lonboard is supported out-of-the-box.</p> <p>Pay attention to the \"Efficient updates\" section of the Shiny documentation. This is the most efficient way to create a reactive map. Take care to not recreate the <code>Map</code> widget from scratch on updates, as that will send data from Python to the browser anew.</p>"},{"location":"ecosystem/shiny/#example","title":"Example","text":"<pre><code>import geopandas as gpd\nfrom shiny import reactive\nfrom shiny.express import input, ui\nfrom shinywidgets import render_widget\n\nfrom lonboard import Map, ScatterplotLayer\n\ncolors = {\n    \"Red\": [200, 0, 0],\n    \"Green\": [0, 200, 0],\n    \"Blue\": [0, 0, 200],\n}\n\nui.input_select(\"color_select\", \"Color\", choices=list(colors.keys()))\n\n\n@render_widget\ndef map():\n    gdf = gpd.read_file(gpd.datasets.get_path(\"naturalearth_cities\"))\n    layer = ScatterplotLayer.from_geopandas(gdf, radius_min_pixels=2)\n    return Map(layer)\n\n\n@reactive.effect\ndef set_fill_color():\n    map.widget.layers[0].get_fill_color = colors[input.color_select()]\n</code></pre>"},{"location":"examples/","title":"Examples","text":"<ul> <li>Speedtest data  using <code>ScatterplotLayer</code></li> <li>North America roads  using <code>PathLayer</code></li> <li>Global boundaries  using <code>PolygonLayer</code></li> <li>U.S. County-to-County Migration  using <code>ArcLayer</code> and <code>BrushingExtension</code></li> <li>Scatterplot with GPU data filtering  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code></li> <li>Color picker integration  using <code>SolidPolygonLayer</code></li> <li>JupyterLab Sidecar integration  using <code>ScatterplotLayer</code> and <code>JupyterLab Sidecar</code></li> <li>Motor Vehicle Crashes in NYC  using <code>ScatterplotLayer</code></li> <li>Rivers in Asia  using <code>PathLayer</code></li> </ul>"},{"location":"examples/#third-party-showcase","title":"Third-party showcase","text":"<p>These examples are maintained by external contributors.</p> <ul> <li>American Community Survey exploration  using <code>ScatterplotLayer</code> and <code>DataFilterExtension</code> by @jaanli.</li> </ul>"},{"location":"examples/data-filter-extension/","title":"DataFilterExtension","text":"<p>As with the <code>ScatterplotLayer</code> example, this example will use data from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry.</p> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom ipywidgets import FloatRangeSlider, jsdlink\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\nfrom lonboard.controls import MultiRangeSlider\nfrom lonboard.layer_extension import DataFilterExtension\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from ipywidgets import FloatRangeSlider, jsdlink from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap from lonboard.controls import MultiRangeSlider from lonboard.layer_extension import DataFilterExtension In\u00a0[3]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\nlocal_path = Path(\"data-filter-extension.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    non_geom_columns = [col for col in columns if col != \"tile\"]\n    gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")\n    gdf.to_parquet(local_path)\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" local_path = Path(\"data-filter-extension.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"avg_u_kbps\", \"avg_lat_ms\", \"devices\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     non_geom_columns = [col for col in columns if col != \"tile\"]     gdf = gpd.GeoDataFrame(df[non_geom_columns], geometry=tile_centroids, crs=\"EPSG:4326\")     gdf.to_parquet(local_path) <p>Taking a quick look at this data, we see that we have 3.2 million records.</p> In\u00a0[4]: Copied! <pre>gdf\n</pre> gdf Out[4]: avg_d_kbps avg_u_kbps avg_lat_ms devices geometry 0 5983 7886 68 1 POINT (-160.01862 70.63722) 1 3748 5841 78 2 POINT (-160.04059 70.63357) 2 3364 6200 78 2 POINT (-160.04059 70.63175) 3 2381 2328 86 1 POINT (-160.03510 70.63357) 4 3047 5356 75 1 POINT (-160.03510 70.63175) ... ... ... ... ... ... 3231240 19528 3200 68 1 POINT (169.81842 -46.29571) 3231241 15693 10359 56 1 POINT (169.81293 -46.30710) 3231242 26747 9674 58 2 POINT (169.66461 -46.42082) 3231243 67995 13564 63 1 POINT (169.65912 -46.45110) 3231244 1230 1798 55 1 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 5 columns</p> <p>Since we want to cross-filter on three attributes at once, we create a <code>DataFilterExtension</code> instance with <code>filter_size=3</code>.</p> In\u00a0[5]: Copied! <pre>filter_extension = DataFilterExtension(filter_size=3)\n</pre> filter_extension = DataFilterExtension(filter_size=3) <p>Similar to the <code>ScatterplotLayer</code> example notebook, we create a normalized download speed to use for a per-point color and radius.</p> In\u00a0[6]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\nnormalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound)\nfill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10)\nradius = normalized_download_speed * 200\n</pre> min_bound = 5000 max_bound = 50000 normalized_download_speed = (gdf[\"avg_d_kbps\"] - min_bound) / (max_bound - min_bound) fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10) radius = normalized_download_speed * 200 <p>Next we prepare the columns that we want to filter on. In this case, we'll filter on the download speed, upload speed, and latency values. These need to be passed in as a single two-dimensional numpy array, so we use <code>numpy.column_stack</code>. Only numeric arrays can be used for GPU-based filtering.</p> <p>We also define initial filter ranges for each of these columns.</p> In\u00a0[7]: Copied! <pre>filter_values = np.column_stack(\n    [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]]\n)\ninitial_filter_range = [\n    [10_000, 50_000],\n    [1000, 10_000],\n    [0, 100],\n]\n</pre> filter_values = np.column_stack(     [gdf[\"avg_d_kbps\"], gdf[\"avg_u_kbps\"], gdf[\"avg_lat_ms\"]] ) initial_filter_range = [     [10_000, 50_000],     [1000, 10_000],     [0, 100], ] <p>Next we create a <code>ScatterplotLayer</code> with our desired attributes. Keep in mind that you must pass the <code>DataFilterExtension</code> instance to the <code>extensions</code> parameter in order to activate its functionality on the layer.</p> In\u00a0[8]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(\n    gdf,\n    extensions=[filter_extension],\n    get_fill_color=fill_color,\n    get_radius=radius,\n    get_filter_value=filter_values,\n    filter_range=initial_filter_range,\n    radius_units=\"meters\",\n    radius_min_pixels=0.1,\n)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(     gdf,     extensions=[filter_extension],     get_fill_color=fill_color,     get_radius=radius,     get_filter_value=filter_values,     filter_range=initial_filter_range,     radius_units=\"meters\",     radius_min_pixels=0.1, ) m = Map(layer) m Out[8]: <p>Only the points that are within all of our initial filter ranges are shown, but we'd like something more interactive. Let's connect sliders to the map.</p> <p>Here we create a separate slider for each attribute and then connect them using a <code>MultiRangeSlider</code>. This defines the slider element but it won't be connected to the map until we use <code>ipywidgets.jsdlink</code> later.</p> In\u00a0[9]: Copied! <pre>download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \")\nupload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \")\nlatency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \")\nmulti_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider])\nmulti_slider\n</pre> download_slider = FloatRangeSlider(value=initial_filter_range[0], min=0, max=70_000, step=0.1, description=\"Download: \") upload_slider = FloatRangeSlider(value=initial_filter_range[1], min=0, max=50_000, step=1, description=\"Upload: \") latency_slider = FloatRangeSlider(value=initial_filter_range[2], min=0, max=500, step=1, description=\"Latency: \") multi_slider = MultiRangeSlider([download_slider, upload_slider, latency_slider]) multi_slider Out[9]: <p>In order to propagate the changes in our sliders to the <code>ScatterplotLayer</code> instance, we use <code>jsdlink</code>. See Linking Widgets in the ipywidgets documentation for more info.</p> In\u00a0[10]: Copied! <pre>_ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\"))\n</pre> _ = jsdlink((multi_slider, \"value\"), (layer, \"filter_range\")) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/data-filter-extension/#datafilterextension","title":"DataFilterExtension\u00b6","text":"<p>The <code>DataFilterExtension</code> adds GPU-based data filtering functionalities to layers, allowing the layer to show/hide objects based on user-defined properties.</p> <p>In this demo we'll use the <code>DataFilterExtension</code> to filter millions of points based on three numeric attributes.</p>"},{"location":"examples/data-filter-extension/#imports","title":"Imports\u00b6","text":""},{"location":"examples/data-filter-extension/#fetch-data","title":"Fetch data\u00b6","text":"<p>We use the same data as the <code>ScatterplotLayer</code> example except that here we download extra columns from the dataset to use for filtering.</p> <p>Columns:</p> <ul> <li><code>avg_d_kbps</code>: the average download speed for that data point in kbps</li> <li><code>avg_u_kbps</code>: the average upload speed for that data point in kbps</li> <li><code>avg_lat_ms</code>: the average latency for that data point in milliseconds</li> <li><code>devices</code>: the number of unique devices contributing tests in the tile.</li> <li><code>tile</code>: the WKT string representing a given zoom-16 Web Mercator tile</li> </ul> <p>We cache the downloaded file locally to <code>data-filter-extension.parquet</code> so that the second time running this notebook will be faster.</p>"},{"location":"examples/global-boundaries/","title":"Municipal Boundaries","text":"In\u00a0[1]: Copied! <pre>! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm\n</pre> ! pip --quiet install pyogrio lonboard requests pyarrow geopandas tqdm <pre>\n[notice] A new release of pip is available: 23.3.2 -&gt; 24.0\n[notice] To update, run: pip install --upgrade pip\n</pre> In\u00a0[2]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport requests\nfrom tqdm import tqdm\n\nfrom lonboard import Map, PolygonLayer\n</pre> from pathlib import Path  import geopandas as gpd import requests from tqdm import tqdm  from lonboard import Map, PolygonLayer <p>The URL for a large data file of CGAZ at ADM1 Districts level:</p> In\u00a0[3]: Copied! <pre>url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\"\nfilepath = \"geoBoundariesCGAZ_ADM1.geojson\"\n</pre> url = \"https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.geojson\" filepath = \"geoBoundariesCGAZ_ADM1.geojson\" <p>We'll go ahead and fetch the GeoJSON data in that format. If this notebook has already been run once and the file exists locally, we'll skip downloading it again.</p> In\u00a0[4]: Copied! <pre>if not Path(filepath).exists():\n    # Make a GET request to download the file\n    # Streaming, so we can iterate over the response and create a progress bar for this large file\n    response = requests.get(url, stream=True)\n\n    # Sizes in bytes.\n    total_size = int(response.headers.get(\"content-length\", 0))\n    block_size = 1024\n\n    with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:\n        with open(filepath, \"wb\") as file:\n            for data in response.iter_content(block_size):\n                progress_bar.update(len(data))\n                file.write(data)\n\n    if total_size != 0 and progress_bar.n != total_size:\n        raise RuntimeError(\"Could not download file\")\n</pre> if not Path(filepath).exists():     # Make a GET request to download the file     # Streaming, so we can iterate over the response and create a progress bar for this large file     response = requests.get(url, stream=True)      # Sizes in bytes.     total_size = int(response.headers.get(\"content-length\", 0))     block_size = 1024      with tqdm(total=total_size, unit=\"B\", unit_scale=True) as progress_bar:         with open(filepath, \"wb\") as file:             for data in response.iter_content(block_size):                 progress_bar.update(len(data))                 file.write(data)      if total_size != 0 and progress_bar.n != total_size:         raise RuntimeError(\"Could not download file\") In\u00a0[5]: Copied! <pre>gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)\n\n# Keep columns necessary for our visualization\ncols = [\"shapeName\", \"shapeID\", \"geometry\"]\ngdf = gdf[cols]\n</pre> gdf = gpd.read_file(filepath, engine=\"pyogrio\", use_arrow=True)  # Keep columns necessary for our visualization cols = [\"shapeName\", \"shapeID\", \"geometry\"] gdf = gdf[cols] <p>We can take a quick look at the shape of this data:</p> In\u00a0[6]: Copied! <pre>gdf.head(10)\n</pre> gdf.head(10) Out[6]: shapeName shapeID geometry 0 Kandahar 12653393B40111500734429 POLYGON ((65.24153 32.28630, 65.12687 32.03679... 1 Zabul 12653393B56617740339660 POLYGON ((67.60666 31.44378, 67.70162 31.51373... 2 Uruzgan 12653393B46006342616872 POLYGON ((66.27519 32.42550, 66.35907 32.60925... 3 Daykundi 12653393B78791504725813 POLYGON ((66.76157 33.25547, 66.92827 33.59694... 4 Ghanzi 12653393B29313712249365 POLYGON ((68.06945 32.04564, 68.06011 32.45619... 5 Paktika 12653393B93572570471663 MULTIPOLYGON (((68.10797 31.74752, 68.10807 31... 6 Khost 12653393B66876919073955 POLYGON ((69.50619 33.02954, 69.50620 33.02954... 7 Paktia 12653393B16859990709200 POLYGON ((68.76070 33.41986, 68.95811 33.18136... 8 Logar 12653393B769191277822 POLYGON ((68.78347 33.62259, 68.99623 33.62635... 9 Wardak 12653393B94990201402617 POLYGON ((68.68691 33.72585, 68.86023 34.01301... <p>Without doing any simplification, the geometries in this dataset contain nearly 9 million coordinates (You can verify this with <code>shapely.get_num_coordinates(gdf[\"geometry\"]).sum()</code>).</p> <p>On recent computers, Lonboard should be able to visualize the entire dataset, but to ensure this dataset works on all computers, we'll select only rows whose geometries are at least partially in the eastern hemisphere:</p> In\u00a0[7]: Copied! <pre>eastern_hemisphere = gdf.cx[0:180, -90:90]\n</pre> eastern_hemisphere = gdf.cx[0:180, -90:90] In\u00a0[8]: Copied! <pre>layer = PolygonLayer.from_geopandas(\n    eastern_hemisphere,\n    get_line_width=20,  # width in default units (meters)\n    line_width_min_pixels=0.2,  # minimum width when zoomed out\n    get_fill_color=[204, 251, 254],  # light blue\n    get_line_color=[37, 36, 34],  # dark border color\n)\n</pre> layer = PolygonLayer.from_geopandas(     eastern_hemisphere,     get_line_width=20,  # width in default units (meters)     line_width_min_pixels=0.2,  # minimum width when zoomed out     get_fill_color=[204, 251, 254],  # light blue     get_line_color=[37, 36, 34],  # dark border color ) In\u00a0[9]: Copied! <pre>m = Map(layer)\nm\n</pre> m = Map(layer) m Out[9]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/global-boundaries/#municipal-boundaries","title":"Municipal Boundaries\u00b6","text":"<p>This example will use data from GeoBoundaries.org's data set of Comprehensive Global Administrative Zones (CGAZ).</p> <p>CGAZ is a set of global composites for administrative boundaries. There are two important distinctions between our global product and individual country downloads. First, extensive simplification is performed to ensure that file sizes are small enough to be used in most traditional desktop software. Second, disputed areas are removed and replaced with polygons following US Department of State definitions. Attribution: www.geoboundaries.org</p> <p>Note: This notebook incurs downloading a 360MB file. To successfully follow this example, please ensure sufficient storage space. The time to download this data will vary based on internet connection speed. We've added a download progress bar to the data retrieval section of this notebook to allow you to view progress and a time estimate for downloading the visualization data.</p>"},{"location":"examples/global-boundaries/#imports","title":"Imports\u00b6","text":""},{"location":"examples/global-boundaries/#visualize-global-boundaries","title":"Visualize Global Boundaries\u00b6","text":"<p>Let's visualize this dataset.</p> <p>We'll start by loading the data into GeoPandas. It's recommended to use the newer <code>pyogrio</code> library for faster data loading.</p>"},{"location":"examples/global-boundaries/#visualizing-the-data","title":"Visualizing the data\u00b6","text":"<p>We can use a <code>PolygonLayer</code> to customize the rendering of this data.</p>"},{"location":"examples/internet-speeds/","title":"Speedtest Data (ScatterplotLayer)","text":"In\u00a0[1]: Copied! <pre>from pathlib import Path\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport shapely\nfrom palettable.colorbrewer.diverging import BrBG_10\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> from pathlib import Path  import geopandas as gpd import numpy as np import pandas as pd import shapely from palettable.colorbrewer.diverging import BrBG_10  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>The URL for a single data file for mobile network speeds in the first quarter of 2019:</p> In\u00a0[2]: Copied! <pre>url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\"\n</pre> url = \"https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet\" <p>The data used in this example is relatively large. In the cell below, we cache the downloading and preparation of the dataset so that it's faster to run this notebook the second time.</p> <p>We fetch two columns \u2014 <code>avg_d_kbps</code> and <code>tile</code> \u2014 from this data file directly from AWS. The <code>pd.read_parquet</code> command will perform a network request for these columns from the data file, so it may take a while on a slow network connection. <code>avg_d_kbps</code> is the average download speed for that data point in kilobits per second. <code>tile</code> is the WKT string representing a given zoom-16 Web Mercator tile.</p> <p>The <code>tile</code> column contains strings representing WKT-formatted geometries. We need to parse those strings into geometries. Then for simplicity we'll convert into their centroids.</p> In\u00a0[3]: Copied! <pre>local_path = Path(\"internet-speeds.parquet\")\nif local_path.exists():\n    gdf = gpd.read_parquet(local_path)\nelse:\n    columns = [\"avg_d_kbps\", \"tile\"]\n    df = pd.read_parquet(url, columns=columns)\n\n    tile_geometries = shapely.from_wkt(df[\"tile\"])\n    tile_centroids = shapely.centroid(tile_geometries)\n    gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')\n    gdf.to_parquet(local_path)\n</pre> local_path = Path(\"internet-speeds.parquet\") if local_path.exists():     gdf = gpd.read_parquet(local_path) else:     columns = [\"avg_d_kbps\", \"tile\"]     df = pd.read_parquet(url, columns=columns)      tile_geometries = shapely.from_wkt(df[\"tile\"])     tile_centroids = shapely.centroid(tile_geometries)     gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs='EPSG:4326')     gdf.to_parquet(local_path) <p>We can take a quick look at this data:</p> In\u00a0[4]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[4]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) <p>To ensure that this demo is snappy on most computers, we'll filter to a bounding box over Europe.</p> <p>If you're on a recent computer, feel free to comment out the next line.</p> In\u00a0[5]: Copied! <pre>gdf = gdf.cx[-11.83:25.5, 34.9:59]\n</pre> gdf = gdf.cx[-11.83:25.5, 34.9:59] <p>Even this filtered data frame still has 800,000 rows, so it's still a lot of data to explore:</p> In\u00a0[6]: Copied! <pre>gdf\n</pre> gdf Out[6]: avg_d_kbps geometry 0 5983 POINT (-160.01862 70.63722) 1 3748 POINT (-160.04059 70.63357) 2 3364 POINT (-160.04059 70.63175) 3 2381 POINT (-160.03510 70.63357) 4 3047 POINT (-160.03510 70.63175) ... ... ... 3231240 19528 POINT (169.81842 -46.29571) 3231241 15693 POINT (169.81293 -46.30710) 3231242 26747 POINT (169.66461 -46.42082) 3231243 67995 POINT (169.65912 -46.45110) 3231244 1230 POINT (168.85162 -46.56075) <p>3231245 rows \u00d7 2 columns</p> <p>To render point data, first create a <code>ScatterplotLayer</code> and then add it to a <code>Map</code> object:</p> In\u00a0[7]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nm = Map(layer)\nm\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) m = Map(layer) m Out[7]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\navg_d_kbps: uint32\ngeometry: fixed_size_list&lt;item: double&gt;[2]\u2026</pre> <p>We can look at the documentation for <code>ScatterplotLayer</code> to see what other rendering options it allows. Let's set the fill color to something other than black:</p> In\u00a0[8]: Copied! <pre>layer.get_fill_color = [0, 0, 200, 200]\n</pre> layer.get_fill_color = [0, 0, 200, 200] <p>Blue is pretty, but the map would be more informative if we colored each point by a relevant characteristic. In this case, we have the download speed associated with each location, so let's use that!</p> <p>Here we compute a linear statistic for the download speed. Given a minimum bound of <code>5000</code> and a maximum bound of <code>50,000</code> the normalized speed is linearly scaled to between 0 and 1.</p> In\u00a0[9]: Copied! <pre>min_bound = 5000\nmax_bound = 50000\ndownload_speed = gdf['avg_d_kbps']\nnormalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound)\n</pre> min_bound = 5000 max_bound = 50000 download_speed = gdf['avg_d_kbps'] normalized_download_speed = (download_speed - min_bound) / (max_bound - min_bound) <p><code>normalized_download_speed</code> is now linearly scaled based on the bounds provided above. Keep in mind that the input range of the colormap is 0-1. So any values that are below 0 will receive the left-most color in the colormap, while any values above 1 will receive the right-most color in the colormap.</p> In\u00a0[10]: Copied! <pre>normalized_download_speed\n</pre> normalized_download_speed Out[10]: <pre>0          0.021844\n1         -0.027822\n2         -0.036356\n3         -0.058200\n4         -0.043400\n             ...   \n3231240    0.322844\n3231241    0.237622\n3231242    0.483267\n3231243    1.399889\n3231244   -0.083778\nName: avg_d_kbps, Length: 3231245, dtype: float64</pre> <p>We can use any colormap provided by the <code>palettable</code> package. Let's inspect the <code>BrBG_10</code> diverging colormap below:</p> In\u00a0[11]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap Out[11]: BrBG  underbad over  <p>Now let's apply the colormap on <code>normalized_download_speed</code> using a helper provided by <code>lonboard</code>. We can set it on <code>layer.get_fill_color</code> to update the existing colors.</p> In\u00a0[12]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7)\n</pre> layer.get_fill_color = apply_continuous_cmap(normalized_download_speed, BrBG_10, alpha=0.7) <p>After running the above cell, you should see the map above update with a different color per point!</p> <p>We can pass an array into any of the \"accessors\" supported by the layer (this is any attribute that starts with <code>get_*</code>).</p> <p>For demonstration purposes, let's also set <code>get_radius</code> to <code>normalized_download_speed</code>.</p> In\u00a0[13]: Copied! <pre>layer.get_radius = normalized_download_speed * 200\nlayer.radius_units = \"meters\"\nlayer.radius_min_pixels = 0.5\n</pre> layer.get_radius = normalized_download_speed * 200 layer.radius_units = \"meters\" layer.radius_min_pixels = 0.5 <p>After running the above cell, you should see the map updated to have a different radius per point!</p>"},{"location":"examples/internet-speeds/#speedtest-data-scatterplotlayer","title":"Speedtest Data (<code>ScatterplotLayer</code>)\u00b6","text":"<p>This example will use data collected from Ookla's Speed Test application and shared publicly in the AWS Open Data Registry. From the AWS page:</p> <p>Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (35MB download).</p>"},{"location":"examples/internet-speeds/#imports","title":"Imports\u00b6","text":""},{"location":"examples/internet-speeds/#fetch-data","title":"Fetch data\u00b6","text":""},{"location":"examples/migration/","title":"U.S. County-to-County Migration","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport requests\nimport shapely\nfrom matplotlib.colors import Normalize\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.experimental import ArcLayer\nfrom lonboard.layer_extension import BrushingExtension\n</pre> import geopandas as gpd import numpy as np import pandas as pd import pyarrow as pa import requests import shapely from matplotlib.colors import Normalize  from lonboard import Map, ScatterplotLayer from lonboard.experimental import ArcLayer from lonboard.layer_extension import BrushingExtension <p>Fetch the data from the version in the <code>deck.gl-data</code> repository.</p> In\u00a0[2]: Copied! <pre>url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\"\nr = requests.get(url)\nsource_data = r.json()\n</pre> url = \"https://raw.githubusercontent.com/visgl/deck.gl-data/master/examples/arc/counties.json\" r = requests.get(url) source_data = r.json() <p>The following cell may be a little hard to follow, but what it's doing is taking the raw data, which represents a graph) of the data and normalizing it to a table structure where each row represents one \"arc\" between a source and target county.</p> <p>This is ported from the original JavaScript here.</p> In\u00a0[3]: Copied! <pre>arcs = []\ntargets = []\nsources = []\npairs = {}\n\nfeatures = source_data[\"features\"]\nfor i, county in enumerate(features):\n    flows = county[\"properties\"][\"flows\"]\n    target_centroid = county[\"properties\"][\"centroid\"]\n    total_value = {\n        \"gain\": 0,\n        \"loss\": 0,\n    }\n\n    for to_id, value in flows.items():\n        if value &gt; 0:\n            total_value[\"gain\"] += value\n        else:\n            total_value[\"loss\"] += value\n\n        # If number is too small, ignore it\n        if abs(value) &lt; 50:\n            continue\n\n        pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))\n        source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]\n        gain = np.sign(flows[to_id])\n\n        # add point at arc source\n        sources.append(\n            {\n                \"position\": source_centroid,\n                \"target\": target_centroid,\n                \"name\": features[int(to_id)][\"properties\"][\"name\"],\n                \"radius\": 3,\n                \"gain\": -gain,\n            }\n        )\n        # eliminate duplicate arcs\n        if pair_key in pairs.keys():\n            continue\n\n        pairs[pair_key] = True\n\n        if gain &gt; 0:\n            arcs.append(\n                {\n                    \"target\": target_centroid,\n                    \"source\": source_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n        else:\n            arcs.append(\n                {\n                    \"target\": source_centroid,\n                    \"source\": target_centroid,\n                    \"value\": flows[to_id],\n                }\n            )\n\n    # add point at arc target\n    targets.append(\n        {\n            **total_value,\n            \"position\": [target_centroid[0], target_centroid[1], 10],\n            \"net\": total_value[\"gain\"] + total_value[\"loss\"],\n            \"name\": county[\"properties\"][\"name\"],\n        }\n    )\n\n# sort targets by radius large -&gt; small\ntargets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True)\nnormalizer = Normalize(0, abs(targets[0][\"net\"]))\n</pre> arcs = [] targets = [] sources = [] pairs = {}  features = source_data[\"features\"] for i, county in enumerate(features):     flows = county[\"properties\"][\"flows\"]     target_centroid = county[\"properties\"][\"centroid\"]     total_value = {         \"gain\": 0,         \"loss\": 0,     }      for to_id, value in flows.items():         if value &gt; 0:             total_value[\"gain\"] += value         else:             total_value[\"loss\"] += value          # If number is too small, ignore it         if abs(value) &lt; 50:             continue          pair_key = \"-\".join(map(str, sorted([i, int(to_id)])))         source_centroid = features[int(to_id)][\"properties\"][\"centroid\"]         gain = np.sign(flows[to_id])          # add point at arc source         sources.append(             {                 \"position\": source_centroid,                 \"target\": target_centroid,                 \"name\": features[int(to_id)][\"properties\"][\"name\"],                 \"radius\": 3,                 \"gain\": -gain,             }         )         # eliminate duplicate arcs         if pair_key in pairs.keys():             continue          pairs[pair_key] = True          if gain &gt; 0:             arcs.append(                 {                     \"target\": target_centroid,                     \"source\": source_centroid,                     \"value\": flows[to_id],                 }             )         else:             arcs.append(                 {                     \"target\": source_centroid,                     \"source\": target_centroid,                     \"value\": flows[to_id],                 }             )      # add point at arc target     targets.append(         {             **total_value,             \"position\": [target_centroid[0], target_centroid[1], 10],             \"net\": total_value[\"gain\"] + total_value[\"loss\"],             \"name\": county[\"properties\"][\"name\"],         }     )  # sort targets by radius large -&gt; small targets = sorted(targets, key=lambda d: abs(d[\"net\"]), reverse=True) normalizer = Normalize(0, abs(targets[0][\"net\"])) <p>We define some color constants, as well as a color lookup array.</p> <p>A nice trick in numpy is that if you have a two-dimensional array like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184]\n]\n</pre> <p>you can perform a lookup based on the index to transform data from one dimensionality to another. In this case, we'll use <code>0</code> and <code>1</code> \u2014 the two available indexes of the array's first dimension \u2014 to create an array of colors.</p> <p>So when we call <code>COLORS[colors_lookup]</code> that creates an output array of something like:</p> <pre>[\n    [166,   3,   3],\n    [ 35, 181, 184],\n    [166,   3,   3],\n    [166,   3,   3]\n]\n</pre> <p>equal to the number of rows in our dataset. We can then pass this to any parameter that accepts a ColorAccessor.</p> In\u00a0[4]: Copied! <pre># migrate out\nSOURCE_COLOR = [166, 3, 3]\n# migrate in\nTARGET_COLOR = [35, 181, 184]\n# Combine into a single arr to use as a lookup table\nCOLORS = np.vstack(\n    [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)]\n)\nSOURCE_LOOKUP = 0\nTARGET_LOOKUP = 1\n</pre> # migrate out SOURCE_COLOR = [166, 3, 3] # migrate in TARGET_COLOR = [35, 181, 184] # Combine into a single arr to use as a lookup table COLORS = np.vstack(     [np.array(SOURCE_COLOR, dtype=np.uint8), np.array(TARGET_COLOR, dtype=np.uint8)] ) SOURCE_LOOKUP = 0 TARGET_LOOKUP = 1 In\u00a0[5]: Copied! <pre>brushing_extension = BrushingExtension()\nbrushing_radius = 200000\n</pre> brushing_extension = BrushingExtension() brushing_radius = 200000 <p>Convert the <code>sources</code> list of dictionaries into a GeoPandas <code>GeoDataFrame</code> to pass into a <code>ScatterplotLayer</code>.</p> In\u00a0[6]: Copied! <pre>source_arr = np.array([source[\"position\"] for source in sources])\nsource_positions = shapely.points(source_arr[:, 0], source_arr[:, 1])\nsource_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],\n    geometry=source_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\nsource_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\nsource_fill_colors = COLORS[source_colors_lookup]\n</pre> source_arr = np.array([source[\"position\"] for source in sources]) source_positions = shapely.points(source_arr[:, 0], source_arr[:, 1]) source_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(sources)[[\"name\", \"radius\", \"gain\"]],     geometry=source_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array source_colors_lookup = np.where(source_gdf[\"gain\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) source_fill_colors = COLORS[source_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for source points:</p> In\u00a0[7]: Copied! <pre>source_layer = ScatterplotLayer.from_geopandas(\n    source_gdf,\n    get_fill_color=source_fill_colors,\n    radius_scale=3000,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> source_layer = ScatterplotLayer.from_geopandas(     source_gdf,     get_fill_color=source_fill_colors,     radius_scale=3000,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) In\u00a0[8]: Copied! <pre>targets_arr = np.array([target[\"position\"] for target in targets])\ntarget_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1])\ntarget_gdf = gpd.GeoDataFrame(\n    pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],\n    geometry=target_positions,\n    crs=\"EPSG:4326\"\n)\n# We use a lookup table (`COLORS`) to apply either the target color or the source color\n# to the array\ntarget_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP)\ntarget_line_colors = COLORS[target_line_colors_lookup]\n</pre> targets_arr = np.array([target[\"position\"] for target in targets]) target_positions = shapely.points(targets_arr[:, 0], targets_arr[:, 1]) target_gdf = gpd.GeoDataFrame(     pd.DataFrame.from_records(targets)[[\"name\", \"gain\", \"loss\", \"net\"]],     geometry=target_positions,     crs=\"EPSG:4326\" ) # We use a lookup table (`COLORS`) to apply either the target color or the source color # to the array target_line_colors_lookup = np.where(target_gdf[\"net\"] &gt; 0, TARGET_LOOKUP, SOURCE_LOOKUP) target_line_colors = COLORS[target_line_colors_lookup] <p>Create a <code>ScatterplotLayer</code> for target points:</p> In\u00a0[9]: Copied! <pre>target_ring_layer = ScatterplotLayer.from_geopandas(\n    target_gdf,\n    get_line_color=target_line_colors,\n    radius_scale=4000,\n    pickable=True,\n    stroked=True,\n    filled=False,\n    line_width_min_pixels=2,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> target_ring_layer = ScatterplotLayer.from_geopandas(     target_gdf,     get_line_color=target_line_colors,     radius_scale=4000,     pickable=True,     stroked=True,     filled=False,     line_width_min_pixels=2,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Note: the <code>ArcLayer</code> can't currently be created from a GeoDataFrame because it needs two point columns, not one. This is a large part of why it's still marked under the \"experimental\" module.</p> <p>Here we pass a numpy array for each point column. This is allowed as long as the shape of the array is <code>(N, 2)</code> or <code>(N, 3)</code> (i.e. 2D or 3D coordinates).</p> In\u00a0[10]: Copied! <pre>value = np.array([arc[\"value\"] for arc in arcs])\nget_source_position = np.array([arc[\"source\"] for arc in arcs])\nget_target_position = np.array([arc[\"target\"] for arc in arcs])\ntable = pa.table({\"value\": value})\n\narc_layer = ArcLayer(\n    table=table,\n    get_source_position=get_source_position,\n    get_target_position=get_target_position,\n    get_source_color=SOURCE_COLOR,\n    get_target_color=TARGET_COLOR,\n    get_width=1,\n    opacity=0.4,\n    pickable=False,\n    extensions=[brushing_extension],\n    brushing_radius=brushing_radius,\n)\n</pre> value = np.array([arc[\"value\"] for arc in arcs]) get_source_position = np.array([arc[\"source\"] for arc in arcs]) get_target_position = np.array([arc[\"target\"] for arc in arcs]) table = pa.table({\"value\": value})  arc_layer = ArcLayer(     table=table,     get_source_position=get_source_position,     get_target_position=get_target_position,     get_source_color=SOURCE_COLOR,     get_target_color=TARGET_COLOR,     get_width=1,     opacity=0.4,     pickable=False,     extensions=[brushing_extension],     brushing_radius=brushing_radius, ) <p>Now we can create a map using these three layers we've created.</p> <p>As you hover over the map, it should render only the arcs near your cursor.</p> <p>You can modify <code>brushing_extension.brushing_radius</code> to control how large the brush is around your cursor.</p> In\u00a0[11]: Copied! <pre>map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10)\nmap_\n</pre> map_ = Map(layers=[source_layer, target_ring_layer, arc_layer], picking_radius=10) map_ Out[11]: In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/migration/#us-county-to-county-migration","title":"U.S. County-to-County Migration\u00b6","text":"<p>This notebook is derived from the original deck.gl example in JavaScript, which you can see here.</p> <p>This dataset originally came from the U.S. Census Bureau and represents people moving in and out of each county between 2009-2013.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/migration/#imports","title":"Imports\u00b6","text":""},{"location":"examples/north-america-roads/","title":"North America Roads","text":"In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport palettable.colorbrewer.diverging\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import palettable.colorbrewer.diverging  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap <p>The \"public\" URL for this dataset from the Natural Earth website is</p> <pre><code>https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>That doesn't appear to work directly inside a notebook, so we'll use the URL the above redirects to:</p> <pre><code>https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\n</code></pre> <p>It's possible this \"private\" URL will change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\"\n</pre> url = \"https://naciscdn.org/naturalearth/10m/cultural/ne_10m_roads_north_america.zip\" <p>We'll use GeoPandas to fetch this data (45MB) over the internet and load it into a <code>GeoDataFrame</code>. This uses the <code>pyogrio</code> engine, which is faster. Ensure you have <code>pyogrio</code> installed.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>This dataframe has a variety of attributes, plus a geometry column with a <code>LineString</code> type.</p> In\u00a0[\u00a0]: Copied! <pre>gdf.head()\n</pre> gdf.head() <p>To ensure that this demo is snappy on most computers, we'll filter to only the contiguous U.S. If you're on a recent computer, feel free to comment out this line.</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"state\"] == \"California\"]\n</pre> gdf = gdf[gdf[\"state\"] == \"California\"] <p>To render <code>LineString</code> data, first create a <code>PathLayer</code> and then add it to a <code>Map</code> object.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = PathLayer.from_geopandas(gdf, width_min_pixels=0.8) map_ = Map(layers=[layer]) map_ <p>We can look at the documentation for <code>PathLayer</code> to see what other rendering options it allows. Let's set the path color to something other than black:</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = [200, 0, 200]\n</pre> layer.get_color = [200, 0, 200] <p>Ok, so we can see the data! That's great! But let's customize the rendering based on an attribute!</p> <p>The <code>scalerank</code> column tells how important the road is in the road network. Let's see what the distribution of values is for this column:</p> In\u00a0[\u00a0]: Copied! <pre>gdf[\"scalerank\"].value_counts().sort_index()\n</pre> gdf[\"scalerank\"].value_counts().sort_index() <p>Ok, so the values range from <code>3</code> to <code>12</code>. To assign a colormap to this column, we need \"normalized\" values that span between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9\n</pre> normalized_scale_rank = (gdf[\"scalerank\"] - 3) / 9 <p>The values of this array now range from 0 to 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_scale_rank.min(), normalized_scale_rank.max()\n</pre> normalized_scale_rank.min(), normalized_scale_rank.max() <p>Let's select a colormap to apply to this data:</p> In\u00a0[\u00a0]: Copied! <pre>cmap = palettable.colorbrewer.diverging.PuOr_10\n</pre> cmap = palettable.colorbrewer.diverging.PuOr_10 In\u00a0[\u00a0]: Copied! <pre>cmap.mpl_colormap\n</pre> cmap.mpl_colormap <p>Now we'll use <code>apply_continuous_cmap</code> onto this array to generate colors for our data. Just set this new array onto the existing layer, and you'll see the map update with the new colors!</p> In\u00a0[\u00a0]: Copied! <pre>layer.get_color = apply_continuous_cmap(\n    normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8\n)\n</pre> layer.get_color = apply_continuous_cmap(     normalized_scale_rank, palettable.colorbrewer.diverging.PuOr_10, alpha=0.8 ) In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/north-america-roads/#north-america-roads","title":"North America Roads\u00b6","text":"<p>This example uses data from Natural Earth to plot the road network in North America. Refer to the data documentation for more information about this dataset.</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (6MB download).</p>"},{"location":"examples/north-america-roads/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>pyogrio</li> </ul>"},{"location":"examples/north-america-roads/#imports","title":"Imports\u00b6","text":""},{"location":"examples/overture-maps/","title":"Overture Maps Buildings","text":"In\u00a0[\u00a0]: Copied! <pre>import numpy as np\nimport overturemaps\nfrom matplotlib.colors import LogNorm\nfrom palettable.colorbrewer.sequential import Oranges_9\n\nfrom lonboard import Map, PolygonLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import numpy as np import overturemaps from matplotlib.colors import LogNorm from palettable.colorbrewer.sequential import Oranges_9  from lonboard import Map, PolygonLayer from lonboard.colormap import apply_continuous_cmap <p>Using this bounding box tool we can find a bounding box covering Manhattan. Choose \"CSV\" in the drop down box, then paste the result in the following cell:</p> In\u00a0[\u00a0]: Copied! <pre>bbox = -74.02169, 40.696423, -73.891338, 40.831263\n</pre> bbox = -74.02169, 40.696423, -73.891338, 40.831263 <p>Ensure you pass the bbox into <code>overturemaps.record_batch_reader</code> as in the next line.</p> <p><code>record_batch_reader</code> returns a RecordBatchReader, an iterator of Arrow RecordBatches. We call <code>.read_all()</code> to materialize all batches into memory. This cell fetches the relevant data from the cloud (AWS S3), so it may take a minute to download.</p> In\u00a0[\u00a0]: Copied! <pre>table = overturemaps.record_batch_reader(\"building\", bbox).read_all()\n\n# Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug\ntable = table.combine_chunks()\n</pre> table = overturemaps.record_batch_reader(\"building\", bbox).read_all()  # Temporarily required as of Lonboard 0.8 to avoid a Lonboard bug table = table.combine_chunks() <p>Let's color buildings by their heights. First, convert the \"height\" column to a numpy array, then replace any null values with <code>1</code>.</p> In\u00a0[\u00a0]: Copied! <pre>heights = table[\"height\"].to_numpy()\nheights = np.nan_to_num(heights, nan=1)\n</pre> heights = table[\"height\"].to_numpy() heights = np.nan_to_num(heights, nan=1) <p>Building heights tend to scale non-linearly. That is, the vast majority of buildings are relatively low, but a few buildings are very tall. So that the low buildings aren't completely washed out, we'll use matplotlib's <code>LogNorm</code> to normalize these values according to a log scale.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = LogNorm(1, heights.max(), clip=True)\nnormalized_heights = normalizer(heights)\n</pre> normalizer = LogNorm(1, heights.max(), clip=True) normalized_heights = normalizer(heights) <p>Then we can apply a colormap to these heights. For this case, we'll use the <code>Oranges_9</code> colormap.</p> In\u00a0[\u00a0]: Copied! <pre>Oranges_9.mpl_colormap\n</pre> Oranges_9.mpl_colormap <p>Using <code>apply_continuous_cmap</code>, we can apply our values onto this colormap.</p> In\u00a0[\u00a0]: Copied! <pre>colors = apply_continuous_cmap(normalized_heights, Oranges_9)\n</pre> colors = apply_continuous_cmap(normalized_heights, Oranges_9) <p>We create a <code>PolygonLayer</code> with our data, passing in the heights and colors from before.</p> In\u00a0[\u00a0]: Copied! <pre>layer = PolygonLayer(\n    # Select only a few attribute columns from the table\n    table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),\n    extruded=True,\n    get_elevation=heights,\n    get_fill_color=colors,\n)\n</pre> layer = PolygonLayer(     # Select only a few attribute columns from the table     table=table.select([\"id\", \"height\", \"geometry\", \"names\"]),     extruded=True,     get_elevation=heights,     get_fill_color=colors, ) <p>We manually set the view state here for the original NYC data so that the map will start pitched. Remove or change this <code>view_state</code> call if you change the input dataset.</p> In\u00a0[\u00a0]: Copied! <pre>view_state = {\n    \"longitude\": -73.98416810282863,\n    \"latitude\": 40.72651721370669,\n    \"zoom\": 12.726630492730596,\n    \"pitch\": 59.80465353190481,\n    \"bearing\": 13.243243243243244,\n}\nm = Map(layer, view_state=view_state)\nm\n</pre> view_state = {     \"longitude\": -73.98416810282863,     \"latitude\": 40.72651721370669,     \"zoom\": 12.726630492730596,     \"pitch\": 59.80465353190481,     \"bearing\": 13.243243243243244, } m = Map(layer, view_state=view_state) m In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/overture-maps/#overture-maps-buildings","title":"Overture Maps Buildings\u00b6","text":"<p>This notebook will give a quick overview of using the new Overture Maps Python library with Lonboard.</p> <p>We'll pass in a bounding box covering New York City and the Overture Python API will fetch only the data inside that bounding box. While Overture's buildings dataset contains 2.3 rows, by using a relatively small bounding box, we can download data for our query relatively quickly (around 30 seconds on my internet connection).</p> <p>This uses the latest advancements of the GeoParquet specification. Data is fetched directly from the cloud with no server in the middle!</p> <p>You can view a hosted version of this notebook on Notebook Sharing Space (26MB download).</p>"},{"location":"examples/overture-maps/#dependendencies","title":"Dependendencies\u00b6","text":"<p>This notebook requires Lonboard (tested with version 0.8) and <code>overturemaps</code> (tested with version 0.4). To install <code>overturemaps</code>, run:</p> <pre><code>pip install overturemaps\n</code></pre>"},{"location":"examples/overture-maps/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/color-picker/","title":"ColorPicker","text":"<p>Example gif:</p> <p></p> In\u00a0[1]: Copied! <pre>import geodatasets\nimport geopandas as gpd\nimport ipywidgets\n\nfrom lonboard import Map, SolidPolygonLayer\n</pre> import geodatasets import geopandas as gpd import ipywidgets  from lonboard import Map, SolidPolygonLayer <p><code>geodatasets.get_path</code> will download the dataset if it hasn't been downloaded and cached before.</p> In\u00a0[2]: Copied! <pre>gdf = gpd.read_file(geodatasets.get_path(\"nybb\"))\n</pre> gdf = gpd.read_file(geodatasets.get_path(\"nybb\")) <p>Create our basic map:</p> In\u00a0[3]: Copied! <pre>layer = SolidPolygonLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\nmap_\n</pre> layer = SolidPolygonLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) map_ <pre>/Users/kyle/github/developmentseed/lonboard/lonboard/_layer.py:99: UserWarning: GeoDataFrame being reprojected to EPSG:4326\n  warnings.warn(\"GeoDataFrame being reprojected to EPSG:4326\")\n</pre> Out[3]: <pre>Map(layers=[SolidPolygonLayer(table=pyarrow.Table\nBoroCode: int64\nBoroName: string\nShape_Leng: double\nShape_Ar\u2026</pre> <p>Create the <code>ColorPicker</code> widget:</p> In\u00a0[4]: Copied! <pre>color_picker = ipywidgets.ColorPicker()\ncolor_picker\n</pre> color_picker = ipywidgets.ColorPicker() color_picker Out[4]: <pre>ColorPicker(value='black')</pre> <p>Note that the \"data\" contained by the <code>color_picker</code> instance is held by the <code>value</code> attribute:</p> In\u00a0[5]: Copied! <pre>color_picker.value\n</pre> color_picker.value Out[5]: <pre>'black'</pre> <p>We'll now link the two widgets together, so that whenever the color picker is updated, <code>ipywidgets</code> will update the value of <code>get_fill_color</code> on the layer.</p> <p>The format of <code>dlink</code> is:</p> <ul> <li>tuple: (source widget, attribute of source widget)</li> <li>tuple: (target widget, attribute of target widget)</li> </ul> <p>So we use the below syntax to link <code>color_picker.value</code> to <code>layer.get_fill_color</code>.</p> <p>We need to use <code>dlink</code>, not <code>link</code>, which creates a one-directional link (from the color picker to the layer) and not a bi-directional link. This is because the color picker and the layer have a different color representation under the hood (<code>ColorPicker</code> stores a hex string; the <code>SolidPolygonLayer</code> works in terms of RGBA color integers).</p> In\u00a0[6]: Copied! <pre>ipywidgets.dlink(\n    (color_picker, 'value'),\n    (layer, 'get_fill_color')\n)\n</pre> ipywidgets.dlink(     (color_picker, 'value'),     (layer, 'get_fill_color') ) Out[6]: <pre>&lt;traitlets.traitlets.directional_link at 0x168822610&gt;</pre> <p>Now click on the color widget and change the color! You need to press Enter to select a specific color.</p> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/color-picker/#colorpicker","title":"ColorPicker\u00b6","text":"<p>Use lonboard with an ipywidgets ColorPicker object.</p>"},{"location":"examples/integrations/color-picker/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li><p>lonboard</p> </li> <li><p>geodatasets</p> <p>Run <code>pip install geodatasets</code> if needed.</p> </li> </ul>"},{"location":"examples/integrations/color-picker/#imports","title":"Imports\u00b6","text":""},{"location":"examples/integrations/sidecar/","title":"Sidecar","text":"<p>The goal of this notebook is to show how using Jupyter Sidecar can help to interactively explore and understand your data.</p> <p>Note that sidecar only works in JupyterLab and will not work in other notebook frontends like Colab.</p> <p></p> In\u00a0[\u00a0]: Copied! <pre>import geopandas as gpd\nimport ipywidgets\nimport matplotlib as mpl\nimport pandas as pd\nfrom palettable.colorbrewer.diverging import BrBG_10\nfrom sidecar import Sidecar\n\nfrom lonboard import Map, ScatterplotLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd import ipywidgets import matplotlib as mpl import pandas as pd from palettable.colorbrewer.diverging import BrBG_10 from sidecar import Sidecar  from lonboard import Map, ScatterplotLayer from lonboard.colormap import apply_continuous_cmap <p>For this example, we'll use a dataset of abandoned cars in Chicago. Since this is a CSV file, we need to pass in the names of the X and Y columns into the \"open options\" to pass to OGR.</p> In\u00a0[\u00a0]: Copied! <pre>url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\"\ngdf = gpd.read_file(\n    url,\n    engine=\"pyogrio\",\n    use_arrow=True,\n    X_POSSIBLE_NAMES=\"Longitude\",\n    Y_POSSIBLE_NAMES=\"Latitude\",\n    KEEP_GEOM_COLUMNS=\"NO\",\n)\ngdf = gdf.set_crs(\"EPSG:4326\")\n</pre> url = \"https://geodacenter.github.io/data-and-lab/data/Abandoned_Vehicles_Map.csv\" gdf = gpd.read_file(     url,     engine=\"pyogrio\",     use_arrow=True,     X_POSSIBLE_NAMES=\"Longitude\",     Y_POSSIBLE_NAMES=\"Latitude\",     KEEP_GEOM_COLUMNS=\"NO\", ) gdf = gdf.set_crs(\"EPSG:4326\") <p>This dataset has some null values for geometries. We'll remove these first:</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf.geometry != None]\n</pre> gdf = gdf[gdf.geometry != None] <p>Let's also remove empty values from another column to make later steps easier (when modifying a single map object with lonboard, you can't change the number of rows).</p> In\u00a0[\u00a0]: Copied! <pre>gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"]\n</pre> gdf = gdf[gdf[\"How Many Days Has the Vehicle Been Reported as Parked?\"] != \"\"] <p>We'll now initialize the <code>Sidecar</code> object, which splits the JupyterLab screen in half, creating the output area for our widget.</p> In\u00a0[\u00a0]: Copied! <pre>sc = Sidecar(title='My Map')\n</pre> sc = Sidecar(title='My Map') <p>Now let's create a map layer from our <code>GeoDataFrame</code>.</p> <p>The default map height is 500 pixels, but we'd like to have a taller map because it's vertical. This API may change in the future.</p> In\u00a0[\u00a0]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap_ = Map(layers=[layer])\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map_ = Map(layers=[layer]) <p>Now we can render this inside the sidecar. You should see the map appear on the right side of your screen.</p> In\u00a0[\u00a0]: Copied! <pre>with sc:\n    display(map_)\n</pre> with sc:     display(map_) <p>It's great that we can see the map, but let's customize the rendering a bit. There's a column in the data titled</p> <p>How Many Days Has the Vehicle Been Reported as Parked?</p> <p>Let's use this column to visualize how long it takes to remove a car in different areas of the city.</p> <p>This is a string column, so first let's convert it to a numeric value.</p> In\u00a0[\u00a0]: Copied! <pre>colname = \"How Many Days Has the Vehicle Been Reported as Parked?\"\ngdf[colname] = pd.to_numeric(gdf[colname])\n</pre> colname = \"How Many Days Has the Vehicle Been Reported as Parked?\" gdf[colname] = pd.to_numeric(gdf[colname]) <p>In order to apply a colormap onto our data, we need to normalize our values to the range of <code>0-1</code>. Matplotlib offers several normalization options. See the tutorial and the <code>matplotlib.colors</code> module.</p> <p>To start, we'll do a simple linear normalization, converting from 0-90 to 0-1. Any values below 0 will be clamped to 0, and any values above 90 will be clamped to 1.</p> In\u00a0[\u00a0]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 90)\nnormalized_values = normalizer(gdf[colname])\n</pre> normalizer = mpl.colors.Normalize(0, 90) normalized_values = normalizer(gdf[colname]) <p>These values now range between 0 and 1:</p> In\u00a0[\u00a0]: Copied! <pre>normalized_values\n</pre> normalized_values In\u00a0[\u00a0]: Copied! <pre>BrBG_10.mpl_colormap\n</pre> BrBG_10.mpl_colormap In\u00a0[\u00a0]: Copied! <pre>layer.radius_min_pixels = 0.5\nlayer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10)\n</pre> layer.radius_min_pixels = 0.5 layer.get_fill_color = apply_continuous_cmap(normalized_values, BrBG_10) <p>Now the circles on the map have colors based on the time they've been parked.</p> <p>But this dataset is skewed with a long tail. Most cars have been sitting way less than 90 days, but a few have been sitting even longer.</p> <p>A logarithmic normalization (aka matplotlib's <code>LogNorm</code>) might be more useful here:</p> In\u00a0[\u00a0]: Copied! <pre>log_normalizer = mpl.colors.LogNorm(3, 90, clip=True)\nlog_values = log_normalizer(gdf[colname])\n</pre> log_normalizer = mpl.colors.LogNorm(3, 90, clip=True) log_values = log_normalizer(gdf[colname]) In\u00a0[\u00a0]: Copied! <pre>layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10)\nlayer.get_radius = log_values * 50\nlayer.radius_units = \"meters\"\n</pre> layer.get_fill_color = apply_continuous_cmap(log_values, BrBG_10) layer.get_radius = log_values * 50 layer.radius_units = \"meters\" In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/integrations/sidecar/#sidecar","title":"Sidecar\u00b6","text":""},{"location":"examples/integrations/sidecar/#dependencies","title":"Dependencies\u00b6","text":"<ul> <li>lonboard</li> <li>sidecar</li> <li>pyogrio</li> </ul> <p>Run</p> <pre><code>pip install sidecar pyogrio\n</code></pre> <p>if needed. You may need to restart Jupyter and/or refresh your browser tab after installing <code>sidecar</code>.</p>"},{"location":"examples/integrations/sidecar/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/","title":"30 Day Map Challenge: Day 1 (Points)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport shapely\n\nfrom lonboard import Map, ScatterplotLayer\n</pre> import geopandas as gpd import matplotlib as mpl import numpy as np import pandas as pd import shapely  from lonboard import Map, ScatterplotLayer In\u00a0[2]: Copied! <pre>path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\"\n</pre> path = \"/Users/kyle/Downloads/Motor_Vehicle_Collisions_-_Crashes_20231101.csv\" <p>Read the data:</p> In\u00a0[3]: Copied! <pre>df = pd.read_csv(path, dtype={\"ZIP CODE\": str})\n</pre> df = pd.read_csv(path, dtype={\"ZIP CODE\": str}) <p>Let's first take a peek at our data:</p> In\u00a0[4]: Copied! <pre>pd.options.display.max_columns = None\ndf.head()\n</pre> pd.options.display.max_columns = None df.head() Out[4]: CRASH DATE CRASH TIME BOROUGH ZIP CODE LATITUDE LONGITUDE LOCATION ON STREET NAME CROSS STREET NAME OFF STREET NAME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED CONTRIBUTING FACTOR VEHICLE 1 CONTRIBUTING FACTOR VEHICLE 2 CONTRIBUTING FACTOR VEHICLE 3 CONTRIBUTING FACTOR VEHICLE 4 CONTRIBUTING FACTOR VEHICLE 5 COLLISION_ID VEHICLE TYPE CODE 1 VEHICLE TYPE CODE 2 VEHICLE TYPE CODE 3 VEHICLE TYPE CODE 4 VEHICLE TYPE CODE 5 0 09/11/2021 2:39 NaN NaN NaN NaN NaN WHITESTONE EXPRESSWAY 20 AVENUE NaN 2.0 0.0 0 0 0 0 2 0 Aggressive Driving/Road Rage Unspecified NaN NaN NaN 4455765 Sedan Sedan NaN NaN NaN 1 03/26/2022 11:45 NaN NaN NaN NaN NaN QUEENSBORO BRIDGE UPPER NaN NaN 1.0 0.0 0 0 0 0 1 0 Pavement Slippery NaN NaN NaN NaN 4513547 Sedan NaN NaN NaN NaN 2 06/29/2022 6:55 NaN NaN NaN NaN NaN THROGS NECK BRIDGE NaN NaN 0.0 0.0 0 0 0 0 0 0 Following Too Closely Unspecified NaN NaN NaN 4541903 Sedan Pick-up Truck NaN NaN NaN 3 09/11/2021 9:35 BROOKLYN 11208 40.667202 -73.866500 (40.667202, -73.8665) NaN NaN 1211      LORING AVENUE 0.0 0.0 0 0 0 0 0 0 Unspecified NaN NaN NaN NaN 4456314 Sedan NaN NaN NaN NaN 4 12/14/2021 8:13 BROOKLYN 11233 40.683304 -73.917274 (40.683304, -73.917274) SARATOGA AVENUE DECATUR STREET NaN 0.0 0.0 0 0 0 0 0 0 NaN NaN NaN NaN NaN 4486609 NaN NaN NaN NaN NaN <p>There are a bunch of columns in this dataset:</p> In\u00a0[5]: Copied! <pre>df.columns\n</pre> df.columns Out[5]: <pre>Index(['CRASH DATE', 'CRASH TIME', 'BOROUGH', 'ZIP CODE', 'LATITUDE',\n       'LONGITUDE', 'LOCATION', 'ON STREET NAME', 'CROSS STREET NAME',\n       'OFF STREET NAME', 'NUMBER OF PERSONS INJURED',\n       'NUMBER OF PERSONS KILLED', 'NUMBER OF PEDESTRIANS INJURED',\n       'NUMBER OF PEDESTRIANS KILLED', 'NUMBER OF CYCLIST INJURED',\n       'NUMBER OF CYCLIST KILLED', 'NUMBER OF MOTORIST INJURED',\n       'NUMBER OF MOTORIST KILLED', 'CONTRIBUTING FACTOR VEHICLE 1',\n       'CONTRIBUTING FACTOR VEHICLE 2', 'CONTRIBUTING FACTOR VEHICLE 3',\n       'CONTRIBUTING FACTOR VEHICLE 4', 'CONTRIBUTING FACTOR VEHICLE 5',\n       'COLLISION_ID', 'VEHICLE TYPE CODE 1', 'VEHICLE TYPE CODE 2',\n       'VEHICLE TYPE CODE 3', 'VEHICLE TYPE CODE 4', 'VEHICLE TYPE CODE 5'],\n      dtype='object')</pre> <p>Let's keep only specific columns that we care about:</p> In\u00a0[6]: Copied! <pre>keep_cols = [\n    \"CRASH DATE\",\n    \"CRASH TIME\",\n    \"LATITUDE\",\n    \"LONGITUDE\",\n    \"NUMBER OF PERSONS INJURED\",\n    \"NUMBER OF PERSONS KILLED\",\n    \"NUMBER OF PEDESTRIANS INJURED\",\n    \"NUMBER OF PEDESTRIANS KILLED\",\n    \"NUMBER OF CYCLIST INJURED\",\n    \"NUMBER OF CYCLIST KILLED\",\n    \"NUMBER OF MOTORIST INJURED\",\n    \"NUMBER OF MOTORIST KILLED\",\n    \"COLLISION_ID\",\n]\ndf = df[keep_cols]\n</pre> keep_cols = [     \"CRASH DATE\",     \"CRASH TIME\",     \"LATITUDE\",     \"LONGITUDE\",     \"NUMBER OF PERSONS INJURED\",     \"NUMBER OF PERSONS KILLED\",     \"NUMBER OF PEDESTRIANS INJURED\",     \"NUMBER OF PEDESTRIANS KILLED\",     \"NUMBER OF CYCLIST INJURED\",     \"NUMBER OF CYCLIST KILLED\",     \"NUMBER OF MOTORIST INJURED\",     \"NUMBER OF MOTORIST KILLED\",     \"COLLISION_ID\", ] df = df[keep_cols] <p>About 11% of the data have missing locations. For this quick demo, I don't have the ability to georeference those rows based on street name, so we'll just remove them. Hopefully there isn't a skew where crashes in some areas are more likely to not be georeferenced.</p> In\u00a0[7]: Copied! <pre>(df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean()\n</pre> (df[\"LONGITUDE\"].isnull() | df[\"LATITUDE\"].isnull()).mean() Out[7]: <pre>0.11342612579376604</pre> <p>Keep only the rows where longitude and latitude are not null.</p> In\u00a0[8]: Copied! <pre>df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())]\n</pre> df = df[(df[\"LONGITUDE\"].notnull() &amp; df[\"LATITUDE\"].notnull())] <p>This dataset has crash data from the end of 2021, all of 2022, and thus far in 2023. For simplicity and regularity, let's choose only data from 2022.</p> <p>We'll convert the date to a datetime column and then select rows where the date happened in 2022:</p> In\u00a0[9]: Copied! <pre>df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"])\ndf = df[df[\"CRASH DATE\"].dt.year == 2022]\n</pre> df[\"CRASH DATE\"] = pd.to_datetime(df[\"CRASH DATE\"]) df = df[df[\"CRASH DATE\"].dt.year == 2022] <p>Let's also convert the time column from a string to a timestamp.</p> In\u00a0[10]: Copied! <pre>df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\")\n</pre> df[\"CRASH TIME\"] = pd.to_datetime(df[\"CRASH TIME\"], format=\"%H:%M\") <p>If we look at the data types in our data, they were inferred as <code>int64</code> and <code>float64</code> data types:</p> In\u00a0[11]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[11]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED               float64\nNUMBER OF PERSONS KILLED                float64\nNUMBER OF PEDESTRIANS INJURED             int64\nNUMBER OF PEDESTRIANS KILLED              int64\nNUMBER OF CYCLIST INJURED                 int64\nNUMBER OF CYCLIST KILLED                  int64\nNUMBER OF MOTORIST INJURED                int64\nNUMBER OF MOTORIST KILLED                 int64\nCOLLISION_ID                              int64\ndtype: object</pre> <p>Knowing that our data holds small values, we can downcast to smaller data types:</p> In\u00a0[12]: Copied! <pre>for numeric_col_name in df.select_dtypes(\"number\").columns:\n    df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\")\n</pre> for numeric_col_name in df.select_dtypes(\"number\").columns:     df[numeric_col_name] = pd.to_numeric(df[numeric_col_name], downcast=\"unsigned\") <p>Now our data types are much smaller:</p> In\u00a0[13]: Copied! <pre>df.dtypes\n</pre> df.dtypes Out[13]: <pre>CRASH DATE                       datetime64[ns]\nCRASH TIME                       datetime64[ns]\nLATITUDE                                float64\nLONGITUDE                               float64\nNUMBER OF PERSONS INJURED                 uint8\nNUMBER OF PERSONS KILLED                  uint8\nNUMBER OF PEDESTRIANS INJURED             uint8\nNUMBER OF PEDESTRIANS KILLED              uint8\nNUMBER OF CYCLIST INJURED                 uint8\nNUMBER OF CYCLIST KILLED                  uint8\nNUMBER OF MOTORIST INJURED                uint8\nNUMBER OF MOTORIST KILLED                 uint8\nCOLLISION_ID                             uint32\ndtype: object</pre> <p>Now let's construct a GeoDataFrame from this data.</p> In\u00a0[14]: Copied! <pre>geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\"))\ngdf = gpd.GeoDataFrame(df, geometry=geometry)\n</pre> geometry = shapely.points(df.pop(\"LONGITUDE\"), df.pop(\"LATITUDE\")) gdf = gpd.GeoDataFrame(df, geometry=geometry) In\u00a0[15]: Copied! <pre>gdf.head()\n</pre> gdf.head() Out[15]: CRASH DATE CRASH TIME NUMBER OF PERSONS INJURED NUMBER OF PERSONS KILLED NUMBER OF PEDESTRIANS INJURED NUMBER OF PEDESTRIANS KILLED NUMBER OF CYCLIST INJURED NUMBER OF CYCLIST KILLED NUMBER OF MOTORIST INJURED NUMBER OF MOTORIST KILLED COLLISION_ID geometry 37 2022-07-12 1900-01-01 17:50:00 0 0 0 0 0 0 0 0 4545699 POINT (-73.96049 40.66330) 40 2022-04-24 1900-01-01 16:45:00 1 0 0 0 0 0 1 0 4521660 POINT (-74.13892 40.60768) 41 2022-04-24 1900-01-01 04:49:00 0 0 0 0 0 0 0 0 4521759 POINT (-73.86990 40.85597) 42 2022-04-22 1900-01-01 17:17:00 1 0 1 0 0 0 0 0 4522226 POINT (-73.93960 40.79028) 43 2022-04-24 1900-01-01 01:30:00 0 0 0 0 0 0 0 0 4522015 POINT (-74.01621 40.64299) <p>If we look at the bounding box of our data, we see that some points are way outside of NYC. Let's filter to include only the data in the NYC region:</p> In\u00a0[16]: Copied! <pre>gdf.total_bounds\n</pre> gdf.total_bounds Out[16]: <pre>array([-74.25496 ,   0.      ,   0.      ,  40.912167])</pre> In\u00a0[17]: Copied! <pre>nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696]\n</pre> nyc_bbox = [-74.382742, 40.428857, -73.452477, 41.092696] In\u00a0[18]: Copied! <pre>gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))]\n</pre> gdf = gdf[gdf.intersects(shapely.box(*nyc_bbox))] In\u00a0[19]: Copied! <pre>layer = ScatterplotLayer.from_geopandas(gdf)\nmap = Map(layers=[layer], _height=800)\nmap\n</pre> layer = ScatterplotLayer.from_geopandas(gdf) map = Map(layers=[layer], _height=800) map Out[19]: <pre>Map(layers=[ScatterplotLayer(table=pyarrow.Table\nCRASH DATE: timestamp[ns]\nCRASH TIME: timestamp[ns]\nNUMBER OF\u2026</pre> <p>Ok cool, we can see something on the map, but let's make our visualization more interesting.</p> <p>Let's compute the number of non-motorists injured in each collision:</p> In\u00a0[20]: Copied! <pre>num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"]\n</pre> num_injured = gdf[\"NUMBER OF PEDESTRIANS INJURED\"] + gdf[\"NUMBER OF CYCLIST INJURED\"] <p>We can see a quick distribution with <code>value_counts</code></p> In\u00a0[21]: Copied! <pre>num_injured.value_counts()\n</pre> num_injured.value_counts() Out[21]: <pre>0    80613\n1    12057\n2      356\n3       37\n4        3\n5        3\nName: count, dtype: int64</pre> In\u00a0[22]: Copied! <pre>layer.get_radius = np.array(num_injured + 1)\nlayer.radius_scale = 50\nlayer.opacity = 0.05\n</pre> layer.get_radius = np.array(num_injured + 1) layer.radius_scale = 50 layer.opacity = 0.05 <p>Next, let's color each dot by time of day. We'll use a cyclic colormap that starts and ends on the same color, because the time of day is cyclic.</p> In\u00a0[23]: Copied! <pre>colormap = mpl.colormaps[\"twilight\"]\ncolormap\n</pre> colormap = mpl.colormaps[\"twilight\"] colormap Out[23]: twilight  underbad over  <p>Next get the minute of day of the crash:</p> In\u00a0[24]: Copied! <pre>minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute\n</pre> minute_of_day = gdf[\"CRASH TIME\"].dt.hour * 60 + gdf[\"CRASH TIME\"].dt.minute <p>To apply a colormap, we need to normalize the <code>minute_of_day</code> series to <code>0-1</code>. We can do this with matplotlib's <code>Normalize</code>. We set the minimum value to 0 and the maximum value to the number of minutes in a day.</p> In\u00a0[25]: Copied! <pre>normalizer = mpl.colors.Normalize(0, 24 * 60)\nnormalized_minutes = normalizer(minute_of_day)\n</pre> normalizer = mpl.colors.Normalize(0, 24 * 60) normalized_minutes = normalizer(minute_of_day) <p>Now <code>normalized_minutes</code> ranges from 0-1:</p> In\u00a0[26]: Copied! <pre>normalized_minutes\n</pre> normalized_minutes Out[26]: <pre>masked_array(data=[0.74305556, 0.69791667, 0.20069444, ..., 0.54166667,\n                   0.625     , 0.60069444],\n             mask=False,\n       fill_value=1e+20)</pre> <p>Next we can apply the matplotlib colormap on our normalized values. We set <code>bytes=True</code> to ensure the output is of <code>uint8</code> data type.</p> In\u00a0[27]: Copied! <pre>colors = colormap(normalized_minutes, bytes=True)\n</pre> colors = colormap(normalized_minutes, bytes=True) In\u00a0[28]: Copied! <pre>colors\n</pre> colors Out[28]: <pre>array([[175,  81,  81, 255],\n       [156,  57,  79, 255],\n       [109, 144, 191, 255],\n       ...,\n       [ 63,  17,  60, 255],\n       [115,  29,  78, 255],\n       [ 99,  24,  75, 255]], dtype=uint8)</pre> In\u00a0[29]: Copied! <pre>layer.get_fill_color = colors\n</pre> layer.get_fill_color = colors In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/1-points/#30-day-map-challenge-day-1-points","title":"30 Day Map Challenge: Day 1 (Points)\u00b6","text":"<p>This is a notebook for day 1 of 30 Day Map Challenge.</p>"},{"location":"examples/map_challenge/1-points/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/1-points/#access-the-data","title":"Access the data\u00b6","text":"<p>This data comes from NYC Open Data, where we use Motor Vehicle Collisions - Crashes. First download this data via <code>Export &gt; CSV</code>, save it somewhere on disk, and update the <code>path</code> variable in the next cell for that location.</p>"},{"location":"examples/map_challenge/6-asia/","title":"30 Day Map Challenge: Day 6 (Asia)","text":"In\u00a0[1]: Copied! <pre>import geopandas as gpd\nfrom palettable.colorbrewer.sequential import Blues_8\n\nfrom lonboard import Map, PathLayer\nfrom lonboard.colormap import apply_continuous_cmap\n</pre> import geopandas as gpd from palettable.colorbrewer.sequential import Blues_8  from lonboard import Map, PathLayer from lonboard.colormap import apply_continuous_cmap In\u00a0[2]: Copied! <pre>url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\"\n</pre> url = \"https://storage.googleapis.com/fao-maps-catalog-data/geonetwork/aquamaps/rivers_asia_37331.zip\" In\u00a0[3]: Copied! <pre>gdf = gpd.read_file(url, engine=\"pyogrio\")\n</pre> gdf = gpd.read_file(url, engine=\"pyogrio\") <p>The <code>Strahler</code> column seems to refer to how important/central the particular river is. It ranges from 1-7.</p> In\u00a0[4]: Copied! <pre>gdf['Strahler'].value_counts()\n</pre> gdf['Strahler'].value_counts() Out[4]: <pre>Strahler\n1    80503\n2    39100\n3    19655\n4     8960\n5     4547\n6     1901\n7      484\nName: count, dtype: int64</pre> <p>We'll use <code>Strahler</code> to plot more central rivers in both a darker blue and with a wider line.</p> In\u00a0[5]: Copied! <pre>layer = PathLayer.from_geopandas(gdf)\nlayer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8)\nlayer.get_width = gdf['Strahler']\nlayer.width_scale = 3000\nlayer.width_min_pixels = 0.5\n\nm = Map(layers=[layer])\nm\n</pre> layer = PathLayer.from_geopandas(gdf) layer.get_color = apply_continuous_cmap(gdf['Strahler'] / 7, Blues_8) layer.get_width = gdf['Strahler'] layer.width_scale = 3000 layer.width_min_pixels = 0.5  m = Map(layers=[layer]) m Out[5]: <pre>Map(layers=[PathLayer(get_color=&lt;pyarrow.lib.FixedSizeListArray object at 0x107cbb220&gt;\n[\n  [\n    8,\n    69,\n  \u2026</pre> In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"examples/map_challenge/6-asia/#30-day-map-challenge-day-6-asia","title":"30 Day Map Challenge: Day 6 (Asia)\u00b6","text":"<p>This is a notebook for day 6 of 30 Day Map Challenge.</p> <p>This data comes from the UN's \"Rivers of South and East Asia\".</p>"},{"location":"examples/map_challenge/6-asia/#imports","title":"Imports\u00b6","text":""},{"location":"examples/map_challenge/6-asia/#fetch-the-data","title":"Fetch the data\u00b6","text":""},{"location":"how-it-works/","title":"How it works?","text":"<p>Lonboard is built on four foundational technologies: deck.gl, GeoArrow, GeoParquet, and anywidget.</p> <ul> <li>deck.gl is a JavaScript geospatial data visualization library. Because deck.gl uses the GPU in your computer to render data, it's capable of performantly rendering very large quantities of data.</li> <li>GeoArrow is a memory format for efficiently representing geospatial vector data. As a memory format, GeoArrow is not compressed and can be used directly.</li> <li>GeoParquet is a file format for efficiently encoding and decoding geospatial vector data. As a file format, GeoParquet contains very efficient compression, and needs to be parsed before it can be used. <sup>1</sup></li> <li>anywidget is a framework for building custom Jupyter widgets that makes the process much easier than before.</li> </ul>"},{"location":"how-it-works/#how-is-it-so-fast","title":"How is it so fast?","text":"<p>Lonboard is so fast because it moves data from Python to JavaScript (in your browser) and then from JavaScript to your Graphics Processing Unit (GPU) more efficiently than ever before.</p> <p>Other Python libraries for interactive maps exist (such as <code>ipyleaflet</code>), and even existing bindings to deck.gl exist (such as <code>pydeck</code>). But those libraries encode data as GeoJSON to copy from Python to the browser. GeoJSON is extremely slow to read and write and results in a very large data file that has to be copied to the browser.</p> <p>With lonboard, the entire pipeline is binary. In Python, GeoPandas to GeoArrow to GeoParquet avoids a text encoding like GeoJSON and results in a compressed binary buffer that can be efficiently copied to the browser. In JavaScript, GeoParquet to GeoArrow offers efficient decoding (in WebAssembly). Then deck.gl is able to interpret the GeoArrow table directly without any parsing (thanks to <code>@geoarrow/deck.gl-layers</code>).</p> <p>GeoPandas is the primary interface for users to add data, allowing lonboard to internally manage the conversion to GeoArrow and its transport to the browser for rendering.</p> <p>Lonboard's goal is to abstract the technical bits of representing and moving data so it can attain its dual goals of performance and ease of use for a vast audience.</p> <ol> <li> <p>lonboard currently doesn't use \"official\" GeoParquet 1.0, because the 1.0 spec requires encoding geometries as Well-Known Binary (WKB) inside of the Parquet file. lonboard uses the highly-efficient GeoArrow encoding inside of GeoParquet (which may become part of the GeoParquet spec in 1.1). This is faster and easier to write when the writer and reader are both using GeoArrow anyways.\u00a0\u21a9</p> </li> </ol>"},{"location":"how-it-works/internals/","title":"Internal Architecture","text":"<p>Last edited April 2024.</p> <p>This document will outline Lonboard's internal architecture, and hopefully should be useful for potential contributors.</p>"},{"location":"how-it-works/internals/#pydeck","title":"Pydeck","text":"<p>Before we consider Lonboard, we should briefly cover how Pydeck manages data transit.</p> <p>Pydeck uses the loaders.gl ecosystem natively supported in deck.gl. This means that pydeck passes the user input directly into the <code>data</code> prop of any given layer. When <code>data</code> is a string, it's interpreted as a URL, passed into loaders.gl machinery to parse the given data format to a list of GeoJSON features. In order to do data-driven styling, users pass in strings like <code>\"@@=properties.valuePerSqm\"</code> to describe the JavaScript callback that should be applied to each GeoJSON feature.</p> <p>In my opinion, this presents performance pitfalls and gives Pydeck a stifling amount of flexibility. The Python API isn't able to do much of anything because the user input could be anything and users end up writing a sort of JavaScript in Python strings.</p>"},{"location":"how-it-works/internals/#lonboard","title":"Lonboard","text":""},{"location":"how-it-works/internals/#goals-and-target-user-persona","title":"Goals and target user persona","text":"<p>While I was never a primary pydeck developer and can't speak to their goals, I can say that Lonboard has a well-formed idea of the target user.</p> <p>When I was learning geospatial data processing in Python, I learned that GeoPandas, Shapely, and others were amazing tools. But I would perform operations and not have a deep understanding of what the output looked like. I wanted some way to quickly get a picture of what my data looked like. This drove me to build <code>keplergl_cli</code>, a single function or CLI command to visualize data in an interactive HTML map. The desire for this simplicity lives on in Lonboard's <code>viz</code> function.</p> <p>This experience informs the target user of Lonboard:</p> <ul> <li>A Python user who does not know or want to know JavaScript.</li> <li>Wants fast exploratory data analysis.</li> <li>Already working with data in Python.</li> <li>A reasonably fast internet connection on a desktop computer.</li> </ul>"},{"location":"how-it-works/internals/#python-classes","title":"Python classes","text":"<p>The <code>Map</code> class is the primary and only widget associated with JavaScript that renders anything. <code>Map</code> subclasses from <code>anywidget.AnyWidget</code>, which dynamically fetches Lonboard's JavaScript ESM bundle. The <code>Map</code> class emulates deck.gl's <code>Deck</code> class. It synchronizes map state and can be passed a sequence of <code>Layer</code> objects.</p> <p><code>Layer</code> classes are designed to map to each underlying deck.gl layer. Most <code>Layer</code> classes subclass from <code>BaseArrowLayer</code>, which stores a <code>pyarrow.Table</code> in the dataclass. This data object gets serialized to Parquet and rendered in a layer provided by <code>@geoarrow/deck.gl-layers</code> as described later on. The primary API for each of these Arrow-based layers is <code>from_geopandas</code>, which converts a <code>GeoDataFrame</code> to a <code>pyarrow.Table</code> with a GeoArrow-represented geometry column. A few layers are not Arrow-based and do not subclass from <code>BaseArrowLayer</code>: primarily <code>BitmapLayer</code> and <code>BitmapTileLayer</code>. These pass user data input directly into a core deck.gl layer.</p> <p>These <code>Layer</code> classes subclass from <code>ipywidgets.Widget</code> but are not associated with any JavaScript of their own. But by being widgets themselves, their data is synced with JavaScript and Python data changes are propagated as events to the core <code>Map</code> object.</p> <p>Each layer has parameters that map to deck.gl accessors and render properties. Each of these has validation on the Python side to catch invalid input early. Accessors are modeled with \"trait\" objects. Scalar accessors are stored as a scalar int/string/float. Function accessors are evaluated in Python and stored as a <code>pyarrow.Array</code>.</p> <p>A variety of layer extensions are supported, which map to deck.gl's upstream layer extensions. These layer extensions dynamically add more parameters to the layers on which they're assigned.</p>"},{"location":"how-it-works/internals/#data-loading","title":"Data loading","text":"<p>While pydeck allows users to pass in URLs to remote files, at the moment this is not possible in Lonboard. With only a few exceptions (e.g. the <code>BitmapLayer</code> and <code>BitmapTileLayer</code>), only data originating in Python is supported in Lonboard. This reduction in scope vastly simplifies Lonboard's internals while not providing much of a hurdle for Lonboard's core audience.</p> <p>Jupyter provides a WebSocket connection between Python and JavaScript. Jupyter widget state from Python classes is serialized over this WebSocket and received in JavaScript. While most widgets synchronize objects through JSON, widgets fully support binary data as well, which Lonboard utilizes.</p>"},{"location":"how-it-works/internals/#simplicity-of-code","title":"Simplicity of code","text":"<p>Supporting only a single data provenance and format makes the internals of Lonboard much more maintainable. Lonboard's JavaScript bindings only need to include a single data parser and can optimize performance for Parquet and Arrow data.</p>"},{"location":"how-it-works/internals/#parquet","title":"Parquet","text":"<p>Parquet was chosen as the underlying data transmission format for its excellent compression, fast read and write speeds, compatibility with GeoArrow and, in turn, compatibility with deck.gl's binary API.</p> <p>I tested with data from the speed test example (3 million points). Saving GeoArrow to Parquet was 135x faster than saving a GeoDataFrame to GeoJSON. The resulting file was 26x smaller. And parsing the Parquet to Arrow on the frontend was 5.6x faster than <code>JSON.parse</code> (including moving the Arrow from WebAssembly to JS).</p> <p>Note that what is sent over the wire is not GeoParquet. GeoParquet 1.0 requires special metadata and WKB-encoded geometries. Using that would require code on the frontend to parse WKB-encoded geometries into GeoArrow. The upcoming GeoParquet 1.1 includes support for GeoArrow but only its \"separated\" coordinates, where <code>x</code> and <code>y</code> coordinate values are in two different buffers (i.e. <code>xxxx</code>, <code>yyyy</code>). deck.gl's binary API currently does not support passing separated coordinates. Therefore Lonboard uses the \"interleaved\" coordinate variant, where <code>x</code> and <code>y</code> coordinates are stored in the same buffer (i.e. <code>xyxyxyxy</code>).</p>"},{"location":"how-it-works/internals/#bootstrapping-geoarrow","title":"Bootstrapping GeoArrow","text":"<p>For performance reasons, Lonboard is very focused on using GeoArrow throughout the pipeline in both Python and JavaScript. At the time when Lonboard was started, there were no user-friendly tools for converting data to GeoArrow in client-side JavaScript. By supporting only data from Python and only Parquet data, we could focus on making data conversions from GeoPandas to GeoArrow in Python efficient and stable. This enables GeoArrow to be \"just an implementation detail\" and out of any primary public APIs while also working to bootstrap a GeoArrow ecosystem in Python and JavaScript.</p>"},{"location":"how-it-works/internals/#authentication","title":"Authentication","text":"<p>Since all data is loaded via Python over the Jupyter-provided WebSocket, Lonboard doesn't need to implement any sort of authentication itself. The user is able to use whatever credentials they're familiar with, say for S3, to access data. Those credentials never need to be passed to the browser.</p> <p>In contrast, having the JavaScript side load data directly from a URL requires some way for users to specify authorization headers or API keys for access to private datasets.</p>"},{"location":"how-it-works/internals/#layer-creation","title":"Layer Creation","text":"<p>With the exception of the <code>BitmapLayer</code>, no direct deck.gl layers are used. deck.gl layers are used exclusively through the <code>@geoarrow/deck.gl-layers</code> glue library. This library connects GeoArrow data to the low-level binary data API of each deck.gl layer. All accessors are passed in directly as binary buffers.</p> <p>This means that in JavaScript the data never leaves an Arrow binary representation. It's parsed from Parquet to Arrow in WebAssembly, the Arrow buffers are copied out of WebAssembly memory, but never parsed to JSON strings or JavaScript objects.</p>"},{"location":"how-it-works/internals/#accessors-and-data-driven-rendering","title":"Accessors and data-driven rendering","text":"<p>Loading data from Python also helps to simplify data-driven rendering.</p>"},{"location":"how-it-works/internals/#no-function-serialization","title":"No function serialization","text":"<p>If we can make the data movement between Python and JavaScript fast enough, we can serialize computed rendering properties rather than the function to generate them. Lonboard serializes data, not functions.</p> <p>Any accessor used to style data is computed in Python, stored as a binary array, serialized to Parquet and sent to the frontend. Other than parsing the input Parquet buffers to Arrow, no data transformations or modifications are done on the JavaScript side.</p> <p>In Pydeck users had to pass something like</p> <pre><code>getRadius=\"@@=properties.valuePerSqm\"\n</code></pre> <p>which would be expanded into</p> <pre><code>getRadius: (object) =&gt; object.properties.valuePerSqm\n</code></pre> <p>on the JavaScript side.</p> <p>I find this syntax to be horrible UX. The user is no longer writing Python, they need to learn a new DSL just for this.</p> <p>RGB Colors for 3 million points take up 9MiB in memory (or 12MiB for RGBA). In the example of speed test data, 12MiB of RGBA data compressed down to 4MiB for transit. Float32 radii values for 3 million points take up 12MiB in memory and 9MiB compressed.</p> <p>Even for users on remote Python environments, this takes no more than a couple seconds to download, and then can render almost instantly. For non-point datasets with a fewer number of geometries, the byte size of accessors are even smaller relative to the geometry buffers.</p>"},{"location":"how-it-works/internals/#update-styling-without-re-sending-data","title":"Update styling without re-sending data","text":"<p>With Lonboard's widget architecture, any accessors other than the geometry are stored separately to the main Arrow table. This means that all accessors and rendering properties can be updated in isolation. The core table does not need to be synced again when a property is updated.</p>"},{"location":"how-it-works/internals/#uses-familiar-objects","title":"Uses familiar objects","text":"<p>Accessors are defined in terms of Numpy arrays, Pandas Series or PyArrow Arrays, which are familiar to Python data scientists.</p>"},{"location":"how-it-works/internals/#full-access-to-python","title":"Full access to Python","text":"<p>Because we serialize data to JavaScript instead of a function in a custom DSL, the user has the full spectrum of Python available to them. They can use one of matplotlib's many normalization helpers before applying a colormap. Or they can use apply some ML model and use its outputs for point radii.</p>"}]}
\ No newline at end of file
diff --git a/v0.8.0/sitemap.xml b/v0.8.0/sitemap.xml
index 6c2db779..64e4d026 100644
--- a/v0.8.0/sitemap.xml
+++ b/v0.8.0/sitemap.xml
@@ -2,222 +2,227 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://developmentseed.org/lonboard/latest/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/CHANGELOG/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/alternatives/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/cli/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/performance/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/basemap/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/colormap/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/controls/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/map/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/traits/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/viz/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layer-extensions/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layer-extensions/brushing-extension/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layer-extensions/collision-filter-extension/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layer-extensions/data-filter-extension/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/arc-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/base-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/bitmap-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/bitmap-tile-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/heatmap-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/path-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/point-cloud-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/polygon-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/scatterplot-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/solid-polygon-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/api/layers/text-layer/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/geoarrow/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/geopandas/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/jupyter-widgets/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/panel/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/ecosystem/shiny/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/data-filter-extension/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/global-boundaries/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/internet-speeds/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/migration/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/north-america-roads/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>https://developmentseed.org/lonboard/latest/examples/overture-maps/</loc>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/integrations/color-picker/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/integrations/sidecar/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/map_challenge/1-points/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/examples/map_challenge/6-asia/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/how-it-works/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://developmentseed.org/lonboard/latest/how-it-works/internals/</loc>
-         <lastmod>2024-04-18</lastmod>
+         <lastmod>2024-04-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file
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