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juba committed May 26, 2024
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2 changes: 1 addition & 1 deletion .nojekyll
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2 changes: 1 addition & 1 deletion development.html
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Expand Up @@ -267,7 +267,7 @@ <h2 class="anchored" data-anchor-id="tests">Tests</h2>
<h2 class="anchored" data-anchor-id="debug-mode">Debug mode</h2>
<p>“Debug mode” outputs the computed JavaScript plot structure (the one passed to <code>Plot.plot</code>).</p>
<p>To activate it, add <code>debug=True</code> when creating your plot generator object:</p>
<div id="d55f334f" class="cell" data-execution_count="1">
<div id="8f852b0c" class="cell" data-execution_count="1">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>op <span class="op">=</span> Obsplot(debug<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>If using the <code>widget</code> renderer, debug output will be displayed in JavaScript console. With the <code>jsdom</code> renderer, it will be displayed directly in the notebook.</p>
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8 changes: 4 additions & 4 deletions gallery_geo.html
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Expand Up @@ -215,7 +215,7 @@ <h1 class="title">Mapping and spatial data</h1>
<h2 class="anchored" data-anchor-id="geo-mark">Geo mark</h2>
<p>The <a href="https://observablehq.com/@observablehq/plot-geo?collection=@observablehq/plot">Geo mark</a> allows to draw geographic features such as points, lines and polygons. These marks data are passed as GeoJSON.</p>
<p>This allows to create choropleth maps such as the following:</p>
<div id="c4d00911" class="cell" data-execution_count="2">
<div id="54da5dbf" class="cell" data-execution_count="2">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Obsplot, Plot</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
Expand Down Expand Up @@ -268,7 +268,7 @@ <h2 class="anchored" data-anchor-id="geo-mark">Geo mark</h2>
</div>
</div>
<p>Of course other marks can be used in conjunction with geo marks. This example represents the density of Walmarts supermarkets and is taken from the <a href="https://observablehq.com/@observablehq/plot-mapping?collection=@observablehq/plot">Mapping notebook</a>.</p>
<div id="40307e1f" class="cell" data-execution_count="3">
<div id="2cde41e7" class="cell" data-execution_count="3">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load US states from TopoJson with geopandas and convert to GeoJson</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>states <span class="op">=</span> gpd.read_file(<span class="st">"data/us-counties-10m.json"</span>, layer<span class="op">=</span><span class="st">"states"</span>).to_json()</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>nation <span class="op">=</span> gpd.read_file(<span class="st">"data/us-counties-10m.json"</span>, layer<span class="op">=</span><span class="st">"nation"</span>).to_json()</span>
Expand Down Expand Up @@ -312,7 +312,7 @@ <h2 class="anchored" data-anchor-id="geo-mark">Geo mark</h2>
<h2 class="anchored" data-anchor-id="raster-mark">Raster mark</h2>
<p>The <a href="https://observablehq.com/@observablehq/plot-raster?collection=@observablehq/plot">Raster mark</a> creates an image from spatial data.</p>
<p>Plot allows to do different type of spatial interpolations, such as <em>nearest</em>, which draws voronoi cells around values:</p>
<div id="f10bf745" class="cell" data-execution_count="4">
<div id="674b2d36" class="cell" data-execution_count="4">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>ca55 <span class="op">=</span> pl.read_csv(<span class="st">"data/ca55-south.csv"</span>)</span>
Expand Down Expand Up @@ -359,7 +359,7 @@ <h2 class="anchored" data-anchor-id="raster-mark">Raster mark</h2>
</div>
</div>
<p>Or the more recent random walk interpolation:</p>
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<div id="2ab6850e" class="cell" data-execution_count="5">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>flare_map(<span class="st">"random-walk"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display">
<figure class="plot-d6a7b5-figure figure" style="max-width: initial; padding: 0px 5px 5px 5px; color: black; background-color: white;"><svg class="plot-d6a7b5" fill="currentColor" font-family="system-ui, sans-serif" font-size="10" text-anchor="middle" width="640" height="500" viewbox="0 0 640 500" xmlns="http://www.w3.org/2000/svg" xlink="http://www.w3.org/1999/xlink" style="color: black; background-color: white;"><style>:where(.plot-d6a7b5) {
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14 changes: 7 additions & 7 deletions gallery_interaction.html

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8 changes: 4 additions & 4 deletions gallery_marks.html
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Expand Up @@ -217,7 +217,7 @@ <h1 class="title">Marks</h1>
<section id="cell-mark" class="level2">
<h2 class="anchored" data-anchor-id="cell-mark">Cell mark</h2>
<p>The following chart, taken from the <a href="https://observablehq.com/@observablehq/plot-cell?collection=@observablehq/plot">Cell Mark notebook</a>, shows the rating of every episode of <em>The Simpsons</em>.</p>
<div id="e3ec3bbe" class="cell" data-execution_count="2">
<div id="f918e24d" class="cell" data-execution_count="2">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Plot, d3, Math, js</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
Expand Down Expand Up @@ -266,7 +266,7 @@ <h2 class="anchored" data-anchor-id="cell-mark">Cell mark</h2>
<section id="faceting" class="level2">
<h2 class="anchored" data-anchor-id="faceting">Faceting</h2>
<p><em>Faceting</em> allows to easily produce multiple plots by partitioning data. The following facet plot is taken from the <a href="https://observablehq.com/@observablehq/plot-facets?collection=@observablehq/plot">Facets notebook</a>.</p>
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<div id="4deadde5" class="cell" data-execution_count="3">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>penguins <span class="op">=</span> pl.read_csv(<span class="st">"data/penguins.csv"</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
Expand Down Expand Up @@ -318,7 +318,7 @@ <h2 class="anchored" data-anchor-id="faceting">Faceting</h2>
<section id="arrow-mark" class="level2">
<h2 class="anchored" data-anchor-id="arrow-mark">Arrow mark</h2>
<p>Some marks allow for nice representation of complex data. The following plot, taken from the <a href="https://observablehq.com/@observablehq/plot-arrow?collection=@observablehq/plot">Arrow mark notebook</a>, shows the evolution of inequality and population in various U.S. cities.</p>
<div id="6641e035" class="cell" data-execution_count="4">
<div id="1baca6a4" class="cell" data-execution_count="4">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>metros <span class="op">=</span> pl.read_csv(<span class="st">"data/metros.csv"</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
Expand Down Expand Up @@ -390,7 +390,7 @@ <h2 class="anchored" data-anchor-id="arrow-mark">Arrow mark</h2>
<section id="contour-mark" class="level2">
<h2 class="anchored" data-anchor-id="contour-mark">Contour mark</h2>
<p>The contour mark allows for nice representations of spatial data. The following example taken from the <a href="https://observablehq.com/@observablehq/plot-contour?collection=@observablehq/plot">Contour mark notebook</a> shows water vapor data from november 2022 (note that data processing and plot code are slightly modified to adapt to <code>pyobsplot</code> specificities).</p>
<div id="a3f50b38" class="cell" data-execution_count="5">
<div id="9e2dc119" class="cell" data-execution_count="5">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>vapor <span class="op">=</span> (</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> pl.read_csv(<span class="st">"data/vapor.csv"</span>, has_header<span class="op">=</span><span class="va">False</span>, null_values<span class="op">=</span><span class="st">"99999.0"</span>)</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> .transpose()</span>
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14 changes: 7 additions & 7 deletions gallery_themes.html

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10 changes: 5 additions & 5 deletions gallery_transforms.html
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Expand Up @@ -218,7 +218,7 @@ <h1 class="title">Transforms</h1>
<section id="group-transform" class="level2">
<h2 class="anchored" data-anchor-id="group-transform">Group transform</h2>
<p><a href="https://observablehq.com/@observablehq/plot-group?collection=@observablehq/plot">Group transform</a> allows to compute summary values before plotting them. It is often used to make bar charts.</p>
<div id="5d6bfdb3" class="cell" data-execution_count="2">
<div id="9ba7dc89" class="cell" data-execution_count="2">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> polars <span class="im">as</span> pl</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pyobsplot <span class="im">import</span> Plot, d3, Math, js</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
Expand Down Expand Up @@ -256,7 +256,7 @@ <h2 class="anchored" data-anchor-id="group-transform">Group transform</h2>
<section id="dodge-transform" class="level2">
<h2 class="anchored" data-anchor-id="dodge-transform">Dodge transform</h2>
<p>The <a href="https://observablehq.com/@observablehq/plot-dodge?collection=@observablehq/plot">Dodge transform</a> allows to pack marks without overlapping. The following chart shows data about IPO offerings in the US.</p>
<div id="8643b145" class="cell" data-execution_count="3">
<div id="836c1e27" class="cell" data-execution_count="3">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datetime <span class="im">import</span> datetime</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>ipos <span class="op">=</span> pl.read_csv(<span class="st">"data/ipos.csv"</span>, try_parse_dates<span class="op">=</span><span class="va">True</span>).<span class="bu">filter</span>(</span>
Expand Down Expand Up @@ -307,7 +307,7 @@ <h2 class="anchored" data-anchor-id="dodge-transform">Dodge transform</h2>
<section id="hexbin-transform" class="level2">
<h2 class="anchored" data-anchor-id="hexbin-transform">Hexbin transform</h2>
<p>The <a href="https://observablehq.com/@observablehq/plot-hexbin?collection=@observablehq/plot">Hexbin transform</a> aggregates two dimensional points into hexagonal bins.</p>
<div id="97658c31" class="cell" data-execution_count="4">
<div id="ff45b058" class="cell" data-execution_count="4">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"inset"</span>: <span class="dv">10</span>,</span>
Expand Down Expand Up @@ -367,7 +367,7 @@ <h2 class="anchored" data-anchor-id="hexbin-transform">Hexbin transform</h2>
<section id="map-transform" class="level2">
<h2 class="anchored" data-anchor-id="map-transform">Map transform</h2>
<p><a href="https://observablehq.com/@observablehq/plot-map?collection=@observablehq/plot">Map transform</a> family can be used to normalize values.</p>
<div id="53c96de0" class="cell" data-execution_count="5">
<div id="5ab4c3b9" class="cell" data-execution_count="5">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>stocks <span class="op">=</span> pl.read_csv(<span class="st">"data/stocks.csv"</span>, try_parse_dates<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>Plot.plot(</span>
Expand Down Expand Up @@ -420,7 +420,7 @@ <h2 class="anchored" data-anchor-id="map-transform">Map transform</h2>
<section id="transforms-composition" class="level2">
<h2 class="anchored" data-anchor-id="transforms-composition">Transforms composition</h2>
<p>Several group and map transforms can be applied and composed to create complex representations, such as this distribution of group ages by US states adapted from the <a href="https://observablehq.com/@observablehq/plot-map?collection=@observablehq/plot">Map transform</a> notebook.</p>
<div id="2d5269d0" class="cell" data-execution_count="6">
<div id="8d85abb8" class="cell" data-execution_count="6">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load and prepare data</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>stateage <span class="op">=</span> (</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> pl.read_csv(<span class="st">"data/us-population-state-age.csv"</span>)</span>
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