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changelog.html

@@ -192,6 +192,7 @@ <h1 class="title">Changelog</h1>
 <section id="pyobsplot-0.4.3-dev" class="level2">
 <h2 class="anchored" data-anchor-id="pyobsplot-0.4.3-dev">pyobsplot 0.4.3 (dev)</h2>
 <ul>
+<li>Plots can now be generated in “png” and “pdf” formats when using the <code>jsdom</code> renderer. This is done by converting figures using <a href="https://typst.app">typst</a>. Many thanks to <span class="citation" data-cites="wirhabenzeit">@wirhabenzeit</span> and <span class="citation" data-cites="harrylojames">@harrylojames</span> for the idea, the underlying code and the feedback.</li>
 <li>Update Observable Plot to 0.6.14</li>
 </ul>
 </section>

development.html

@@ -266,7 +266,7 @@ <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="bc017968" class="cell" data-execution_count="1">
+<div id="0af2da70" 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(render<span class="op">=</span><span class="st">"widget"</span>, 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>

gallery_geo.html

@@ -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="23bcfa2a" class="cell" data-execution_count="2">
+<div id="be2516ce" 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>
@@ -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="a6cc8f60" class="cell" data-execution_count="3">
+<div id="75076e32" 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>
@@ -306,7 +306,7 @@ <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="6a75af38" class="cell" data-execution_count="4">
+<div id="637efbd8" 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>
@@ -347,7 +347,7 @@ <h2 class="anchored" data-anchor-id="raster-mark">Raster mark</h2>
 </div>
 </div>
 <p>Or the more recent random walk interpolation:</p>
-<div id="3f0ce8fe" class="cell" data-execution_count="5">
+<div id="3ae49f9e" 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">
 <div>

gallery_marks.html

@@ -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="72d9bde0" class="cell" data-execution_count="2">
+<div id="057d3c97" 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>
@@ -260,7 +260,7 @@ <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>
-<div id="10e64ed4" class="cell" data-execution_count="3">
+<div id="837b4f6a" 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>
@@ -306,7 +306,7 @@ <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="e1e57da9" class="cell" data-execution_count="4">
+<div id="b915802f" 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>
@@ -378,7 +378,7 @@ <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="39148027" class="cell" data-execution_count="5">
+<div id="11c1bc28" 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>

gallery_transforms.html

@@ -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="7060c3be" class="cell" data-execution_count="2">
+<div id="d0068b20" 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>
@@ -250,7 +250,7 @@ <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="46453e2e" class="cell" data-execution_count="3">
+<div id="49318be0" 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>
@@ -295,7 +295,7 @@ <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="28343990" class="cell" data-execution_count="4">
+<div id="8c7666a4" 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>
@@ -355,7 +355,7 @@ <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="8d8c9298" class="cell" data-execution_count="5">
+<div id="2f31ae17" 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>
@@ -402,7 +402,7 @@ <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="90b2672d" class="cell" data-execution_count="6">
+<div id="d7cb7996" 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>