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+78df2dd9

changelog.html

@@ -162,6 +162,7 @@ <h1 class="title">Changelog</h1>
 <section id="pyobsplot-0.5.1-dev" class="level2">
 <h2 class="anchored" data-anchor-id="pyobsplot-0.5.1-dev">pyobsplot 0.5.1-dev</h2>
 <ul>
+<li><code>Plot.plot()</code> performance should now be equivalent to the one of a generator object</li>
 <li>New shortcut syntax <code>Plot.line(...).plot()</code> is now available (#18, thanks <span class="citation" data-cites="harrylojames">@harrylojames</span>)</li>
 </ul>
 </section>

development.html

@@ -331,7 +331,7 @@ <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="0ec8fe84" class="cell" data-execution_count="1">
+<div id="501c7706" 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>

gallery_geo.html

@@ -280,7 +280,7 @@ <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="6168c81a" class="cell" data-execution_count="2">
+<div id="2098a3ac" 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>
@@ -333,7 +333,7 @@ <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="aa1f138a" class="cell" data-execution_count="3">
+<div id="1126418f" 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>
@@ -377,7 +377,7 @@ <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="2b99b52d" class="cell" data-execution_count="4">
+<div id="f1747b96" 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>
@@ -424,7 +424,7 @@ <h2 class="anchored" data-anchor-id="raster-mark">Raster mark</h2>
 </div>
 </div>
 <p>Or the more recent random walk interpolation:</p>
-<div id="c9c802c2" class="cell" data-execution_count="5">
+<div id="0ad7624f" 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: rgb(0, 0, 0); background-color: rgb(255, 255, 255);" typstbg="#FFFFFF" typstfg="#000000" typstcaption="#777777"><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: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><style>:where(.plot-d6a7b5) {

gallery_marks.html

@@ -282,7 +282,7 @@ <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="09b8d273" class="cell" data-execution_count="2">
+<div id="c59c9055" 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>
@@ -331,7 +331,7 @@ <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="e0a3ff05" class="cell" data-execution_count="3">
+<div id="897a2777" 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>
@@ -383,7 +383,7 @@ <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="d7c0235d" class="cell" data-execution_count="4">
+<div id="5a64ba12" 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>
@@ -455,7 +455,7 @@ <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="b1e8ed27" class="cell" data-execution_count="5">
+<div id="466f9239" 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

@@ -283,7 +283,7 @@ <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="61e55bc2" class="cell" data-execution_count="2">
+<div id="95e91834" 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>
@@ -321,7 +321,7 @@ <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="105fdb72" class="cell" data-execution_count="3">
+<div id="a9e333c2" 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>
@@ -372,7 +372,7 @@ <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="8cb15494" class="cell" data-execution_count="4">
+<div id="4b94a8dc" 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>
@@ -432,7 +432,7 @@ <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="2f95f61a" class="cell" data-execution_count="5">
+<div id="1d6392d9" 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>
@@ -485,7 +485,7 @@ <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="1e4128f0" class="cell" data-execution_count="6">
+<div id="5861360c" 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>