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Emily Boudreaux
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<div class="headertitle"><div class="title">GridFire Python Usage Guide</div></div>
</div><!--header-->
<div class="contents">
<div class="textblock"><p><a class="anchor" id="autotoc_md23"></a></p>
<div class="textblock"><p><a class="anchor" id="autotoc_md55"></a></p>
<p>This tutorial walks you through installing GridFire’s Python bindings, choosing engines and views thoughtfully, running a simulation, and visualizing your results.</p>
<hr />
<h1><a class="anchor" id="autotoc_md25"></a>
<h1><a class="anchor" id="autotoc_md57"></a>
1. Installation</h1>
<h2><a class="anchor" id="autotoc_md26"></a>
<h2><a class="anchor" id="autotoc_md58"></a>
1.1 PyPI Release</h2>
<p>The quickest way to get started is: </p><div class="fragment"><div class="line">pip install gridfire</div>
</div><!-- fragment --><h2><a class="anchor" id="autotoc_md27"></a>
</div><!-- fragment --><h2><a class="anchor" id="autotoc_md59"></a>
1.2 Development from Source</h2>
<p>If you want the cutting-edge features or need to hack the C++ backend: </p><div class="fragment"><div class="line">git clone https://github.com/4DSTAR/GridFire.git</div>
<div class="line">cd GridFire</div>
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</div><!-- fragment --><p>You can also build manually with Meson (generally end users will not need to do this): </p><div class="fragment"><div class="line">meson setup build-python</div>
<div class="line">meson compile -C build_gridfire</div>
</div><!-- fragment --><hr />
<h1><a class="anchor" id="autotoc_md29"></a>
<h1><a class="anchor" id="autotoc_md61"></a>
2. Why These Engines and Views?</h1>
<p>GridFire’s design balances physical fidelity and performance. Here’s why we pick each component:</p>
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</ol>
<p>By composing these views in sequence, you can tailor accuracy vs performance for your scientific question. Commonly one might use a flow like <b>GraphEngine → Partitioning → Adaptive</b> to capture both full-network physics and manageable stiffness.</p>
<hr />
<h1><a class="anchor" id="autotoc_md31"></a>
<h1><a class="anchor" id="autotoc_md63"></a>
3. Step-by-Step Example</h1>
<p>Adapted from <a href="../../tests/python/test.py"><code>tests/python/test.py</code></a>. Comments explain each choice.</p>
<div class="fragment"><div class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</div>
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<div class="line"> </div>
<div class="line"><span class="comment"># 8. Final result:</span></div>
<div class="line">print(f<span class="stringliteral">&quot;Final H-1 fraction: {netOut.composition.getMassFraction(&#39;H-1&#39;)}&quot;</span>)</div>
<div class="ttc" id="anamespacegridfire_1_1solver_html"><div class="ttname"><a href="namespacegridfire_1_1solver.html">gridfire::solver</a></div><div class="ttdef"><b>Definition</b> <a href="solver_8h_source.html#l00015">solver.h:15</a></div></div>
<div class="ttc" id="anamespacegridfire_1_1solver_html"><div class="ttname"><a href="namespacegridfire_1_1solver.html">gridfire::solver</a></div><div class="ttdef"><b>Definition</b> solver.h:15</div></div>
</div><!-- fragment --><p><b>Why these choices?</b> <br />
</p><ul>
<li><b>buildDepth=2</b>: In Emily’s preliminary tests, depth=2 captures key reaction loops without the overhead of a full network. <br />
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<li><b>Implicit solver</b>: Rosenbrock4 handles stiff systems robustly, letting you push to longer <code>tMax</code>.</li>
</ul>
<hr />
<h1><a class="anchor" id="autotoc_md33"></a>
<h1><a class="anchor" id="autotoc_md65"></a>
4. Visualizing Reaction Networks</h1>
<p>GridFire engines and views provide built-in export methods for Graphviz DOT and CSV formats:</p>
<div class="fragment"><div class="line"><span class="comment"># Export the base network to DOT for Graphviz</span></div>
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<div class="line">df.to_csv(<span class="stringliteral">&#39;H1_evolution.csv&#39;</span>, index=<span class="keyword">False</span>)</div>
</div><!-- fragment --><p> Then plot in pandas or Excel for custom figures.</p>
<hr />
<h1><a class="anchor" id="autotoc_md35"></a>
<h1><a class="anchor" id="autotoc_md67"></a>
5. Beyond the Basics</h1>
<ul>
<li><b>Custom Partition Functions</b>: In Python, subclass <code><a class="el" href="classgridfire_1_1partition_1_1_partition_function.html" title="Abstract interface for evaluating nuclear partition functions.">gridfire.partition.PartitionFunction</a></code>, override <code>evaluate</code>, <code>supports</code>, and <code>clone</code> to implement new weighting schemes. <br />