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docs(docs): rebuilt for version v0.7.1_rc2

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Emily M. Boudreaux
2025-11-28 09:46:00 -05:00
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<title>GridFire: GridFire Python Usage Guide</title>
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<div id="projectname">GridFire<span id="projectnumber">&#160;v0.7.0_rc2</span>
<div id="projectname">GridFire<span id="projectnumber">&#160;v0.7.1_rc2</span>
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<div id="projectbrief">General Purpose Nuclear Network</div>
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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_md62"></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>
<div class="textblock"><p><a class="anchor" id="autotoc_md61"></a> 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_md64"></a>
<h1><a class="anchor" id="autotoc_md63"></a>
1. Installation</h1>
<h2><a class="anchor" id="autotoc_md65"></a>
<h2><a class="anchor" id="autotoc_md64"></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_md66"></a>
</div><!-- fragment --><h2><a class="anchor" id="autotoc_md65"></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_md68"></a>
<h1><a class="anchor" id="autotoc_md67"></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>
<ol type="1">
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<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_md70"></a>
<h1><a class="anchor" id="autotoc_md69"></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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<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_md72"></a>
<h1><a class="anchor" id="autotoc_md71"></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_md74"></a>
<h1><a class="anchor" id="autotoc_md73"></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 />
<li><b>Custom Partition Functions</b>: In Python, subclass <code><a class="el" href="classgridfire_1_1partition_1_1PartitionFunction.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 />
</li>
<li><b>Parameter Studies</b>: Loop over <code>buildDepth</code>, solver tolerances, or initial compositions to get a sense of the sensitity of the network to input conditions or build a monte carlo grid. <br />
</li>
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