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<!DOCTYPE html>
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<title>cdsaxs package — cdsaxs v0.0.1 documentation</title>
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<section id="cdsaxs-package">
<h1>cdsaxs package<a class="headerlink" href="#cdsaxs-package" title="Permalink to this heading"></a></h1>
<section id="subpackages">
<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this heading"></a></h2>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="cdsaxs.simulations.html">cdsaxs.simulations package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="cdsaxs.simulations.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="cdsaxs.simulations.html#module-cdsaxs.simulations.base">cdsaxs.simulations.base module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Geometry"><code class="docutils literal notranslate"><span class="pre">Geometry</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Geometry.convert_to_dataframe"><code class="docutils literal notranslate"><span class="pre">Geometry.convert_to_dataframe()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Geometry.rescale_fitparams"><code class="docutils literal notranslate"><span class="pre">Geometry.rescale_fitparams()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation"><code class="docutils literal notranslate"><span class="pre">Simulation</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation.geometry"><code class="docutils literal notranslate"><span class="pre">Simulation.geometry</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation.set_from_fitter"><code class="docutils literal notranslate"><span class="pre">Simulation.set_from_fitter()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation.simulate_diffraction"><code class="docutils literal notranslate"><span class="pre">Simulation.simulate_diffraction()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="cdsaxs.simulations.html#module-cdsaxs.simulations.stacked_trapezoid">cdsaxs.simulations.stacked_trapezoid module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction.calculate_coefficients"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction.calculate_coefficients()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction.calculate_form_factor"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction.calculate_form_factor()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction.correct_form_factor_intensity"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction.correct_form_factor_intensity()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction.corrections_dwi0bk"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction.corrections_dwi0bk()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidDiffraction.trapezoid_form_factor"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidDiffraction.trapezoid_form_factor()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.xp"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.xp</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.from_fitter"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.from_fitter</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.initial_guess"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.initial_guess</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.initial_guess_dataframe"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.initial_guess_dataframe</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.symmetric"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.symmetric</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.calculate_ycoords"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.calculate_ycoords()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.calculate_shift"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.calculate_shift()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.convert_to_dataframe"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.convert_to_dataframe()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.rescale_fitparams"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.rescale_fitparams()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.check_physical_validity"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.check_physical_validity()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id0"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.calculate_shift()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id1"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.calculate_ycoords()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id2"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.check_physical_validity()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id3"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.convert_to_dataframe()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id4"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.rescale_fitparams()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidGeometry.set_initial_guess_dataframe"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidGeometry.set_initial_guess_dataframe()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.qys"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.qys</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.qzs"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.qzs</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.xp"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.xp</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.from_fitter"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.from_fitter</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.TrapezoidGeometry"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.TrapezoidGeometry</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.TrapezoidDiffraction"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.TrapezoidDiffraction</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.__init__"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.__init__()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.simulate_diffraction"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.simulate_diffraction()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.set_from_fitter"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.set_from_fitter()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.stacked_trapezoid.StackedTrapezoidSimulation.geometry"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.geometry()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id5"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.geometry</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id6"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.set_from_fitter()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#id7"><code class="docutils literal notranslate"><span class="pre">StackedTrapezoidSimulation.simulate_diffraction()</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="cdsaxs.simulations.html#module-cdsaxs.simulations.strong_castle">cdsaxs.simulations.strong_castle module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleDiffraction"><code class="docutils literal notranslate"><span class="pre">StrongCastleDiffraction</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleDiffraction.correct_form_factor_intensity"><code class="docutils literal notranslate"><span class="pre">StrongCastleDiffraction.correct_form_factor_intensity()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleGeometry"><code class="docutils literal notranslate"><span class="pre">StrongCastleGeometry</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleGeometry.calculate_ycoords"><code class="docutils literal notranslate"><span class="pre">StrongCastleGeometry.calculate_ycoords()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleGeometry.check_initial_guess"><code class="docutils literal notranslate"><span class="pre">StrongCastleGeometry.check_initial_guess()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleGeometry.remove_fixed_params"><code class="docutils literal notranslate"><span class="pre">StrongCastleGeometry.remove_fixed_params()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleGeometry.set_n1_n2"><code class="docutils literal notranslate"><span class="pre">StrongCastleGeometry.set_n1_n2()</span></code></a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.strong_castle.StrongCastleSimulation"><code class="docutils literal notranslate"><span class="pre">StrongCastleSimulation</span></code></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="cdsaxs.simulations.html#module-cdsaxs.simulations">Module contents</a></li>
</ul>
</li>
</ul>
</div>
</section>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
</section>
<section id="module-cdsaxs.fitter">
<span id="cdsaxs-fitter-module"></span><h2>cdsaxs.fitter module<a class="headerlink" href="#module-cdsaxs.fitter" title="Permalink to this heading"></a></h2>
<p>This module contains the Fitter class, which is designed to fit the cdsaxs experimental data using the CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
and then do a statstical analysis of the best fit parameters using the MCMC (Markov Chain Monte Carlo) algorithm.</p>
<dl class="simple">
<dt>Classes:</dt><dd><p>Fitter: A class that fits the cdsaxs experimental data using the CMA-ES and MCMC algorithms.</p>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cdsaxs.fitter.</span></span><span class="sig-name descname"><span class="pre">Fitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Simulation</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation" title="cdsaxs.simulations.base.Simulation"><span class="pre">Simulation</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">exp_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>This class is designed to fit the cdsaxs experimental data using the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) and then do a statstical analysis
of the best fit parameters using the MCMC (Markov Chain Monte Carlo) algorithm. It takes an instance of the Simulation class and fits this simulated data to the
experimental data.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.Simulation">
<span class="sig-name descname"><span class="pre">Simulation</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.Simulation" title="Permalink to this definition"></a></dt>
<dd><p>An instance of the Simulation class representing the simulated diffraction pattern.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="cdsaxs.simulations.html#cdsaxs.simulations.base.Simulation" title="cdsaxs.simulations.base.Simulation">Simulation</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.exp_data">
<span class="sig-name descname"><span class="pre">exp_data</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.exp_data" title="Permalink to this definition"></a></dt>
<dd><p>Experimental diffraction data.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>numpy.ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.np">
<span class="sig-name descname"><span class="pre">np</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.np" title="Permalink to this definition"></a></dt>
<dd><p>NumPy or CuPy module, depending on whether GPU acceleration is used.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>module</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.best_fit_cmaes">
<span class="sig-name descname"><span class="pre">best_fit_cmaes</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.best_fit_cmaes" title="Permalink to this definition"></a></dt>
<dd><p>List containing the best fit parameters obtained using the CMA-ES algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list or None</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.cmaes">
<span class="sig-name descname"><span class="pre">cmaes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.cmaes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.cmaes" title="Permalink to this definition"></a></dt>
<dd><p>Perform fitting using the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.mcmc">
<span class="sig-name descname"><span class="pre">mcmc</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cdsaxs.fitter.Fitter.mcmc" title="Permalink to this definition"></a></dt>
<dd><p>Give a set of statstical data on the best fit parameters using the MCMC (Markov Chain Monte Carlo) algorithm.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="id0">
<span class="sig-name descname"><span class="pre">cmaes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ngen</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">popsize</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_default</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">restarts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolhistfun</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ftarget</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">restart_from_best</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dir_save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.cmaes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition"></a></dt>
<dd><p>Fit experimental data using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm.</p>
<p>This method utilizes a modified version of the CMA-ES algorithm to fit experimental data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sigma</strong> (<em>float</em>) – The initial standard deviation for each parameter.</p></li>
<li><p><strong>ngen</strong> (<em>int</em>) – The number of generations to run the algorithm.</p></li>
<li><p><strong>popsize</strong> (<em>int</em>) – The size of the population (number of candidate solutions) in each generation.</p></li>
<li><p><strong>mu</strong> (<em>int</em>) – The number of parents/points for recombination.</p></li>
<li><p><strong>n_default</strong> (<em>int</em>) – The number of parameters to be optimized.</p></li>
<li><p><strong>restarts</strong> (<em>int</em>) – The number of restarts allowed during the optimization process.</p></li>
<li><p><strong>tolhistfun</strong> (<em>float</em>) – The tolerance for the history of the best fitness value.</p></li>
<li><p><strong>ftarget</strong> (<em>float</em>) – The target fitness value.</p></li>
<li><p><strong>restart_from_best</strong> (<em>bool</em><em>, </em><em>optional</em>) – Determines whether to restart from the best individual found so far. Default is True.</p></li>
<li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>optional</em>) – Controls whether to print progress information during optimization. Default is True.</p></li>
<li><p><strong>dir_save</strong> (<em>str</em><em>, </em><em>optional</em>) – The directory to save the output. Default is None.</p></li>
<li><p><strong>test</strong> (<em>bool</em><em>, </em><em>optional</em>) – Controls whether to test the function and return best value instead of performing the full optimization process. If True, the function returns best value. Default is False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A tuple containing the best fit parameters and the corresponding fitness value.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>tuple</p>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="id1">
<span class="sig-name descname"><span class="pre">best_fit_cmaes</span></span><a class="headerlink" href="#id1" title="Permalink to this definition"></a></dt>
<dd><p>List containing the best fit parameters obtained using the CMA-ES algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list or None</p>
</dd>
</dl>
</dd></dl>
<p class="rubric">Notes</p>
<p>This method is modified from deap/algorithms.py to return a list of populations instead of the final population and to incorporate additional termination criteria based on neuromorphic algorithms. The function was originally extracted from XiCam and has been modified for specific use cases.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.do_stats">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">do_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ci</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.do_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.do_stats" title="Permalink to this definition"></a></dt>
<dd><p>This method generates a set of statistical data on the best fit parameters obtained from the MCMC fitting process.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>df</strong> (<em>pandas.DataFrame</em>) – The DataFrame containing the best fit parameters.</p></li>
<li><p><strong>ci</strong> (<em>float</em><em>, </em><em>optional</em>) – The confidence interval. Default is 0.95.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A DataFrame containing the statistical data on the best fit parameters.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>pandas.DataFrame</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.mcmc_bestfit_stats">
<span class="sig-name descname"><span class="pre">mcmc_bestfit_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">N</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsteps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nwalkers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gaussian_move</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dir_save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.mcmc_bestfit_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.mcmc_bestfit_stats" title="Permalink to this definition"></a></dt>
<dd><p>Generate a set of statstical data on the best fit parameters using the MCMC (Markov Chain Monte Carlo) algorithm. Two kinds of options for moves to explore solution space are provided gaussian and stretch move. Default is strech move and recommended.</p>
<p>This method utilizes the emcee package’s implementation of the MCMC algorithm and generates a csv file with the statistical data of the best fit parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>N</strong> (<em>int</em>) – The number of parameters to be optimized.</p></li>
<li><p><strong>sigma</strong> (<em>float</em><em> or </em><em>list</em>) – The initial standard deviation for each parameter. If a float is provided, it is applied to all parameters. If a list is provided, each parameter is initialized with the corresponding value.</p></li>
<li><p><strong>nsteps</strong> (<em>int</em>) – The number of MCMC steps to perform.</p></li>
<li><p><strong>nwalkers</strong> (<em>int</em>) – The number of MCMC walkers to use.</p></li>
<li><p><strong>gaussian_move</strong> (<em>bool</em><em>, </em><em>optional</em>) – Determines whether to use Gaussian moves for proposal distribution. If True, Gaussian moves are used. If False, stretch moves are used. Default is False.</p></li>
<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The seed for the random number generator. If None, a random seed is generated. Default is None.</p></li>
<li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>optional</em>) – Controls whether to print progress information during fitting. If True, progress information is printed. Default is False.</p></li>
<li><p><strong>test</strong> (<em>bool</em><em>, </em><em>optional</em>) – Controls whether to test the function and return mean values instead of performing the full fitting process. If True, the function returns mean values. Default is True.</p></li>
<li><p><strong>tau</strong> (<em>float</em><em>, </em><em>optional</em>) – The autocorrelation time to find burnin steps. If None, is provided emcee package is used to estimate the autocorrelation time. If emcee fails to do so 1/3 of the first nsteps are discarded. Default is None.</p></li>
<li><p><strong>c</strong> (<em>float</em><em>, </em><em>optional</em>) – Empirical factor to modify the MCMC acceptance rate. Default is 1e-5.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>None</p>
</dd>
</dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.best_uncorr">
<span class="sig-name descname"><span class="pre">best_uncorr</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.best_uncorr" title="Permalink to this definition"></a></dt>
<dd><p>The best uncorrected individual obtained from the MCMC fitting process.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>numpy.ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.best_fitness">
<span class="sig-name descname"><span class="pre">best_fitness</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.best_fitness" title="Permalink to this definition"></a></dt>
<dd><p>The fitness value of the best individual obtained from the MCMC fitting process.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.minfitness_each_gen">
<span class="sig-name descname"><span class="pre">minfitness_each_gen</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.minfitness_each_gen" title="Permalink to this definition"></a></dt>
<dd><p>The minimum fitness value at each generation during the MCMC fitting process.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>numpy.ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.Sampler">
<span class="sig-name descname"><span class="pre">Sampler</span></span><a class="headerlink" href="#cdsaxs.fitter.Fitter.Sampler" title="Permalink to this definition"></a></dt>
<dd><p>An instance of emcee.EnsembleSampler with detailed output of the MCMC algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>emcee.EnsembleSampler</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.plot_correlation">
<span class="sig-name descname"><span class="pre">plot_correlation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dir_save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.plot_correlation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.plot_correlation" title="Permalink to this definition"></a></dt>
<dd><p>Generate a corner plot of the best fit parameters obtained from the MCMC fitting process.</p>
<p>This method utilizes the corner package to generate a corner plot of the best fit parameters obtained from the MCMC fitting process.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file</strong> (<em>str</em>) – The path to the file containing the data.</p></li>
<li><p><strong>dir_save</strong> (<em>str</em><em>, </em><em>optional</em>) – The directory to save the output. If not provided, the plot will be displayed instead of being saved.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>None</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.save_population">
<span class="sig-name descname"><span class="pre">save_population</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">population_arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fitness_arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dir_save</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cmaes'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.save_population"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.save_population" title="Permalink to this definition"></a></dt>
<dd><p>Save the population array to a csv file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>population</strong> (<em>numpy.ndarray</em>) – The population array to save.</p></li>
<li><p><strong>dir_save</strong> (<em>str</em>) – The directory to save the output.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>None</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.fitter.Fitter.set_best_fit_cmaes">
<span class="sig-name descname"><span class="pre">set_best_fit_cmaes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">best_fit</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/fitter.html#Fitter.set_best_fit_cmaes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.fitter.Fitter.set_best_fit_cmaes" title="Permalink to this definition"></a></dt>
<dd><p>Set the best fit parameters obtained using the CMA-ES algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>best_fit</strong> (<em>pandas.DataFrame</em>) – The best fit parameters obtained using the CMA-ES algorithm.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>None</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
<section id="module-cdsaxs.residual">
<span id="cdsaxs-residual-module"></span><h2>cdsaxs.residual module<a class="headerlink" href="#module-cdsaxs.residual" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cdsaxs.residual.</span></span><span class="sig-name descname"><span class="pre">Residual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_mode='cmaes'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xp=<module</span> <span class="pre">'numpy'</span> <span class="pre">from</span> <span class="pre">'/opt/hostedtoolcache/Python/3.11.11/x64/lib/python3.11/site-packages/numpy/__init__.py'></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Simulation:</span> <span class="pre">Simulation</span> <span class="pre">|</span> <span class="pre">None</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">best_fit=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.residual.Residual" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>A class to calculate the residual between experimental and simulated data, used for fitness evaluation in optimization algorithms.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.mdata">
<span class="sig-name descname"><span class="pre">mdata</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.mdata" title="Permalink to this definition"></a></dt>
<dd><p>numpy.ndarray
Experimental intensity data.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.mfit_mode">
<span class="sig-name descname"><span class="pre">mfit_mode</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.mfit_mode" title="Permalink to this definition"></a></dt>
<dd><p>str
Method to calculate fitness, differentiating between ‘cmaes’ and ‘mcmc’.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.xp">
<span class="sig-name descname"><span class="pre">xp</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.xp" title="Permalink to this definition"></a></dt>
<dd><p>module
NumPy or CuPy module.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.Simulation">
<span class="sig-name descname"><span class="pre">Simulation</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.Simulation" title="Permalink to this definition"></a></dt>
<dd><p>Optional[‘Simulation’]
Class to simulate the diffraction pattern (for now only StackedTrapezoidSimulation).</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.c">
<span class="sig-name descname"><span class="pre">c</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.c" title="Permalink to this definition"></a></dt>
<dd><p>float
Empirical factor to modify the MCMC acceptance rate.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.best_fit">
<span class="sig-name descname"><span class="pre">best_fit</span></span><a class="headerlink" href="#cdsaxs.residual.Residual.best_fit" title="Permalink to this definition"></a></dt>
<dd><p>list or None
List containing the best fit parameters obtained from the optimization algorithm (optional).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.__call__">
<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.residual.Residual.__call__" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the residual between experimental and simulated data.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.log_error">
<span class="sig-name descname"><span class="pre">log_error</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual.log_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.residual.Residual.log_error" title="Permalink to this definition"></a></dt>
<dd><p>Return the difference between experimental and simulated data using the log error.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdsaxs.residual.Residual.fix_fitness_mcmc">
<span class="sig-name descname"><span class="pre">fix_fitness_mcmc</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual.fix_fitness_mcmc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#cdsaxs.residual.Residual.fix_fitness_mcmc" title="Permalink to this definition"></a></dt>
<dd><p>Fix the fitness for the MCMC algorithm using the Metropolis-Hastings criterion.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="id2">
<span class="sig-name descname"><span class="pre">fix_fitness_mcmc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fitness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual.fix_fitness_mcmc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id2" title="Permalink to this definition"></a></dt>
<dd><p>Metropolis-Hastings criterion: acceptance probability equal to ratio between P(new)/P(old)
where P is proportional to probability distribution we want to find
for our case we assume that probability of our parameters being the best is proportional to a Gaussian centered at fitness=0
where fitness can be log, abs, squared error, etc.
emcee expects the fitness function to return ln(P(new)), P(old) is auto-calculated</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>fitness</strong> – float
Fitness value to be fixed.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><dl class="simple">
<dt>float</dt><dd><p>Fixed fitness value.</p>
</dd>
</dl>
</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="id3">
<span class="sig-name descname"><span class="pre">log_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exp_i_array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sim_i_array</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cdsaxs/residual.html#Residual.log_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id3" title="Permalink to this definition"></a></dt>
<dd><p>Return the difference between two set of values (experimental and
simulated data), using the log error</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>exp_i_array</strong> (<em>numpy.ndarray</em><em>(</em><em>(</em><em>n</em><em>)</em><em>)</em>) – Experimental intensities data</p></li>
<li><p><strong>sim_i_array</strong> (<em>numpy.ndarray</em><em>(</em><em>(</em><em>n</em><em>)</em><em>)</em>) – Simulated intensities data</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><dl class="simple">
<dt>numpy.ndarray</dt><dd><p>Difference between experimental and simulated data using the log error.</p>
</dd>
</dl>
</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Returns</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
</section>
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