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<label class="md-nav__link md-nav__link--active" for="__toc">


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Development
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Writing Code
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Publishing
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<label class="md-nav__title" for="__toc">
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<a href="#writing-code" class="md-nav__link">
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Writing Code
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<h1 id="development">Development<a class="headerlink" href="#development" title="Permanent link">#</a></h1>
<h2 id="writing-code">Writing Code<a class="headerlink" href="#writing-code" title="Permanent link">#</a></h2>
<p>To create a development environment, you must have <a href="https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html"><code>mamba</code> installed</a>.</p>
<p>A development conda environment can be created and activated with:</p>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>make<span class="w"> </span>env
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<p>To serve the documentation locally:</p>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>mkdocs<span class="w"> </span>serve
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<h2 id="publishing">Publishing<a class="headerlink" href="#publishing" title="Permanent link">#</a></h2>
<h3 id="pypi">PyPI<a class="headerlink" href="#pypi" title="Permanent link">#</a></h3>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>pip<span class="w"> </span>install<span class="w"> </span>build<span class="w"> </span>twine
</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>python<span class="w"> </span>-m<span class="w"> </span>build
</span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a>twine<span class="w"> </span>check<span class="w"> </span>dist/*
</span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a>twine<span class="w"> </span>upload<span class="w"> </span>dist/*
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<p>A Python package for detecting equilibration in timeseries data where an initial transient is followed by a stationary distribution. Two main approaches are implemented, which differ in the way they account for autocorrelation:</p>
<p>A Python package for detecting equilibration in time series where an initial transient is followed by a stationary distribution. Two main approaches are implemented, which differ in the way they account for autocorrelation:</p>
<ul>
<li><code>detect_equilibration_init_seq</code>: This uses the initial sequence methods of Geyer (<a href="https://www.jstor.org/stable/2246094">Geyer, 1992</a>) to determine the truncation point of the sum of autocovariances. Chodera's method (<a href="https://pubs.acs.org/doi/full/10.1021/acs.jctc.5b00784">Chodera, 2016</a>) of simply truncating the autocovariance series at the first negative value is also implemented.</li>
<li><code>detect_equilibration_window</code>: This uses window methods (see <a href="https://www.jstor.org/stable/2246094">Geyer</a> again) when calculating the
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