deploy: b47ed65

_modules/mdaencore/similarity.html

@@ -138,11 +138,11 @@ <h1>Source code for mdaencore.similarity</h1><div class="highlight"><pre>
 <span class="sd">described in :footcite:p:`Tiberti2015`.</span>
 
 <span class="sd">The module includes facilities for handling ensembles and trajectories through</span>
-<span class="sd">the :class:`Universe` class, performing clustering or dimensionality reduction</span>
-<span class="sd">of the ensemble space, estimating multivariate probability distributions from</span>
-<span class="sd">the input data, and more. ENCORE can be used to compare experimental and</span>
-<span class="sd">simulation-derived ensembles, as well as estimate the convergence of</span>
-<span class="sd">trajectories from time-dependent simulations.</span>
+<span class="sd">the :class:`~MDAnalysis.core.universe.Universe` class, performing clustering</span>
+<span class="sd">or dimensionality reduction of the ensemble space, estimating multivariate</span>
+<span class="sd">probability distributions from the input data, and more. ENCORE can be used to</span>
+<span class="sd">compare experimental and simulation-derived ensembles, as well as estimate the</span>
+<span class="sd">convergence of trajectories from time-dependent simulations.</span>
 
 <span class="sd">ENCORE includes three different methods for calculations of similarity measures</span>
 <span class="sd">between ensembles implemented in individual functions:</span>
@@ -1188,13 +1188,13 @@ <h1>Source code for mdaencore.similarity</h1><div class="highlight"><pre>
 <span class="sd">    --------</span>
 <span class="sd">    To calculate the Clustering Ensemble similarity, two ensembles are</span>
 <span class="sd">    created as Universe object using a topology file and two trajectories. The</span>
-<span class="sd">    topology- and trajectory files used are obtained from the MDAnalysis</span>
-<span class="sd">    test suite for two different simulations of the protein AdK.</span>
-<span class="sd">    To use a different clustering method, set the parameter clustering_method</span>
-<span class="sd">    (Note that the sklearn module must be installed). Likewise, different parameters</span>
-<span class="sd">    for the same clustering method can be explored by adding different</span>
-<span class="sd">    instances of the same clustering class.</span>
-<span class="sd">    Here the simplest case of just two instances of :class:`Universe` is illustrated:</span>
+<span class="sd">    topology- and trajectory files used are obtained from the MDAnalysis test</span>
+<span class="sd">    suite for two different simulations of the protein AdK. To use a different</span>
+<span class="sd">    clustering method, set the parameter clustering_method (Note that the</span>
+<span class="sd">    sklearn module must be installed). Likewise, different parameters for the</span>
+<span class="sd">    same clustering method can be explored by adding different instances of</span>
+<span class="sd">    the same clustering class. Here the simplest case of just two instances</span>
+<span class="sd">    of :class:`~MDAnalysis.core.universe.Universe` is illustrated:</span>
 
 <span class="sd">        &gt;&gt;&gt; from MDAnalysis import Universe</span>
 <span class="sd">        &gt;&gt;&gt; import mdaencore as encore</span>
@@ -1471,8 +1471,8 @@ <h1>Source code for mdaencore.similarity</h1><div class="highlight"><pre>
 <span class="sd">    To use a different dimensional reduction methods, simply set the</span>
 <span class="sd">    parameter dimensionality_reduction_method. Likewise, different parameters</span>
 <span class="sd">    for the same clustering method can be explored by adding different</span>
-<span class="sd">    instances of the same method  class.</span>
-<span class="sd">    Here the simplest case of comparing just two instances of :class:`Universe` is</span>
+<span class="sd">    instances of the same method  class. Here the simplest case of comparing</span>
+<span class="sd">    just two instances of :class:`~MDAnalysis.core.universe.Universe` is</span>
 <span class="sd">    illustrated:</span>
 
 <span class="sd">        &gt;&gt;&gt; from MDAnalysis import Universe</span>
@@ -1487,7 +1487,7 @@ <h1>Source code for mdaencore.similarity</h1><div class="highlight"><pre>
 
 <span class="sd">    In addition to the quantitative similarity estimate, the dimensional</span>
 <span class="sd">    reduction can easily be visualized, see the ``Example`` section in</span>
-<span class="sd">    :mod:`mdaencore.dimensionality_reduction.reduce_dimensionality``</span>
+<span class="sd">    :mod:`mdaencore.dimensionality_reduction.reduce_dimensionality`</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
 

_sources/index.rst.txt

@@ -19,7 +19,7 @@ ensembles described in :footcite:p:`LindorffLarsen2009`. The implementation and
 are described in :footcite:p:`Tiberti2015`.
 
 The module includes facilities for handling ensembles and trajectories through
-the :class:`Universe` class, performing clustering or dimensionality reduction
+the :class:`~MDAnalysis.core.universe.Universe` class, performing clustering or dimensionality reduction
 of the ensemble space, estimating multivariate probability distributions from
 the input data, and more. ENCORE can be used to compare experimental and
 simulation-derived ensembles, as well as estimate the convergence of

encore/similarity.html

@@ -151,11 +151,11 @@
 The implementation and examples are also further
 described in <a class="footnote-reference brackets" href="#footcite-tiberti2015" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
 <p>The module includes facilities for handling ensembles and trajectories through
-the <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> class, performing clustering or dimensionality reduction
-of the ensemble space, estimating multivariate probability distributions from
-the input data, and more. ENCORE can be used to compare experimental and
-simulation-derived ensembles, as well as estimate the convergence of
-trajectories from time-dependent simulations.</p>
+the <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> class, performing clustering
+or dimensionality reduction of the ensemble space, estimating multivariate
+probability distributions from the input data, and more. ENCORE can be used to
+compare experimental and simulation-derived ensembles, as well as estimate the
+convergence of trajectories from time-dependent simulations.</p>
 <p>ENCORE includes three different methods for calculations of similarity measures
 between ensembles implemented in individual functions:</p>
 <ul class="simple">
@@ -414,13 +414,13 @@ <h2>Functions for ensemble comparisons<a class="headerlink" href="#functions-for
 <p class="rubric">Examples</p>
 <p>To calculate the Clustering Ensemble similarity, two ensembles are
 created as Universe object using a topology file and two trajectories. The
-topology- and trajectory files used are obtained from the MDAnalysis
-test suite for two different simulations of the protein AdK.
-To use a different clustering method, set the parameter clustering_method
-(Note that the sklearn module must be installed). Likewise, different parameters
-for the same clustering method can be explored by adding different
-instances of the same clustering class.
-Here the simplest case of just two instances of <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> is illustrated:</p>
+topology- and trajectory files used are obtained from the MDAnalysis test
+suite for two different simulations of the protein AdK. To use a different
+clustering method, set the parameter clustering_method (Note that the
+sklearn module must be installed). Likewise, different parameters for the
+same clustering method can be explored by adding different instances of
+the same clustering class. Here the simplest case of just two instances
+of <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> is illustrated:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis</span> <span class="kn">import</span> <span class="n">Universe</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mdaencore</span> <span class="k">as</span> <span class="nn">encore</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis.tests.datafiles</span> <span class="kn">import</span> <span class="n">PSF</span><span class="p">,</span> <span class="n">DCD</span><span class="p">,</span> <span class="n">DCD2</span>
@@ -521,8 +521,8 @@ <h2>Functions for ensemble comparisons<a class="headerlink" href="#functions-for
 To use a different dimensional reduction methods, simply set the
 parameter dimensionality_reduction_method. Likewise, different parameters
 for the same clustering method can be explored by adding different
-instances of the same method  class.
-Here the simplest case of comparing just two instances of <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> is
+instances of the same method  class. Here the simplest case of comparing
+just two instances of <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> is
 illustrated:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis</span> <span class="kn">import</span> <span class="n">Universe</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mdaencore</span> <span class="k">as</span> <span class="nn">encore</span>
@@ -537,7 +537,7 @@ <h2>Functions for ensemble comparisons<a class="headerlink" href="#functions-for
 </div>
 <p>In addition to the quantitative similarity estimate, the dimensional
 reduction can easily be visualized, see the <code class="docutils literal notranslate"><span class="pre">Example</span></code> section in
-<code class="xref py py-mod docutils literal notranslate"><span class="pre">mdaencore.dimensionality_reduction.reduce_dimensionality`</span></code></p>
+<a class="reference internal" href="dimensionality_reduction.html#module-mdaencore.dimensionality_reduction.reduce_dimensionality" title="mdaencore.dimensionality_reduction.reduce_dimensionality"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mdaencore.dimensionality_reduction.reduce_dimensionality</span></code></a></p>
 </dd></dl>
 
 </section>
@@ -622,13 +622,13 @@ <h2>Function reference<a class="headerlink" href="#function-reference" title="Li
 <p class="rubric">Examples</p>
 <p>To calculate the Clustering Ensemble similarity, two ensembles are
 created as Universe object using a topology file and two trajectories. The
-topology- and trajectory files used are obtained from the MDAnalysis
-test suite for two different simulations of the protein AdK.
-To use a different clustering method, set the parameter clustering_method
-(Note that the sklearn module must be installed). Likewise, different parameters
-for the same clustering method can be explored by adding different
-instances of the same clustering class.
-Here the simplest case of just two instances of <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> is illustrated:</p>
+topology- and trajectory files used are obtained from the MDAnalysis test
+suite for two different simulations of the protein AdK. To use a different
+clustering method, set the parameter clustering_method (Note that the
+sklearn module must be installed). Likewise, different parameters for the
+same clustering method can be explored by adding different instances of
+the same clustering class. Here the simplest case of just two instances
+of <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> is illustrated:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis</span> <span class="kn">import</span> <span class="n">Universe</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mdaencore</span> <span class="k">as</span> <span class="nn">encore</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis.tests.datafiles</span> <span class="kn">import</span> <span class="n">PSF</span><span class="p">,</span> <span class="n">DCD</span><span class="p">,</span> <span class="n">DCD2</span>
@@ -976,8 +976,8 @@ <h2>Function reference<a class="headerlink" href="#function-reference" title="Li
 To use a different dimensional reduction methods, simply set the
 parameter dimensionality_reduction_method. Likewise, different parameters
 for the same clustering method can be explored by adding different
-instances of the same method  class.
-Here the simplest case of comparing just two instances of <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> is
+instances of the same method  class. Here the simplest case of comparing
+just two instances of <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> is
 illustrated:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">MDAnalysis</span> <span class="kn">import</span> <span class="n">Universe</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mdaencore</span> <span class="k">as</span> <span class="nn">encore</span>
@@ -992,7 +992,7 @@ <h2>Function reference<a class="headerlink" href="#function-reference" title="Li
 </div>
 <p>In addition to the quantitative similarity estimate, the dimensional
 reduction can easily be visualized, see the <code class="docutils literal notranslate"><span class="pre">Example</span></code> section in
-<code class="xref py py-mod docutils literal notranslate"><span class="pre">mdaencore.dimensionality_reduction.reduce_dimensionality`</span></code></p>
+<a class="reference internal" href="dimensionality_reduction.html#module-mdaencore.dimensionality_reduction.reduce_dimensionality" title="mdaencore.dimensionality_reduction.reduce_dimensionality"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mdaencore.dimensionality_reduction.reduce_dimensionality</span></code></a></p>
 </dd></dl>
 
 <dl class="py function">

index.html

@@ -115,7 +115,7 @@ <h1>Welcome to mdaencore’s documentation!<a class="headerlink" href="#welcome-
 ensembles described in <a class="footnote-reference brackets" href="#footcite-lindorfflarsen2009" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>. The implementation and examples
 are described in <a class="footnote-reference brackets" href="#footcite-tiberti2015" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
 <p>The module includes facilities for handling ensembles and trajectories through
-the <code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code> class, performing clustering or dimensionality reduction
+the <a class="reference external" href="https://docs.mdanalysis.org/stable/documentation_pages/core/universe.html#MDAnalysis.core.universe.Universe" title="(in MDAnalysis v2.6.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Universe</span></code></a> class, performing clustering or dimensionality reduction
 of the ensemble space, estimating multivariate probability distributions from
 the input data, and more. ENCORE can be used to compare experimental and
 simulation-derived ensembles, as well as estimate the convergence of