Update from scikit-bio/scikit-bio@d8b1187

Author: qiyunzhu

docs/dev/generated/skbio.stats.distance.DistanceMatrix.html

@@ -786,7 +786,7 @@ <h1>skbio.stats.distance.DistanceMatrix<a class="headerlink" href="#skbio-stats-
 <tr class="row-even"><td><p><a class="reference internal" href="skbio.stats.distance.DistanceMatrix.from_iterable.html#skbio.stats.distance.DistanceMatrix.from_iterable" title="skbio.stats.distance.DistanceMatrix.from_iterable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_iterable</span></code></a>(iterable, metric[, key, keys, ...])</p></td>
 <td><p>Create DistanceMatrix from all pairs in an iterable given a metric.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.DistanceMatrix.permute.html#skbio.stats.distance.DistanceMatrix.permute" title="skbio.stats.distance.DistanceMatrix.permute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permute</span></code></a>([condensed])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.DistanceMatrix.permute.html#skbio.stats.distance.DistanceMatrix.permute" title="skbio.stats.distance.DistanceMatrix.permute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permute</span></code></a>([condensed, seed])</p></td>
 <td><p>Randomly permute both rows and columns in the matrix.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="skbio.stats.distance.DistanceMatrix.read.html#skbio.stats.distance.DistanceMatrix.read" title="skbio.stats.distance.DistanceMatrix.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>(file[, format])</p></td>

docs/dev/generated/skbio.stats.distance.DistanceMatrix.permute.html

@@ -717,19 +717,25 @@
 <h1>skbio.stats.distance.DistanceMatrix.permute<a class="headerlink" href="#skbio-stats-distance-distancematrix-permute" title="Link to this heading">#</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="skbio.stats.distance.DistanceMatrix.permute">
-<span class="sig-prename descclassname"><span class="pre">DistanceMatrix.</span></span><span class="sig-name descname"><span class="pre">permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condensed</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 external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_base.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.DistanceMatrix.permute" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DistanceMatrix.</span></span><span class="sig-name descname"><span class="pre">permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condensed</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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_base.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.DistanceMatrix.permute" title="Link to this definition">#</a></dt>
 <dd><p>Randomly permute both rows and columns in the matrix.</p>
 <p>Randomly permutes the ordering of rows and columns in the matrix. The
 same permutation is applied to both rows and columns in order to
 maintain symmetry and hollowness. Only the rows/columns in the distance
 matrix are permuted; the IDs are <em>not</em> permuted.</p>
-<dl class="field-list simple">
+<dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>condensed</strong><span class="classifier">bool, optional</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, return the permuted distance matrix in condensed
 format. Otherwise, return the permuted distance matrix as a new
 <code class="docutils literal notranslate"><span class="pre">DistanceMatrix</span></code> instance.</p>
 </dd>
+<dt><strong>seed</strong><span class="classifier">int, Generator or RandomState, optional</span></dt><dd><p>A user-provided random seed or random generator instance. See
+<code class="xref py py-func docutils literal notranslate"><span class="pre">details</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 0.6.3.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>

docs/dev/generated/skbio.stats.distance.DistanceMatrix.to_series.html

@@ -717,7 +717,7 @@
 <h1>skbio.stats.distance.DistanceMatrix.to_series<a class="headerlink" href="#skbio-stats-distance-distancematrix-to-series" title="Link to this heading">#</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="skbio.stats.distance.DistanceMatrix.to_series">
-<span class="sig-prename descclassname"><span class="pre">DistanceMatrix.</span></span><span class="sig-name descname"><span class="pre">to_series</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_base.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.DistanceMatrix.to_series" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DistanceMatrix.</span></span><span class="sig-name descname"><span class="pre">to_series</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_base.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.DistanceMatrix.to_series" title="Link to this definition">#</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">pandas.Series</span></code> from this <code class="docutils literal notranslate"><span class="pre">DistanceMatrix</span></code>.</p>
 <p>The series will contain distances in condensed form: only distances
 from one matrix triangle are included, and the diagonal is excluded.

docs/dev/generated/skbio.stats.distance.anosim.html

@@ -712,7 +712,7 @@
 <h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance-anosim" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="skbio.stats.distance.anosim">
-<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">anosim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</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">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_anosim.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.anosim" title="Link to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">anosim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</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">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_anosim.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.anosim" title="Link to this definition">#</a></dt>
 <dd><p>Test for significant differences between groups using ANOSIM.</p>
 <p>Analysis of Similarities (ANOSIM) is a non-parametric method that tests
 whether two or more groups of objects (e.g., samples) are significantly
@@ -725,9 +725,9 @@ <h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance
 (controlled via <cite>permutations</cite>). An R statistic is computed for each
 permutation and the p-value is the proportion of permuted R statisics that
 are equal to or greater than the original (unpermuted) R statistic.</p>
-<dl class="field-list simple">
+<dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><dl class="simple">
+<dd class="field-odd"><dl>
 <dt><strong>distance_matrix</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
 between samples of microbial communities).</p>
 </dd>
@@ -752,6 +752,12 @@ <h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance
 statistical significance calculations will be skipped and the p-value
 will be <code class="docutils literal notranslate"><span class="pre">np.nan</span></code>.</p>
 </dd>
+<dt><strong>seed</strong><span class="classifier">int, Generator or RandomState, optional</span></dt><dd><p>A user-provided random seed or random generator instance. See
+<code class="xref py py-func docutils literal notranslate"><span class="pre">details</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 0.6.3.</span></p>
+</div>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -798,18 +804,16 @@ <h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance
 <span class="gp">&gt;&gt;&gt; </span><span class="n">grouping</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Group1&#39;</span><span class="p">,</span> <span class="s1">&#39;Group1&#39;</span><span class="p">,</span> <span class="s1">&#39;Group2&#39;</span><span class="p">,</span> <span class="s1">&#39;Group2&#39;</span><span class="p">]</span>
 </pre></div>
 </div>
-<p>Run ANOSIM using 99 permutations to calculate the p-value:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="c1"># make output deterministic; not necessary for normal use</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">skbio.stats.distance</span> <span class="kn">import</span> <span class="n">anosim</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">anosim</span><span class="p">(</span><span class="n">dm</span><span class="p">,</span> <span class="n">grouping</span><span class="p">,</span> <span class="n">permutations</span><span class="o">=</span><span class="mi">99</span><span class="p">)</span>
+<p>Run ANOSIM using 99 permutations to calculate the p-value. The seed is to
+make the output deterministic. You may skip it if that’s not necessary.</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">skbio.stats.distance</span> <span class="kn">import</span> <span class="n">anosim</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">anosim</span><span class="p">(</span><span class="n">dm</span><span class="p">,</span> <span class="n">grouping</span><span class="p">,</span> <span class="n">permutations</span><span class="o">=</span><span class="mi">99</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 <span class="go">method name               ANOSIM</span>
 <span class="go">test statistic name            R</span>
 <span class="go">sample size                    4</span>
 <span class="go">number of groups               2</span>
 <span class="go">test statistic              0.25</span>
-<span class="go">p-value                     0.67</span>
+<span class="go">p-value                     0.66</span>
 <span class="go">number of permutations        99</span>
 <span class="go">Name: ANOSIM results, dtype: object</span>
 </pre></div>
@@ -834,18 +838,17 @@ <h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance
 <code class="docutils literal notranslate"><span class="pre">Group</span></code> column specifies the same grouping as the vector we used in the
 previous examples:</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># make output deterministic; not necessary for normal use</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="p">{</span><span class="s1">&#39;Group&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;s2&#39;</span><span class="p">:</span> <span class="s1">&#39;Group1&#39;</span><span class="p">,</span> <span class="s1">&#39;s3&#39;</span><span class="p">:</span> <span class="s1">&#39;Group2&#39;</span><span class="p">,</span> <span class="s1">&#39;s4&#39;</span><span class="p">:</span> <span class="s1">&#39;Group2&#39;</span><span class="p">,</span>
 <span class="gp">... </span>               <span class="s1">&#39;s5&#39;</span><span class="p">:</span> <span class="s1">&#39;Group3&#39;</span><span class="p">,</span> <span class="s1">&#39;s1&#39;</span><span class="p">:</span> <span class="s1">&#39;Group1&#39;</span><span class="p">}})</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">anosim</span><span class="p">(</span><span class="n">dm</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">permutations</span><span class="o">=</span><span class="mi">99</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">anosim</span><span class="p">(</span><span class="n">dm</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="s1">&#39;Group&#39;</span><span class="p">,</span> <span class="n">permutations</span><span class="o">=</span><span class="mi">99</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 <span class="go">method name               ANOSIM</span>
 <span class="go">test statistic name            R</span>
 <span class="go">sample size                    4</span>
 <span class="go">number of groups               2</span>
 <span class="go">test statistic              0.25</span>
-<span class="go">p-value                     0.67</span>
+<span class="go">p-value                     0.66</span>
 <span class="go">number of permutations        99</span>
 <span class="go">Name: ANOSIM results, dtype: object</span>
 </pre></div>