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50
docs/pyerrors/misc.html
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@ -117,12 +117,17 @@
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> means -- list containing the mean value of each observable.</span>
<span class="sd"> cov -- covariance matrix for the data to be geneated.</span>
<span class="sd"> name -- ensemble name for the data to be geneated.</span>
<span class="sd"> tau -- can either be a real number or a list with an entry for</span>
<span class="sd"> every dataset.</span>
<span class="sd"> samples -- number of samples to be generated for each observable.</span>
<span class="sd"> means : list</span>
<span class="sd"> list containing the mean value of each observable.</span>
<span class="sd"> cov : numpy.ndarray</span>
<span class="sd"> covariance matrix for the data to be generated.</span>
<span class="sd"> name : str</span>
<span class="sd"> ensemble name for the data to be geneated.</span>
<span class="sd"> tau : float or list</span>
<span class="sd"> can either be a real number or a list with an entry for</span>
<span class="sd"> every dataset.</span>
<span class="sd"> samples : int</span>
<span class="sd"> number of samples to be generated for each observable.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">means</span><span class="p">)</span> <span class="o">==</span> <span class="n">cov</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
@ -246,12 +251,17 @@ path to the file</li>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> means -- list containing the mean value of each observable.</span>
<span class="sd"> cov -- covariance matrix for the data to be geneated.</span>
<span class="sd"> name -- ensemble name for the data to be geneated.</span>
<span class="sd"> tau -- can either be a real number or a list with an entry for</span>
<span class="sd"> every dataset.</span>
<span class="sd"> samples -- number of samples to be generated for each observable.</span>
<span class="sd"> means : list</span>
<span class="sd"> list containing the mean value of each observable.</span>
<span class="sd"> cov : numpy.ndarray</span>
<span class="sd"> covariance matrix for the data to be generated.</span>
<span class="sd"> name : str</span>
<span class="sd"> ensemble name for the data to be geneated.</span>
<span class="sd"> tau : float or list</span>
<span class="sd"> can either be a real number or a list with an entry for</span>
<span class="sd"> every dataset.</span>
<span class="sd"> samples : int</span>
<span class="sd"> number of samples to be generated for each observable.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">means</span><span class="p">)</span> <span class="o">==</span> <span class="n">cov</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
@ -280,11 +290,17 @@ path to the file</li>
<h6 id="parameters">Parameters</h6>
<ul>
<li><strong>means -- list containing the mean value of each observable.</strong></li>
<li><strong>cov -- covariance matrix for the data to be geneated.</strong></li>
<li><strong>name -- ensemble name for the data to be geneated.</strong></li>
<li><strong>tau -- can either be a real number or a list with an entry for</strong>: every dataset.</li>
<li><strong>samples -- number of samples to be generated for each observable.</strong></li>
<li><strong>means</strong> (list):
list containing the mean value of each observable.</li>
<li><strong>cov</strong> (numpy.ndarray):
covariance matrix for the data to be generated.</li>
<li><strong>name</strong> (str):
ensemble name for the data to be geneated.</li>
<li><strong>tau</strong> (float or list):
can either be a real number or a list with an entry for
every dataset.</li>
<li><strong>samples</strong> (int):
number of samples to be generated for each observable.</li>
</ul>
</div>

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docs/search.js
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