documentation

doc/html/_sources/case-integral.rst.txt

@@ -66,7 +66,7 @@ applied to a much more singular function::
     Itrap_g = 10.3633 ± 4.0e+03
 
 The correct answer is 16.6 ± 1.9. We need a much larger number of integrand 
-samples ``n+1`` (100x larger) to get reasonable results.
+samples ``n`` (100x larger) to get reasonable results.
 
 Leveraging Existing Code
 ---------------------------
@@ -89,7 +89,7 @@ of ``I`` with respect to each of these parameters
 --- :math:`[dI/dp_0, dI/dp_1, ...]` --- again 
 evaluated with their mean values.
 
-The integral ``I`` (with the parameters replaced by their mean values )
+The integral ``I`` (with the parameters replaced by their mean values)
 can be evaluated using standard routines as no |GVar|\s are involved.
 The derivatives with respect to the endpoints are also easily evaluated:
 

doc/html/case-integral.html

@@ -102,7 +102,7 @@ <h2>The Problem<a class="headerlink" href="#the-problem" title="Permalink to thi
 </pre></div>
 </div>
 <p>The correct answer is 16.6 ± 1.9. We need a much larger number of integrand
-samples <code class="docutils literal notranslate"><span class="pre">n+1</span></code> (100x larger) to get reasonable results.</p>
+samples <code class="docutils literal notranslate"><span class="pre">n</span></code> (100x larger) to get reasonable results.</p>
 </section>
 <section id="leveraging-existing-code">
 <h2>Leveraging Existing Code<a class="headerlink" href="#leveraging-existing-code" title="Permalink to this heading">¶</a></h2>
@@ -122,7 +122,7 @@ <h2>Leveraging Existing Code<a class="headerlink" href="#leveraging-existing-cod
 of <code class="docutils literal notranslate"><span class="pre">I</span></code> with respect to each of these parameters
 — <img class="math" src="_images/math/826eb1603997b91edc64a24e1700a40d03109a8f.svg" alt="[dI/dp_0, dI/dp_1, ...]"/> — again
 evaluated with their mean values.</p>
-<p>The integral <code class="docutils literal notranslate"><span class="pre">I</span></code> (with the parameters replaced by their mean values )
+<p>The integral <code class="docutils literal notranslate"><span class="pre">I</span></code> (with the parameters replaced by their mean values)
 can be evaluated using standard routines as no <a class="reference internal" href="gvar.html#gvar.GVar" title="gvar.GVar"><code class="xref py py-class docutils literal notranslate"><span class="pre">gvar.GVar</span></code></a>s are involved.
 The derivatives with respect to the endpoints are also easily evaluated:</p>
 <div class="math">

doc/html/gvar.html

@@ -3094,25 +3094,13 @@ <h2>PDF-related Objects<a class="headerlink" href="#pdf-related-objects" title="
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>g</strong> – <a class="reference internal" href="#gvar.GVar" title="gvar.GVar"><code class="xref py py-class docutils literal notranslate"><span class="pre">gvar.GVar</span></code></a> or array of <a class="reference internal" href="#gvar.GVar" title="gvar.GVar"><code class="xref py py-class docutils literal notranslate"><span class="pre">gvar.GVar</span></code></a>s, or dictionary of <a class="reference internal" href="#gvar.GVar" title="gvar.GVar"><code class="xref py py-class docutils literal notranslate"><span class="pre">gvar.GVar</span></code></a>s
 or arrays of <a class="reference internal" href="#gvar.GVar" title="gvar.GVar"><code class="xref py py-class docutils literal notranslate"><span class="pre">gvar.GVar</span></code></a>s.</p></li>
-<li><p><strong>svdcut</strong> (<em>float</em><em> or </em><em>None</em>) – If nonzero, singularities in the correlation
+<li><p><strong>svdcut</strong> (<em>float</em>) – If nonzero, singularities in the correlation
 matrix are regulated using <a class="reference internal" href="#gvar.regulate" title="gvar.regulate"><code class="xref py py-func docutils literal notranslate"><span class="pre">gvar.regulate()</span></code></a>
 with an SVD cutoff <code class="docutils literal notranslate"><span class="pre">svdcut</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">svdcut=1e-12</span></code>.</p></li>
 <li><p><strong>eps</strong> (<em>non-negative float</em>) – If positive, singularities in the correlation matrix
 for <code class="docutils literal notranslate"><span class="pre">g</span></code> are regulated using <a class="reference internal" href="#gvar.regulate" title="gvar.regulate"><code class="xref py py-func docutils literal notranslate"><span class="pre">gvar.regulate()</span></code></a>
 with cutoff <code class="docutils literal notranslate"><span class="pre">eps</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">svdcut</span></code> is specified (and
 not <code class="docutils literal notranslate"><span class="pre">None</span></code>).</p></li>
-<li><p><strong>svdcut</strong> – If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, replace
-covariance matrix of <code class="docutils literal notranslate"><span class="pre">g</span></code> with a new matrix whose
-small eigenvalues are modified: eigenvalues smaller than
-<code class="docutils literal notranslate"><span class="pre">svdcut</span></code> times the maximum eigenvalue <code class="docutils literal notranslate"><span class="pre">eig_max</span></code> are
-replaced by <code class="docutils literal notranslate"><span class="pre">svdcut*eig_max</span></code>. This can ameliorate
-problems caused by roundoff errors when inverting the
-covariance matrix. It increases the uncertainty associated
-with the modified eigenvalues and so is conservative.
-Setting <code class="docutils literal notranslate"><span class="pre">svdcut=None</span></code> or <code class="docutils literal notranslate"><span class="pre">svdcut=0</span></code> leaves the
-covariance matrix unchanged. If <code class="docutils literal notranslate"><span class="pre">svdcut</span></code> is negative
-eigenmodes with eigenvalues smaller than <code class="docutils literal notranslate"><span class="pre">|svdcut|*eig_max</span></code>
-are discarded. Default is <code class="docutils literal notranslate"><span class="pre">1e-12</span></code>.</p></li>
 <li><p><strong>noise</strong> (<em>bool</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> adds noise to the corrections caused by
 <code class="docutils literal notranslate"><span class="pre">svdcut/eps</span></code> (see documentation for <a class="reference internal" href="#gvar.regulate" title="gvar.regulate"><code class="xref py py-func docutils literal notranslate"><span class="pre">gvar.regulate()</span></code></a>).
 Default value is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
@@ -3339,7 +3327,7 @@ <h2>PDF-related Objects<a class="headerlink" href="#pdf-related-objects" title="
 <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>chiv</strong> – An array of values for <img class="math" src="_images/math/7f2539bf7c52accbb29ae629466468c7c593dcc7.svg" alt="\chi(p)"/>
+<li><p><strong>chiv</strong> – An array of values for <img class="math" src="_images/math/290db8021d950c02bfbccdd738eeadbf3c6062b7.svg" alt="\chi_i(p)"/>
 evaluate a point <code class="docutils literal notranslate"><span class="pre">p</span></code> in paramter space. In batch
 mode the array has an extra batch index,
 either on the right (<code class="docutils literal notranslate"><span class="pre">mode='rbatch'</span></code>) or on the l

tests/test_gvar.py

@@ -2898,7 +2898,7 @@ def _distribute_gvars(self, gvlist):
     yaml = None
 
 def olddump(g, outputfile, method='pickle', use_json=False):
-    """ Serialize a collection ``g`` of |GVar|\s into file ``outputfile``.
+    """ Serialize a collection ``g`` of |GVar| objects into file ``outputfile``.
 
     Old verion, here for testing purposes only.