documentation

doc/html/vegas.html

@@ -1382,12 +1382,13 @@ <h2>PDFIntegrator Objects<a class="headerlink" href="#pdfintegrator-objects" tit
 <span class="n">r</span> <span class="o">=</span> <span class="n">g_ev</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;p&gt; =&#39;</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="s1">&#39;(vegas)&#39;</span><span class="p">)</span>
 
-<span class="c1"># sample g_pdf(p) and use sample to evaluate &lt;p&gt; and &lt;cos(p0)&gt;</span>
+<span class="c1"># sample g_pdf(p)</span>
 <span class="n">wgts</span><span class="p">,</span> <span class="n">p_samples</span> <span class="o">=</span> <span class="n">g_ev</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">nbatch</span><span class="o">=</span><span class="mi">40_000</span><span class="p">)</span>
-<span class="n">p_avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wgts</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">p_samples</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">cosp_avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wgts</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p_samples</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># evaluate mean values &lt;p&gt; and &lt;cos(p0)&gt;</span>
+<span class="n">p_avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wgts</span> <span class="o">*</span> <span class="n">p_samples</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">cosp0_avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">wgts</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p_samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;p&gt; =&#39;</span><span class="p">,</span> <span class="n">p_avg</span><span class="p">,</span> <span class="s1">&#39;(sample)&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;cos(p)&gt; =&#39;</span><span class="p">,</span> <span class="n">cosp_avg</span><span class="p">,</span> <span class="s1">&#39;(sample)&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&lt;cos(p0)&gt; =&#39;</span><span class="p">,</span> <span class="n">cosp0_avg</span><span class="p">,</span> <span class="s1">&#39;(sample)&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Here <code class="docutils literal notranslate"><span class="pre">p_samples[d,</span> <span class="pre">i]</span></code> is a batch of about 40,000 random samples
@@ -1400,8 +1401,8 @@ <h2>PDFIntegrator Objects<a class="headerlink" href="#pdfintegrator-objects" tit
 batch index to the leftmost position: <code class="docutils literal notranslate"><span class="pre">p_samples[i,</span> <span class="pre">d]</span></code>.)
 The output from this script is:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">p</span><span class="o">&gt;</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.40</span><span class="p">(</span><span class="mi">17</span><span class="p">)</span> <span class="mf">0.40</span><span class="p">(</span><span class="mi">17</span><span class="p">)]</span> <span class="p">(</span><span class="n">vegas</span><span class="p">)</span>
-<span class="o">&lt;</span><span class="n">p</span><span class="o">&gt;</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.39998773</span> <span class="mf">0.39984626</span><span class="p">]</span> <span class="p">(</span><span class="n">sample</span><span class="p">)</span>
-<span class="o">&lt;</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">&gt;</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.90745871</span> <span class="mf">0.9075133</span> <span class="p">]</span> <span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+<span class="o">&lt;</span><span class="n">p</span><span class="o">&gt;</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.40011804</span> <span class="mf">0.39999454</span><span class="p">]</span> <span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+<span class="o">&lt;</span><span class="n">cos</span><span class="p">(</span><span class="n">p0</span><span class="p">)</span><span class="o">&gt;</span> <span class="o">=</span> <span class="mf">0.9074221724843065</span> <span class="p">(</span><span class="n">sample</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Samples are also useful for making histograms and contour

src/vegas/__init__.py

@@ -1055,13 +1055,14 @@ def g_pdf(p):
             r = g_ev.stats()
             print('<p> =', r, '(vegas)')
 
-            # sample g_pdf(p) and use sample to evaluate <p> and <cos(p0)>
+            # sample g_pdf(p) 
             wgts, p_samples = g_ev.sample(nbatch=40_000)
-            p_avg = np.sum(wgts[None, :] * p_samples, axis=1)
-            cosp_avg = np.sum(wgts[None, :] * np.cos(p_samples), axis=1)
+            # evaluate mean values <p> and <cos(p0)>
+            p_avg = np.sum(wgts * p_samples, axis=1)
+            cosp0_avg = np.sum(wgts * np.cos(p_samples[0]))
             print('<p> =', p_avg, '(sample)')
-            print('<cos(p)> =', cosp_avg, '(sample)')
-
+            print('<cos(p0)> =', cosp0_avg, '(sample)')
+        
         Here ``p_samples[d, i]`` is a batch of about 40,000 random samples 
         for parameter ``p[d]`` drawn from the (bimodal) distribution with 
         PDF ``g_pdf(p)``. Index ``d=0,1`` labels directions in parameter 
@@ -1073,8 +1074,8 @@ def g_pdf(p):
         The output from this script is::
 
             <p> = [0.40(17) 0.40(17)] (vegas)
-            <p> = [0.39998773 0.39984626] (sample)
-            <cos(p)> = [0.90745871 0.9075133 ] (sample)
+            <p> = [0.40011804 0.39999454] (sample)
+            <cos(p0)> = 0.9074221724843065 (sample)
         
         Samples are also useful for making histograms and contour 
         plots of the probability density. For example, the following 

tests/test_vegas.py

@@ -1343,7 +1343,7 @@ def f(p):
         self.assertTrue(r[1].shape == (2,3))
 
     def test_PDFIntegrator_sample(self):   ## add dictionaries
-        nbatch = 100_000
+        nbatch = 100000
         cov1 = np.array([[1., 0.99], [0.99, 1]])
         D = np.array([2, 1e-1])
         cov1 = D[None, :] * cov1 * D[:, None]