doc

.github/workflows/wheels.yml

@@ -10,13 +10,13 @@ jobs:
       # matrix:
       #   os:  [ubuntu-20.04]
       matrix:
-        os: [ubuntu-20.04, windows-2022, macos-12]
+        os: [ubuntu-latest, windows-latest, macos-latest]
         # see https://github.com/actions/runner-images
     steps:
       - uses: actions/checkout@v4
 
       # Used to host cibuildwheel
-      - uses: actions/setup-python@v3
+      - uses: actions/setup-python@v5
 
       - name: Install cibuildwheel
         run: python -m pip install cibuildwheel==2.16.2
@@ -35,6 +35,7 @@ jobs:
           CIBW_TEST_COMMAND: "pytest {project}/tests"    
           CIBW_TEST_SKIP: "*_arm64 *_universal2:arm64"
 
-      - uses: actions/upload-artifact@v3
+      - uses: actions/upload-artifact@v4
         with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
           path: ./wheelhouse/*.whl

doc/html/_sources/tutorial.rst.txt

@@ -888,7 +888,7 @@ variables, ``a[0]`` and ``a[1]``, and a third uncorrelated variable ``b``
 that is uniformly distributed on the interval [0,2] (see the :mod:`gvar` 
 documentation for more information).
 We use the integrator to calculated  the expectation value of 
-``fp = a[0] * a[1] + b`` and ``fp**2``, so we can compute the 
+``fp = a[0]*a[1] + 3*b`` and ``fp**2``, so we can compute the 
 mean and standard 
 deviation of the ``fp`` |~| distribution. The output from this code 
 shows that the Gaussian approximation 5.0(3.8) for the mean and 
@@ -911,7 +911,7 @@ than ``f_f2(p)`` above)::
     def f(p):
         a = p['a']
         b = p['b']
-        fp = a[0] * a[1] + b
+        fp = a[0] * a[1] + 3 * b
         return dict(a=a, b=b, fp=fp)
 
     r = g_ev.stats(f)
@@ -958,7 +958,7 @@ each quantity come from the :mod:`vegas` integrations.
 
 The last line in 
 the code above displays the histogram
-for ``fp``, which confirms that it is not particularly Gaussian:
+for ``fp``, which confirms that it is not quite Gaussian:
 
 .. image:: eg7b-plt.*
    :width: 70%

doc/html/tutorial.html

@@ -1024,7 +1024,7 @@ <h2>PDF Integrals<a class="headerlink" href="#pdf-integrals" title="Permalink to
 that is uniformly distributed on the interval [0,2] (see the <code class="xref py py-mod docutils literal notranslate"><span class="pre">gvar</span></code>
 documentation for more information).
 We use the integrator to calculated  the expectation value of
-<code class="docutils literal notranslate"><span class="pre">fp</span> <span class="pre">=</span> <span class="pre">a[0]</span> <span class="pre">*</span> <span class="pre">a[1]</span> <span class="pre">+</span> <span class="pre">b</span></code> and <code class="docutils literal notranslate"><span class="pre">fp**2</span></code>, so we can compute the
+<code class="docutils literal notranslate"><span class="pre">fp</span> <span class="pre">=</span> <span class="pre">a[0]*a[1]</span> <span class="pre">+</span> <span class="pre">3*b</span></code> and <code class="docutils literal notranslate"><span class="pre">fp**2</span></code>, so we can compute the
 mean and standard
 deviation of the <code class="docutils literal notranslate"><span class="pre">fp</span></code>   distribution. The output from this code
 shows that the Gaussian approximation 5.0(3.8) for the mean and
@@ -1058,7 +1058,7 @@ <h2>PDF Integrals<a class="headerlink" href="#pdf-integrals" title="Permalink to
 <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
     <span class="n">a</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
     <span class="n">b</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
-    <span class="n">fp</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">b</span>
+    <span class="n">fp</span> <span class="o">=</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">b</span>
     <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b</span><span class="p">,</span> <span class="n">fp</span><span class="o">=</span><span class="n">fp</span><span class="p">)</span>
 
 <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="n">f</span><span class="p">)</span>
@@ -1114,7 +1114,7 @@ <h2>PDF Integrals<a class="headerlink" href="#pdf-integrals" title="Permalink to
 each quantity come from the <a class="reference internal" href="vegas.html#module-vegas" title="vegas: Adaptive multidimensional Monte Carlo integration"><code class="xref py py-mod docutils literal notranslate"><span class="pre">vegas</span></code></a> integrations.</p>
 <p>The last line in
 the code above displays the histogram
-for <code class="docutils literal notranslate"><span class="pre">fp</span></code>, which confirms that it is not particularly Gaussian:</p>
+for <code class="docutils literal notranslate"><span class="pre">fp</span></code>, which confirms that it is not quite Gaussian:</p>
 <a class="reference internal image-reference" href="_images/eg7b-plt.png"><img alt="_images/eg7b-plt.png" src="_images/eg7b-plt.png" style="width: 70%;" /></a>
 <p>The discussion in <a class="reference internal" href="outliers.html#case-curve-fitting"><span class="std std-ref">Case Study: Bayesian Curve Fitting</span></a> illustrates how
 <a class="reference internal" href="vegas.html#vegas.PDFIntegrator" title="vegas.PDFIntegrator"><code class="xref py py-class docutils literal notranslate"><span class="pre">vegas.PDFIntegrator</span></code></a> can be used with a non-Gaussian PDF in two

doc/source/tutorial.rst

@@ -888,7 +888,7 @@ variables, ``a[0]`` and ``a[1]``, and a third uncorrelated variable ``b``
 that is uniformly distributed on the interval [0,2] (see the :mod:`gvar` 
 documentation for more information).
 We use the integrator to calculated  the expectation value of 
-``fp = a[0] * a[1] + b`` and ``fp**2``, so we can compute the 
+``fp = a[0]*a[1] + 3*b`` and ``fp**2``, so we can compute the 
 mean and standard 
 deviation of the ``fp`` |~| distribution. The output from this code 
 shows that the Gaussian approximation 5.0(3.8) for the mean and 
@@ -911,7 +911,7 @@ than ``f_f2(p)`` above)::
     def f(p):
         a = p['a']
         b = p['b']
-        fp = a[0] * a[1] + b
+        fp = a[0] * a[1] + 3 * b
         return dict(a=a, b=b, fp=fp)
 
     r = g_ev.stats(f)
@@ -958,7 +958,7 @@ each quantity come from the :mod:`vegas` integrations.
 
 The last line in 
 the code above displays the histogram
-for ``fp``, which confirms that it is not particularly Gaussian:
+for ``fp``, which confirms that it is not quite Gaussian:
 
 .. image:: eg7b-plt.*
    :width: 70%