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1_quadrature.html

@@ -324,7 +324,7 @@ <h2><span class="section-number">1.8. </span>Implementing quadrature rules in Py
 <p>You can implement
 <code class="xref py py-meth docutils literal notranslate"><span class="pre">integrate()</span></code> in one line
 using a <a class="reference external" href="https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions">list
-comprehension</a> and <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.dot()</span></code></a>.</p>
+comprehension</a> and <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.dot()</span></code></a>.</p>
 </div>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>

2_finite_elements.html

@@ -361,7 +361,7 @@ <h2><span class="section-number">2.5. </span>Implementing finite elements in Pyt
 <code class="docutils literal notranslate"><span class="pre">test/test_04_init_finite_element.py</span></code>.</p>
 </div></div><div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.linalg.inv.html#numpy.linalg.inv" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.linalg.inv()</span></code></a> function may be
+<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.linalg.inv.html#numpy.linalg.inv" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.linalg.inv()</span></code></a> function may be
 used to invert the matrix.</p>
 </div>
 </section>
@@ -507,15 +507,15 @@ <h2><span class="section-number">2.8. </span>Gradients of basis functions<a clas
 <code class="docutils literal notranslate"><span class="pre">test/test_03_vandermonde_matrix.py</span></code>.</p>
 </div></div><div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose" title="(in NumPy v1.24)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transpose()</span></code></a> method of numpy arrays enables
+<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose" title="(in NumPy v1.26)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transpose()</span></code></a> method of numpy arrays enables
 generalised transposes swapping any dimensions.</p>
 </div>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
 <p>At least one of the natural ways of implementing this function
 results in a whole load of <code class="xref py py-data docutils literal notranslate"><span class="pre">nan</span></code> values in the generalised
 Vandermonde matrix. In this case, you might find
-<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.nan_to_num.html#numpy.nan_to_num" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.nan_to_num()</span></code></a> useful.</p>
+<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.nan_to_num.html#numpy.nan_to_num" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.nan_to_num()</span></code></a> useful.</p>
 </div>
 <div class="proof proof-type-exercise" id="id13">
 
@@ -552,7 +552,7 @@ <h2><span class="section-number">2.8. </span>Gradients of basis functions<a clas
 </div>
 </details><div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.einsum()</span></code></a> function implements generalised tensor
+<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.einsum()</span></code></a> function implements generalised tensor
 contractions using <a class="reference external" href="http://mathworld.wolfram.com/EinsteinSummation.html">Einstein summation notation</a>. For
 example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s2">&quot;ijk,jl-&gt;ilk&quot;</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">C</span><span class="p">)</span>

4_function_spaces.html

@@ -334,11 +334,11 @@ <h2><span class="section-number">4.5. </span>Implementing function spaces in Pyt
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
 <p><code class="xref py py-attr docutils literal notranslate"><span class="pre">cell_nodes</span></code> needs to
-be integer-valued. If you choose to use <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.zeros()</span></code></a>
+be integer-valued. If you choose to use <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.zeros()</span></code></a>
 to create a matrix which you then populate with values, you
 need to explicitly specify that you want a matrix of
 integers. This can be achieved by passing the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> argument
-to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.zeros()</span></code></a>. For example <code class="docutils literal notranslate"><span class="pre">numpy.zeros((nrows,</span> <span class="pre">ncols),</span> <span class="pre">dtype=int)</span></code>.</p>
+to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.zeros()</span></code></a>. For example <code class="docutils literal notranslate"><span class="pre">numpy.zeros((nrows,</span> <span class="pre">ncols),</span> <span class="pre">dtype=int)</span></code>.</p>
 </div>
 <p class="rubric">Footnotes</p>
 <dl class="footnote brackets">

5_functions.html

@@ -385,10 +385,10 @@ <h3><span class="section-number">5.3.3. </span>Implementing integration<a class=
 basis functions at each quadrature point.</p></li>
 <li><p>Visit each cell in turn.</p></li>
 <li><p>Construct the <code class="xref py py-meth docutils literal notranslate"><span class="pre">jacobian()</span></code> for that cell
-and take the absolute value of its determinant (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.absolute.html#numpy.absolute" title="(in NumPy v1.24)"><code class="xref py py-data docutils literal notranslate"><span class="pre">numpy.absolute</span></code></a>
-and <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.linalg.det.html#numpy.linalg.det" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.linalg.det()</span></code></a> will be useful here).</p></li>
+and take the absolute value of its determinant (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.absolute.html#numpy.absolute" title="(in NumPy v1.26)"><code class="xref py py-data docutils literal notranslate"><span class="pre">numpy.absolute</span></code></a>
+and <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.linalg.det.html#numpy.linalg.det" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.linalg.det()</span></code></a> will be useful here).</p></li>
 <li><p>Sum all of the arrays you have constructed over the correct
-indices to a contribution to the integral (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.einsum()</span></code></a>
+indices to a contribution to the integral (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.einsum()</span></code></a>
 may be useful for this).</p></li>
 </ol>
 </div>

6_finite_element_problems.html

@@ -256,11 +256,11 @@ <h3><span class="section-number">6.2.3. </span>A note on matrix insertion<a clas
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span><span class="p">[</span><span class="n">nodes</span><span class="p">,</span> <span class="n">nodes</span><span class="p">]</span> <span class="o">+=</span> <span class="n">m</span> <span class="c1"># DON&#39;T DO THIS!</span>
 </pre></div>
 </div>
-<p>Unfortunately, <a class="reference external" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="(in NumPy v1.24)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a> interprets this as an instruction to
+<p>Unfortunately, <a class="reference external" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="(in NumPy v1.26)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a> interprets this as an instruction to
 insert a vector into the diagonal of <code class="docutils literal notranslate"><span class="pre">A</span></code>, and will complain that
 the two-dimensional right hand side does not match the
 one-dimensional left hand side. Instead, one has to employ the
-<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ix_.html#numpy.ix_" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.ix_()</span></code></a> function:</p>
+<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ix_.html#numpy.ix_" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.ix_()</span></code></a> function:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)]</span> <span class="o">+=</span> <span class="n">m</span> <span class="c1"># DO THIS!</span>
 </pre></div>
 </div>
@@ -317,13 +317,13 @@ <h3><span class="section-number">6.2.4. </span>Sparse matrices<a class="headerli
 are avoided.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>The <a class="reference external" href="http://scipy.github.io/devdocs/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.11.0.dev0+2027.1651835)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> package provides convenient interfaces
+<p>The <a class="reference external" href="http://scipy.github.io/devdocs/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.13.0.dev)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> package provides convenient interfaces
 which enable Python code to employ a variety of sparse matrix
 formats using essentially identical operations to the dense matrix
 case. The skeleton code already contains commands to construct
 empty sparse matrices and to solve the resulting linear system. You
 may, if you wish, experiment with choosing other sparse formats
-from <a class="reference external" href="http://scipy.github.io/devdocs/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.11.0.dev0+2027.1651835)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a>, but it is very strongly suggested that
+from <a class="reference external" href="http://scipy.github.io/devdocs/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.13.0.dev)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a>, but it is very strongly suggested that
 you do <strong>not</strong> switch to a dense numpy array; unless, that is, you
 particularly enjoy running out of memory on your computer!</p>
 </div>