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Author: lucascolley

generated/array_api_extra.at.html

@@ -399,7 +399,7 @@ <h1>array_api_extra.at<a class="headerlink" href="#array-api-extra-at" title="Li
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>If x is a read-only numpy array, they are the same as:</p>
+<p>If x is a read-only NumPy array, they are the same as:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">2</span>
 </pre></div>

generated/array_api_extra.lazy_apply.html

@@ -291,7 +291,7 @@ <h1>array_api_extra.lazy_apply<a class="headerlink" href="#array-api-extra-lazy-
 </p></li>
 <li><p><strong>*args</strong> (<em>Array</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.13)"><em>complex</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em> | </em><em>None</em>) – <p>One or more Array API compliant arrays, Python scalars, or None’s.</p>
 <p>If <cite>as_numpy=True</cite>, you need to be able to apply <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="(in NumPy v2.2)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.asarray()</span></code></a> to
-non-None args to convert them to numpy; read notes below about specific
+non-None args to convert them to NumPy; read notes below about specific
 backends.</p>
 </p></li>
 <li><p><strong>shape</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Sequence</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em><em>, </em><em>...</em><em>]</em><em>]</em><em>, </em><em>optional</em>) – Output shape or sequence of output shapes, one for each output of <cite>func</cite>.
@@ -300,7 +300,7 @@ <h1>array_api_extra.lazy_apply<a class="headerlink" href="#array-api-extra-lazy-
 dtype(s) must belong to the same array namespace as the input arrays.
 Default: infer the result type(s) from the input arrays.</p></li>
 <li><p><strong>as_numpy</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – <dl class="simple">
-<dt>If True, convert the input arrays to NumPy before passing them to <cite>func</cite>.</dt><dd><p>This is particularly useful to make numpy-only functions, e.g. written in Cython</p>
+<dt>If True, convert the input arrays to NumPy before passing them to <cite>func</cite>.</dt><dd><p>This is particularly useful to make NumPy-only functions, e.g. written in Cython</p>
 </dd>
 <dt>or Numba, work transparently with array API-compliant arrays.</dt><dd><p>Default: False.</p>
 </dd>
@@ -344,8 +344,8 @@ <h1>array_api_extra.lazy_apply<a class="headerlink" href="#array-api-extra-lazy-
 <dt>Sparse</dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">as_numpy=True</span></code>, by default sparse prevents implicit densification through
 <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="(in NumPy v2.2)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.asarray()</span></code></a>. <a class="reference external" href="https://sparse.pydata.org/en/stable/operations.html#package-configuration">This safety mechanism can be disabled</a>.</p>
 </dd>
-<dt>Dask</dt><dd><p>This allows applying eager functions to dask arrays.
-The dask graph won’t be computed.</p>
+<dt>Dask</dt><dd><p>This allows applying eager functions to Dask arrays.
+The Dask graph won’t be computed.</p>
 <p><cite>lazy_apply</cite> doesn’t know if <cite>func</cite> reduces along any axes; also, shape
 changes are non-trivial in chunked Dask arrays. For these reasons, all inputs
 will be rechunked into a single chunk.</p>

generated/array_api_extra.testing.lazy_xp_function.html

@@ -287,11 +287,11 @@ <h1>array_api_extra.testing.lazy_xp_function<a class="headerlink" href="#array-a
 <li><p><strong>func</strong> (<em>callable</em>) – Function to be tested.</p></li>
 <li><p><strong>allow_dask_compute</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>optional</em>) – <p>Number of times <cite>func</cite> is allowed to internally materialize the Dask graph. This
 is typically triggered by <code class="docutils literal notranslate"><span class="pre">bool()</span></code>, <code class="docutils literal notranslate"><span class="pre">float()</span></code>, or <code class="docutils literal notranslate"><span class="pre">np.asarray()</span></code>.</p>
-<p>Set to 1 if you are aware that <cite>func</cite> converts the input parameters to numpy and
+<p>Set to 1 if you are aware that <cite>func</cite> converts the input parameters to NumPy and
 want to let it do so at least for the time being, knowing that it is going to be
 extremely detrimental for performance.</p>
 <p>If a test needs values higher than 1 to pass, it is a canary that the conversion
-to numpy/bool/float is happening multiple times, which translates to multiple
+to NumPy/bool/float is happening multiple times, which translates to multiple
 computations of the whole graph. Short of making the function fully lazy, you
 should at least add explicit calls to <code class="docutils literal notranslate"><span class="pre">np.asarray()</span></code> early in the function.
 <em>Note:</em> the counter of <cite>allow_dask_compute</cite> resets after each call to <cite>func</cite>, so