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<section id="nep-30-duck-typing-for-numpy-arrays-implementation">
<span id="nep30"></span><h1>NEP 30 — Duck typing for NumPy arrays - implementation<a class="headerlink" href="#nep-30-duck-typing-for-numpy-arrays-implementation" title="Link to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Peter Andreas Entschev <<a class="reference external" href="mailto:pentschev%40nvidia.com">pentschev<span>@</span>nvidia<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Author<span class="colon">:</span></dt>
<dd class="field-even"><p>Stephan Hoyer <<a class="reference external" href="mailto:shoyer%40google.com">shoyer<span>@</span>google<span>.</span>com</a>></p>
</dd>
<dt class="field-odd">Status<span class="colon">:</span></dt>
<dd class="field-odd"><p>Superseded</p>
</dd>
<dt class="field-even">Replaced-By<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference internal" href="nep-0056-array-api-main-namespace.html#nep56"><span class="std std-ref">NEP 56 — Array API standard support in NumPy’s main namespace</span></a></p>
</dd>
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Standards Track</p>
</dd>
<dt class="field-even">Created<span class="colon">:</span></dt>
<dd class="field-even"><p>2019-07-31</p>
</dd>
<dt class="field-odd">Updated<span class="colon">:</span></dt>
<dd class="field-odd"><p>2019-07-31</p>
</dd>
<dt class="field-even">Resolution<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/">https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>We propose the <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> protocol, following the high-level overview
described in NEP 22, allowing downstream libraries to return arrays of their
defined types, in contrast to <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code>, that coerces those <code class="docutils literal notranslate"><span class="pre">array_like</span></code>
objects to NumPy arrays.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<p>NumPy’s API, including array definitions, is implemented and mimicked in
countless other projects. By definition, many of those arrays are fairly
similar in how they operate to the NumPy standard. The introduction of
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> allowed dispatching of functions implemented by several
of these projects directly via NumPy’s API. This introduces a new requirement,
returning the NumPy-like array itself, rather than forcing a coercion into a
pure NumPy array.</p>
<p>For the purpose above, NEP 22 introduced the concept of duck typing to NumPy
arrays. The suggested solution described in the NEP allows libraries to avoid
coercion of a NumPy-like array to a pure NumPy array where necessary, while
still allowing that NumPy-like array libraries that do not wish to implement
the protocol to coerce arrays to a pure NumPy array via <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code>.</p>
<section id="usage-guidance">
<h3>Usage Guidance<a class="headerlink" href="#usage-guidance" title="Link to this heading">#</a></h3>
<p>Code that uses <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code> is meant for supporting other ndarray-like objects
that “follow the NumPy API”. That is an ill-defined concept at the moment –
every known library implements the NumPy API only partly, and many deviate
intentionally in at least some minor ways. This cannot be easily remedied, so
for users of <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code> we recommend the following strategy: check if the
NumPy functionality used by the code that follows your use of <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code>
is present in Dask, CuPy and Sparse. If so, it’s reasonable to expect any duck
array to work here. If not, we suggest you indicate in your docstring what kinds
of duck arrays are accepted, or what properties they need to have.</p>
<p>To exemplify the usage of duck arrays, suppose one wants to take the <code class="docutils literal notranslate"><span class="pre">mean()</span></code>
of an array-like object <code class="docutils literal notranslate"><span class="pre">arr</span></code>. Using NumPy to achieve that, one could write
<code class="docutils literal notranslate"><span class="pre">np.asarray(arr).mean()</span></code> to achieve the intended result. If <code class="docutils literal notranslate"><span class="pre">arr</span></code> is not
a NumPy array, this would create an actual NumPy array in order to call
<code class="docutils literal notranslate"><span class="pre">.mean()</span></code>. However, if the array is an object that is compliant with the NumPy
API (either in full or partially) such as a CuPy, Sparse or a Dask array, then
that copy would have been unnecessary. On the other hand, if one were to use the new
<code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> protocol: <code class="docutils literal notranslate"><span class="pre">np.duckarray(arr).mean()</span></code>, and <code class="docutils literal notranslate"><span class="pre">arr</span></code> is an object
compliant with the NumPy API, it would simply be returned rather than coerced
into a pure NumPy array, avoiding unnecessary copies and potential loss of
performance.</p>
</section>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<p>The implementation idea is fairly straightforward, requiring a new function
<code class="docutils literal notranslate"><span class="pre">duckarray</span></code> to be introduced in NumPy, and a new method <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> in
NumPy-like array classes. The new <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> method shall return the
downstream array-like object itself, such as the <code class="docutils literal notranslate"><span class="pre">self</span></code> object, while the
<code class="docutils literal notranslate"><span class="pre">__array__</span></code> method raises <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>. Alternatively, the <code class="docutils literal notranslate"><span class="pre">__array__</span></code>
method could create an actual NumPy array and return that.</p>
<p>The new NumPy <code class="docutils literal notranslate"><span class="pre">duckarray</span></code> function can be implemented as follows:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">duckarray</span><span class="p">(</span><span class="n">array_like</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">array_like</span><span class="p">,</span> <span class="s1">'__duckarray__'</span><span class="p">):</span>
<span class="k">return</span> <span class="n">array_like</span><span class="o">.</span><span class="n">__duckarray__</span><span class="p">()</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">array_like</span><span class="p">)</span>
</pre></div>
</div>
<section id="example-for-a-project-implementing-numpy-like-arrays">
<h3>Example for a project implementing NumPy-like arrays<a class="headerlink" href="#example-for-a-project-implementing-numpy-like-arrays" title="Link to this heading">#</a></h3>
<p>Now consider a library that implements a NumPy-compatible array class called
<code class="docutils literal notranslate"><span class="pre">NumPyLikeArray</span></code>, this class shall implement the methods described above, and
a complete implementation would look like the following:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">NumPyLikeArray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">__duckarray__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">__array__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"NumPyLikeArray can not be converted to a NumPy "</span>
<span class="s2">"array. You may want to use np.duckarray() instead."</span><span class="p">)</span>
</pre></div>
</div>
<p>The implementation above exemplifies the simplest case, but the overall idea
is that libraries will implement a <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> method that returns the
original object, and an <code class="docutils literal notranslate"><span class="pre">__array__</span></code> method that either creates and returns an
appropriate NumPy array, or raises a``TypeError`` to prevent unintentional use
as an object in a NumPy array (if <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code> is called on an arbitrary
object that does not implement <code class="docutils literal notranslate"><span class="pre">__array__</span></code>, it will create a NumPy array
scalar).</p>
<p>In case of existing libraries that don’t already implement <code class="docutils literal notranslate"><span class="pre">__array__</span></code> but
would like to use duck array typing, it is advised that they introduce
both <code class="docutils literal notranslate"><span class="pre">__array__</span></code> and``__duckarray__`` methods.</p>
</section>
</section>
<section id="usage">
<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading">#</a></h2>
<p>An example of how the <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> protocol could be used to write a
<code class="docutils literal notranslate"><span class="pre">stack</span></code> function based on <code class="docutils literal notranslate"><span class="pre">concatenate</span></code>, and its produced outcome, can be
seen below. The example here was chosen not only to demonstrate the usage of
the <code class="docutils literal notranslate"><span class="pre">duckarray</span></code> function, but also to demonstrate its dependency on the NumPy
API, demonstrated by checks on the array’s <code class="docutils literal notranslate"><span class="pre">shape</span></code> attribute. Note that the
example is merely a simplified version of NumPy’s actual implementation of
<code class="docutils literal notranslate"><span class="pre">stack</span></code> working on the first axis, and it is assumed that Dask has implemented
the <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> method.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">duckarray_stack</span><span class="p">(</span><span class="n">arrays</span><span class="p">):</span>
<span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">duckarray</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">]</span>
<span class="n">shapes</span> <span class="o">=</span> <span class="p">{</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">}</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shapes</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'all input arrays must have the same shape'</span><span class="p">)</span>
<span class="n">expanded_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">arr</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">]</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">expanded_arrays</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">dask_arr</span> <span class="o">=</span> <span class="n">dask</span><span class="o">.</span><span class="n">array</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">np_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">np_like</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">duckarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">dask_arr</span><span class="p">))</span> <span class="c1"># Returns dask.array</span>
<span class="n">duckarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">np_arr</span><span class="p">))</span> <span class="c1"># Returns dask.array</span>
<span class="n">duckarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">np_like</span><span class="p">))</span> <span class="c1"># Returns dask.array</span>
</pre></div>
</div>
<p>In contrast, using only <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code> (at the time of writing of this NEP, this
is the usual method employed by library developers to ensure arrays are
NumPy-like) has a different outcome:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">asarray_stack</span><span class="p">(</span><span class="n">arrays</span><span class="p">):</span>
<span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">asanyarray</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">]</span>
<span class="c1"># The remaining implementation is the same as that of</span>
<span class="c1"># ``duckarray_stack`` above</span>
<span class="n">asarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">dask_arr</span><span class="p">))</span> <span class="c1"># Returns np.ndarray</span>
<span class="n">asarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">np_arr</span><span class="p">))</span> <span class="c1"># Returns np.ndarray</span>
<span class="n">asarray_stack</span><span class="p">((</span><span class="n">dask_arr</span><span class="p">,</span> <span class="n">np_like</span><span class="p">))</span> <span class="c1"># Returns np.ndarray</span>
</pre></div>
</div>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>This proposal does not raise any backward compatibility issues within NumPy,
given that it only introduces a new function. However, downstream libraries
that opt to introduce the <code class="docutils literal notranslate"><span class="pre">__duckarray__</span></code> protocol may choose to remove the
ability of coercing arrays back to a NumPy array via <code class="docutils literal notranslate"><span class="pre">np.array</span></code> or
<code class="docutils literal notranslate"><span class="pre">np.asarray</span></code> functions, preventing unintended effects of coercion of such
arrays back to a pure NumPy array (as some libraries already do, such as CuPy
and Sparse), but still leaving libraries not implementing the protocol with the
choice of utilizing <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code> to promote <code class="docutils literal notranslate"><span class="pre">array_like</span></code> objects to pure
NumPy arrays.</p>
</section>
<section id="previous-proposals-and-discussion">
<h2>Previous proposals and discussion<a class="headerlink" href="#previous-proposals-and-discussion" title="Link to this heading">#</a></h2>
<p>The duck typing protocol proposed here was described in a high level in
<a class="reference internal" href="nep-0022-ndarray-duck-typing-overview.html#nep22"><span class="std std-ref">NEP 22</span></a>.</p>
<p>Additionally, longer discussions about the protocol and related proposals
took place in
<a class="reference external" href="https://github.com/numpy/numpy/issues/13831">numpy/numpy #13831</a></p>
</section>
<section id="copyright">
<h2>Copyright<a class="headerlink" href="#copyright" title="Link to this heading">#</a></h2>
<p>This document has been placed in the public domain.</p>
</section>
</section>
</article>
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