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<li class="toctree-l2"><a class="reference internal" href="nep-0032-remove-financial-functions.html">NEP 32 — Remove the financial functions from NumPy</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0055-string_dtype.html">NEP 55 — Add a UTF-8 variable-width string DType to NumPy</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0056-array-api-main-namespace.html">NEP 56 — Array API standard support in NumPy's main namespace</a></li>
</ul>
</details></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0002-warnfix.html">NEP 2 — A proposal to build numpy without warning with a big set of warning flags</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0037-array-module.html">NEP 37 — A dispatch protocol for NumPy-like modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0047-array-api-standard.html">NEP 47 — Adopting the array API standard</a></li>
</ul>
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<section id="nep-16-an-abstract-base-class-for-identifying-duck-arrays">
<span id="nep16"></span><h1>NEP 16 — An abstract base class for identifying “duck arrays”<a class="headerlink" href="#nep-16-an-abstract-base-class-for-identifying-duck-arrays" 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>Nathaniel J. Smith <<a class="reference external" href="mailto:njs%40pobox.com">njs<span>@</span>pobox<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Withdrawn</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>2018-03-06</p>
</dd>
<dt class="field-odd">Resolution<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="github reference external" href="https://github.com/numpy/numpy/pull/12174">numpy/numpy#12174</a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This NEP has been withdrawn in favor of the protocol based approach
described in
<a class="reference external" href="nep-0022-ndarray-duck-typing-overview.html">NEP 22</a></p>
</div>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>We propose to add an abstract base class <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> so that
third-party classes can declare their ability to “quack like” an
<code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, and an <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code> function that performs
similarly to <code class="docutils literal notranslate"><span class="pre">asarray</span></code> except that it passes through
<code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> instances unchanged.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Link to this heading">#</a></h2>
<p>Many functions, in NumPy and in third-party packages, start with some
code like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">myfunc</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="o">...</span>
</pre></div>
</div>
<p>This ensures that <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code> are <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code> objects, so
<code class="docutils literal notranslate"><span class="pre">myfunc</span></code> can carry on assuming that they’ll act like ndarrays both
semantically (at the Python level), and also in terms of how they’re
stored in memory (at the C level). But many of these functions only
work with arrays at the Python level, which means that they don’t
actually need <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> objects <em>per se</em>: they could work just as
well with any Python object that “quacks like” an ndarray, such as
sparse arrays, dask’s lazy arrays, or xarray’s labeled arrays.</p>
<p>However, currently, there’s no way for these libraries to express that
their objects can quack like an ndarray, and there’s no way for
functions like <code class="docutils literal notranslate"><span class="pre">myfunc</span></code> to express that they’d be happy with
anything that quacks like an ndarray. The purpose of this NEP is to
provide those two features.</p>
<p>Sometimes people suggest using <code class="docutils literal notranslate"><span class="pre">np.asanyarray</span></code> for this purpose, but
unfortunately its semantics are exactly backwards: it guarantees that
the object it returns uses the same memory layout as an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>,
but tells you nothing at all about its semantics, which makes it
essentially impossible to use safely in practice. Indeed, the two
<code class="docutils literal notranslate"><span class="pre">ndarray</span></code> subclasses distributed with NumPy – <code class="docutils literal notranslate"><span class="pre">np.matrix</span></code> and
<code class="docutils literal notranslate"><span class="pre">np.ma.masked_array</span></code> – do have incompatible semantics, and if they
were passed to a function like <code class="docutils literal notranslate"><span class="pre">myfunc</span></code> that doesn’t check for them
as a special-case, then it may silently return incorrect results.</p>
<section id="declaring-that-an-object-can-quack-like-an-array">
<h3>Declaring that an object can quack like an array<a class="headerlink" href="#declaring-that-an-object-can-quack-like-an-array" title="Link to this heading">#</a></h3>
<p>There are two basic approaches we could use for checking whether an
object quacks like an array. We could check for a special attribute on
the class:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">quacks_like_array</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">),</span> <span class="s2">"__quacks_like_array__"</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
</pre></div>
</div>
<p>Or, we could define an <a class="reference external" href="https://docs.python.org/3/library/collections.abc.html">abstract base class (ABC)</a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">quacks_like_array</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">AbstractArray</span><span class="p">)</span>
</pre></div>
</div>
<p>If you look at how ABCs work, this is essentially equivalent to
keeping a global set of types that have been declared to implement the
<code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> interface, and then checking it for membership.</p>
<p>Between these, the ABC approach seems to have a number of advantages:</p>
<ul class="simple">
<li><p>It’s Python’s standard, “one obvious way” of doing this.</p></li>
<li><p>ABCs can be introspected (e.g. <code class="docutils literal notranslate"><span class="pre">help(np.AbstractArray)</span></code> does
something useful).</p></li>
<li><p>ABCs can provide useful mixin methods.</p></li>
<li><p>ABCs integrate with other features like mypy type-checking,
<code class="docutils literal notranslate"><span class="pre">functools.singledispatch</span></code>, etc.</p></li>
</ul>
<p>One obvious thing to check is whether this choice affects speed. Using
the attached benchmark script on a CPython 3.7 prerelease (revision
c4d77a661138d, self-compiled, no PGO), on a Thinkpad T450s running
Linux, we find:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ndarray_obj</span><span class="p">)</span> <span class="mi">330</span> <span class="n">ns</span>
<span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span> <span class="mi">1400</span> <span class="n">ns</span>
<span class="n">Attribute</span> <span class="n">check</span><span class="p">,</span> <span class="n">success</span> <span class="mi">80</span> <span class="n">ns</span>
<span class="n">Attribute</span> <span class="n">check</span><span class="p">,</span> <span class="n">failure</span> <span class="mi">80</span> <span class="n">ns</span>
<span class="n">ABC</span><span class="p">,</span> <span class="n">success</span> <span class="n">via</span> <span class="n">subclass</span> <span class="mi">340</span> <span class="n">ns</span>
<span class="n">ABC</span><span class="p">,</span> <span class="n">success</span> <span class="n">via</span> <span class="n">register</span><span class="p">()</span> <span class="mi">700</span> <span class="n">ns</span>
<span class="n">ABC</span><span class="p">,</span> <span class="n">failure</span> <span class="mi">370</span> <span class="n">ns</span>
</pre></div>
</div>
<p>Notes:</p>
<ul>
<li><p>The first two lines are included to put the other lines in context.</p></li>
<li><p>This used 3.7 because both <code class="docutils literal notranslate"><span class="pre">getattr</span></code> and ABCs are receiving
substantial optimizations in this release, and it’s more
representative of the long-term future of Python. (Failed
<code class="docutils literal notranslate"><span class="pre">getattr</span></code> doesn’t necessarily construct an exception object
anymore, and ABCs were reimplemented in C.)</p></li>
<li><p>The “success” lines refer to cases where <code class="docutils literal notranslate"><span class="pre">quacks_like_array</span></code> would
return True. The “failure” lines are cases where it would return
False.</p></li>
<li><p>The first measurement for ABCs is subclasses defined like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyArray</span><span class="p">(</span><span class="n">AbstractArray</span><span class="p">):</span>
<span class="o">...</span>
</pre></div>
</div>
<p>The second is for subclasses defined like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyArray</span><span class="p">:</span>
<span class="o">...</span>
<span class="n">AbstractArray</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">MyArray</span><span class="p">)</span>
</pre></div>
</div>
<p>I don’t know why there’s such a large difference between these.</p>
</li>
</ul>
<p>In practice, either way we’d only do the full test after first
checking for well-known types like <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, <code class="docutils literal notranslate"><span class="pre">list</span></code>, etc. <a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/core/src/private/get_attr_string.h">This
is how NumPy currently checks for other double-underscore attributes</a>
and the same idea applies here to either approach. So these numbers
won’t affect the common case, just the case where we actually have an
<code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code>, or else another third-party object that will end up
going through <code class="docutils literal notranslate"><span class="pre">__array__</span></code> or <code class="docutils literal notranslate"><span class="pre">__array_interface__</span></code> or end up as an
object array.</p>
<p>So in summary, using an ABC will be slightly slower than using an
attribute, but this doesn’t affect the most common paths, and the
magnitude of slowdown is fairly small (~250 ns on an operation that
already takes longer than that). Furthermore, we can potentially
optimize this further (e.g. by keeping a tiny LRU cache of types that
are known to be AbstractArray subclasses, on the assumption that most
code will only use one or two of these types at a time), and it’s very
unclear that this even matters – if the speed of <code class="docutils literal notranslate"><span class="pre">asarray</span></code> no-op
pass-throughs were a bottleneck that showed up in profiles, then
probably we would have made them faster already! (It would be trivial
to fast-path this, but we don’t.)</p>
<p>Given the semantic and usability advantages of ABCs, this seems like
an acceptable trade-off.</p>
</section>
<section id="specification-of-asabstractarray">
<h3>Specification of <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code><a class="headerlink" href="#specification-of-asabstractarray" title="Link to this heading">#</a></h3>
<p>Given <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code>, the definition of <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code> is simple:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">asabstractarray</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">AbstractArray</span><span class="p">):</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dtype</span> <span class="o">!=</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
<span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">return</span> <span class="n">a</span>
<span class="k">return</span> <span class="n">asarray</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
</pre></div>
</div>
<p>Things to note:</p>
<ul>
<li><p><code class="docutils literal notranslate"><span class="pre">asarray</span></code> also accepts an <code class="docutils literal notranslate"><span class="pre">order=</span></code> argument, but we don’t
include that here because it’s about details of memory
representation, and the whole point of this function is that you use
it to declare that you don’t care about details of memory
representation.</p></li>
<li><p>Using the <code class="docutils literal notranslate"><span class="pre">astype</span></code> method allows the <code class="docutils literal notranslate"><span class="pre">a</span></code> object to decide how to
implement casting for its particular type.</p></li>
<li><p>For strict compatibility with <code class="docutils literal notranslate"><span class="pre">asarray</span></code>, we skip calling
<code class="docutils literal notranslate"><span class="pre">astype</span></code> when the dtype is already correct. Compare:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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="go"># astype() always returns a view:</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="ow">is</span> <span class="n">a</span>
<span class="go">False</span>
<span class="go"># asarray() returns the original object if possible:</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="ow">is</span> <span class="n">a</span>
<span class="go">True</span>
</pre></div>
</div>
</li>
</ul>
</section>
<section id="what-exactly-are-you-promising-if-you-inherit-from-abstractarray">
<h3>What exactly are you promising if you inherit from <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code>?<a class="headerlink" href="#what-exactly-are-you-promising-if-you-inherit-from-abstractarray" title="Link to this heading">#</a></h3>
<p>This will presumably be refined over time. The ideal of course is that
your class should be indistinguishable from a real <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, but
nothing enforces that except the expectations of users. In practice,
declaring that your class implements the <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> interface
simply means that it will start passing through <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code>,
and so by subclassing it you’re saying that if some code works for
<code class="docutils literal notranslate"><span class="pre">ndarray</span></code>s but breaks for your class, then you’re willing to accept
bug reports on that.</p>
<p>To start with, we should declare <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> to be an abstract
method, and add the <code class="docutils literal notranslate"><span class="pre">NDArrayOperatorsMixin</span></code> methods as mixin
methods.</p>
<p>Declaring <code class="docutils literal notranslate"><span class="pre">astype</span></code> as an <code class="docutils literal notranslate"><span class="pre">@abstractmethod</span></code> probably makes sense as
well, since it’s used by <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code>. We might also want to go
ahead and add some basic attributes like <code class="docutils literal notranslate"><span class="pre">ndim</span></code>, <code class="docutils literal notranslate"><span class="pre">shape</span></code>,
<code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<p>Adding new abstract methods will be a bit tricky, because ABCs enforce
these at subclass time; therefore, simply adding a new
<cite>@abstractmethod</cite> will be a backwards compatibility break. If this
becomes a problem then we can use some hacks to implement an
<cite>@upcoming_abstractmethod</cite> decorator that only issues a warning if the
method is missing, and treat it like a regular deprecation cycle. (In
this case, the thing we’d be deprecating is “support for abstract
arrays that are missing feature X”.)</p>
</section>
<section id="naming">
<h3>Naming<a class="headerlink" href="#naming" title="Link to this heading">#</a></h3>
<p>The name of the ABC doesn’t matter too much, because it will only be
referenced rarely and in relatively specialized situations. The name
of the function matters a lot, because most existing instances of
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> should be replaced by this, and in the future it’s what
everyone should be reaching for by default unless they have a specific
reason to use <code class="docutils literal notranslate"><span class="pre">asarray</span></code> instead. This suggests that its name really
should be <em>shorter</em> and <em>more memorable</em> than <code class="docutils literal notranslate"><span class="pre">asarray</span></code>… which
is difficult. I’ve used <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code> in this draft, but I’m not
really happy with it, because it’s too long and people are unlikely to
start using it by habit without endless exhortations.</p>
<p>One option would be to actually change <code class="docutils literal notranslate"><span class="pre">asarray</span></code>'s semantics so
that <em>it</em> passes through <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> objects unchanged. But I’m
worried that there may be a lot of code out there that calls
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> and then passes the result into some C function that
doesn’t do any further type checking (because it knows that its caller
has already used <code class="docutils literal notranslate"><span class="pre">asarray</span></code>). If we allow <code class="docutils literal notranslate"><span class="pre">asarray</span></code> to return
<code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> objects, and then someone calls one of these C
wrappers and passes it an <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> object like a sparse
array, then they’ll get a segfault. Right now, in the same situation,
<code class="docutils literal notranslate"><span class="pre">asarray</span></code> will instead invoke the object’s <code class="docutils literal notranslate"><span class="pre">__array__</span></code> method, or
use the buffer interface to make a view, or pass through an array with
object dtype, or raise an error, or similar. Probably none of these
outcomes are actually desirable in most cases, so maybe making it a
segfault instead would be OK? But it’s dangerous given that we don’t
know how common such code is. OTOH, if we were starting from scratch
then this would probably be the ideal solution.</p>
<p>We can’t use <code class="docutils literal notranslate"><span class="pre">asanyarray</span></code> or <code class="docutils literal notranslate"><span class="pre">array</span></code>, since those are already
taken.</p>
<p>Any other ideas? <code class="docutils literal notranslate"><span class="pre">np.cast</span></code>, <code class="docutils literal notranslate"><span class="pre">np.coerce</span></code>?</p>
</section>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<ol class="arabic simple">
<li><p>Rename <code class="docutils literal notranslate"><span class="pre">NDArrayOperatorsMixin</span></code> to <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code> (leaving
behind an alias for backwards compatibility) and make it an ABC.</p></li>
<li><p>Add <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code> (or whatever we end up calling it), and
probably a C API equivalent.</p></li>
<li><p>Begin migrating NumPy internal functions to using
<code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code> where appropriate.</p></li>
</ol>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>This is purely a new feature, so there are no compatibility issues.
(Unless we decide to change the semantics of <code class="docutils literal notranslate"><span class="pre">asarray</span></code> itself.)</p>
</section>
<section id="rejected-alternatives">
<h2>Rejected alternatives<a class="headerlink" href="#rejected-alternatives" title="Link to this heading">#</a></h2>
<p>One suggestion that has come up is to define multiple abstract classes
for different subsets of the array interface. Nothing in this proposal
stops either NumPy or third-parties from doing this in the future, but
it’s very difficult to guess ahead of time which subsets would be
useful. Also, “the full ndarray interface” is something that existing
libraries are written to expect (because they work with actual
ndarrays) and test (because they test with actual ndarrays), so it’s
by far the easiest place to start.</p>
</section>
<section id="links-to-discussion">
<h2>Links to discussion<a class="headerlink" href="#links-to-discussion" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://mail.python.org/pipermail/numpy-discussion/2018-March/077767.html">https://mail.python.org/pipermail/numpy-discussion/2018-March/077767.html</a></p></li>
</ul>
</section>
<section id="appendix-benchmark-script">
<h2>Appendix: benchmark script<a class="headerlink" href="#appendix-benchmark-script" title="Link to this heading">#</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">perf</span>
<span class="kn">import</span> <span class="nn">abc</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">class</span> <span class="nc">NotArray</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">AttrArray</span><span class="p">:</span>
<span class="n">__array_implementer__</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">class</span> <span class="nc">ArrayBase</span><span class="p">(</span><span class="n">abc</span><span class="o">.</span><span class="n">ABC</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ABCArray1</span><span class="p">(</span><span class="n">ArrayBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ABCArray2</span><span class="p">:</span>
<span class="k">pass</span>
<span class="n">ArrayBase</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">ABCArray2</span><span class="p">)</span>
<span class="n">not_array</span> <span class="o">=</span> <span class="n">NotArray</span><span class="p">()</span>
<span class="n">attr_array</span> <span class="o">=</span> <span class="n">AttrArray</span><span class="p">()</span>
<span class="n">abc_array_1</span> <span class="o">=</span> <span class="n">ABCArray1</span><span class="p">()</span>
<span class="n">abc_array_2</span> <span class="o">=</span> <span class="n">ABCArray2</span><span class="p">()</span>
<span class="c1"># Make sure ABC cache is primed</span>
<span class="nb">isinstance</span><span class="p">(</span><span class="n">not_array</span><span class="p">,</span> <span class="n">ArrayBase</span><span class="p">)</span>
<span class="nb">isinstance</span><span class="p">(</span><span class="n">abc_array_1</span><span class="p">,</span> <span class="n">ArrayBase</span><span class="p">)</span>
<span class="nb">isinstance</span><span class="p">(</span><span class="n">abc_array_2</span><span class="p">,</span> <span class="n">ArrayBase</span><span class="p">)</span>
<span class="n">runner</span> <span class="o">=</span> <span class="n">perf</span><span class="o">.</span><span class="n">Runner</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">t</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">statement</span><span class="p">):</span>
<span class="n">runner</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">statement</span><span class="p">,</span> <span class="nb">globals</span><span class="o">=</span><span class="nb">globals</span><span class="p">())</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"np.asarray([])"</span><span class="p">,</span> <span class="s2">"np.asarray([])"</span><span class="p">)</span>
<span class="n">arrobj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"np.asarray(arrobj)"</span><span class="p">,</span> <span class="s2">"np.asarray(arrobj)"</span><span class="p">)</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"attr, False"</span><span class="p">,</span>
<span class="s2">"getattr(not_array, '__array_implementer__', False)"</span><span class="p">)</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"attr, True"</span><span class="p">,</span>
<span class="s2">"getattr(attr_array, '__array_implementer__', False)"</span><span class="p">)</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"ABC, False"</span><span class="p">,</span> <span class="s2">"isinstance(not_array, ArrayBase)"</span><span class="p">)</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"ABC, True, via inheritance"</span><span class="p">,</span> <span class="s2">"isinstance(abc_array_1, ArrayBase)"</span><span class="p">)</span>
<span class="n">t</span><span class="p">(</span><span class="s2">"ABC, True, via register"</span><span class="p">,</span> <span class="s2">"isinstance(abc_array_2, ArrayBase)"</span><span class="p">)</span>
</pre></div>
</div>
</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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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#abstract">Abstract</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#detailed-description">Detailed description</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#declaring-that-an-object-can-quack-like-an-array">Declaring that an object can quack like an array</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#specification-of-asabstractarray">Specification of <code class="docutils literal notranslate"><span class="pre">asabstractarray</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#what-exactly-are-you-promising-if-you-inherit-from-abstractarray">What exactly are you promising if you inherit from <code class="docutils literal notranslate"><span class="pre">AbstractArray</span></code>?</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#naming">Naming</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#rejected-alternatives">Rejected alternatives</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#appendix-benchmark-script">Appendix: benchmark script</a></li>
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