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Implementation of convolve (#2205)
Add implementation of `dpnp.convolve`. Implementation is mostly based on already existing functionality developed for `dpnp.correlate` Similar to [scipy.signal.convolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.convolve.html) `method` keyword is introduced, but unlike `scipy.signal.convolve` `dpnp.convolve` works only with 1-d arrays. - [x] Have you provided a meaningful PR description? - [x] Have you added a test, reproducer or referred to issue with a reproducer? - [x] Have you tested your changes locally for CPU and GPU devices? - [x] Have you made sure that new changes do not introduce compiler warnings? - [x] Have you checked performance impact of proposed changes? - [x] If this PR is a work in progress, are you filing the PR as a draft? --------- Co-authored-by: Vahid Tavanashad <[email protected]> Co-authored-by: Anton <[email protected]> f13e9a4
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<span class="n">dpnp</span><span class="o">.</span><span class="n">check_supported_arrays_type</span><span class="p">(</span><span class="o">*</span><span class="n">arys</span><span class="p">)</span>
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<span class="n">result</span> <span class="o">=</span> <span class="n">ary</span>

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@@ -176,7 +176,6 @@ <h1>Source code for dpnp.dpnp_iface_mathematical</h1><div class="highlight"><pre
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<span class="s2">&quot;clip&quot;</span><span class="p">,</span>
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<span class="s2">&quot;conj&quot;</span><span class="p">,</span>
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<span class="s2">&quot;conjugate&quot;</span><span class="p">,</span>
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<span class="s2">&quot;convolve&quot;</span><span class="p">,</span>
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<span class="s2">&quot;copysign&quot;</span><span class="p">,</span>
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<span class="s2">&quot;cross&quot;</span><span class="p">,</span>
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<span class="s2">&quot;cumprod&quot;</span><span class="p">,</span>
@@ -883,27 +882,6 @@ <h1>Source code for dpnp.dpnp_iface_mathematical</h1><div class="highlight"><pre
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<span class="n">conj</span> <span class="o">=</span> <span class="n">conjugate</span>
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<div class="viewcode-block" id="convolve">
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<a class="viewcode-back" href="../../reference/generated/dpnp.convolve.html#dpnp.convolve">[docs]</a>
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<span class="k">def</span><span class="w"> </span><span class="nf">convolve</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;full&quot;</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Returns the discrete, linear convolution of two one-dimensional sequences.</span>
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<span class="sd"> For full documentation refer to :obj:`numpy.convolve`.</span>
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<span class="sd"> Examples</span>
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<span class="sd"> --------</span>
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<span class="sd"> &gt;&gt;&gt; ca = dpnp.convolve([1, 2, 3], [0, 1, 0.5])</span>
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<span class="sd"> &gt;&gt;&gt; print(ca)</span>
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<span class="sd"> [0. , 1. , 2.5, 4. , 1.5]</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">return</span> <span class="n">call_origin</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">convolve</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">v</span><span class="o">=</span><span class="n">v</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">)</span></div>
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<span class="n">_COPYSIGN_DOCSTRING</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
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<span class="s2">Composes a floating-point value with the magnitude of `x1_i` and the sign of</span>
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<span class="s2">`x2_i` for each element of input arrays `x1` and `x2`.</span>

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