Add implementation of dpnp.histogram (#1785)

* Implement dpnp.histogram

* Updated third party tests for histogram

* Updated histogram tests due to new dpnp.cumsum() impl

* Applied pre-commit checks

* Added dpnp tests

* Added CFD tests

* Renamed test_histogram.py

* Updated tests to run on Iris Xe

* Get rid of block size

* Addressed review comments

* Leftovers from rebase

* Cast bin edges to default floating type if density is enabled 8d04592

Author: github-actions[bot]

.buildinfo

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+  <title>dpnp.dpnp_flatiter &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
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     </script>
 
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+  <title>dpnp.dpnp_iface &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
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@@ -308,8 +308,8 @@ <h1>Source code for dpnp.dpnp_iface</h1><div class="highlight"><pre>
 <span class="sd">            - &#39;unsafe&#39; means any data conversions may be done.</span>
 
 <span class="sd">    copy : bool, optional</span>
-<span class="sd">        By default, ``astype`` always returns a newly allocated array. If this</span>
-<span class="sd">        is set to ``False``, and the `dtype`, `order`, and `subok` requirements</span>
+<span class="sd">        By default, astype always returns a newly allocated array. If this</span>
+<span class="sd">        is set to ``False``, and the dtype, order, and subok requirements</span>
 <span class="sd">        are satisfied, the input array is returned instead of a copy.</span>
 
 <span class="sd">    Returns</span>

_modules/dpnp/dpnp_iface.html

@@ -12,7 +12,7 @@
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+  <title>dpnp.dpnp_iface_counting &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
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-  <title>dpnp.dpnp_iface_histograms &mdash; Data Parallel Extension for NumPy 0.15.0dev1+62.g651e98a28a documentation</title>
+  <title>dpnp.dpnp_iface_histograms &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
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-  <title>dpnp.dpnp_iface_indexing &mdash; Data Parallel Extension for NumPy 0.15.0dev1+62.g651e98a28a documentation</title>
+  <title>dpnp.dpnp_iface_indexing &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
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             Data Parallel Extension for NumPy
           </a>
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@@ -529,12 +529,12 @@ <h1>Source code for dpnp.dpnp_iface_indexing</h1><div class="highlight"><pre>
 <span class="sd">    out : one dpnp.ndarray or tuple of dpnp.ndarray</span>
 <span class="sd">        If sparse is ``False``:</span>
 <span class="sd">        Returns one array of grid indices,</span>
-<span class="sd">        ``grid.shape = (len(dimensions),) + tuple(dimensions)``.</span>
+<span class="sd">        grid.shape = (len(dimensions),) + tuple(dimensions).</span>
 
 <span class="sd">        If sparse is ``True``:</span>
 <span class="sd">        Returns a tuple of arrays,</span>
 <span class="sd">        with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)</span>
-<span class="sd">        with dimensions[i] in the i-th place.</span>
+<span class="sd">        with dimensions[i] in the ith place.</span>
 
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
@@ -907,8 +907,7 @@ <h1>Source code for dpnp.dpnp_iface_indexing</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../reference/generated/dpnp.select.html#dpnp.select">[docs]</a>
 <span class="k">def</span> <span class="nf">select</span><span class="p">(</span><span class="n">condlist</span><span class="p">,</span> <span class="n">choicelist</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return an array drawn from elements in `choicelist`, depending on</span>
-<span class="sd">    conditions.</span>
+<span class="sd">    Return an array drawn from elements in choicelist, depending on conditions.</span>
 
 <span class="sd">    For full documentation refer to :obj:`numpy.select`.</span>
 
@@ -1065,8 +1064,7 @@ <h1>Source code for dpnp.dpnp_iface_indexing</h1><div class="highlight"><pre>
 <span class="sd">    &gt;&gt;&gt; import dpnp as np</span>
 <span class="sd">    &gt;&gt;&gt; a = np.array([[10, 30, 20], [60, 40, 50]])</span>
 
-<span class="sd">    We can sort either by using :obj:`dpnp.sort` directly, or</span>
-<span class="sd">    :obj:`dpnp.argsort` and this function:</span>
+<span class="sd">    We can sort either by using sort directly, or argsort and this function</span>
 
 <span class="sd">    &gt;&gt;&gt; np.sort(a, axis=1)</span>
 <span class="sd">    array([[10, 20, 30],</span>
@@ -1094,7 +1092,7 @@ <h1>Source code for dpnp.dpnp_iface_indexing</h1><div class="highlight"><pre>
 <span class="sd">           [60]])</span>
 
 <span class="sd">    If we want to get the max and min at the same time, we can stack the</span>
-<span class="sd">    indices first:</span>
+<span class="sd">    indices first</span>
 
 <span class="sd">    &gt;&gt;&gt; ai_min = np.argmin(a, axis=1, keepdims=True)</span>
 <span class="sd">    &gt;&gt;&gt; ai_max = np.argmax(a, axis=1, keepdims=True)</span>

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@@ -43,7 +43,7 @@
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     </script>
 
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+  <title>dpnp.dpnp_iface_linearalgebra &mdash; Data Parallel Extension for NumPy 0.15.0dev1+43.g8d0459283 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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@@ -43,7 +43,7 @@
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@@ -344,7 +344,7 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
 <span class="sd">    See Also</span>
 <span class="sd">    --------</span>
 <span class="sd">    :obj:`dpnp.einsum` : Einstein summation convention..</span>
-<span class="sd">    :obj:`dpnp.dot` : Generalized matrix product,</span>
+<span class="sd">    :obj:`dpnp.dot` : Generalised matrix product,</span>
 <span class="sd">                      using second last dimension of `b`.</span>
 <span class="sd">    :obj:`dpnp.tensordot` : Sum products over arbitrary axes.</span>
 
@@ -409,10 +409,7 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
 <a class="viewcode-back" href="../../reference/generated/dpnp.kron.html#dpnp.kron">[docs]</a>
 <span class="k">def</span> <span class="nf">kron</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="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Kronecker product of two arrays.</span>
-
-<span class="sd">    Computes the Kronecker product, a composite array made of blocks of the</span>
-<span class="sd">    second array scaled by the first.</span>
+<span class="sd">    Returns the kronecker product of two arrays.</span>
 
 <span class="sd">    For full documentation refer to :obj:`numpy.kron`.</span>
 
@@ -713,7 +710,7 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
 <span class="sd">    out : dpnp.ndarray</span>
-<span class="sd">        Returns the tensor dot product of `a` and `b`.</span>
+<span class="sd">        Returns the tensordot product of `a` and `b`.</span>
 
 <span class="sd">    See Also</span>
 <span class="sd">    --------</span>
@@ -792,7 +789,7 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
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+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Axes must be a nonnegative integer.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
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         <span class="n">axes_b</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">axes</span><span class="p">))</span>
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