Using sphinxcontrib.spelling as extension (#1800)

* Using sphinxcontrib.spelling as extension

* Installing enchant package required by sphinxcontrib-spelling

* Run a spelling checker for docs

* Installing pyenchant package

* Aligned docstrings of elementwise functions with dpctl.tensor

* Aligned docstrings of returns in elementwise functions with dpctl.tensor

* Install enchant apt package

* Extend dictionary with dpnp, dtype, ndarray, ufunc and ufuncs words

* Fixed spelling issues and added 15 new words to known list

* Fixed spelling issues and added 7 new words to known list

* Fixed spelling issues and added 15 new words to known list

* Fixed spelling issues and added 10 new words to known list

* Fixed spelling issues and added 4 new words to known list

* Fixed spelling issues and added 7 new words to known list

* Added missing docstrings to ndarray methods

* Fixed building issue in ndarray.rst

* Resolved a warning: term not in glossary: 'universal function'

* Resolve a warning after rebase with master c348bf4

Author: github-actions[bot]

.buildinfo

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 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: c95e5698925b98627c84d79a866befa6
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+  <title>dpnp.dpnp_flatiter &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
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+        <script src="../../_static/documentation_options.js?v=ff0417e1"></script>
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@@ -12,7 +12,7 @@
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-  <title>dpnp.dpnp_iface &mdash; Data Parallel Extension for NumPy 0.15.0dev1+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 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+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface_counting &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
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_modules/dpnp/dpnp_iface_arraycreation.html

@@ -12,7 +12,7 @@
     </script>
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>dpnp.dpnp_iface_histograms &mdash; Data Parallel Extension for NumPy 0.15.0dev1+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface_histograms &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
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@@ -23,7 +23,7 @@
 
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@@ -300,7 +300,7 @@ <h1>Source code for dpnp.dpnp_iface_histograms</h1><div class="highlight"><pre>
 <a class="viewcode-back" href="../../reference/generated/dpnp.histogram.html#dpnp.histogram">[docs]</a>
 <span class="k">def</span> <span class="nf">histogram</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Compute the histogram of a dataset.</span>
+<span class="sd">    Compute the histogram of a data set.</span>
 
 <span class="sd">    For full documentation refer to :obj:`numpy.histogram`.</span>
 

_modules/dpnp/dpnp_iface_counting.html

@@ -12,7 +12,7 @@
     </script>
 
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-  <title>dpnp.dpnp_iface_indexing &mdash; Data Parallel Extension for NumPy 0.15.0dev1+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface_indexing &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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@@ -43,7 +43,7 @@
             Data Parallel Extension for NumPy
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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 ith place.</span>
+<span class="sd">        with dimensions[i] in the i-th place.</span>
 
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
@@ -907,7 +907,8 @@ <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 conditions.</span>
+<span class="sd">    Return an array drawn from elements in `choicelist`, depending on</span>
+<span class="sd">    conditions.</span>
 
 <span class="sd">    For full documentation refer to :obj:`numpy.select`.</span>
 
@@ -1064,7 +1065,8 @@ <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 sort directly, or argsort and this function</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">    &gt;&gt;&gt; np.sort(a, axis=1)</span>
 <span class="sd">    array([[10, 20, 30],</span>
@@ -1092,7 +1094,7 @@ <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>

_modules/dpnp/dpnp_iface_histograms.html

@@ -12,7 +12,7 @@
     </script>
 
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-  <title>dpnp.dpnp_iface_libmath &mdash; Data Parallel Extension for NumPy 0.15.0dev1+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface_libmath &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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@@ -43,7 +43,7 @@
             Data Parallel Extension for NumPy
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@@ -12,7 +12,7 @@
     </script>
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>dpnp.dpnp_iface_linearalgebra &mdash; Data Parallel Extension for NumPy 0.15.0dev1+49.g5dd1c4c7be documentation</title>
+  <title>dpnp.dpnp_iface_linearalgebra &mdash; Data Parallel Extension for NumPy 0.15.0dev1+44.gc348bf4590 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
 
@@ -23,7 +23,7 @@
 
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         <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-        <script src="../../_static/documentation_options.js?v=c3fade41"></script>
+        <script src="../../_static/documentation_options.js?v=ff0417e1"></script>
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@@ -43,7 +43,7 @@
             Data Parallel Extension for NumPy
           </a>
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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` : Generalised matrix product,</span>
+<span class="sd">    :obj:`dpnp.dot` : Generalized 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>
 
@@ -409,7 +409,10 @@ <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">    Returns the kronecker product of two arrays.</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">    For full documentation refer to :obj:`numpy.kron`.</span>
 
@@ -710,7 +713,7 @@ <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 tensordot product of `a` and `b`.</span>
+<span class="sd">        Returns the tensor dot product of `a` and `b`.</span>
 
 <span class="sd">    See Also</span>
 <span class="sd">    --------</span>
@@ -789,7 +792,7 @@ <h1>Source code for dpnp.dpnp_iface_linearalgebra</h1><div class="highlight"><pr
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Axes must be an integer.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
         <span class="k">if</span> <span class="n">axes</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <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>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Axes must be a non-negative integer.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span>
         <span class="n">axes_a</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="o">-</span><span class="n">axes</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
         <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>
     <span class="k">else</span><span class="p">:</span>