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8 changes: 4 additions & 4 deletions sed/latest/_modules/index.html
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Overview: module code &#8212; SED 0.1.10a6 documentation</title>
<title>Overview: module code &#8212; SED 0.1.10a5 documentation</title>



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<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=3ee479438cf8b5e0d341" />
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DOCUMENTATION_OPTIONS.theme_version = '0.15.3';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://raw.githubusercontent.com/OpenCOMPES/docs/main/sed/switcher.json';
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</script>
<link rel="index" title="Index" href="../genindex.html" />
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<p class="title logo__title">SED 0.1.10a6 documentation</p>
<p class="title logo__title">SED 0.1.10a5 documentation</p>

</a></div>

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40 changes: 19 additions & 21 deletions sed/latest/_modules/sed/binning/numba_bin.html
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<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>sed.binning.numba_bin &#8212; SED 0.1.10a6 documentation</title>
<title>sed.binning.numba_bin &#8212; SED 0.1.10a5 documentation</title>



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DOCUMENTATION_OPTIONS.theme_version = '0.15.3';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://raw.githubusercontent.com/OpenCOMPES/docs/main/sed/switcher.json';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.1.10a6';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.1.10a5';
DOCUMENTATION_OPTIONS.show_version_warning_banner = true;
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<link rel="index" title="Index" href="../../../genindex.html" />
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<p class="title logo__title">SED 0.1.10a6 documentation</p>
<p class="title logo__title">SED 0.1.10a5 documentation</p>

</a></div>

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<h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;This file contains code for binning using numba precompiled code for the</span>
<span class="sd">sed.binning module</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Sequence</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>

<span class="kn">import</span> <span class="nn">numba</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
Expand All @@ -472,7 +470,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span class="sd"> bit integers.</span>

<span class="sd"> Args:</span>
<span class="sd"> sample (np.ndarray): The data to be histogram&#39;d with shape N,D.</span>
<span class="sd"> sample (np.ndarray): The data to be histogrammed with shape N,D.</span>
<span class="sd"> bins (Sequence[int]): The number of bins for each dimension D.</span>
<span class="sd"> ranges (np.ndarray): A sequence of length D, each an optional (lower,</span>
<span class="sd"> upper) tuple giving the outer bin edges to be used if the edges are</span>
Expand All @@ -497,7 +495,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndims</span><span class="p">):</span>
<span class="n">delta</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">((</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">bins</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">hist</span><span class="o">.</span><span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">//</span> <span class="n">hist</span><span class="o">.</span><span class="n">itemsize</span>
<span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">hist</span><span class="o">.</span><span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">//</span> <span class="n">hist</span><span class="o">.</span><span class="n">itemsize</span> <span class="c1"># pylint: disable=E1136</span>

<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">is_inside</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="k">def</span> <span class="nf">_hist_from_bins</span><span class="p">(</span>
<span class="n">sample</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">bins</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
<span class="n">shape</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">,</span>
<span class="n">shape</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Numba powered binning method, similar to np.histogramdd.</span>

Expand All @@ -569,7 +567,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span class="sd"> sample (np.ndarray) : the array of shape (N,D) on which to compute the histogram</span>
<span class="sd"> bins (Sequence[np.ndarray]): array of shape (N,D) defining the D bins on which</span>
<span class="sd"> to compute the histogram, i.e. the desired output axes.</span>
<span class="sd"> shape (Tuple): shape of the resulting array. Workaround for the fact numba</span>
<span class="sd"> shape (tuple): shape of the resulting array. Workaround for the fact numba</span>
<span class="sd"> does not allow to create tuples.</span>
<span class="sd"> Returns:</span>
<span class="sd"> hist: the computed n-dimensional histogram</span>
Expand Down Expand Up @@ -607,10 +605,10 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<a class="viewcode-back" href="../../../sed/binning.html#sed.binning.numba_bin.numba_histogramdd">[docs]</a>
<span class="k">def</span> <span class="nf">numba_histogramdd</span><span class="p">(</span>
<span class="n">sample</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">bins</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
<span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">ranges</span><span class="p">:</span> <span class="n">Sequence</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Multidimensional histogram function, powered by Numba.</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Multidimensional histogramming function, powered by Numba.</span>

<span class="sd"> Behaves in total much like numpy.histogramdd. Returns uint32 arrays.</span>
<span class="sd"> This was chosen because it has a significant performance improvement over</span>
Expand All @@ -620,8 +618,8 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span class="sd"> sizes.</span>

<span class="sd"> Args:</span>
<span class="sd"> sample (np.ndarray): The data to be histogram&#39;d with shape N,D</span>
<span class="sd"> bins (Union[int, Sequence[int], Sequence[np.ndarray], np.ndarray]): The number</span>
<span class="sd"> sample (np.ndarray): The data to be histogrammed with shape N,D</span>
<span class="sd"> bins (int | Sequence[int] | Sequence[np.ndarray] | np.ndarray): The number</span>
<span class="sd"> of bins for each dimension D, or a sequence of bin edges on which to calculate</span>
<span class="sd"> the histogram.</span>
<span class="sd"> ranges (Sequence, optional): The range(s) to use for binning when bins is a sequence</span>
Expand All @@ -634,7 +632,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span class="sd"> RuntimeError: Internal shape error after binning</span>

<span class="sd"> Returns:</span>
<span class="sd"> Tuple[np.ndarray, List[np.ndarray]]: 2-element tuple of The computed histogram</span>
<span class="sd"> tuple[np.ndarray, list[np.ndarray]]: 2-element tuple of The computed histogram</span>
<span class="sd"> and s list of D arrays describing the bin edges for each dimension.</span>

<span class="sd"> - **hist**: The computed histogram</span>
Expand Down Expand Up @@ -666,7 +664,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>

<span class="c1"># method == &quot;array&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">bins</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="nb">list</span><span class="p">(</span><span class="n">bins</span><span class="p">))</span>
<span class="n">bins</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="nb">list</span><span class="p">(</span><span class="n">bins</span><span class="p">))</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">_hist_from_bins</span><span class="p">(</span>
<span class="n">sample</span><span class="p">,</span>
<span class="nb">tuple</span><span class="p">(</span><span class="n">bins</span><span class="p">),</span>
Expand All @@ -692,7 +690,7 @@ <h1>Source code for sed.binning.numba_bin</h1><div class="highlight"><pre>
<span class="n">bins</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">bins</span><span class="p">)</span>

<span class="c1"># Create edge arrays</span>
<span class="n">edges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">edges</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">nbin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">num_cols</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_cols</span><span class="p">):</span>
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