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+<!DOCTYPE html>
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+<html lang="en" data-content_root="../../">
+  <head>
+    <meta charset="utf-8" />
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+    <title>dolomite_matrix.choose_dense_chunk_sizes &#8212; dolomite-matrix 0.0.post1.dev1+gdc536bf documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+  <h1>Source code for dolomite_matrix.choose_dense_chunk_sizes</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
+
+
+<div class="viewcode-block" id="choose_dense_chunk_sizes">
+<a class="viewcode-back" href="../../api/dolomite_matrix.html#dolomite_matrix.choose_dense_chunk_sizes.choose_dense_chunk_sizes">[docs]</a>
+<span class="k">def</span> <span class="nf">choose_dense_chunk_sizes</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="nb">int</span><span class="p">],</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">min_extent</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">memory</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mf">1e7</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Chosen some chunk sizes to use for a dense HDF5 dataset. For each</span>
+<span class="sd">    dimension, we consider a slice of the array that consists of the full</span>
+<span class="sd">    extent of all other dimensions. We want this slice to occupy less than</span>
+<span class="sd">    ``memory`` in memory, and we resize the slice along the current dimension</span>
+<span class="sd">    to achieve this. The chosen chunk size is then defined as the size of the</span>
+<span class="sd">    slice along the current dimension. This ensures that efficient iteration</span>
+<span class="sd">    along each dimension will not use any more than ``memory`` bytes.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        shape: Shape of the array.</span>
+
+<span class="sd">        size: Size of each array element in bytes.</span>
+
+<span class="sd">        min_extent: </span>
+<span class="sd">            Minimum extent of each chunk dimension, to avoid problems</span>
+<span class="sd">            with excessively small chunk sizes when the data is large.</span>
+
+<span class="sd">        memory:</span>
+<span class="sd">            Size of the (conceptual) memory buffer to use for storing blocks of</span>
+<span class="sd">            data during iteration through the array, in bytes.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple containing the chunk dimensions.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">num_elements</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">memory</span> <span class="o">/</span> <span class="n">size</span><span class="p">)</span>
+    <span class="n">chunks</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span>
+        <span class="n">otherdim</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">d2</span><span class="p">,</span> <span class="n">s2</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> <span class="c1"># just calculating it again to avoid overflow issues.</span>
+            <span class="k">if</span> <span class="n">d2</span> <span class="o">!=</span> <span class="n">d</span><span class="p">:</span>
+                <span class="n">otherdim</span> <span class="o">*=</span> <span class="n">s2</span>
+
+        <span class="n">proposed</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_elements</span> <span class="o">/</span> <span class="n">otherdim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">proposed</span> <span class="o">&gt;</span> <span class="n">s</span><span class="p">:</span>
+            <span class="n">proposed</span> <span class="o">=</span> <span class="n">s</span>
+        <span class="k">elif</span> <span class="n">proposed</span> <span class="o">&lt;</span> <span class="n">min_extent</span><span class="p">:</span>
+            <span class="n">proposed</span> <span class="o">=</span> <span class="n">min_extent</span>
+
+        <span class="n">chunks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">proposed</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="p">(</span><span class="o">*</span><span class="n">chunks</span><span class="p">,)</span></div>
+
+</pre></div>
+
+          </div>
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diff --git a/_modules/dolomite_matrix/load_hdf5_dense_array.html b/_modules/dolomite_matrix/load_hdf5_dense_array.html
@@ -0,0 +1,138 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>dolomite_matrix.load_hdf5_dense_array &#8212; dolomite-matrix 0.0.post1.dev1+gdc536bf documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=fa44fd50" />
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+  <h1>Source code for dolomite_matrix.load_hdf5_dense_array</h1><div class="highlight"><pre>
+<span></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">numpy</span> <span class="kn">import</span> <span class="n">bool_</span>
+<span class="kn">from</span> <span class="nn">filebackedarray</span> <span class="kn">import</span> <span class="n">Hdf5DenseArray</span>
+<span class="kn">from</span> <span class="nn">dolomite_base</span> <span class="kn">import</span> <span class="n">acquire_file</span>
+
+
+<div class="viewcode-block" id="load_hdf5_dense_array">
+<a class="viewcode-back" href="../../api/dolomite_matrix.html#dolomite_matrix.load_hdf5_dense_array.load_hdf5_dense_array">[docs]</a>
+<span class="k">def</span> <span class="nf">load_hdf5_dense_array</span><span class="p">(</span><span class="n">meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">project</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Hdf5DenseArray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Load a HDF5-backed dense array.  In general, this function should be</span>
+<span class="sd">    called via :py:meth:`~dolomite_base.load_object.load_object`.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        meta: Metadata for this HDF5 array.</span>
+
+<span class="sd">        project: Value specifying the project of interest. This is most</span>
+<span class="sd">            typically a string containing a file path to a staging directory</span>
+<span class="sd">            but may also be an application-specific object that works with</span>
+<span class="sd">            :py:meth:`~dolomite_base.acquire_file.acquire_file`.</span>
+
+<span class="sd">        kwargs: Further arguments, ignored.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        A HDF5-backed dense array.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fpath</span> <span class="o">=</span> <span class="n">acquire_file</span><span class="p">(</span><span class="n">project</span><span class="p">,</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;path&quot;</span><span class="p">])</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;hdf5_dense_array&quot;</span><span class="p">][</span><span class="s2">&quot;dataset&quot;</span><span class="p">]</span>
+
+    <span class="n">dtype</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">meta</span><span class="p">[</span><span class="s2">&quot;array&quot;</span><span class="p">][</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;boolean&quot;</span><span class="p">:</span>
+        <span class="n">dtype</span> <span class="o">=</span> <span class="n">bool_</span>
+
+    <span class="k">return</span> <span class="n">Hdf5DenseArray</span><span class="p">(</span><span class="n">fpath</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">native_order</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+
+</pre></div>
+
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