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Expand Up @@ -532,6 +532,8 @@ <h1>Source code for cil.framework.framework</h1><div class="highlight"><pre>
<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<div class="viewcode-block" id="Partitioner">
<a class="viewcode-back" href="../../../../framework/#cil.framework.Partitioner">[docs]</a>
<span class="k">class</span> <span class="nc">Partitioner</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Interface for AcquisitionData to be able to partition itself in a number of batches.</span>

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<span class="n">ags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ag</span><span class="p">)</span>
<span class="k">return</span> <span class="n">BlockGeometry</span><span class="p">(</span><span class="o">*</span><span class="n">ags</span><span class="p">)</span>

<div class="viewcode-block" id="Partitioner.partition">
<a class="viewcode-back" href="../../../../framework/#cil.framework.Partitioner.partition">[docs]</a>
<span class="k">def</span> <span class="nf">partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Partition the data into ``num_batches`` batches using the specified ``mode``.</span>

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<span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="n">Partitioner</span><span class="o">.</span><span class="n">RANDOM_PERMUTATION</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_random_permutation</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown partition mode </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mode</span><span class="p">))</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Unknown partition mode </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mode</span><span class="p">))</span></div>


<span class="k">def</span> <span class="nf">_partition_deterministic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&#39;&#39;&#39;Partition the data into ``num_batches`` batches.</span>
Expand Down Expand Up @@ -712,7 +717,8 @@ <h1>Source code for cil.framework.framework</h1><div class="highlight"><pre>

<span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>

<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_deterministic</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_deterministic</span><span class="p">(</span><span class="n">num_batches</span><span class="p">,</span> <span class="n">stagger</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">)</span></div>


<span class="k">def</span> <span class="nf">find_key</span><span class="p">(</span><span class="n">dic</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;return the key of dictionary dic given the value&quot;&quot;&quot;</span>
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Expand Up @@ -174,6 +174,64 @@ This provide a base class that will behave as normal :code:`DataContainer`.
:members:
:inherited-members:

Partitioner
===========

This method partitions an instance of tomography :code:`AcquisitionData` into a number of batches. For example, to use with a stochastic optimisation method.

The partitioning is done by taking batches of angles and the corresponding data collected by taking projections along these angles. The partitioner method chooses what angles go in which batch depending on the `mode` and takes in an `AquisitionData` object and outputs a `BlockDataContainer` where each element in the block is `AquisitionData` object with the batch of data and corresponding geometry.
We consider a **batch** to be a subset of the :code:`AcquisitionData` and the verb, **to partition**, to be the act of splitting into batches.


For example:

.. code-block :: python
from cil.utilities import dataexample
from cil.plugins.astra.operators import ProjectionOperator
# get the data
data = dataexample.SIMULATED_PARALLEL_BEAM_DATA.get()
data.reorder('astra')
data = data.get_slice(vertical='centre')
# create the geometries
ag = data.geometry
ig = ag.get_ImageGeometry()
# partition the data into batches contained in the elements of a BlockDataContainer
data_partitioned = data.partition(num_batches=10, mode='staggered') # Choose mode from `sequential`, `staggered` or `random_permutation`
# From the partitioned data build a BlockOperator container the projectors for each batch
A_partitioned = ProjectionOperator(ig, data_partitioned.geometry, device = "cpu")
print('The total number of angles is ', len(data.geometry.angles))
print('The first 30 angles are ', data.geometry.angles[:30])
print('In batch zero the number of angles is ', len(data_partitioned[0].geometry.angles))
print('The angles in batch zero are ', data_partitioned[0].geometry.angles)
print('The angles in batch one are ', data_partitioned[1].geometry.angles)
.. code-block :: RST
The total number of angles is 300
The first 30 angles are [ 0. 1.2 2.4 3.6 4.8 6. 7.2 8.4 9.6 10.8 12. 13.2 14.4 15.6
16.8 18. 19.2 20.4 21.6 22.8 24. 25.2 26.4 27.6 28.8 30. 31.2 32.4
33.6 34.8]
In batch zero the number of angles is 30
The angles in batch zero are [ 0. 12. 24. 36. 48. 60. 72. 84. 96. 108. 120. 132. 144. 156.
168. 180. 192. 204. 216. 228. 240. 252. 264. 276. 288. 300. 312. 324.
336. 348.]
The angles in batch one are [ 1.2 13.2 25.2 37.2 49.2 61.2 73.2 85.2 97.2 109.2 121.2 133.2
145.2 157.2 169.2 181.2 193.2 205.2 217.2 229.2 241.2 253.2 265.2 277.2
289.2 301.2 313.2 325.2 337.2 349.2]
The :code:`partition` method is defined as part of:

.. autoclass:: cil.framework.Partitioner
:members:


DataOrder
=========
.. autoclass:: cil.framework.DataOrder
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