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<li class="toctree-l1 current"><a class="current reference internal" href="#">Datasets</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#cdtools.datasets.CDataset"><code class="docutils literal notranslate"><span class="pre">CDataset</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="#cdtools.datasets.CDataset.__init__"><code class="docutils literal notranslate"><span class="pre">CDataset.__init__()</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="#cdtools.datasets.Ptycho2DDataset"><code class="docutils literal notranslate"><span class="pre">Ptycho2DDataset</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="#cdtools.datasets.Ptycho2DDataset.__init__"><code class="docutils literal notranslate"><span class="pre">Ptycho2DDataset.__init__()</span></code></a></li>
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<li class="toctree-l3"><a class="reference internal" href="#cdtools.datasets.Ptycho2DDataset.to_cxi"><code class="docutils literal notranslate"><span class="pre">Ptycho2DDataset.to_cxi()</span></code></a></li>
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<section id="module-cdtools.datasets">
<span id="datasets"></span><h1>Datasets<a class="headerlink" href="#module-cdtools.datasets" title="Link to this heading"></a></h1>
<p>This module contains all the datasets for interacting with ptychography data</p>
<p>All the access to data from standard ptychography and CDI experiments is
coordinated through the various datasets defined in this module. They make use
of the lower-level data reading and writing functions defined in tools.data,
but critically all of these datasets subclass torch.Dataset. This allows
them to be used as standard torch datasets during reconstructions, which
helps make it easy to use the various data-handling strategies that are
implemented by default in pytorch (such as drawing data in a random order,
drawing minibatches, etc.)</p>
<p>New Datasets can be defined a subclass of the main CDataset class defined
in the base.py file, and should define the following functions:</p>
<ul class="simple">
<li><p>__init__</p></li>
<li><p>__len__</p></li>
<li><p>_load</p></li>
<li><p>to</p></li>
<li><p>from_cxi</p></li>
<li><p>to_cxi</p></li>
<li><p>inspect</p></li>
</ul>
<p>Example implementations of all these functions can be found in the code
for the Ptycho2DDataset class. In addition, it is recommended to read
through the tutorial section on defining a new CDI dataset before
attempting to do so</p>
<dl class="py class">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cdtools.datasets.</span></span><span class="sig-name descname"><span class="pre">CDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entry_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wavelength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">background</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset" title="Link to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
<p>The base dataset class which all other datasets subclass</p>
<p>Subclasses torch.utils.data.Dataset</p>
<p>This base dataset class defines the functionality which should be
common to all subclassed datasets. This includes the loading and
storage of the metadata portions of .cxi files, as well as the tools
needed to allow for easy mixing of data on the CPU and GPU.</p>
<p>The __init__ function allows construction from python objects.</p>
<p>The detector_geometry dictionary is defined to have the
entries defined by the outputs of data.get_detector_geometry.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>entry_info</strong> (<em>dict</em>) – A dictionary containing the entry_info metadata</p></li>
<li><p><strong>sample_info</strong> (<em>dict</em>) – A dictionary containing the sample_info metadata</p></li>
<li><p><strong>wavelength</strong> (<em>float</em>) – The wavelength of light used in the experiment</p></li>
<li><p><strong>detector_geometry</strong> (<em>dict</em>) – A dictionary containing the various detector geometry
parameters</p></li>
<li><p><strong>mask</strong> (<em>array</em>) – A mask for the detector, defined as 1 for live pixels, 0
for dead</p></li>
<li><p><strong>background</strong> (<em>array</em>) – An initial guess for the not-previously-subtracted
detector background</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entry_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wavelength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">detector_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">background</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.__init__" title="Link to this definition"></a></dt>
<dd><p>The __init__ function allows construction from python objects.</p>
<p>The detector_geometry dictionary is defined to have the
entries defined by the outputs of data.get_detector_geometry.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>entry_info</strong> (<em>dict</em>) – A dictionary containing the entry_info metadata</p></li>
<li><p><strong>sample_info</strong> (<em>dict</em>) – A dictionary containing the sample_info metadata</p></li>
<li><p><strong>wavelength</strong> (<em>float</em>) – The wavelength of light used in the experiment</p></li>
<li><p><strong>detector_geometry</strong> (<em>dict</em>) – A dictionary containing the various detector geometry
parameters</p></li>
<li><p><strong>mask</strong> (<em>array</em>) – A mask for the detector, defined as 1 for live pixels, 0
for dead</p></li>
<li><p><strong>background</strong> (<em>array</em>) – An initial guess for the not-previously-subtracted
detector background</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.to">
<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.to" title="Link to this definition"></a></dt>
<dd><p>Sends the relevant data to the given device and dtype</p>
<p>This function sends the stored mask and background to the
specified device and dtype</p>
<p>Accepts the same parameters as torch.Tensor.to</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.get_as">
<span class="sig-name descname"><span class="pre">get_as</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.get_as" title="Link to this definition"></a></dt>
<dd><p>Sets the dataset to return data on the given device and dtype</p>
<p>Oftentimes there isn’t room to store an entire dataset on a GPU,
but it is still worth running the calculation on the GPU even with
the overhead incurred by transferring data back and forth. In that
case, get_as can be used instead of to, to declare a set of
device and dtype that the data should be returned as, whenever it
is accessed through the __getitem__ function (as it would be in
any reconstructions).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>torch.Tensor.to</strong> (<em>Accepts the same parameters as</em>)</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset._load">
<span class="sig-name descname"><span class="pre">_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset._load" title="Link to this definition"></a></dt>
<dd><p>Internal function to load data</p>
<p>In all subclasses of CDataset, a _load function should be defined.
This function is used internally by the global __getitem__ function
defined in the base class, which handles moving data around when
the dataset is (for example) storing the data on the CPU but
getting data as GPU tensors.</p>
<p>It should accept an index or slice, and return output as a tuple.
The first item of the tuple is a tuple containing the inputs to
the forward model for the related ptychography model. The second
item of the tuple should be the set of diffraction patterns
associated with the returned inputs.</p>
<p>Since there is no kind of data stored in a CDataset, this
function is defined as returing a NotImplemented Error</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.from_cxi">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_cxi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cxi_file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.from_cxi" title="Link to this definition"></a></dt>
<dd><p>Generates a new CDataset from a .cxi file directly</p>
<p>This is the most commonly used constructor for CDatasets and
subclasses thereof. It populates the dataset using the information
in a .cxi file. It can either take an h5py.File object directly,
or a filename or pathlib object pointing to the file</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>file</strong> (<em>str</em><em>, </em><em>pathlib.Path</em><em>, or </em><em>h5py.File</em>) – The .cxi file to load from</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>dataset</strong> – The constructed dataset object</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="#cdtools.datasets.CDataset" title="cdtools.datasets.CDataset">CDataset</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.to_cxi">
<span class="sig-name descname"><span class="pre">to_cxi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cxi_file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.to_cxi" title="Link to this definition"></a></dt>
<dd><p>Saves out a CDataset as a .cxi file</p>
<p>This function saves all the compatible information in a CDataset
object into a .cxi file. This is useful for saving out modified
or simulated datasets</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>cxi_file</strong> (<em>str</em><em>, </em><em>pathlib.Path</em><em>, or </em><em>h5py.File</em>) – The .cxi file to write to</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.CDataset.inspect">
<span class="sig-name descname"><span class="pre">inspect</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.CDataset.inspect" title="Link to this definition"></a></dt>
<dd><p>The prototype for the inspect function</p>
<p>In all subclasses of CDataset, an inspect function should be
defined which opens a tool that shows the data in a natural
layout for that kind of experiment. In the base class, no actual
data is stored, so this is defined to raise a NotImplementedError</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">cdtools.datasets.</span></span><span class="sig-name descname"><span class="pre">Ptycho2DDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">translations</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patterns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intensities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset" title="Link to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#cdtools.datasets.CDataset" title="cdtools.datasets.base.CDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">CDataset</span></code></a></p>
<p>The standard dataset for a 2D ptychography scan</p>
<p>Subclasses datasets.CDataset</p>
<p>This class loads and saves 2D ptychography scan data from .cxi files.
It should save and load files compatible with most reconstruction
programs, although it is only tested against SHARP.</p>
<p>The __init__ function allows construction from python objects.</p>
<p>The detector_geometry dictionary is defined to have the
entries defined by the outputs of data.get_detector_geometry.</p>
<p>Note that the created dataset object will not copy the data in the
patterns parameter in order to avoid doubling the memory requiement
for large datasets.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>translations</strong> (<em>array</em>) – An nx3 array containing the probe translations at each scan point</p></li>
<li><p><strong>patterns</strong> (<em>array</em>) – An nxmxl array containing the full stack of measured diffraction patterns</p></li>
<li><p><strong>axes</strong> (<em>list</em><em>(</em><em>str</em><em>)</em>) – A list of names for the axes of the probe translations</p></li>
<li><p><strong>entry_info</strong> (<em>dict</em>) – A dictionary containing the entry_info metadata</p></li>
<li><p><strong>sample_info</strong> (<em>dict</em>) – A dictionary containing the sample_info metadata</p></li>
<li><p><strong>wavelength</strong> (<em>float</em>) – The wavelength of light used in the experiment</p></li>
<li><p><strong>detector_geometry</strong> (<em>dict</em>) – A dictionary containing the various detector geometry
parameters</p></li>
<li><p><strong>mask</strong> (<em>array</em>) – A mask for the detector, defined as 1 for live pixels, 0
for dead</p></li>
<li><p><strong>background</strong> (<em>array</em>) – An initial guess for the not-previously-subtracted
detector background</p></li>
<li><p><strong>intensities</strong> (<em>array</em>) – A list of measured shot-to-shot intensities</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">translations</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patterns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intensities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.__init__" title="Link to this definition"></a></dt>
<dd><p>The __init__ function allows construction from python objects.</p>
<p>The detector_geometry dictionary is defined to have the
entries defined by the outputs of data.get_detector_geometry.</p>
<p>Note that the created dataset object will not copy the data in the
patterns parameter in order to avoid doubling the memory requiement
for large datasets.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>translations</strong> (<em>array</em>) – An nx3 array containing the probe translations at each scan point</p></li>
<li><p><strong>patterns</strong> (<em>array</em>) – An nxmxl array containing the full stack of measured diffraction patterns</p></li>
<li><p><strong>axes</strong> (<em>list</em><em>(</em><em>str</em><em>)</em>) – A list of names for the axes of the probe translations</p></li>
<li><p><strong>entry_info</strong> (<em>dict</em>) – A dictionary containing the entry_info metadata</p></li>
<li><p><strong>sample_info</strong> (<em>dict</em>) – A dictionary containing the sample_info metadata</p></li>
<li><p><strong>wavelength</strong> (<em>float</em>) – The wavelength of light used in the experiment</p></li>
<li><p><strong>detector_geometry</strong> (<em>dict</em>) – A dictionary containing the various detector geometry
parameters</p></li>
<li><p><strong>mask</strong> (<em>array</em>) – A mask for the detector, defined as 1 for live pixels, 0
for dead</p></li>
<li><p><strong>background</strong> (<em>array</em>) – An initial guess for the not-previously-subtracted
detector background</p></li>
<li><p><strong>intensities</strong> (<em>array</em>) – A list of measured shot-to-shot intensities</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset._load">
<span class="sig-name descname"><span class="pre">_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset._load" title="Link to this definition"></a></dt>
<dd><p>Internal function to load data</p>
<p>This function is used internally by the global __getitem__ function
defined in the base class, which handles moving data around when
the dataset is (for example) storing the data on the CPU but
getting data as GPU tensors.</p>
<p>It loads data in the format (inputs, output)</p>
<p>The inputs for a 2D ptychogaphy data set are:</p>
<ol class="arabic simple">
<li><p>The indices of the patterns to use</p></li>
<li><p>The recorded probe positions associated with those points</p></li>
</ol>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>index</strong> (<em>int</em><em> or </em><em>slice</em>) – The index or indices of the scan points to use</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><ul class="simple">
<li><p><strong>inputs</strong> (<em>tuple</em>) – A tuple of the inputs to the related forward models</p></li>
<li><p><strong>outputs</strong> (<em>tuple</em>) – The output pattern or stack of output patterns</p></li>
</ul>
</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.to">
<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.to" title="Link to this definition"></a></dt>
<dd><p>Sends the relevant data to the given device and dtype</p>
<p>This function sends the stored translations, patterns,
mask and background to the specified device and dtype</p>
<p>Accepts the same parameters as torch.Tensor.to</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.from_cxi">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_cxi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cxi_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_patterns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.from_cxi" title="Link to this definition"></a></dt>
<dd><p>Generates a new Ptycho2DDataset from a .cxi file directly</p>
<p>This generates a new Ptycho2DDataset from a .cxi file storing
a 2D ptychography scan.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file</strong> (<em>str</em><em>, </em><em>pathlib.Path</em><em>, or </em><em>h5py.File</em>) – The .cxi file to load from</p></li>
<li><p><strong>cut_zeros</strong> (<em>bool</em>) – Default True, whether to set all negative data to zero</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><strong>dataset</strong> – The constructed dataset object</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="#cdtools.datasets.Ptycho2DDataset" title="cdtools.datasets.Ptycho2DDataset">Ptycho2DDataset</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.to_cxi">
<span class="sig-name descname"><span class="pre">to_cxi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cxi_file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.to_cxi" title="Link to this definition"></a></dt>
<dd><p>Saves out a Ptycho2DDataset as a .cxi file</p>
<p>This function saves all the compatible information in a
Ptycho2DDataset object into a .cxi file. This saved .cxi file
should be compatible with any standard .cxi file based
reconstruction tool, such as SHARP.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>cxi_file</strong> (<em>str</em><em>, </em><em>pathlib.Path</em><em>, or </em><em>h5py.File</em>) – The .cxi file to write to</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.inspect">
<span class="sig-name descname"><span class="pre">inspect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logarithmic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'um'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_mean_pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.inspect" title="Link to this definition"></a></dt>
<dd><p>Launches an interactive plot for perusing the data</p>
<p>This launches an interactive plotting tool in matplotlib that
shows the spatial map constructed from the integrated intensity
at each position on the left, next to a panel on the right that
can display a base-10 log plot of the detector readout at each
position.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.plot_mean_pattern">
<span class="sig-name descname"><span class="pre">plot_mean_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.plot_mean_pattern" title="Link to this definition"></a></dt>
<dd><p>Plots the mean diffraction pattern across the dataset</p>
<p>The output is normalized so that the summed intensity on the
detector is equal to the total intensity of light that passed
through the sample within each detector conjugate field of view.</p>
<p>The plot is plotted as log base 10 of the output plus log_offset.
By default, log_offset is set equal to 1, which is a good level for
shot-noise limited data captured in units of photons. More
generally, log_offset should be set roughly at the background noise
level.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.split">
<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.split" title="Link to this definition"></a></dt>
<dd><p>Splits a dataset into two pseudorandomly selected sub-datasets</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.pad">
<span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_pad</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.pad" title="Link to this definition"></a></dt>
<dd><p>Pads all the diffraction patterns by a speficied amount</p>
<p>This is useful for scenarios where the diffraction is strong, even
near the edge of the detector. In this scenario, the discrete version
of the ptychography model will alias. Padding the diffraction patterns
to increase their size and masking off the outer region can account
for this effect.</p>
<p>If to_pad is an integer, the patterns will be padded on all sides by
this value. If it is a tuple of length 2, then the patterns will be
padded (left/right, top/bottom, left/right). If a tuple of length 4,
the padding is done as (left, right, top, bottom), following the
convention for torch.nn.functional.pad</p>
<p>Any mask and background data which is stored with the dataset will be
padded along with the diffraction patterns</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>to_pad</strong> (<em>int</em><em> or </em><em>tuple</em><em>(</em><em>int</em><em>)</em>) – The number of pixels to pad by.</p></li>
<li><p><strong>value</strong> (<em>float</em>) – Optional, the fill value to pad with. Default is 0</p></li>
<li><p><strong>mask</strong> (<em>bool</em>) – Optional, whether to mask off the new pixels. Default is True</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.downsample">
<span class="sig-name descname"><span class="pre">downsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.downsample" title="Link to this definition"></a></dt>
<dd><p>Downsamples all diffraction patterns by the specified factor</p>
<p>This is an easy way to shrink the amount of data you need to work with
if the speckle size is much larger than the detector pixel size.</p>
<p>The downsampling factor must be an integer. The size of the output
patterns are reduced by the specified factor, with each output pixel
equal to the sum of a <factor> x <factor> region of pixels in the
input pattern. This summation is done by pytorch.functional.avg_pool2d.</p>
<p>Any mask and background data which is stored with the dataset is
downsampled with the data. The background is downsampled using the same
method as the data. The mask is expanded so that any output pixel
containing a masked pixel will be masked.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>factor</strong> (<em>int</em>) – Default 2, the factor to downsample by</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.remove_translations_mask">
<span class="sig-name descname"><span class="pre">remove_translations_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_remove</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.remove_translations_mask" title="Link to this definition"></a></dt>
<dd><p>Removes one or more translation positions, and their associated
properties, from the dataset using logical indexing.</p>
<p>This takes a 1D mask (boolean torch tensor) with the length
self.translations.shape[0] (i.e., the number of individual
translated points). Patterns, translations, and intensities
associated with indices that are “True” will be removed.</p>
<section id="parameters">
<h2>Parameters:<a class="headerlink" href="#parameters" title="Link to this heading"></a></h2>
<dl class="simple">
<dt>mask_remove<span class="classifier">1D torch.tensor(dtype=torch.bool)</span></dt><dd><p>The boolean mask indicating which elements are to be removed from
the dataset. True indicates that the corresponding element will be
removed.</p>
</dd>
</dl>
</section>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="cdtools.datasets.Ptycho2DDataset.crop_translations">
<span class="sig-name descname"><span class="pre">crop_translations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">roi</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#cdtools.datasets.Ptycho2DDataset.crop_translations" title="Link to this definition"></a></dt>
<dd><p>Shrinks the range of translation positions that are analyzed</p>
<p>This deletes all diffraction patterns associated with x- and
y-translations that lie outside of a specified rectangular
region of interest. In essence, this operation crops the “relative
displacement map” (shown in self.inspect()) down to the region of
interest.</p>
<section id="id1">
<h2>Parameters:<a class="headerlink" href="#id1" title="Link to this heading"></a></h2>
<dl class="simple">
<dt>roi<span class="classifier">tuple(float, float, float, float)</span></dt><dd><p>The translation-x and -y coordinates that define the rectangular
region of interest as (in units of meters)
(left, right, bottom, top). The definition of these bounds are
based on how an image is normally displayed with matplotlib’s
imshow. The order in which these elements are defined in roi
do not matter as long as roi[:2] and roi[2:] correspond with
the x and y coordinates, respectively.</p>
</dd>
</dl>
</section>
</dd></dl>
</dd></dl>
</section>
</div>
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