iris.AbstractRawDataset

class iris.AbstractRawDataset(source=None, metadata=None)

Abstract base class for ultrafast electron diffraction data set. AbstractRawDataset allows for enforced metadata types and values, as well as a standard interface. For example, AbstractRawDataset implements the context manager interface.

Minimally, the following method must be implemented in subclasses:

  • raw_data

It is suggested to also implement the following magic method:

  • __init__
  • __exit__

For better results or performance during reduction, the following methods can be specialized:

  • reduced

A list of concrete implementations of AbstractRawDatasets is available in the implementations class attribute. Subclasses are automatically added.

The call signature must remain the same for all overwritten methods.

Parameters:
  • source (object) – Data source, for example a directory or external file.
  • metadata (dict or None, optional) – Metadata and experimental parameters. Dictionary keys that are not valid metadata, they are ignored. Metadata can also be set directly later.
Raises:

TypeError : if an item from the metadata has an unexpected type.

__init__(source=None, metadata=None)
Parameters:
  • source (object) – Data source, for example a directory or external file.
  • metadata (dict or None, optional) – Metadata and experimental parameters. Dictionary keys that are not valid metadata, they are ignored. Metadata can also be set directly later.
Raises:

TypeError : if an item from the metadata has an unexpected type.

Methods

__init__([source, metadata])
param source:Data source, for example a directory or external file.
iterscan(scan, **kwargs) Generator function of diffraction patterns as part of a scan, in time-delay order.
raw_data(timedelay[, scan]) Returns an array of the image at a timedelay and scan.
reduced([exclude_scans, align, normalize, …]) Generator of reduced dataset.
update_metadata(metadata) Update metadata from a dictionary.
__exit__(*exc)

Raise any exception triggered within the runtime context.

__init__(source=None, metadata=None)
Parameters:
  • source (object) – Data source, for example a directory or external file.
  • metadata (dict or None, optional) – Metadata and experimental parameters. Dictionary keys that are not valid metadata, they are ignored. Metadata can also be set directly later.
Raises:

TypeError : if an item from the metadata has an unexpected type.

__repr__()

Return repr(self).

iterscan(scan, **kwargs)

Generator function of diffraction patterns as part of a scan, in time-delay order.

Parameters:
  • scan (int) – Scan from which to yield the data.
  • kwargs – Keyword-arguments are passed to raw_data method.
Yields:

data (~numpy.ndarray, ndim 2)

See also

itertime()
generator of diffraction patterns for a single time-delay, in scan order
itertime(timedelay, exclude_scans=None, **kwargs)

Generator function of diffraction patterns of the same time-delay, in scan order.

Parameters:
  • timedelay (float) –
  • from which to yield the data. (Scan) –
exclude_scans : iterable or None, optional
These scans will be skipped.
kwargs
Keyword-arguments are passed to raw_data method.
Yields:data (~numpy.ndarray, ndim 2)

See also

iterscan()
generator of diffraction patterns for a single scan, in time-delay order
metadata

Experimental parameters and dataset metadata as a dictionary.

raw_data(timedelay, scan=1, **kwargs)

Returns an array of the image at a timedelay and scan.

Parameters:
  • timdelay (float) – Acquisition time-delay.
  • scan (int, optional) – Scan number. Default is 1.
  • kwargs – Keyword-arguments are ignored.
Returns:

arr

Return type:

~numpy.ndarray, ndim 2

Raises:
  • ValueError : if timedelay or scan are invalid / out of bounds.
  • IOError : Filename is not associated with an image/does not exist.
reduced(exclude_scans=None, align=True, normalize=True, mask=None, processes=1, dtype=<class 'float'>)

Generator of reduced dataset. The reduced diffraction patterns are generated in order of time-delay.

This particular implementation normalizes diffracted intensity of pictures acquired at the same time-delay while rejecting masked pixels.

Parameters:
  • exclude_scans (iterable or None, optional) – These scans will be skipped when reducing the dataset.
  • align (bool, optional) – If True (default), raw diffraction patterns will be aligned using the masked normalized cross-correlation approach. See skued.align for more information.
  • normalize (bool, optional) – If True (default), equivalent diffraction pictures (e.g. same time-delay, different scans) are normalized to the same diffracted intensity.
  • mask (array-like of bool or None, optional) – If not None, pixels where mask = True are ignored for certain operations (e.g. alignment).
  • processes (int or None, optional) – Number of Processes to spawn for processing.
  • dtype (numpy.dtype or None, optional) – Reduced patterns will be cast to dtype.
Yields:

pattern (~numpy.ndarray, ndim 2)

update_metadata(metadata)

Update metadata from a dictionary. Only appropriate keys are used; irrelevant keys are ignored.

Parameters:metadata (dictionary) – See AbstractRawDataset.valid_metadata for valid keys.