Ewoks tasks reference#

Each Darfix widget has an underlying Ewoks task that takes care of the processing. The Ewoks tasks can be used independently for headless processing.

The documentation for these Ewoks tasks is given below.

Binning#

Rescale images of a Darfix dataset by a given factor.

Identifier:: darfix.tasks.binning.Binning
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
scale* : float: Factor to rescale images of the dataset.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

BlindSourceSeparation#

Perform blind source separation on a Darfix dataset. Blind source separation (BSS) comprises all techniques that try to decouple a set of source signals from a set of mixed signals with unknown (or very little) information.

Supported methods are PCA, NICA, NMF and NICA_NMF.

Identifier:: darfix.tasks.blind_source_separation.BlindSourceSeparation
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
method* : darfix.tasks.blind_source_separation.Method: Method to use for blind source separation
n_comp : int | None= None: Number of components to extract
save : bool= False
processing_order : int= 0

Outputs:

W
comp
dataset

ChiGradientCorrection#

Apply optical chi gradient correction to the chi COM map.

Identifier:: darfix.tasks.chi_gradient_correction.ChiGradientCorrection
Task type:: class

Inputs:

grain_properties* : darfix.core.grainplot.GrainProperties

Outputs:

grain_properties : darfix.core.grainplot.GrainProperties

DimensionDefinition#

Fit dimension of given dataset. If dims are provided then will use them. else will call ‘find_dimensions’ with the provided tolerance or the default one.

Identifier:: darfix.tasks.dimension_definition.DimensionDefinition
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset

Input dataset containing a stack of images

dims : dict[int, Any] | None= None

Dimensions to use for the dataset. If not provided, the task will try to find dimensions from metadata.

Examples:

{0: {'name': 'diffrx', 'size': 5, 'range': [0.0, 5.0, 1.0]}, 1: {'name': 'diffry', 'size': 10, 'range': [0.0, 10.0, 1.0]}}

is_zigzag : bool | None= None

Set to True if the scan was a zigzag scan (slow motor moving back and forth). Defaults to False.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

GrainPlot#

Generates and saves maps of Center of Mass, FWHM, Skewness, Kurtosis, Orientation distribution and Mosaicity.

Identifier:: darfix.tasks.grain_plot.GrainPlot
Task type:: class

Inputs:

dataset : None | darfix.core.dataset.Dataset= None: Input dataset containing a stack of images. If set, the grain properties are computed from the stack of images.
grain_properties : darfix.core.grainplot.GrainProperties | None= None: Input grain properties. Can be set directly instead of dataset input. Only Mosaicity data would be recomputed.
dimensions : tuple[int, int]= (0, 1): Dimension indices to use for the maps. Default is (0, 1), which means the two first dimensions.
range : tuple[Tuple[float, float] | None, Tuple[float, float] | None]= (None, None): Dimensionrange for the two dimensions. If None, use the Center of Mass min and max for the both dimensions.
save_maps : bool= True: Whether to save the maps to file. Default is True.
filename : str | None= None: Only used if save_maps is True. Filename to save the maps to. Default is ‘maps.h5’ in the dataset directory.
orientation_img_origin : Literal['dims', 'center']= dims: Deprecated. Not used anymore.
kernel_average_misorientation_size : int= 1: Kernel size for kernel average misorientation computation

Outputs:

grain_properties : darfix.core.grainplot.GrainProperties | None: Computed grain properties (Or input grain_properties passthrough)

HDF5DataSelection#

Loads data and positioner metadata from a hdf5 file to create a Darfix dataset.

Identifier:: darfix.tasks.hdf5_data_selection.HDF5DataSelection
Task type:: class

Inputs:

raw_input_file* : str

Path to the raw input file.

Examples:

'/path/to/awesome/file.h5'

raw_detector_data_path* : str

Path to the raw detector data in the input file.

Examples:

'/1.1/measurement/pco_ff'

raw_metadata_path : str | None= None

Path to the raw metadata in the input file.

Examples:

'/1.1/instrument/positioners'

dark_input_file : str | None= None

Path to the dark input file. Default is None.

Examples:

'/path/to/dark/file.h5'

dark_detector_data_path : str | None= None

Path to the dark detector data in the input file. Default is None.

Examples:

'/1.1/measurement/pco_ff'

workflow_title : str=

Title of the dataset for display purpose. Empty if not provided.

treated_data_dir : str | None= None

Processed output directory. If not provided, will try to find PROCESSED_DATA directory.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

ConcatenateHDF5Scans#

Concatenate a set of scans / entries contained in ‘input_file’. If entries_to_concatenate is None then all entries will be concatenated

‘detector_data_path’ is the pattern to find all the detector data path. It

is expected to look like ‘{scan}/instrument/measurement/my_detector’. In this case it will look for each scan at the same location. So for the scan ‘1.1’ it will look for ‘1.1/instrument/measurement/my_detector’. For the scan 2.1 it will look for ‘2.1/instrument/measurement/my_detector’… If the {detector} is provided then all the dataset part of the upper path will be browse in order to ‘guess’ the detector to be used. Using the ‘find_detector’ function. * ‘positioners_group_path’ is the pattern to find all positioner groups. And should look like ‘{scan}/instrument/positioners’ For the scan ‘1.1’ it will look for ‘1.1/instrument/positioners’…

Identifier:: darfix.tasks.hdf5_scans_concatenation.ConcatenateHDF5Scans
Task type:: class

Inputs:

input_file* : str

Path to the input file containing scans to concatenate.

Examples:

'/path/to/input/file.h5'

entries_to_concatenate : tuple[str, ...] | None= None

Entries (scans) in the file to concatenate. If not provided, all entries will be concatenated.

Examples:

('/1.1', '/2.1', '/3.1')

detector_data_path : str= {scan}/measurement/{detector}

Path pattern to the detector data in the input file. If {detector} is in the pattern then all the datasets from the subpath will be browsed in order to ‘guess’ the detector to be used. If not provided, {scan}/measurement/{detector} is the default pattern

Examples:

'{scan}/instrument/measurement/my_detector'
'{scan}/instrument/measurement/{detector}'

duplicate_detector_frames : bool= False

If True will avoid creating a VDS for detector frames and create a standard HDF5 dataset. !!! Will duplicate the frames !!!.

positioners_group_path : str= {scan}/instrument/positioners

Path pattern to the positioners group in the input file. If not provided, {scan}/instrument/positioners is the default pattern

Examples:

'{scan}/instrument/positioners'

overwrite : bool= False

If True then will overwrite the output file if it exists. False if not provided.

output_file : str | None= None

Path to the output file. Must be provided if guess_output_file is False.

Examples:

'/path/to/output/file.h5'

guess_output_file : bool= True

If True then will try to guess the output file from the input file. False if not provided.

Outputs:

output_file

LoadDataset#

Load a preprocessed dataset.

Identifier:: darfix.tasks.load_dataset.LoadDataset
Task type:: class

Inputs:

filename* : str: File name of the Darfix save

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

MetadataTask#

‘Place holder’ task for the MetadataWidgetOW. This widget only does display of information an no processing. But having this task allow use to have it compatible with ewoks.

Identifier:: darfix.tasks.metadata.MetadataTask
Task type:: class

Inputs:

dataset*

NoiseRemoval#

Apply a list of noise removal operations on a Darfix dataset.

Identifier:: darfix.tasks.noise_removal.NoiseRemoval
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset

Input dataset containing a stack of images

operations : list[dict[str, Any]] | ewokscore.missing_data.MissingData= []

List of noise removal operations to apply to the dataset. Empty list if not provided.”

Available operations :

‘Operation.THRESHOLD’: Threshold operation. Parameters: ‘bottom’ (float) and ‘top’ (float). Keep value only if it is between bottom and top.
‘Operation.HP’: Hot Pixel removal using median filter operation. Parameters: ‘kernel_size’ (int).
‘Operation.BS’: Background subtraction operation. Parameters: ‘method’ (“mean” | “median”) and ‘background_type’ (“Data” | “Unused data (after partition)” | “Dark data”).
‘Operation.MASK’: Mask removal operation. Parameters: ‘mask’ (numpy.ndarray 2D containing 0 and 1 where 0 indicates the pixels to be removed).

Examples:

[{'type': 'THRESHOLD', 'parameters': {'bottom': 10.0, 'top': 1000.0}}, {'type': 'HP', 'parameters': {'kernel_size': 3}}]

copy_dataset : bool= False

If True, operations are applied on a copy of the input dataset. Else, operations are applied directly on the input dataset

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

PCA#

Compute Principal Component Analysis on a Darfix dataset.

More about PCA : https://en.wikipedia.org/wiki/Principal_component_analysis

Identifier:: darfix.tasks.pca.PCA
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
num_components : int | None= None: Number of principal components to compute.

Outputs:

dataset
vals

Projection#

Removes one dimension by projecting (summing) all images in this dimension.

Details in https://gitlab.esrf.fr/XRD/darfix/-/issues/37

Identifier:: darfix.tasks.projection.Projection
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
dimension* : Sequence[int]: Dimensions indices to project the data onto.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

RockingCurves#

Analyze the rocking curve of each pixel of each image of the darfix dataset by fitting to a peak shape, e.g. a Gaussian.

Identifier:: darfix.tasks.rocking_curves.RockingCurves
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
output_filename : str | pathlib.Path | None= None: Output filename to save the rocking curves results. Results are not saved if not provided
save_maps : bool= True: Whether to save the maps to file. Default is True.
fit_options : darfix.processing.rocking_curve_fit_options.RockingCurveFitOptions= RockingCurveFitOptions(n_iter_max=100, y_threshold=15, max_peaks_count=1, gaussian_filtering_sigma=0.5, discard_zero_values=True)

Outputs:

dataset
maps

RoiSelection#

Apply a Region of Interest (ROI) selection on a Darfix dataset.

Identifier:: darfix.tasks.roi.RoiSelection
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images.
roi_origin : numpy.ndarray | list[float] | None= None: Origin of the ROI to apply. If not provided, dataset will be unchanged.
roi_size : numpy.ndarray | list[float] | None= None: Size of the ROI to apply. If not provided, dataset will be unchanged.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

RSMHistogram#

Computes Reciprocal Space Map histogram.

Identifier:: darfix.tasks.rsm_histogram.RSMHistogram
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images.
Q* : Tuple[float, float, float]: Scattering vector in oriented pseudocubic coordinates.
a* : float: Pseudocubic lattice parameter.
map_range* : float: Range (in all 3 directions) of the histogram. Center-to-edge distance.
detector* : str: Detector type for the RSM computation.
units : Literal['poulsen', 'gorfman'] | None= None: Either ‘poulsen’ [10.1107/S1600576717011037] or ‘gorfman’ [https://arxiv.org/pdf/2110.14311.pdf]. ‘poulsen’ if not provided
n : Tuple[float, float, float] | None= None: Surface normal of the sample in oriented pseudocubic hkl
map_shape : Tuple[float, float, float] | None= None: Number of bins in each direction.
energy : float | None= None

Outputs:

hist_edges
hist_values

SaveDataset#

Save a preprocessed dataset.

Identifier:: darfix.tasks.save_dataset.SaveDataset
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset
filename* : str: File name of the darfix save (*.h5)

Outputs:

filename : str: File name of the darfix save (*.h5). Useful to combinate with Load task.

ShiftCorrection#

Identifier:: darfix.tasks.shift_correction.ShiftCorrection
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images
shift : Sequence[float] | Sequence[Sequence[float]] | None= None: Shift to apply to the images. If not provided, dataset will be unchanged.
selected_axis : int | None= None: Selected dimension axis. If not None. We considere a linear shift along this dimension. Darfix convention is : dimension with axis 0 is the fast motor.
copy_dataset : bool= False: If True, operations are applied on a copy of the input dataset. Else, operations are applied directly on the input dataset

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

StrainPlot#

Compute strain given COM maps of motor OBPITCH or CCMTH/MU

Identifier:: darfix.tasks.strain_plot.StrainPlot
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset
com_reference : float | None= None: Reference value for the Center Of Mass (COM) of ccmth (or obpitch). If None, the median of COM ccmth (or obpitch) is taken.
filename : str | None= None: Filename to save the strain map to. Default is ‘strain.h5’ in the dataset directory.

Outputs:

strain

TransformationMatrixComputation#

Computes transformation matrix and attach it to the dataset

Identifier:: darfix.tasks.transformation.TransformationMatrixComputation
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images.
kind : Literal['rsm', 'magnification'] | bool | None= None
orientation : int | None= None: Used only with kind=’magnification’.
magnification : float | None= None: To be used only with kind=’magnification’. Magnification factor to apply to the dataset.
pixelSize : Literal['Basler', 'PcoEdge_2x', 'PcoEdge_10x'] | None= None: To be used only with kind=’rsm’, distance in micrometers of each pixel.
rotate : bool= False: To be used only with kind=’rsm’, if True the images with transformation are rotated 90 degrees.

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

WeakBeam#

Obtain dataset with filtered weak beam and recover its Center of Mass. Save file with this COM for further processing.

Identifier:: darfix.tasks.weak_beam.WeakBeam
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images.
nvalue : float | None= None: Increase or decrease the top threshold (threshold = nvalue * std)
title : str=: Title for the output file. If not provided, title is empty.
copy_dataset : bool= False: If True, operations are applied on a copy of the input dataset. Else, operations are applied directly on the input dataset

Outputs:

dataset : darfix.core.dataset.Dataset: Output darfix dataset

ZSum#

Sum all images of the dataset or images along a given dimension.

Identifier:: darfix.tasks.zsum.ZSum
Task type:: class

Inputs:

dataset* : darfix.core.dataset.Dataset: Input dataset containing a stack of images.
selected_axis : int | None= None: Selected axis in dataset.data. The zsum is computed for each value of this axis.

Outputs:

zsum