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DiffractionFocussing v2¶

Summary ¶

Algorithm to focus powder diffraction data into a number of histograms according to a grouping scheme defined in a CalFile.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	A 2D workspace with X values of d-spacing, Q or TOF (TOF support deprecated on 29/04/21)
OutputWorkspace	Output	MatrixWorkspace	Mandatory	The result of diffraction focussing of InputWorkspace
GroupingFileName	Input	string		Optional: The name of the CalFile with grouping data. Allowed extensions: [‘.cal’]
GroupingWorkspace	Input	GroupingWorkspace		Optional: GroupingWorkspace to use instead of a grouping file.
PreserveEvents	Input	boolean	True	Keep the output workspace as an EventWorkspace, if the input has events (default). If false, then the workspace gets converted to a Workspace2D histogram.
DMin	Input	dbl list		Minimum x values, one value for each output specta or single value which is common to all
DMax	Input	dbl list		Maximum x values, one value for each output specta or single value which is common to all
Delta	Input	dbl list		Step parameters for rebin, positive values are constant step-size, negative are logorithmic. One value for each output specta or single value which is common to all

Description ¶

Example of RAW GEM data focused across the 5 detector banks Given an InputWorkspace and a Grouping filename, the algorithm performs the following:

The calibration file is read and a map of corresponding udet-group is created.
The algorithm determines the X boundaries for each group as the upper and lower limits of all contributing detectors. It then calculates a logarithmic step that preserves the number of bins in the initial workspace. It assumes that the entire data set uses logarithmic binning in the process (i.e. it does not check for constant width binning).
All histograms are read and rebinned to the new grid for their group.
A new workspace with N histograms is created.

Within the CalFile any detectors with the ‘select’ flag can be set to zero or with a group number of 0 or -ve groups are not included in the analysis.

Since the new X boundaries depend on the group and not the entire workspace, this focusing algorithm does not create overestimated X ranges for multi-group instruments. However it is important to remember that this means that this algorithm outputs a ragged workspace. Some 2D and 3D plots will not display the data correctly.

The DiffractionFocussing algorithm uses GroupDetectors algorithm to combine data from several spectra according to GroupingFileName file which is a CalFile.

For EventWorkspaces ¶

The algorithm can be used with an EventWorkspace input, and will create an EventWorkspace output if a different workspace is specified.

The main difference vs. using a Workspace2D is that the event lists from all the incoming pixels are simply appended in the grouped spectra; this means that you can rebin the resulting spectra to finer bins with no loss of data. In fact, it is unnecessary to bin your incoming data at all; binning can be performed as the very last step.

Rebin parameters ¶

By default DiffractionFocussing will use the min and max of each spectra and the number of bins in the input workspace to automatically determine the output binning. An alternative is to provide the min, max and delta for each output spectra similar to RebinRagged v2. The minimum and maximum values that are specified are interpreted as one value per spectrum. If there is only one value overall, it is used for all of the spectra. The Delta parameter is required and can either be a single number which is common to all, or one number per spectra. Positive values are interpreted as constant step-size. Negative are logarithmic.

Usage ¶

Note

To run these usage examples please first download the usage data, and add these to your path. In Mantid this is done using Manage User Directories.

Example - Diffraction focussing of HRPD data:

# Load HRP dataset
ws = Load("HRP39180.RAW")

# specify groupping file, here using CalFile format
cal_file = "hrpd_new_072_01_corr.cal"

# For HRPD data, perform a unit conversion TOF->d-spacing, taking into account detector position offsets
ws = AlignDetectors(InputWorkspace='ws',CalibrationFile=cal_file)
# Focus the data
ws = DiffractionFocussing(InputWorkspace='ws',GroupingFileName=cal_file)

print("The 51st y-value is: %.3f" % ws.readY(0)[50])

Output:

The 51st y-value is: 900.148

Example - Demonstrating option PreserveEvents:

# create event workspace with one bank of 4 detectors
ws = CreateSampleWorkspace(WorkspaceType="Event", XUnit="dSpacing", NumBanks=1, BankPixelWidth=2)

# focus data in 4 spectra to 2 spectra according to .cal file and don't perserve events
ws = DiffractionFocussing(InputWorkspace='ws', GroupingFileName="4detector_cal_example_file.cal" \
     , PreserveEvents=False)

print("Number of focussed spectra: " + str(ws.getNumberHistograms()))
print("What type is the workspace after focussing: " + ws.id())

Output:

Number of focussed spectra: 2
What type is the workspace after focussing: Workspace2D

Example - Defining binning parameters:

# Load HRP dataset
ws = Load("HRP39180.RAW")

# specify groupping file, here using CalFile format
cal_file = "hrpd_new_072_01_corr.cal"

# For HRPD data, perform a unit conversion TOF->d-spacing, taking into account detector position offsets
ws = AlignDetectors(InputWorkspace='ws',CalibrationFile=cal_file)
# Focus the data with defined binning parameters
ws = DiffractionFocussing(InputWorkspace='ws',GroupingFileName=cal_file, DMin=[0.6,1.0,2.2], DMax=[1.0,1.5,4.0], Delta=0.1)

print(f"Output has {ws.getNumberHistograms()} spectra with number of bins {len(ws.readY(0))}, {len(ws.readY(1))} and {len(ws.readY(2))}")

Output:

Output has 3 spectra with number of bins 4, 5 and 18

DiffractionFocussing v2¶

Summary ¶

See Also ¶

Properties ¶

Description ¶

For EventWorkspaces ¶

Rebin parameters ¶

Usage ¶

Previous Versions ¶

Version 1 ¶

Source ¶