PoldiAutoCorrelation v6#

Summary#

Performs correlation analysis of POLDI 2D-data.

Properties#

Name	Direction	Type	Default	Description
InputWorkspace	Input	`MatrixWorkspace`	Mandatory	The workspace containing the input data
OutputWorkspace	Output	`MatrixWorkspace`	Mandatory	Output workspace containing the correlation spectrum.
WavelengthMin	Input	number	1.1	Minium wavelength to consider.
WavelengthMax	Input	number	5	Maximum wavelength to consider.
InterpolationMethod	Input	string	Linear	Interpolation used when adding intensity to a given bin in correlation function - ‘Nearest’ is quicker but potentially less accurate. Allowed values: [‘Linear’, ‘Nearest’]
NGroups	Input	number	1	Number of groups to split poldi detectors into (returns a spectrum per group). Cannot exceed the number of spectra, or the number of unmasked spectra if GroupingMode is ‘Unmasked’.
GroupingMode	Input	string	All	How detectors are split into NGroups when spectra are masked. ‘All’ splits the detectors into groups first and then removes any which have been masked (giving consistent but potentially uneven groups). ‘Unmasked’ removes the masked detectors first and then splits what is left. Allowed values: [‘All’, ‘Unmasked’]

Description#

POLDI is a pulse overlap diffractometer and therefore the arrival time of a neutron does not uniquely determine the time-of-flight of the neutron (and hence the d-spacing in a given detector). Neutrons of different wavelength can arrive at the same time having passed through the chopper in a different pulse/slit. Indeed, neutrons corresponding to the same d-spacing are observed at many different arrival times in a given detector due to the overlapping pulses measured.

Raw POLDI 1.2 data (i.e. post 2D detector upgrade) for silicon powder standard.#

The output of PoldiAutoCorrelation is a correlation spectrum, similar to the spectrum of a conventional TOF diffractometer. Bragg reflections will appear in this correlation spectrum as peaks surrounded by a dip in the background. The positions of the Bragg peaks can be determined to a high accuracy in the correlation spectrum and indexed accordingly.

PoldiAutoCorrelation performs a type of average of the intensity over different arrival times from each pulse corresponding to a given d-spacing for each detector: the correlation intensity at a given d-spacing is the inverse of the sum of the inverse intensities at each possible arrival time (see Eq. 8 in [1]). A high correlation intensity implies a consistently high intensity was measured at all arrival times due to all possible chopper openings/pulses.

In general the arrival time of a neutron for a given d-spacing will not coincide with a bin-center in the data - some interpolation is required. There are two interpolation methods which can be specified with the InterpolationMethod parameter: Linear (default - as in [1]) and Nearest. The Nearest method is a roughly a factor of 2-3 quicker but is potentially less accurate.

Masked spectra are omitted from the calculations. The way this interacts with the detector grouping behaviour is controlled by the GroupingMode. All splits the detectors into groups first and then removes any which have been masked (giving consistent but potentially uneven groups). A group in which every detector is masked gives a spectrum of zeros, so NGroups can be as large as the number of spectra. Unmasked removes the masked detectors first and then splits whatever is left into the requested number of groups, so NGroups cannot exceed the number of unmasked spectra.

Note by convention the correlation spectrum is converted from d-spacing into momentum transfer.

Further details of the POLDI instrument and the reduction can be found in [1].

Correlation spectrum of silicon powder standard.#

Usage#

Note POLDI 1.2 is still under development, the data are currently saved in ASCII format with no meta-data required for the reduction. Currently there is a helper function load_poldi that reads the ASCII file, loads an instrument definition and adds the appropriate meta-data to the workspace.

from mantid.simpleapi import *
from mantid.api import FileFinder
from plugins.algorithms.poldi_utils import load_poldi

fpath_data = FileFinder.getFullPath("poldi_448x500_chopper5k_silicon.txt")
fpath_idf = FileFinder.getFullPath("POLDI_Definition_448_calibrated.xml")

# load the raw data
ws = load_poldi(fpath_data, fpath_idf, chopper_speed=5000, t0=5.855e-02, t0_const=-9.00)
print(f"The workspace contains {ws.getNumberHistograms()} spectra.")

ws_corr = PoldiAutoCorrelation(InputWorkspace=ws, OutputWorkspace='ws_corr')
print(f"The correlation spectrum has {ws_corr.blocksize()} bins.")

Output:

The workspace contains 448 spectra.
The correlation spectrum has 2833 bins.

References#

Categories: AlgorithmIndex | SINQ\Poldi

Source#

Python: PoldiAutoCorrelation6.py