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EnggCalibrate.py
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EnggCalibrate.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
from mantid.kernel import *
from mantid.api import *
import mantid.simpleapi as mantid
class EnggCalibrate(PythonAlgorithm):
INDICES_PROP_NAME = 'SpectrumNumbers'
def category(self):
return "Diffraction\\Engineering"
def seeAlso(self):
return [ "EnggCalibrateFull" ]
def name(self):
return "EnggCalibrate"
def summary(self):
return ("This algorithm is deprecated as of May 2021, consider using PDCalibration instead."
"Calibrates one or more detector banks (or group(s) of detectors) by performing single peak "
"fitting.")
def PyInit(self):
self.declareProperty(MatrixWorkspaceProperty("InputWorkspace", "", Direction.Input),
doc="Workspace with the calibration run to use.")
import EnggUtils
self.declareProperty(FloatArrayProperty("ExpectedPeaks",
values=EnggUtils.default_ceria_expected_peaks(),
direction=Direction.Input),
doc="A list of dSpacing values where peaks are expected.")
self.declareProperty(FileProperty(name="ExpectedPeaksFromFile", defaultValue="", action=FileAction.OptionalLoad,
extensions=[".csv"]),
doc="Load from file a list of dSpacing values to be translated into TOF to find expected "
"peaks. This takes precedence over 'ExpectedPeaks' if both options are given.")
peaks_grp = 'Peaks to fit'
self.setPropertyGroup('ExpectedPeaks', peaks_grp)
self.setPropertyGroup('ExpectedPeaksFromFile', peaks_grp)
self.declareProperty(MatrixWorkspaceProperty("VanadiumWorkspace", "", Direction.Input, PropertyMode.Optional),
doc='Workspace with the Vanadium (correction and calibration) run. Alternatively, '
'when the Vanadium run has been already processed, the properties can be used')
self.declareProperty(ITableWorkspaceProperty("VanIntegrationWorkspace", "", Direction.Input,
PropertyMode.Optional),
doc='Results of integrating the spectra of a Vanadium run, with one column '
'(integration result) and one row per spectrum. This can be used in '
'combination with OutVanadiumCurveFits from a previous execution and '
'VanadiumWorkspace to provide pre-calculated values for Vanadium correction.')
self.declareProperty(MatrixWorkspaceProperty('VanCurvesWorkspace', '', Direction.Input, PropertyMode.Optional),
doc='A workspace2D with the fitting workspaces corresponding to the instrument banks. '
'This workspace has three spectra per bank, as produced by the algorithm Fit. '
'This is meant to be used as an alternative input VanadiumWorkspace for testing and '
'performance reasons. If not given, no workspace is generated.')
vana_grp = 'Vanadium (open beam) properties'
self.setPropertyGroup('VanadiumWorkspace', vana_grp)
self.setPropertyGroup('VanIntegrationWorkspace', vana_grp)
self.setPropertyGroup('VanCurvesWorkspace', vana_grp)
self.declareProperty("Bank", '', StringListValidator(EnggUtils.ENGINX_BANKS),
direction=Direction.Input,
doc="Which bank to calibrate. It can be specified as 1 or 2, or "
"equivalently, North or South. See also " + self.INDICES_PROP_NAME + " "
"for a more flexible alternative to select specific detectors")
self.declareProperty(self.INDICES_PROP_NAME, '', direction=Direction.Input,
doc='Sets the spectrum numbers for the detectors '
'that should be considered in the calibration (all others will be '
'ignored). This option cannot be used together with Bank, as they overlap. '
'You can give multiple ranges, for example: "0-99", or "0-9, 50-59, 100-109".')
banks_grp = 'Banks / spectra'
self.setPropertyGroup('Bank', banks_grp)
self.setPropertyGroup(self.INDICES_PROP_NAME, banks_grp)
self.declareProperty(ITableWorkspaceProperty("DetectorPositions", "", Direction.Input, PropertyMode.Optional),
"Calibrated detector positions. If not specified, default ones (from the "
"current instrument definition) are used.")
self.declareProperty('OutputParametersTableName', '', direction=Direction.Input,
doc='Name for a table workspace with the calibration parameters calculated '
'from this algorithm: difc and zero parameters for GSAS. these two parameters '
'are added as two columns in a single row. If not given, no table is '
'generated.')
self.declareProperty("DIFA", 0.0, direction=Direction.Output,
doc="Calibration parameter DIFA for the bank or range of pixels/detectors given")
self.declareProperty("DIFC", 0.0, direction=Direction.Output,
doc="Calibration parameter DIFC for the bank or range of pixels/detectors given")
self.declareProperty("TZERO", 0.0, direction=Direction.Output,
doc="Calibration parameter TZERO for the bank or range of pixels/detectors given")
self.declareProperty(ITableWorkspaceProperty("FittedPeaks", "", Direction.Output),
doc="Information on fitted peaks as produced by the (child) algorithm EnggFitPeaks.")
out_grp = 'Outputs'
self.setPropertyGroup('DetectorPositions', out_grp)
self.setPropertyGroup('OutputParametersTableName', out_grp)
self.setPropertyGroup('DIFA', out_grp)
self.setPropertyGroup('DIFC', out_grp)
self.setPropertyGroup('TZERO', out_grp)
self.setPropertyGroup('FittedPeaks', out_grp)
def validateInputs(self):
issues = dict()
if not self.getPropertyValue("ExpectedPeaksFromFile") and not self.getPropertyValue('ExpectedPeaks'):
issues['ExpectedPeaks'] = ("Cannot run this algorithm without any expected peak. Please provide "
"either a list of peaks or a file with a list of peaks")
return issues
def PyExec(self):
mantid.logger.warning("EnggCalibrate is deprecated as of May 2021. Please use PDCalibration instead.")
import EnggUtils
max_reports = 20
prog = Progress(self, start=0, end=1, nreports=max_reports)
# Get peaks in dSpacing from file
prog.report("Reading peaks")
expected_peaks_dsp = EnggUtils.read_in_expected_peaks(filename=self.getPropertyValue("ExpectedPeaksFromFile"),
expected_peaks=self.getProperty('ExpectedPeaks').value)
if len(expected_peaks_dsp) < 1:
raise ValueError("Cannot run this algorithm without any input expected peaks")
prog.report('Focusing the input workspace')
focused_ws = self._focus_run(self.getProperty('InputWorkspace').value,
self.getProperty("VanadiumWorkspace").value,
self.getProperty('Bank').value,
self.getProperty(self.INDICES_PROP_NAME).value,
prog)
prog.report('Fitting parameters for the focused run')
difa, difc, zero, fitted_peaks = self._fit_params(focused_ws, expected_peaks_dsp, prog)
self.log().information("Fitted {0} peaks. Resulting DIFA: {1}, DIFC: {2}, TZERO: {3}".
format(fitted_peaks.rowCount(), difa, difc, zero))
self.log().information("Peaks fitted: {0}, centers in ToF: {1}".
format(fitted_peaks.column("dSpacing"),
fitted_peaks.column("X0")))
prog.report("Producing outputs")
self._produce_outputs(difa, difc, zero, fitted_peaks)
prog.report(max_reports, "Calibration complete")
def _fit_params(self, focused_ws, expected_peaks_d, prog):
"""
Fit the GSAS parameters that this algorithm produces: DIFC and TZERO. Fits a
number of peaks starting from the expected peak positions. Then it fits a line
on the peak positions to produce the DIFC and TZERO as used in GSAS.
@param focused_ws :: focused workspace to do the fitting on
@param expected_peaks_d :: expected peaks, used as intial peak positions for the
fitting, in d-spacing units
@param prog :: progress reporter
@returns a tuple with three GSAS calibration parameters (DIFA, DIFC, ZERO),
and a list of peak centers as fitted
"""
fit_alg = self.createChildAlgorithm('EnggFitPeaks')
fit_alg.setProperty('InputWorkspace', focused_ws)
fit_alg.setProperty('WorkspaceIndex', 0) # There should be only one index anyway
fit_alg.setProperty('ExpectedPeaks', expected_peaks_d)
# we could also pass raw 'ExpectedPeaks' and 'ExpectedPeaksFromFile' to
# EnggFitPaks, but better to check inputs early, before this
fit_alg.execute()
fitted_peaks = fit_alg.getProperty('FittedPeaks').value
difc_alg = self.createChildAlgorithm('EnggFitTOFFromPeaks')
difc_alg.setProperty('FittedPeaks', fitted_peaks)
prog.report("Performing fit")
difc_alg.execute()
prog.report("Fit complete")
difa = difc_alg.getProperty('DIFA').value
difc = difc_alg.getProperty('DIFC').value
zero = difc_alg.getProperty('TZERO').value
return difa, difc, zero, fitted_peaks
def _focus_run(self, ws, vanadium_ws, bank, indices, prog):
"""
Focuses the input workspace by running EnggFocus as a child algorithm, which will produce a
single spectrum workspace.
@param ws :: workspace to focus
@param vanadium_ws :: workspace with Vanadium run for corrections
@param bank :: the focusing will be applied on the detectors of this bank
@param indices :: list of indices to consider, as an alternative to bank (bank and indices are
mutually exclusive)
@return focused (summed) workspace
"""
prog.report("Initialising EnggFocus")
engg_focus_params = dict()
detector_positions = self.getProperty('DetectorPositions').value
if detector_positions:
engg_focus_params["DetectorPositions"] = detector_positions
if vanadium_ws:
engg_focus_params["VanadiumWorkspace"] = vanadium_ws
van_integration_ws = self.getProperty('VanIntegrationWorkspace').value
if van_integration_ws:
engg_focus_params["VanIntegrationWorkspace"] = van_integration_ws
van_curves_ws = self.getProperty('VanCurvesWorkspace').value
if van_curves_ws:
engg_focus_params['VanCurvesWorkspace'] = van_curves_ws
prog.report("Running EnggFocus")
return mantid.EnggFocus(InputWorkspace=ws, Bank=bank, SpectrumNumbers=indices, StoreInADS=False,
startProgress=0.3, endProgress=0.6, **engg_focus_params)
def _produce_outputs(self, difa, difc, zero, fitted_peaks):
"""
Just fills in the output properties as requested
@param difa :: the DIFA GSAS parameter as fitted here
@param difc :: the DIFC GSAS parameter as fitted here
@param zero :: the TZERO GSAS parameter as fitted here
@param fitted_peaks :: table workspace with peak parameters (one peak per row)
"""
import EnggUtils
self.setProperty('DIFA', difa)
self.setProperty('DIFC', difc)
self.setProperty('TZERO', zero)
self.setProperty('FittedPeaks', fitted_peaks)
# make output table if requested
table_name = self.getPropertyValue("OutputParametersTableName")
if '' != table_name:
EnggUtils.generate_output_param_table(table_name, difa, difc, zero)
self.log().information("Output parameters added into a table workspace: %s" % table_name)
AlgorithmFactory.subscribe(EnggCalibrate)