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EnggSaveGSASIIFitResultsToHDF5.py
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EnggSaveGSASIIFitResultsToHDF5.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.api import *
from mantid.kernel import *
import h5py
import numpy
class EnggSaveGSASIIFitResultsToHDF5(PythonAlgorithm):
PROP_BANKIDS = "BankIDs"
PROP_LATTICE_PARAMS = "LatticeParamWorkspaces"
PROP_SIGMA = "Sigma"
PROP_GAMMA = "Gamma"
PROP_RWP = "Rwp"
PROP_XMIN = "XMin"
PROP_XMAX = "XMax"
PROP_PAWLEY_DMIN = "PawleyDMin"
PROP_PAWLEY_NEGATIVE_WEIGHT = "PawleyNegativeWeight"
PROP_REFINE_GAMMA = "RefineGamma"
PROP_REFINE_SIGMA = "RefineSigma"
PROP_REFINEMENT_METHOD = "RefinementMethod"
PROP_RUN_NUMBERS = "RunNumbers"
PROP_FILENAME = "Filename"
BANK_GROUP_NAME = "Bank {}".format
RUN_GROUP_NAME = "Run {}".format
LATTICE_PARAMS_GROUP_NAME = "Lattice Parameters"
REFINEMENT_PARAMS_GROUP_NAME = "Refinement Parameters"
FIT_RESULTS_GROUP_NAME = "GSAS-II Fitting"
LATTICE_PARAMS = ["a", "b", "c", "alpha", "beta", "gamma", "volume"]
PAWLEY_REFINEMENT = "Pawley refinement"
RIETVELD_REFINEMENT = "Rietveld refinement"
RWP_DATASET_NAME = "Rwp"
PROFILE_COEFFS_GROUP_NAME = "Profile Coefficients"
WRONG_NUMBER_PARAM_MSG = "One {} value must be supplied for every run {}"
def category(self):
return "DataHandling"
def name(self):
return "EnggSaveGSASIIFitResultsToHDF5"
def summary(self):
return "Save input parameters and fit results from GSASIIRefineFitPeaks to an HDF5 file, indexed by bank ID"
def validateInputs(self):
issues = {}
lattice_param_wss = self.getProperty(self.PROP_LATTICE_PARAMS).value
for ws_name in lattice_param_wss:
if not mtd.doesExist(ws_name):
issues[self.PROP_LATTICE_PARAMS] = "The workspace {} does not exist".format(ws_name)
num_runs = len(lattice_param_wss)
issues.update(self._wrong_number_warning_dict(self.PROP_BANKIDS, num_runs))
issues.update(self._wrong_number_warning_dict(self.PROP_RWP, num_runs))
refine_sigma = self.getProperty(self.PROP_REFINE_SIGMA).value
if refine_sigma:
issues.update(self._wrong_number_warning_dict(self.PROP_SIGMA, num_runs,
" when {} is True".format(self.PROP_REFINE_SIGMA)))
refine_gamma = self.getProperty(self.PROP_REFINE_GAMMA).value
if refine_gamma:
issues.update(self._wrong_number_warning_dict(self.PROP_GAMMA, num_runs,
" when {} is True".format(self.PROP_REFINE_GAMMA)))
if num_runs > 1:
issues.update(self._wrong_number_warning_dict(self.PROP_RUN_NUMBERS, num_runs,
" when saving multiple refinement results"))
return issues
def PyInit(self):
self.declareProperty(StringArrayProperty(name=self.PROP_LATTICE_PARAMS),
doc="Table workspace containing lattice parameters")
self.declareProperty(IntArrayProperty(name=self.PROP_RUN_NUMBERS),
doc="The run number of each fitted run")
self.declareProperty(IntArrayProperty(name=self.PROP_BANKIDS),
doc="Bank IDs of the workspaces a fit was run on "
"(1 for each workspace, in order. 1 for North, 2 for South)")
self.declareProperty(name=self.PROP_REFINEMENT_METHOD, defaultValue=self.PAWLEY_REFINEMENT,
validator=StringListValidator([self.PAWLEY_REFINEMENT, self.RIETVELD_REFINEMENT]),
doc="Which refinement method was used")
self.declareProperty(name=self.PROP_XMIN, defaultValue=0.0,
doc="Minimum TOF value used for refinement")
self.declareProperty(name=self.PROP_XMAX, defaultValue=0.0,
doc="Maximum TOF value used for refinement")
self.declareProperty(name=self.PROP_PAWLEY_DMIN, defaultValue=0.0,
doc="Minimum d spacing used for Pawley refinement")
self.declareProperty(name=self.PROP_PAWLEY_NEGATIVE_WEIGHT, defaultValue=0.0,
doc="Negative weight penalty used in Pawley refinement")
self.declareProperty(name=self.PROP_REFINE_SIGMA, defaultValue=False,
doc="Whether to sigma profile coefficient was refined")
self.declareProperty(name=self.PROP_REFINE_GAMMA, defaultValue=False,
doc="Whether gamma profile coefficient was refined")
self.declareProperty(FloatArrayProperty(name=self.PROP_SIGMA),
doc="GSAS-II profile coefficient sigma, "
"one for each run (or none if sigma wasn't refined)")
self.declareProperty(FloatArrayProperty(name=self.PROP_GAMMA),
doc="GSAS-II profile coefficient gamma, "
"one for each run (or none if gamma wasn't refined)")
self.declareProperty(FloatArrayProperty(name=self.PROP_RWP),
doc="Weighted profile R-factor, 'goodness-of-fit' measure. "
"One for each run")
self.declareProperty(FileProperty(name=self.PROP_FILENAME, defaultValue="", action=FileAction.Save,
extensions=[".hdf5", ".h5", ".hdf"]), doc="HDF5 file to save to")
def PyExec(self):
output_file_name = self.getProperty(self.PROP_FILENAME).value
lattice_param_ws_names = self.getProperty(self.PROP_LATTICE_PARAMS).value
run_numbers = self.getProperty(self.PROP_RUN_NUMBERS).value
bankIDs = self.getProperty(self.PROP_BANKIDS).value
rwps = self.getProperty(self.PROP_RWP).value
refine_sigma = self.getProperty(self.PROP_REFINE_SIGMA).value
refine_gamma = self.getProperty(self.PROP_REFINE_GAMMA).value
sigmas = self.getProperty(self.PROP_SIGMA).value
gammas = self.getProperty(self.PROP_GAMMA).value
with h5py.File(output_file_name, "a") as output_file:
for i, ws_name in enumerate(lattice_param_ws_names):
if len(lattice_param_ws_names) > 1:
top_level_group = output_file.require_group(self.RUN_GROUP_NAME(run_numbers[i]))
else:
top_level_group = output_file
bank_group = top_level_group.require_group(self.BANK_GROUP_NAME(bankIDs[i]))
if self.FIT_RESULTS_GROUP_NAME in bank_group:
del bank_group[self.FIT_RESULTS_GROUP_NAME]
fit_results_group = bank_group.create_group(self.FIT_RESULTS_GROUP_NAME)
self._save_refinement_params(fit_results_group)
self._save_lattice_params(fit_results_group, lattice_params_ws=mtd[ws_name])
self._save_rwp(fit_results_group, rwp=rwps[i])
self._save_profile_coefficients(fit_results_group,
refine_sigma=refine_sigma, refine_gamma=refine_gamma,
sigmas=sigmas, gammas=gammas, index=i)
def _wrong_number_warning_dict(self, prop_name, num_runs, condition=""):
prop = self.getProperty(prop_name).value
if len(prop) != num_runs:
return {prop_name: self.WRONG_NUMBER_PARAM_MSG.format(prop_name, condition)}
return {}
def _save_lattice_params(self, results_group, lattice_params_ws):
lattice_params_group = results_group.create_group(name=self.LATTICE_PARAMS_GROUP_NAME)
lattice_params = lattice_params_ws.row(0)
for param_name in self.LATTICE_PARAMS:
lattice_params_group.create_dataset(name=param_name, data=lattice_params[param_name])
def _save_profile_coefficients(self, results_group, refine_sigma, refine_gamma, sigmas, gammas, index):
if refine_sigma or refine_gamma:
coeffs_group = results_group.create_group(name=self.PROFILE_COEFFS_GROUP_NAME)
if refine_sigma:
coeffs_group.create_dataset(name="Sigma", data=sigmas[index])
if refine_gamma:
coeffs_group.create_dataset(name="Gamma", data=gammas[index])
def _copy_property_to_dataset(self, group, property_name):
group.create_dataset(name=property_name, data=self.getProperty(property_name).value)
def _save_refinement_params(self, results_group):
refinement_params_group = results_group.create_group(name=self.REFINEMENT_PARAMS_GROUP_NAME)
mandatory_properties = {self.PROP_REFINEMENT_METHOD,
self.PROP_REFINE_SIGMA,
self.PROP_REFINE_GAMMA,
self.PROP_XMIN,
self.PROP_XMAX}
for prop_name in mandatory_properties:
self._copy_property_to_dataset(refinement_params_group, prop_name)
if self.getProperty(self.PROP_REFINEMENT_METHOD).value == self.PAWLEY_REFINEMENT:
self._copy_property_to_dataset(refinement_params_group, self.PROP_PAWLEY_DMIN)
self._copy_property_to_dataset(refinement_params_group, self.PROP_PAWLEY_NEGATIVE_WEIGHT)
def _save_rwp(self, results_group, rwp):
results_group.create_dataset(self.RWP_DATASET_NAME, data=numpy.array(rwp))
AlgorithmFactory.subscribe(EnggSaveGSASIIFitResultsToHDF5)