# 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
# SPDX - License - Identifier: GPL - 3.0 +
from __future__ import (absolute_import, division, print_function)
import numpy as np
import mantid.simpleapi as mantid
from isis_powder.routines import absorb_corrections, common
from isis_powder.routines.common_enums import WORKSPACE_UNITS
from isis_powder.routines.run_details import create_run_details_object, get_cal_mapping_dict
from isis_powder.polaris_routines import polaris_advanced_config
def calculate_van_absorb_corrections(ws_to_correct, multiple_scattering, is_vanadium):
mantid.MaskDetectors(ws_to_correct, SpectraList=list(range(1, 55)))
absorb_dict = polaris_advanced_config.absorption_correction_params
sample_details_obj = absorb_corrections.create_vanadium_sample_details_obj(config_dict=absorb_dict)
ws_to_correct = absorb_corrections.run_cylinder_absorb_corrections(
ws_to_correct=ws_to_correct, multiple_scattering=multiple_scattering, sample_details_obj=sample_details_obj,
is_vanadium=is_vanadium)
return ws_to_correct
def _get_run_numbers_for_key(current_mode_run_numbers, key):
err_message = "this must be under the relevant Rietveld or PDF mode."
return common.cal_map_dictionary_key_helper(current_mode_run_numbers, key=key,
append_to_error_message=err_message)
def _get_current_mode_dictionary(run_number_string, inst_settings):
mapping_dict = get_cal_mapping_dict(run_number_string, inst_settings.cal_mapping_path)
if inst_settings.mode is None:
ws = mantid.Load('POLARIS'+run_number_string+'.nxs')
mode, cropping_vals = _determine_chopper_mode(ws)
inst_settings.mode = mode
inst_settings.focused_cropping_values = cropping_vals
mantid.DeleteWorkspace(ws)
# Get the current mode "Rietveld" or "PDF" run numbers
return common.cal_map_dictionary_key_helper(mapping_dict, inst_settings.mode)
def get_run_details(run_number_string, inst_settings, is_vanadium_run):
mode_run_numbers = _get_current_mode_dictionary(run_number_string, inst_settings)
# Get empty and vanadium
err_message = "this must be under the relevant Rietveld or PDF mode."
empty_runs = common.cal_map_dictionary_key_helper(mode_run_numbers,
key="empty_run_numbers", append_to_error_message=err_message)
vanadium_runs = common.cal_map_dictionary_key_helper(mode_run_numbers, key="vanadium_run_numbers",
append_to_error_message=err_message)
grouping_file_name = inst_settings.grouping_file_name
return create_run_details_object(run_number_string=run_number_string, inst_settings=inst_settings,
is_vanadium_run=is_vanadium_run, empty_run_number=empty_runs,
vanadium_string=vanadium_runs, grouping_file_name=grouping_file_name)
def save_unsplined_vanadium(vanadium_ws, output_path):
converted_workspaces = []
if vanadium_ws.id() != "Workspace2D":
for ws_index in range(vanadium_ws.getNumberOfEntries()):
ws = vanadium_ws.getItem(ws_index)
previous_units = ws.getAxis(0).getUnit().unitID()
if previous_units != WORKSPACE_UNITS.tof:
ws = mantid.ConvertUnits(InputWorkspace=ws, Target=WORKSPACE_UNITS.tof)
ws = mantid.RenameWorkspace(InputWorkspace=ws, OutputWorkspace="van_bank_{}".format(ws_index + 1))
converted_workspaces.append(ws)
converted_group = mantid.GroupWorkspaces(",".join(ws.name() for ws in converted_workspaces))
mantid.SaveNexus(InputWorkspace=converted_group, Filename=output_path, Append=False)
mantid.DeleteWorkspace(converted_group)
else:
mantid.SaveNexus(InputWorkspace=vanadium_ws, Filename=output_path, Append=False)
def generate_ts_pdf(run_number, focus_file_path, merge_banks=False, q_lims=None, cal_file_name=None, sample_details=None):
focused_ws = _obtain_focused_run(run_number, focus_file_path)
if merge_banks:
pdf_output = _generate_grouped_ts_pdf(run_number, focused_ws, q_lims, cal_file_name, sample_details)
else:
focused_ws = mantid.ConvertUnits(InputWorkspace=focused_ws.name(), Target="MomentumTransfer")
pdf_output = mantid.PDFFourierTransform(Inputworkspace=focused_ws, InputSofQType="S(Q)", PDFType="G(r)",
Filter=True)
pdf_output = mantid.RebinToWorkspace(WorkspaceToRebin=pdf_output, WorkspaceToMatch=pdf_output[4],
PreserveEvents=True)
common.remove_intermediate_workspace('focused_ws')
return pdf_output
def _obtain_focused_run(run_number, focus_file_path):
"""
Searches for the focused workspace to use (based on user specified run number) in the ADS and then the output
directory.
If unsuccessful, a ValueError exception is thrown.
:param run_number: The run number to search for.
:param focus_file_path: The expected file path for the focused file.
:return: The focused workspace.
"""
# Try the ADS first to avoid undesired loading
if mantid.mtd.doesExist('%s-Results-TOF-Grp' % run_number):
focused_ws = mantid.mtd['%s-Results-TOF-Grp' % run_number]
elif mantid.mtd.doesExist('%s-Results-D-Grp' % run_number):
focused_ws = mantid.mtd['%s-Results-D-Grp' % run_number]
else:
# Check output directory
print('No loaded focused files found. Searching in output directory...')
try:
focused_ws = mantid.LoadNexus(Filename=focus_file_path, OutputWorkspace='focused_ws').OutputWorkspace
except ValueError:
raise ValueError("Could not find focused file for run number:%s\n"
"Please ensure a focused file has been produced and is located in the output directory."
% run_number)
return focused_ws
def _generate_grouped_ts_pdf(run_number, focused_ws, q_lims, cal_file_name, sample_details):
focused_ws = mantid.ConvertUnits(InputWorkspace=focused_ws, Target="MomentumTransfer", EMode='Elastic')
min_x = np.inf
max_x = -np.inf
num_x = -np.inf
for ws in focused_ws:
x_data = ws.dataX(0)
min_x = min(np.min(x_data), min_x)
max_x = max(np.max(x_data), max_x)
num_x = max(x_data.size, num_x)
width_x = (max_x-min_x)/num_x
binning = [min_x, width_x, max_x]
focused_ws = mantid.Rebin(InputWorkspace=focused_ws, Params=binning)
focused_data_combined = mantid.ConjoinSpectra(InputWorkspaces=focused_ws)
mantid.ConvertFromDistribution(Workspace=focused_data_combined)
raw_ws = mantid.LoadRaw(Filename='POL'+str(run_number))
mantid.SetSample(InputWorkspace=raw_ws,
Geometry=common.generate_sample_geometry(sample_details),
Material=common.generate_sample_material(sample_details))
monitor = mantid.ExtractSpectra(InputWorkspace=raw_ws, WorkspaceIndexList=[11])
monitor = mantid.ConvertUnits(InputWorkspace=monitor, Target="Wavelength")
x_data = monitor.dataX(0)
min_x = np.min(x_data)
max_x = np.max(x_data)
width_x = (max_x-min_x)/x_data.size
fit_spectra = mantid.FitIncidentSpectrum(InputWorkspace=monitor,
BinningForCalc=[min_x, 1*width_x, max_x],
BinningForFit=[min_x, 10*width_x, max_x],
FitSpectrumWith="CubicSpline")
placzek = mantid.CalculatePlaczekSelfScattering(InputWorkspace=raw_ws, IncidentSpecta=fit_spectra)
cal_workspace = mantid.LoadCalFile(InputWorkspace=placzek,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False)
placzek = mantid.DiffractionFocussing(InputWorkspace=placzek, GroupingFilename=cal_file_name)
n_pixel = np.zeros(placzek.getNumberHistograms())
for i in range(cal_workspace.getNumberHistograms()):
grouping = cal_workspace.dataY(i)
if grouping[0] > 0:
n_pixel[int(grouping[0]-1)] += 1
correction_ws = mantid.CreateWorkspace(DataY=n_pixel, DataX=[0, 1], NSpec=placzek.getNumberHistograms())
placzek = mantid.Divide(LHSWorkspace=placzek, RHSWorkspace=correction_ws)
mantid.ConvertToDistribution(Workspace=placzek)
placzek = mantid.ConvertUnits(InputWorkspace=placzek, Target="MomentumTransfer", EMode='Elastic')
placzek = mantid.RebinToWorkspace(WorkspaceToRebin=placzek, WorkspaceToMatch=focused_data_combined)
mantid.ConvertFromDistribution(Workspace=placzek)
mantid.Subtract(LHSWorkspace=focused_data_combined,
RHSWorkspace=placzek,
OutputWorkspace=focused_data_combined)
if type(q_lims) == str:
q_min = []
q_max = []
try:
with open(q_lims, 'r') as f:
line_list = [line.rstrip('\n') for line in f]
for line in line_list[:-1]:
value_list = line.split()
q_min.append(value_list[2])
q_max.append(value_list[3])
except IOError:
raise RuntimeError("q_lims is not valid")
elif type(q_lims) == list or type(q_lims) == np.ndarray:
q_min = q_lims[0, :]
q_max = q_lims[1, :]
else:
raise RuntimeError("q_lims is not valid")
bin_width = np.inf
for i in range(q_min.size):
pdf_x_array = focused_data_combined.readX(i)
q_min[i] = pdf_x_array[np.amin(np.where(pdf_x_array >= q_min[i]))]
q_max[i] = pdf_x_array[np.amax(np.where(pdf_x_array <= q_max[i]))]
bin_width = min(pdf_x_array[1] - pdf_x_array[0], bin_width)
mantid.MatchSpectra(InputWorkspace=focused_data_combined,
OutputWorkspace=focused_data_combined,
ReferenceSpectrum=1)
focused_data_combined = mantid.CropWorkspaceRagged(InputWorkspace=focused_data_combined, XMin=q_min, XMax=q_max)
focused_data_combined = mantid.Rebin(InputWorkspace=focused_data_combined,
Params=[min(q_min), bin_width, max(q_max)])
focused_data_combined = mantid.SumSpectra(InputWorkspace=focused_data_combined,
WeightedSum=True,
MultiplyBySpectra=False)
pdf_output = mantid.PDFFourierTransform(Inputworkspace=focused_data_combined,
InputSofQType="S(Q)",
PDFType="G(r)",
Filter=True)
common.remove_intermediate_workspace(fit_spectra)
common.remove_intermediate_workspace(monitor)
return pdf_output
def _determine_chopper_mode(ws):
if ws.getRun().hasProperty('Frequency'):
frequency = ws.getRun()['Frequency'].lastValue()
print("No chopper mode provided")
if frequency == 50:
print("automatically chose Rietveld")
return 'Rietveld', polaris_advanced_config.rietveld_focused_cropping_values
if frequency == 0:
print("automatically chose PDF")
return 'PDF', polaris_advanced_config.pdf_focused_cropping_values
else:
raise ValueError("Chopper frequency not in log data. Please specify a chopper mode")