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Commit 8468019a authored by Harriet Brown's avatar Harriet Brown
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Move Workflow to seperate function, add workspace cleanup

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scripts/Diffraction/isis_powder/polaris_routines/polaris_algs.py
+ 72
− 137
View file @ 8468019a
......@@ -7,7 +7,6 @@
from __future__ import (absolute_import, division, print_function)
import numpy as np
from mantid.api import WorkspaceGroup
import mantid.simpleapi as mantid
from isis_powder.routines import absorb_corrections, common
......@@ -85,98 +84,51 @@ def save_unsplined_vanadium(vanadium_ws, output_path):
def generate_ts_pdf(run_number, focus_file_path, merge_banks=False, q_lims=None, cal_file_name=None):
focused_ws = _obtain_focused_run(run_number, focus_file_path)
pdf_output = mantid.ConvertUnits(InputWorkspace=focused_ws.name(), Target="MomentumTransfer")
focused_ws = mantid.ConvertUnits(InputWorkspace=focused_ws.name(), Target="MomentumTransfer")
if merge_banks:
group_bin_min = None
group_bin_max = None
group_bin_width = None
for i in range(pdf_output.getNumberOfEntries()):
x_array = pdf_output.getItem(i).readX(0)
bin_min = x_array[0]
bin_max = x_array[-1]
bin_width = (x_array[-1] - x_array[0])/x_array.size
binning = [bin_min, bin_width, bin_max]
if not group_bin_min:
group_bin_min = bin_min
group_bin_max = bin_max
group_bin_width = bin_width
else:
group_bin_min = min(group_bin_min, bin_min)
group_bin_max = min(group_bin_max, bin_max)
group_bin_width = min(group_bin_width, bin_width)
fit_spectra = mantid.FitIncidentSpectrum(InputWorkspace=pdf_output.getItem(i),
BinningForFit=binning,
BinningForCalc=binning,
FitSpectrumWith="GaussConvCubicSpline")
placzek_self_scattering = mantid.CalculatePlaczekSelfScattering(InputWorkspace=fit_spectra)
cal_workspace = mantid.LoadCalFile(
InputWorkspace=placzek_self_scattering,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False)
focused_correction = None
for spec_index in range(placzek_self_scattering.getNumberHistograms()):
if cal_workspace.dataY(spec_index)[0] == i + 1:
if focused_correction is None:
focused_correction = placzek_self_scattering.dataY(spec_index)
else:
focused_correction = np.add(focused_correction, placzek_self_scattering.dataY(spec_index))
mantid.CreateWorkspace(DataX=placzek_self_scattering.dataX(0),
DataY=focused_correction,
Distribution=True,
UnitX='MomentumTransfer',
OutputWorkspace='focused_correction_ws')
mantid.Rebin(InputWorkspace=pdf_output.getItem(i), OutputWorkspace='pdf_rebined', Params=binning)
mantid.Rebin(InputWorkspace='focused_correction_ws', OutputWorkspace='focused_correction_ws', Params=binning)
mantid.Subtract(LHSWorkspace='pdf_rebined', RHSWorkspace='focused_correction_ws', OutputWorkspace=pdf_output.getItem(i))
binning = [group_bin_min, group_bin_width, group_bin_max]
pdf_output = mantid.Rebin(InputWorkspace=pdf_output, Params=binning)
pdf_output_combined = mantid.ConjoinSpectra(InputWorkspaces='pdf_output')
mantid.MatchSpectra(InputWorkspace=pdf_output_combined, OutputWorkspace=pdf_output_combined, ReferenceSpectrum=1)
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")
pdf_x_array = pdf_output_combined.readX(0)
for i in range(q_min.size):
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 = pdf_x_array[1] - pdf_x_array[0]
pdf_output_combined = mantid.CropWorkspaceRagged(InputWorkspace=pdf_output_combined, XMin=q_min, XMax=q_max)
pdf_output_combined = mantid.Rebin(InputWorkspace=pdf_output_combined, Params=[min(q_min), bin_width, max(q_max)])
pdf_output_combined = mantid.SumSpectra(InputWorkspace=pdf_output_combined, WeightedSum=True, MultiplyBySpectra=False)
pdf_fourier_transform = mantid.PDFFourierTransform(Inputworkspace=pdf_output_combined, InputSofQType="S(Q)",
PDFType="G(r)", Filter=True)
return pdf_fourier_transform
pdf_output = mantid.PDFFourierTransform(Inputworkspace=pdf_output, InputSofQType="S(Q)", PDFType="G(r)",
Filter=True)
pdf_output = mantid.RebinToWorkspace(WorkspaceToRebin=pdf_output, WorkspaceToMatch=pdf_output[4],
PreserveEvents=True)
pdf_output = _generate_grouped_ts_pdf(focused_ws, q_lims, cal_file_name)
else:
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 _generate_grouped_ts_pdf(focused_data, q_lims, cal_file_name):
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(focused_ws, q_lims, cal_file_name):
group_bin_min = None
group_bin_max = None
group_bin_width = None
for i in range(focused_data.getNumberOfEntries()):
x_array = focused_data.getItem(i).readX(0)
for i in range(focused_ws.getNumberOfEntries()):
x_array = focused_ws.getItem(i).readX(0)
bin_min = x_array[0]
bin_max = x_array[-1]
bin_width = (x_array[-1] - x_array[0]) / x_array.size
......@@ -189,17 +141,16 @@ def _generate_grouped_ts_pdf(focused_data, q_lims, cal_file_name):
group_bin_min = min(group_bin_min, bin_min)
group_bin_max = min(group_bin_max, bin_max)
group_bin_width = min(group_bin_width, bin_width)
fit_spectra = mantid.FitIncidentSpectrum(InputWorkspace=focused_data.getItem(i),
fit_spectra = mantid.FitIncidentSpectrum(InputWorkspace=focused_ws.getItem(i),
BinningForFit=binning,
BinningForCalc=binning,
FitSpectrumWith="GaussConvCubicSpline")
placzek_self_scattering = mantid.CalculatePlaczekSelfScattering(InputWorkspace=fit_spectra)
cal_workspace = mantid.LoadCalFile(
InputWorkspace=placzek_self_scattering,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False)
cal_workspace = mantid.LoadCalFile(InputWorkspace=placzek_self_scattering,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False)
focused_correction = None
for spec_index in range(placzek_self_scattering.getNumberHistograms()):
if cal_workspace.dataY(spec_index)[0] == i + 1:
......@@ -207,19 +158,19 @@ def _generate_grouped_ts_pdf(focused_data, q_lims, cal_file_name):
focused_correction = placzek_self_scattering.dataY(spec_index)
else:
focused_correction = np.add(focused_correction, placzek_self_scattering.dataY(spec_index))
mantid.CreateWorkspace(DataX=placzek_self_scattering.dataX(0),
DataY=focused_correction,
Distribution=True,
UnitX='MomentumTransfer',
OutputWorkspace='focused_correction_ws')
mantid.Rebin(InputWorkspace=focused_data.getItem(i), OutputWorkspace='focused_rebined', Params=binning)
mantid.Rebin(InputWorkspace='focused_correction_ws', OutputWorkspace='focused_correction_ws', Params=binning)
mantid.Subtract(LHSWorkspace='focused_rebined', RHSWorkspace='focused_correction_ws',
OutputWorkspace=focused_data.getItem(i))
focused_correction_ws = mantid.CreateWorkspace(DataX=placzek_self_scattering.dataX(0),
DataY=focused_correction,
Distribution=True,
UnitX='MomentumTransfer')
mantid.Rebin(InputWorkspace=focused_ws.getItem(i), OutputWorkspace=focused_ws.getItem(i), Params=binning)
focused_correction_ws = mantid.Rebin(InputWorkspace=focused_correction_ws, Params=binning)
mantid.Subtract(LHSWorkspace=focused_ws.getItem(i),
RHSWorkspace=focused_correction_ws,
OutputWorkspace=focused_ws.getItem(i))
binning = [group_bin_min, group_bin_width, group_bin_max]
focused_data = mantid.Rebin(InputWorkspace=focused_data, Params=binning)
pdf_output_combined = mantid.ConjoinSpectra(InputWorkspaces=focused_data)
mantid.MatchSpectra(InputWorkspace=pdf_output_combined, OutputWorkspace=pdf_output_combined, ReferenceSpectrum=1)
focused_data = mantid.Rebin(InputWorkspace=focused_ws, Params=binning)
focused_data_combined = mantid.ConjoinSpectra(InputWorkspaces=focused_data)
mantid.MatchSpectra(InputWorkspace=focused_data_combined, OutputWorkspace=focused_data_combined, ReferenceSpectrum=1)
if type(q_lims) == str:
q_min = []
q_max = []
......@@ -237,44 +188,28 @@ def _generate_grouped_ts_pdf(focused_data, q_lims, cal_file_name):
q_max = q_lims[1, :]
else:
raise RuntimeError("q_lims is not valid")
pdf_x_array = pdf_output_combined.readX(0)
pdf_x_array = focused_data_combined.readX(0)
for i in range(q_min.size):
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 = pdf_x_array[1] - pdf_x_array[0]
focused_data_combined = mantid.CropWorkspaceRagged(InputWorkspace=pdf_output_combined, XMin=q_min, XMax=q_max)
focused_data_combined = mantid.Rebin(InputWorkspace=pdf_output_combined, Params=[min(q_min), bin_width, max(q_max)])
focused_data_combined = mantid.SumSpectra(InputWorkspace=pdf_output_combined, WeightedSum=True,
MultiplyBySpectra=False)
pdf_output = mantid.PDFFourierTransform(Inputworkspace=pdf_output_combined, InputSofQType="S(Q)",
PDFType="G(r)", Filter=True)
return pdf_output
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)
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
common.remove_intermediate_workspace(cal_workspace)
common.remove_intermediate_workspace(fit_spectra)
common.remove_intermediate_workspace(focused_correction_ws)
common.remove_intermediate_workspace(focused_data)
common.remove_intermediate_workspace(placzek_self_scattering)
return pdf_output
def _determine_chopper_mode(ws):
......
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