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Commit 62b18404 authored by Zhou, Wenduo's avatar Zhou, Wenduo
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Refs #14796. Fixed some issues.

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Framework/PythonInterface/plugins/algorithms/SNSPowderReduction.py
+ 82
− 53
View file @ 62b18404
......@@ -266,7 +266,8 @@ class SNSPowderReduction(DataProcessorAlgorithm):
self.COMPRESS_TOL_TOF = 0.01
# Process data
workspacelist = [] # all data workspaces that will be converted to d-spacing in the end
# List stores the workspacename of all data workspaces that will be converted to d-spacing in the end.
workspacelist = []
samwksplist = []
self._lowResTOFoffset = self.getProperty("LowResolutionSpectraOffset").value
......@@ -297,13 +298,14 @@ class SNSPowderReduction(DataProcessorAlgorithm):
if self._splitws is not None:
raise NotImplementedError("Summing spectra and filtering events are not supported simultaneously.")
sam_ws_ = self._focusAndSum(samRuns, SUFFIX, timeFilterWall, calib,\
preserveEvents=preserveEvents)
assert isinstance(sam_ws_, str)
sam_ws_name = self._focusAndSum(samRuns, SUFFIX, timeFilterWall, calib,
preserveEvents=preserveEvents)
assert isinstance(sam_ws_name, str), 'Returned from _focusAndSum() must be a string but not' \
'%s. ' % str(type(sam_ws_name))
samRuns = [sam_ws_]
workspacelist.append(str(sam_ws_))
samwksplist.append(str(sam_ws_))
samRuns = [sam_ws_name]
workspacelist.append(sam_ws_name)
samwksplist.append(sam_ws_name)
# ENDIF (SUM)
# Process each sample run
......@@ -320,23 +322,18 @@ class SNSPowderReduction(DataProcessorAlgorithm):
if isinstance(returned, list):
# Returned with a list of workspaces
focusedwksplist = returned
run_index = 0
for run in focusedwksplist:
assert isinstance(run, str)
if run is not None:
samwksplist.append(run)
workspacelist.append(str(run))
else:
self.log().warning("Found a None entry in returned focused workspaces. "
"Index = %d." % run_index)
# ENDIF
run_index += 1
for sam_ws_name in focusedwksplist:
assert isinstance(sam_ws_name, str), 'Impossible to have a non-string value in ' \
'returned focused workspaces\' names.'
samwksplist.append(sam_ws_name)
workspacelist.append(sam_ws_name)
# END-FOR
else:
run = returned
assert isinstance(run, str)
samwksplist.append(run)
workspacelist.append(str(run))
# returned as a single workspace
sam_ws_name = returned
assert isinstance(sam_ws_name, str)
samwksplist.append(sam_ws_name)
workspacelist.append(sam_ws_name)
# ENDIF
# ENDIF
# ENDFOR
......@@ -684,54 +681,85 @@ class SNSPowderReduction(DataProcessorAlgorithm):
return outName
#pylint: disable=too-many-arguments
def _focusAndSum(self, runnumbers, extension, filterWall, calib, preserveEvents=True):
"""Load, sum, and focus data in chunks"""
def _focusAndSum(self, run_number_list, extension, filterWall, calib, preserveEvents=True):
"""Load, sum, and focus data in chunks
Purpose:
Load, sum and focus data in chunks;
Requirements:
1. input run numbers are in numpy array or list
Guarantees:
The experimental runs are focused and summed together
@param run_number_list:
@param extension:
@param filterWall:
@param calib:
@param preserveEvents:
@return: string as the summed workspace's name
"""
# Check requirements
assert isinstance(run_number_list, numpy.array) or isinstance(run_number_list, list)
sumRun = None
info = None
for temp in runnumbers:
runnumber = temp
self.log().information("[Sum] Process run number %s. " %(str(runnumber)))
for run_number in run_number_list:
# check
error_message = 'Run number %s should be integer or numpy.int32. But it is %s.' % (str(run_number),
str(type(run_number)))
assert isinstance(run_number, int) or isinstance(run_number, numpy.int32), error_message
self.log().information("[Sum] Process run number %d. " % run_number)
# focus one run
out_ws_name = self._focusChunks(run_number, extension, filterWall, calib,
normalisebycurrent=False,
preserveEvents=preserveEvents)
assert isinstance(out_ws_name, str), 'Output from _focusChunks() should be a string but' \
' not %s.' % str(type(out_ws_name))
assert self.does_workspace_exist(out_ws_name)
temp = self._focusChunks(temp, extension, filterWall, calib,\
normalisebycurrent=False,
preserveEvents=preserveEvents)
tempinfo = self._getinfo(temp)
# sum reduced runs
if sumRun is None:
sumRun = temp
# First run. No need to sumRun
sumRun = out_ws_name
info = tempinfo
else:
# Non-first run. Add this run to current summed run
self.checkInfoMatch(info, tempinfo)
sumRun = api.Plus(LHSWorkspace=sumRun, RHSWorkspace=temp, OutputWorkspace=sumRun,
ClearRHSWorkspace=allEventWorkspaces(sumRun, temp))
if sumRun.id() == EVENT_WORKSPACE_ID:
sumRun = api.CompressEvents(InputWorkspace=sumRun, OutputWorkspace=sumRun,\
Tolerance=self.COMPRESS_TOL_TOF) # 10ns
api.DeleteWorkspace(str(temp))
# add current workspace to sub sum
temp_ws = api.Plus(LHSWorkspace=sumRun, RHSWorkspace=out_ws_name, OutputWorkspace=sumRun,
ClearRHSWorkspace=allEventWorkspaces(sumRun, out_ws_name))
if temp_ws.id() == EVENT_WORKSPACE_ID:
temp_ws = api.CompressEvents(InputWorkspace=sumRun, OutputWorkspace=sumRun,
Tolerance=self.COMPRESS_TOL_TOF) # 10ns
assert temp_ws is not None
# after adding all events, delete the current workspace.
api.DeleteWorkspace(out_ws_name)
# ENDIF
# ENDFOR (processing each)
if self._normalisebycurrent is True:
sumRun = api.NormaliseByCurrent(InputWorkspace=sumRun,
OutputWorkspace=sumRun)
sumRun.getRun()['gsas_monitor'] = 1
temp_ws = api.NormaliseByCurrent(InputWorkspace=sumRun,
OutputWorkspace=sumRun)
temp_ws.getRun()['gsas_monitor'] = 1
return sumRun
#pylint: disable=too-many-arguments,too-many-locals,too-many-branches
def _focusChunks(self, runnumber, extension, filterWall, calib,
normalisebycurrent, splitwksp=None, preserveEvents=True):
""" Load, (optional) split and focus data in chunks
Arguments:
- runnumber : integer for run number
- normalisebycurrent: Set to False if summing runs for correct math
- splitwksp: SplittersWorkspace (if None then no split)
- filterWall: Enabled if splitwksp is defined
Return:
"""
Load, (optional) split and focus data in chunks
@param runnumber: integer for run number
@param extension:
@param filterWall: Enabled if splitwksp is defined
@param calib:
@param normalisebycurrent: Set to False if summing runs for correct math
@param splitwksp: SplittersWorkspace (if None then no split)
@param preserveEvents:
@return: a string as the returned workspace's name or a list of strings as the returned workspaces' names
in the case that split workspace is used.
"""
# generate the workspace name
self.log().information("_focusChunks(): runnumber = %d, extension = %s" % (runnumber, extension))
......@@ -771,7 +799,7 @@ class SNSPowderReduction(DataProcessorAlgorithm):
# Split the workspace if it is required
if do_split_raw_wksp is True:
output_ws_name_list = self._split_workspace(raw_ws_name_chunk, splitwksp)
output_ws_name_list = self._split_workspace(raw_ws_name_chunk, splitwksp.name())
else:
# Histogram data
output_ws_name_list = [raw_ws_name_chunk]
......@@ -1346,7 +1374,8 @@ class SNSPowderReduction(DataProcessorAlgorithm):
"""
# Check requirements
assert isinstance(raw_ws_name, str), 'Raw workspace name must be a string.'
assert isinstance(split_ws_name, str)
assert isinstance(split_ws_name, str), 'Input split workspace name must be string,' \
'but not of type %s' % str(type(split_ws_name))
assert self.does_workspace_exist(split_ws_name)
raw_ws = self.get_workspace(workspace_name=raw_ws_name)
......
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