def do_background_test(background_int, median_lo, median_hi, sigma, mask_zero,\
start_index=None, end_index=None):
"""
Run the background tests
Required inputs:
background_int - An integrated workspace
median_lo - Fraction of median to consider counting low
median_hi - Fraction of median to consider counting high
sigma - Error criterion as a multiple of error bar
mask_zero - If True, zero background counts will be considered a fail
"""
logger.notice('Running background count test')
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_background_test'
mask_bkgd, num_failures = MedianDetectorTest(
InputWorkspace=background_int,
StartWorkspaceIndex=start_index,
EndWorkspaceIndex=end_index,
SignificanceTest=sigma,
LowThreshold=median_lo,
HighThreshold=median_hi,
LowOutlier=0.0,
HighOutlier=1e100,
ExcludeZeroesFromMedian=True)
#TODO: Looks like hack! why it returns negative value
return mask_bkgd, abs(num_failures)
def do_background_test(background_int, median_lo, median_hi, sigma, mask_zero,
start_index=None, end_index=None):
"""
Run the background tests
Required inputs:
background_int - An integrated workspace
median_lo - Fraction of median to consider counting low
median_hi - Fraction of median to consider counting high
sigma - Error criterion as a multiple of error bar
mask_zero - If True, zero background counts will be considered a fail
"""
logger.notice('Running background count test')
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_background_test'
mask_bkgd, num_failures = MedianDetectorTest(InputWorkspace=background_int,
StartWorkspaceIndex=start_index, EndWorkspaceIndex=end_index,
SignificanceTest=sigma,
LowThreshold=median_lo, HighThreshold=median_hi,
LowOutlier=0.0, HighOutlier=1e100, ExcludeZeroesFromMedian=True)
return mask_bkgd, num_failures
def do_bleed_test(sample_run, max_framerate, ignored_pixels):
"""Runs the CreatePSDBleedMask algorithm
Input:
sample_run - The run number of the sample
max_framerate - The maximum allowed framerate in a tube. If None, the instrument defaults are used.
ignored_pixels - The number of central pixels to ignore. If None, the instrument defaults are used.
"""
logger.notice('Running PSD bleed test')
# Load the sample run
data_ws = common.load_run(sample_run)
if max_framerate is None:
max_framerate = float(data_ws.getInstrument().getNumberParameter('max-tube-framerate')[0])
if ignored_pixels is None:
ignored_pixels = int(data_ws.getInstrument().getNumberParameter('num-ignored-pixels')[0])
else:
# Make sure it is an int
ignored_pixels = int(ignored_pixels)
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_bleed__test'
bleed_test, num_failed = CreatePSDBleedMask(InputWorkspace=data_ws, OutputWorkspace=ws_name,
MaxTubeFramerate=max_framerate,
NIgnoredCentralPixels=ignored_pixels)
return bleed_test, num_failed
def do_bleed_test(sample_run, max_framerate, ignored_pixels):
"""Runs the CreatePSDBleedMask algorithm
Input:
sample_run - The run number of the sample
max_framerate - The maximum allowed framerate in a tube. If None, the instrument defaults are used.
ignored_pixels - The number of central pixels to ignore. If None, the instrument defaults are used.
"""
#NOTE: Should be deployed on non-normalized workspace only!
logger.notice('Running PSD bleed test')
# Load the sample run
if __Reducer__: # Try to use generic loader which would work with files or workspaces alike
sample_run = __Reducer__.get_run_descriptor(sample_run)
data_ws = sample_run.get_workspace(
) # this will load data if necessary
ws_name = data_ws.name() + '_bleed'
else:
# may be sample run is already a run descriptor despite __Reducer__ have not been exposed
data_ws = sample_run.get_workspace(
) # this will load data if necessary
ws_name = data_ws.name() + '_bleed'
if max_framerate is None: #get defaults
max_framerate = float(data_ws.getInstrument().getNumberParameter(
'max-tube-framerate')[0])
if ignored_pixels is None: #get defaults
ignored_pixels = int(data_ws.getInstrument().getNumberParameter(
'num-ignored-pixels')[0])
else:
# Make sure it is an int
ignored_pixels = int(ignored_pixels)
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_bleed__test'
# Check if all necessary logs present in the workspace,as nxs workspace log names are different
# from a raw file workspace logs.
try:
nFrames = data_ws.getRun().getLogData('goodfrm').value
except RuntimeError:
try:
nFrames = data_ws.getRun().getLogData('good_frames').lastValue()
AddSampleLog(Workspace=data_ws,
LogName='goodfrm',
LogText=str(int(nFrames)),
LogType='Number')
except RuntimeError:
raise RuntimeError("Bleed test fails as no appropriate 'good_frames' or 'goodfrm' log is loaded with ws: {0}\n"
"Disable bleed test by setting diag_bleed_test=False or add 'goodfrm' log to the workspace\n"\
.format(data_ws.name()))
bleed_test, num_failed = CreatePSDBleedMask(
InputWorkspace=data_ws,
OutputWorkspace=ws_name,
MaxTubeFramerate=max_framerate,
NIgnoredCentralPixels=ignored_pixels)
return bleed_test, num_failed
def iliad_wrapper(*args):
#seq = inspect.stack()
# output workspace name.
try:
_,r = funcreturns.lhs_info('both')
out_ws_name = r[0]
except:
out_ws_name = None
host = args[0]
if len(args) > 1:
input_file = args[1]
if len(args) > 2:
output_directory = args[2]
else:
output_directory = None
else:
input_file = None
output_directory = None
# add input file folder to data search directory if file has it
if input_file and isinstance(input_file,str):
data_path = os.path.dirname(input_file)
if len(data_path) > 0:
try:
config.appendDataSearchDir(str(data_path))
args[1] = os.path.basename(input_file)
#pylint: disable=bare-except
except: # if mantid is not available, this should ignore config
pass
if output_directory:
config['defaultsave.directory'] = str(output_directory)
#pylint: disable=protected-access
if host._run_from_web:
#pylint: disable=protected-access
web_vars = host._wvs.get_all_vars()
host.reducer.prop_man.set_input_parameters(**web_vars)
else:
pass # we should set already set up variables using
custom_print_function = host.set_custom_output_filename()
if not custom_print_function is None:
PropertyManager.save_file_name.set_custom_print(custom_print_function)
#
rez = reduce(*args)
# prohibit returning workspace to web services.
#pylint: disable=protected-access
if host._run_from_web and not isinstance(rez,str):
rez = ""
else:
if isinstance(rez, list):
# multirep run, just return as it is
return rez
if not(rez is None) and out_ws_name and rez.name() != out_ws_name:
# the function does not return None, pylint is wrong
#pylint: disable=W1111
rez = RenameWorkspace(InputWorkspace=rez, OutputWorkspace=out_ws_name)
return rez
def sqw(self,wksp_in,qbin):
"""
convert to SmodQw assume direct geom requires string of rebin parameters
sqw(w1,'0,.1,12')
"""
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
#ei= (wksp_in.getRun().getLogData("Ei").value)
#wksp_in=mtd[wksp_in]
SofQW3(InputWorkspace=wksp_in,OutputWorkSpace=wksp_out,QAxisBinning=qbin,EMode="Direct")
##comment lines were code for correcting interceting area rebin as coded in sofqw2, sofqw3 seems to work correctly
#CloneWorkspace(InputWorkspace=wksp_in,OutputWorkspace='tmp')
#CreateSingleValuedWorkspace(OutputWorkspace='scale',DataValue='0',ErrorValue='0')
#Multiply(LHSWorkspace='tmp',RHSWorkspace='scale',OutputWorkspace='tmp')
#CreateSingleValuedWorkspace(OutputWorkspace='scale2',DataValue='1',ErrorValue='0')
#Plus(LHSWorkspace='tmp',RHSWorkspace='scale2',OutputWorkspace='tmp')
#SofQW3(InputWorkspace='tmp',OutputWorkspace='tmp',QAxisBinning=qbin,EMode='Direct')
#SetUncertainties(InputWorkSpace='tmp',OutputWorkSpace='tmp')
#Divide(LHSWorkspace=wksp_out,RHSWorkspace='tmp',OutputWorkspace=wksp_out)
#DeleteWorkspace('tmp')
#DeleteWorkspace('scale')
#DeleteWorkspace('scale2')
return mtd[wksp_out]
def sqw(self, wksp_in, qbin):
"""
convert to SmodQw assume direct geom requires string of rebin parameters
sqw(w1,'0,.1,12')
"""
n, r = funcreturns.lhs_info('both')
wksp_out = r[0]
#ei= (wksp_in.getRun().getLogData("Ei").value)
#wksp_in=mtd[wksp_in]
SofQW3(InputWorkspace=wksp_in,
OutputWorkSpace=wksp_out,
QAxisBinning=qbin,
EMode="Direct")
##comment lines were code for correcting interceting area rebin as coded in sofqw2, sofqw3 seems to work correctly
#CloneWorkspace(InputWorkspace=wksp_in,OutputWorkspace='tmp')
#CreateSingleValuedWorkspace(OutputWorkspace='scale',DataValue='0',ErrorValue='0')
#Multiply(LHSWorkspace='tmp',RHSWorkspace='scale',OutputWorkspace='tmp')
#CreateSingleValuedWorkspace(OutputWorkspace='scale2',DataValue='1',ErrorValue='0')
#Plus(LHSWorkspace='tmp',RHSWorkspace='scale2',OutputWorkspace='tmp')
#SofQW3(InputWorkspace='tmp',OutputWorkspace='tmp',QAxisBinning=qbin,EMode='Direct')
#SetUncertainties(InputWorkSpace='tmp',OutputWorkSpace='tmp')
#Divide(LHSWorkspace=wksp_out,RHSWorkspace='tmp',OutputWorkspace=wksp_out)
#DeleteWorkspace('tmp')
#DeleteWorkspace('scale')
#DeleteWorkspace('scale2')
return mtd[wksp_out]
def transpose(wksp_in):
"""
transpose workspace
"""
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
Transpose(InputWorkspace=wksp_in,OutputWorkspace=wksp_out)
return mtd[wksp_out]
def do_bleed_test(sample_run, max_framerate, ignored_pixels):
"""Runs the CreatePSDBleedMask algorithm
Input:
sample_run - The run number of the sample
max_framerate - The maximum allowed framerate in a tube. If None, the instrument defaults are used.
ignored_pixels - The number of central pixels to ignore. If None, the instrument defaults are used.
"""
#NOTE: Should be deployed on non-normalized workspace only!
logger.notice('Running PSD bleed test')
# Load the sample run
if __Reducer__: # Try to use generic loader which would work with files or workspaces alike
sample_run = __Reducer__.get_run_descriptor(sample_run)
data_ws = sample_run.get_workspace() # this will load data if necessary
ws_name = data_ws.name()+'_bleed'
else:
# may be sample run is already a run descriptor despite __Reducer__ have not been exposed
data_ws = sample_run.get_workspace() # this will load data if necessary
ws_name = data_ws.name()+'_bleed'
if max_framerate is None: #get defaults
max_framerate = float(data_ws.getInstrument().getNumberParameter('max-tube-framerate')[0])
if ignored_pixels is None: #get defaults
ignored_pixels = int(data_ws.getInstrument().getNumberParameter('num-ignored-pixels')[0])
else:
# Make sure it is an int
ignored_pixels = int(ignored_pixels)
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_bleed__test'
# Check if all necessary logs present in the workspace,as nxs workspace log names are different
# from a raw file workspace logs.
try:
nFrames= data_ws.getRun().getLogData('goodfrm').value
except RuntimeError:
try:
nFrames = data_ws.getRun().getLogData('good_frames').lastValue()
AddSampleLog(Workspace=data_ws, LogName='goodfrm', LogText=str(int(nFrames)), LogType='Number')
except RuntimeError:
raise RuntimeError("Bleed test fails as no appropriate 'good_frames' or 'goodfrm' log is loaded with ws: {0}\n"
"Disable bleed test by setting diag_bleed_test=False or add 'goodfrm' log to the workspace\n"\
.format(data_ws.name()))
bleed_test, num_failed = CreatePSDBleedMask(InputWorkspace=data_ws, OutputWorkspace=ws_name,
MaxTubeFramerate=max_framerate,
NIgnoredCentralPixels=ignored_pixels)
return bleed_test, num_failed
def sqwfast(self,wksp_in,qbin):
"""
convert to SmodQw assume direct geom requires string of rebin parameters
sqw(w1,'0,.1,12')
"""
try:
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
#ei= (wksp_in.getRun().getLogData("Ei").value)
SofQW(wksp_in,OutputWorkspace=wksp_out,QAxisBinning=qbin,EMode="Direct",EFixed=str(ei))
return mtd[wksp_out]
except:
print 'no output workpsace defined'
def iliad_wrapper(*args):
#seq = inspect.stack()
# output workspace name.
try:
n,r = funcreturns.lhs_info('both')
out_ws_name = r[0]
except:
out_ws_name = None
host = args[0]
if len(args)>1:
input_file = args[1]
if len(args)>2:
output_directory = args[2]
else:
output_directory =None
else:
input_file=None
output_directory=None
# add input file folder to data search directory if file has it
if input_file and isinstance(input_file,str):
data_path = os.path.dirname(input_file)
if len(data_path)>0:
try:
config.appendDataSearchDir(str(data_path))
args[1] = os.path.basename(input_file)
except: # if mantid is not available, this should ignore config
pass
if output_directory:
config['defaultsave.directory'] = output_directory
if host._run_from_web:
web_vars = dict(host._wvs.standard_vars.items()+host._wvs.advanced_vars.items())
host.reducer.prop_man.set_input_parameters(**web_vars)
else:
pass # we should set already set up variables using
rez = reduce(*args)
# prohibit returning workspace to web services.
if host._run_from_web and not isinstance(rez,str):
rez=""
else:
if isinstance(rez,list):
# multirep run, just return as it is
return rez
if out_ws_name and rez.name() != out_ws_name :
rez=RenameWorkspace(InputWorkspace=rez,OutputWorkspace=out_ws_name)
return rez
def do_second_white_test(white_counts, comp_white_counts, tiny, large, out_lo, out_hi,
median_lo, median_hi, sigma, variation,
start_index=None, end_index=None):
"""
Run additional tests comparing given another white beam count workspace, comparing
to the first
Required inputs:
white_counts - A workspace containing the integrated counts from a
white beam vanadium run
comp_white_counts - A workspace containing the integrated counts from a
white beam vanadium run
tiny - Minimum threshold for acceptance
large - Maximum threshold for acceptance
median_lo - Fraction of median to consider counting low
median_hi - Fraction of median to consider counting high
signif - Counts within this number of multiples of the
standard dev will be kept
variation - Defines a range within which the ratio of the two counts is
allowed to fall in terms of the number of medians
"""
logger.notice('Running second white beam test')
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_second_white_test'
# Make sure we are a MatrixWorkspace
white_counts = ConvertToMatrixWorkspace(InputWorkspace=white_counts,OutputWorkspace=white_counts)
comp_white_counts = ConvertToMatrixWorkspace(InputWorkspace=comp_white_counts,OutputWorkspace=comp_white_counts)
# Do the white beam test
__second_white_tests, failed = do_white_test(comp_white_counts, tiny, large, median_lo, median_hi,
sigma, start_index, end_index)
# and now compare it with the first
effic_var, num_failed = DetectorEfficiencyVariation(WhiteBeamBase=white_counts, WhiteBeamCompare=comp_white_counts,
OutputWorkspace=ws_name,
Variation=variation, StartWorkspaceIndex=start_index,
EndWorkspaceIndex=end_index)
DeleteWorkspace(Workspace=str(__second_white_tests))
# Mask those that failed
maskWS = effic_var
MaskDetectors(Workspace=white_counts, MaskedWorkspace=maskWS)
MaskDetectors(Workspace=comp_white_counts, MaskedWorkspace=maskWS)
return maskWS, num_failed
def do_second_white_test(white_counts, comp_white_counts, tiny, large, out_lo, out_hi,
median_lo, median_hi, sigma, variation,
start_index=None, end_index=None):
"""
Run additional tests comparing given another white beam count workspace, comparing
to the first
Required inputs:
white_counts - A workspace containing the integrated counts from a
white beam vanadium run
comp_white_counts - A workspace containing the integrated counts from a
white beam vanadium run
tiny - Minimum threshold for acceptance
large - Maximum threshold for acceptance
median_lo - Fraction of median to consider counting low
median_hi - Fraction of median to consider counting high
signif - Counts within this number of multiples of the
standard dev will be kept
variation - Defines a range within which the ratio of the two counts is
allowed to fall in terms of the number of medians
"""
logger.notice('Running second white beam test')
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_second_white_test'
# Make sure we are a MatrixWorkspace
white_counts = ConvertToMatrixWorkspace(InputWorkspace=white_counts,OutputWorkspace=white_counts)
comp_white_counts = ConvertToMatrixWorkspace(InputWorkspace=comp_white_counts,OutputWorkspace=comp_white_counts)
# Do the white beam test
__second_white_tests, failed = do_white_test(comp_white_counts, tiny, large, median_lo, median_hi,
sigma, start_index, end_index)
# and now compare it with the first
effic_var, num_failed = DetectorEfficiencyVariation(WhiteBeamBase=white_counts, WhiteBeamCompare=comp_white_counts,
OutputWorkspace=ws_name,
Variation=variation, StartWorkspaceIndex=start_index,
EndWorkspaceIndex=end_index)
DeleteWorkspace(Workspace=str(__second_white_tests))
# Mask those that failed
maskWS = effic_var
MaskDetectors(Workspace=white_counts, MaskedWorkspace=maskWS)
MaskDetectors(Workspace=comp_white_counts, MaskedWorkspace=maskWS)
return maskWS, num_failed
def sqw(wksp_in,qbin):
"""
convert to SmodQw assume direct geom requires string of rebin parameters
sqw(w1,'0,.1,12')
"""
try:
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
ei= (wksp_in.getRun().getLogData("Ei").value)
SofQW2(wksp_in, OutputWorkspace=wksp_out,QAxisBinning=qbin,EMode="Direct",EFixed=str(ei))
Transpose(InputWorkspace=wksp_out,OutputWorkspace=wksp_out)
return mtd[wksp_out]
except:
print 'no output workpsace defined'
def calculate_resolution(input_data, mass, index=0):
"""
Run the VesuvioResolution function to produce a workspace
with the value of the Vesuvio resolution.
@param input_data The original TOF data
@param mass The mass defining the recoil peak in AMU
@param index An optional index to specify the spectrum to use
"""
from mantid.api import AlgorithmManager, AnalysisDataService
from mantid.kernel.funcreturns import lhs_info
# Grab the name of the variable that this function call is assigned to
try:
output_name = lhs_info("names")[0]
except IndexError:
# No variable specified
name_stem = str(input_data)
output_name = name_stem + "_res" + str(index)
function = "name=VesuvioResolution, Mass=%f" % mass
# execute the resolution function using fit.
# functions can't currently be executed as stand alone objects,
# so for now we will run fit with zero iterations to achieve the same result.
fit = mantid.api.AlgorithmManager.createUnmanaged('Fit')
fit.initialize()
fit.setChild(True)
fit.setLogging(False)
mantid.simpleapi._set_properties(fit, function, input_data, MaxIterations=0,
CreateOutput=True, Output=output_name,WorkspaceIndex=index)
fit.execute()
values_ws = fit.getProperty("OutputWorkspace").value
# extract just the function values
extract = mantid.api.AlgorithmManager.createUnmanaged('ExtractSingleSpectrum')
extract.initialize()
extract.setChild(True)
extract.setLogging(False)
extract.setProperty("InputWorkspace", values_ws)
extract.setProperty("OutputWorkspace", "__unused_for_child")
extract.setProperty("WorkspaceIndex", 1)
extract.execute()
calculated = extract.getProperty("OutputWorkspace").value
AnalysisDataService.addOrReplace(output_name, calculated)
return calculated
def QCut(self,Emin,Emax,Qmin,delQ,Qmax,**kwargs):
intmin=Emin
intmax=Emax
cutmin=Qmin
delcut=delQ
cutmax=Qmax
try:
n,r=funcreturns.lhs_info('both')
name=r[0]
if kwargs.has_key('shoelace'):
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle=kwargs.get_key('Handle')
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,cutName=name,shoelace=True,Handle=fighandle)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,cutName=name,shoelace=True)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',cutName=name,shoelace=True)
else:
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle=kwargs.get('Handle')
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,cutName=name,Handle=fighandle)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,cutName=name)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',cutName=name)
except:
if kwargs.has_key('shoelace'):
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle=kwargs.get('Handle')
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,shoelace=True,Handle=fighandle)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,shoelace=True)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',shoelace=True)
else:
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle=kwargs.get('Handle')
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True,Handle=fighandle)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q',over=True)
else:
self.cut(self.data,intmin,intmax,cutmin,delcut,cutmax,along='q')
def cutMatplotlib(self,direction,intMin,intMax,minX,delX,maxX,**kwargs):
#keywords, wkspout= name of output
# wkspIn= input data
#deal with the call type and where to get the output workspace name from
if kwargs.has_key('wkspout'):
cut_name=kwargs.get('wkspout')
else:
n,r=funcreturns.lhs_info('both')
cut_name=r[0]
if direction == 'x':
if kwargs.has_key('wkspIn'):
wkspinName=kwargs.get('wkspIn')
Rebin2D(InputWorkspace=wkspinName,OutputWorkspace=cut_name,Axis1Binning=str(minX)+','+str(delX)+','+str(maxX),Axis2Binning=str(intMin)+','+str(intMax-intMin)+','+str(intMax),UseFractionalArea='1' )
else:
Rebin2D(InputWorkspace=self.data,OutputWorkspace=cut_name,Axis1Binning=str(minX)+','+str(delX)+','+str(maxX),Axis2Binning=str(intMin)+','+str(intMax-intMin)+','+str(intMax),UseFractionalArea='1' )
if direction == 'y':
if kwargs.has_key('wkspin'):
wkspinName=kwargs.get('wkspin')
Rebin2D(InputWorkspace=wkspinName,OutputWorkspace=cut_name,Axis1Binning=str(intMin)+','+str(intMax-intMin)+','+str(intMax),Axis2Binning=str(minX)+','+str(delX)+','+str(maxX),UseFractionalArea='1')
Transpose(InputWorkspace=cut_name,OutputWorkspace=cut_name)
else:
Rebin2D(InputWorkspace=self.data,OutputWorkspace=cut_name,Axis1Binning=str(intMin)+','+str(intMax-intMin)+','+str(intMax),Axis2Binning=str(minX)+','+str(delX)+','+str(maxX),UseFractionalArea='1')
Transpose(InputWorkspace=cut_name,OutputWorkspace=cut_name)
ReplaceSpecialValues(InputWorkspace=cut_name,OutputWorkspace=cut_name,NaNValue='0',InfinityValue='0')
cutDataWorkSpace=mtd[cut_name]
evals=cutDataWorkSpace.extractE()
xvals=cutDataWorkSpace.extractX()
yvals=cutDataWorkSpace.extractY()
NewXvals=( xvals + numpy.roll(xvals,-1) )/2 # calculate the bin center
NewXvals = numpy.delete(NewXvals,-1) # remove the last element which is junk
yvals=yvals[0]
evals=evals[0]
output=[NewXvals,yvals,evals]
return output
def do_bleed_test(sample_run, max_framerate, ignored_pixels):
"""Runs the CreatePSDBleedMask algorithm
Input:
sample_run - The run number of the sample
max_framerate - The maximum allowed framerate in a tube. If None, the instrument defaults are used.
ignored_pixels - The number of central pixels to ignore. If None, the instrument defaults are used.
"""
# NOTE: it was deployed on loaded workspace and now it works on normalized workspace. Is this acceptable?
logger.notice('Running PSD bleed test')
# Load the sample run
if __Reducer__: # Try to use generic loader which would work with files or workspaces alike
sample_run = __Reducer__.get_run_descriptor(sample_run)
data_ws = sample_run.get_workspace() # this will load data if necessary
ws_name = sample_run.get_ws_name()+'_bleed'
else:
# may be sample run is already a run descriptor despite __Reducer__ have not been exposed
data_ws = sample_run.get_workspace() # this will load data if necessary
ws_name = sample_run.get_ws_name()+'_bleed'
if max_framerate is None:
max_framerate = float(data_ws.getInstrument().getNumberParameter('max-tube-framerate')[0])
if ignored_pixels is None:
ignored_pixels = int(data_ws.getInstrument().getNumberParameter('num-ignored-pixels')[0])
else:
# Make sure it is an int
ignored_pixels = int(ignored_pixels)
# What shall we call the output
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
ws_name = lhs_names[0]
else:
ws_name = '__do_bleed__test'
bleed_test, num_failed = CreatePSDBleedMask(InputWorkspace=data_ws, OutputWorkspace=ws_name,
MaxTubeFramerate=max_framerate,
NIgnoredCentralPixels=ignored_pixels)
return bleed_test, num_failed
def diagnose(self, white, **kwargs):
"""
Run diagnostics on the provided workspaces.
This method does some additional processing before moving on to the diagnostics:
1) Computes the white beam integrals, converting to energy
2) Computes the background integral using the instrument defined range
3) Computes a total count from the sample
These inputs are passed to the diagnostics functions
Required inputs:
white - A workspace, run number or filepath of a white beam run. A workspace is assumed to
have simple been loaded and nothing else.
Optional inputs:
sample - A workspace, run number or filepath of a sample run. A workspace is assumed to
have simple been loaded and nothing else. (default = None)
second_white - If provided an additional set of tests is performed on this. (default = None)
hard_mask - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
huge - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
van_out_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
van_out_hi - Upper bound defining outliers as fraction of median value (default = 100.)
van_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
van_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the\n"
"difference with respect to the median value must also exceed this number of error bars (default=0.0)
samp_zero - If true then zeroes in the vanadium data will count as failed (default = True)
samp_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
samp_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the\n"
"difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation - The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum framerate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
"""
lhs_names = funcreturns.lhs_info('names')
if len(lhs_names) > 0:
var_name = lhs_names[0]
else:
var_name = "diag_mask"
# Check for any keywords that have not been supplied and put in the defaults
for par in self.diag_params:
arg = par.lstrip('diag_')
if arg not in kwargs:
kwargs[arg] = getattr(self, par)
# Get the white beam vanadium integrals
whiteintegrals = self.do_white(white, None, None,None) # No grouping yet
if 'second_white' in kwargs:
second_white = kwargs['second_white']
if second_white is None:
del kwargs['second_white']
else:
other_whiteintegrals = self.do_white(second_white, None, None,None) # No grouping yet
kwargs['second_white'] = other_whiteintegrals
# Get the background/total counts from the sample if present
if 'sample' in kwargs:
sample = kwargs['sample']
del kwargs['sample']
# If the bleed test is requested then we need to pass in the sample_run as well
if kwargs.get('bleed_test', False):
kwargs['sample_run'] = sample
# Set up the background integrals
result_ws = common.load_runs(sample)
result_ws = self.normalise(result_ws, result_ws.name(), self.normalise_method)
if 'bkgd_range' in kwargs:
bkgd_range = kwargs['bkgd_range']
del kwargs['bkgd_range']
else:
bkgd_range = self.background_range
background_int = Integration(result_ws,
RangeLower=bkgd_range[0],RangeUpper=bkgd_range[1],
IncludePartialBins=True)
total_counts = Integration(result_ws, IncludePartialBins=True)
background_int = ConvertUnits(background_int, "Energy", AlignBins=0)
background_int *= 1.7016e8
diagnostics.normalise_background(background_int, whiteintegrals, kwargs.get('second_white',None))
kwargs['background_int'] = background_int
kwargs['sample_counts'] = total_counts
# If we have a hard_mask, check the instrument name is defined
if 'hard_mask' in kwargs:
if 'instrument_name' not in kwargs:
kwargs['instrument_name'] = self.instr_name
# Check how we should run diag
if self.diag_spectra is None:
# Do the whole lot at once
diagnostics.diagnose(whiteintegrals, **kwargs)
else:
banks = self.diag_spectra.split(";")
bank_spectra = []
for b in banks:
token = b.split(",") # b = "(,)"
if len(token) != 2:
raise ValueError("Invalid bank spectra specification in diag %s" % self.diag_spectra)
start = int(token[0].lstrip('('))
end = int(token[1].rstrip(')'))
bank_spectra.append((start,end))
for index, bank in enumerate(bank_spectra):
kwargs['start_index'] = bank[0] - 1
kwargs['end_index'] = bank[1] - 1
diagnostics.diagnose(whiteintegrals, **kwargs)
if 'sample_counts' in kwargs:
DeleteWorkspace(Workspace='background_int')
DeleteWorkspace(Workspace='total_counts')
if 'second_white' in kwargs:
DeleteWorkspace(Workspace=kwargs['second_white'])
# Return a mask workspace
diag_mask, det_ids = ExtractMask(InputWorkspace=whiteintegrals,OutputWorkspace=var_name)
DeleteWorkspace(Workspace=whiteintegrals)
self.spectra_masks = diag_mask
return diag_mask
def arb_units(wb_run,sample_run,ei_guess,rebin,map_file='default',monovan_run=None,**kwargs):
""" One step conversion of run into workspace containing information about energy transfer
Usage:
>>arb_units(wb_run,sample_run,ei_guess,rebin)
>>arb_units(wb_run,sample_run,ei_guess,rebin,**arguments)
>>arb_units(wb_run,sample_run,ei_guess,rebin,mapfile,**arguments)
>>arb_units(wb_run Whitebeam run number or file name or workspace
sample_run sample run number or file name or workspace
ei_guess Ei guess
rebin Rebin parameters
mapfile Mapfile -- if absent/'default' the defaults from IDF are used
monovan_run If present will do the absolute units normalization. Number of additional parameters
specified in **kwargs is usually requested for this. If they are absent, program uses defaults,
but the defaults (e.g. sample_mass or sample_rmm ) are usually incorrect for a particular run.
arguments The dictionary containing additional keyword arguments.
The list of allowed additional arguments is defined in InstrName_Parameters.xml file, located in
MantidPlot->View->Preferences->Mantid->Directories->Parameter Definitions
with run numbers as input:
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70]) # will run on default instrument
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70],'mari_res', additional keywords as required)
>>dgreduce.arb_units(1000,10001,80,'-10,.1,70','mari_res',fixei=True)
A detector calibration file must be specified if running the reduction with workspaces as input
namely:
>>w2=iliad("wb_wksp","run_wksp",ei,rebin_params,mapfile,det_cal_file=cal_file
,diag_remove_zero=False,norm_method='current')
type help() for the list of all available keywords. All availible keywords are provided in InstName_Parameters.xml file
Some samples are:
norm_method =[monitor-1],[monitor-2][Current]
background =False , True
fixei =False , True
save_format =['.spe'],['.nxspe'],'none'
detector_van_range =[20,40] in mev
bkgd_range =[15000,19000] :integration range for background tests
second_white - If provided an additional set of tests is performed on this. (default = None)
hardmaskPlus - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
diag_van_median_rate_limit_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
diag_van_median_rate_limit_hi - Upper bound defining outliers as fraction of median value (default = 100.)
diag_van_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
diag_van_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
diag_van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars (default=0.0)
diag_remove_zero - If true then zeroes in the vanadium data will count as failed (default = True)
diag_samp_samp_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
diag_samp_samp_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
diag_samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the"
difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation -The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum framerate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
diag_remove_zero =True, False (default):Diag zero counts in background range
bleed=True , turn bleed correction on and off on by default for Merlin and LET
sum =True,False(default) , sum multiple files
det_cal_file= a valid detector block file and path or a raw file. Setting this
will use the detector calibraion from the specified file NOT the
input raw file
mask_run = RunNumber to use for diag instead of the input run number
one2one =True, False :Reduction will not use a mapping file
hardmaskPlus=Filename :load a hardmarkfile and apply together with diag mask
hardmaskOnly=Filename :load a hardmask and use as only mask
"""
global Reducer
if Reducer is None or Reducer.instrument is None:
raise ValueError("instrument has not been defined, call setup(instrument_name) first.")
# --------------------------------------------------------------------------------------------------------
# Deal with mandatory parameters for this and may be some top level procedures
# --------------------------------------------------------------------------------------------------------
Reducer.log("****************************************************************");
if isinstance(sample_run,api.Workspace) or (isinstance(sample_run,str) and sample_run in mtd):
Reducer.log('*** DGreduce run for: {0:>20} : Workspace name: {1:<20} '.format(Reducer.instr_name,str(sample_run)))
else:
Reducer.log('*** DGreduce run for: {0:>20} : Run number/s : {1:<20} '.format(Reducer.instr_name,str(sample_run)))
try:
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
except:
if sample_run == 0:
#deal with the current run being parsed as 0 rather than 00000
sample_run='00000'
wksp_out=Reducer.instr_name+str(sample_run)+'.spe'
if kwargs.has_key('sum') and kwargs.get('sum')==True:
wksp_out=inst_name+str(sample_run[0])+'sum'+'.spe'
start_time=time.time()
if sample_run=='00000' and mtd.doesExist(inst_name+'00000.raw')==True:
Reducer.log('Deleting previous instance of temp data')
DeleteWorkspace(Workspace=inst_name+'00000.raw')
# we may want to run absolute units normalization and this function has been called with monovan run or helper procedure
abs_units_defaults_check = False
if monovan_run != None :
# check if mono-vanadium is provided as multiple files list or just put in brackets occasionally
Reducer.log("****************************************************************");
Reducer.log('*** Output will be in absolute units of mb/str/mev/fu')
if isinstance(monovan_run,list):
if len(monovan_run)>1:
raise IOError(' Can currently work only with single monovan file but list supplied')
else:
monovan_run = monovan_run[0];
abs_units_defaults_check =True
if '_defaults_have_changed' in kwargs:
del kwargs['_defaults_have_changed']
abs_units_defaults_check =False
if "wb_for_monovanadium" in kwargs :
wb_for_monovanadium = kwargs['wb_for_monovanadium']
del kwargs['wb_for_monovanadium']
else:
wb_for_monovanadium = wb_run;
if isinstance(ei_guess,str):
ei_guess = float(ei_guess)
# set rebinning range
Reducer.energy_bins = rebin
Reducer.incident_energy = ei_guess;
if Reducer.energy_bins[2] > ei_guess:
Reducer.log('Error: rebin max rebin range {0:f} exceeds incident energy {1:f}'.format(Reducer.energy_bins[2],ei_guess),'Error')
return
# Process old legacy parameters which are easy to re-define in dgreduce rather then transfer through Mantid
program_args = process_legacy_parameters(**kwargs)
# set non-default reducers parameters and check if all optional keys provided as parameters are acceptable and have been defined in IDF
changed_Keys=Reducer.set_input_parameters(**program_args);
# inform user about changed parameters
Reducer.log("*** Provisional Incident energy: {0:>12.3f} mEv".format(ei_guess))
Reducer.log("****************************************************************");
for key in changed_Keys:
val = getattr(Reducer,key);
Reducer.log(" Value of : {0:<25} is set to : {1:<20} ".format(key,val))
save_dir = config.getString('defaultsave.directory')
Reducer.log("****************************************************************");
if monovan_run != None and not('van_mass' in changed_Keys or 'vanadium-mass' in changed_Keys) :
Reducer.log("*** Monochromatic vanadium mass used : {0} ".format(Reducer.van_mass))
Reducer.log("*** By default results are saved into: {0}".format(save_dir));
Reducer.log("****************************************************************");
#do we run absolute units normalization and need to warn users if the parameters needed for that have not changed from defaults
if abs_units_defaults_check :
Reducer.check_abs_norm_defaults_changed(changed_Keys);
#process complex parameters
# map file given in parameters overrides default map file
if map_file != 'default' :
Reducer.map_file = map_file
# defaults can be None too, but can be a file
if Reducer.map_file == None:
Reducer.log('one2one map selected')
if Reducer.det_cal_file != None :
if isinstance(Reducer.det_cal_file,str) and not Reducer.det_cal_file in mtd : # it is a file
Reducer.log('Setting detector calibration file to '+Reducer.det_cal_file)
else:
Reducer.log('Setting detector calibration to {0}, which is probably a workspace '.format(str(Reducer.det_cal_file)))
else:
Reducer.log('Setting detector calibration to detector block info from '+str(sample_run))
# check if reducer can find all non-run files necessary for the reduction before starting long run.
Reducer.check_necessary_files(monovan_run);
print 'Output will be normalized to', Reducer.normalise_method
if (numpy.size(sample_run)) > 1 and Reducer.sum_runs:
#this sums the runs together before passing the summed file to the rest of the reduction
#this circumvents the inbuilt method of summing which fails to sum the files for diag
#the D.E.C. tries to be too clever so we have to fool it into thinking the raw file is already exists as a workpsace
sumfilename=Reducer.instr_name+str(sample_run[0])+'.raw'
sample_run =sum_files(Reducer.instr_name,sumfilename, sample_run)
common.apply_calibration(Reducer.instr_name,sample_run,Reducer.det_cal_file)
#sample_run = RenameWorkspace(InputWorkspace=accum,OutputWorkspace=inst_name+str(sample_run[0])+'.raw')
if Reducer.mask_run == None :
mask_run=sample_run
masking = None;
masks_done=False
if not Reducer.run_diagnostics:
header="Diagnostics including hard masking is skipped "
masks_done = True;
if Reducer.save_and_reuse_masks :
raise NotImplementedError("Save and reuse masks option is not yet implemented")
mask_file_name = common.create_resultname(str(mask_run),Reducer.instr_name,'_masks.xml')
mask_full_file = FileFinder.getFullPath(mask_file_name)
if len(mask_full_file) > 0 :
masking = LoadMask(Instrument=Reducer.instr_name,InputFile=mask_full_file,OutputWorkspace=mask_file_name)
#Reducer.hard_mask_file = mask_full_file;
#Reducer.use_hard_mask_only = True
masks_done=True
header="Masking fully skipped and processed {0} spectra and {1} bad spectra "
else:
pass
#-------------------------------------------------------------------------------------------------------------------------------------------------------
# Here we give control to the Reducer
# --------------------------------------------------------------------------------------------------------
# diag the sample and detector vanadium. It will deal with hard mask only if it is set that way
if not masks_done:
print '########### Run diagnose for sample run ##############################'
masking = Reducer.diagnose(wb_run,sample = mask_run,
second_white = None,print_results=True)
header = "Diag Processed workspace with {0:d} spectra and masked {1:d} bad spectra"
# Calculate absolute units:
if monovan_run != None :
if Reducer.mono_correction_factor == None :
if Reducer.use_sam_msk_on_monovan == True:
Reducer.log(' Applying sample run mask to mono van')
else:
if not Reducer.use_hard_mask_only : # in this case the masking2 is different but points to the same workspace Should be better soulution for that.
print '########### Run diagnose for monochromatic vanadium run ##############'
masking2 = Reducer.diagnose(wb_for_monovanadium,sample=monovan_run,
second_white = None,rint_results=True)
masking += masking2
DeleteWorkspace(masking2)
else: # if Reducer.mono_correction_factor != None :
pass
# save mask if it does not exist and has been already loaded
if Reducer.save_and_reuse_masks and not masks_done:
SaveMask(InputWorkspace=masking,OutputFile = mask_file_name,GroupedDetectors=True)
# Very important statement propagating masks for further usage in convert_to_energy
Reducer.spectra_masks=masking
# estimate and report the number of failing detectors
failed_sp_list,nSpectra = get_failed_spectra_list_from_masks(masking)
nMaskedSpectra = len(failed_sp_list)
# this tells turkey in case of hard mask only but everything else semens work fine
print header.format(nSpectra,nMaskedSpectra)
#Run the conversion first on the sample
deltaE_wkspace_sample = Reducer.convert_to_energy(sample_run, ei_guess, wb_run)
# calculate absolute units integral and apply it to the workspace
if monovan_run != None or Reducer.mono_correction_factor != None :
deltaE_wkspace_sample = apply_absolute_normalization(Reducer,deltaE_wkspace_sample,monovan_run,ei_guess,wb_run)
# Hack for multirep
#if isinstance(monovan_run,int):
# filename = common.find_file(monovan_run)
# output_name = common.create_dataname(filename);
# DeleteWorkspace(output_name);
results_name = deltaE_wkspace_sample.name();
if results_name != wksp_out:
RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)
ei= (deltaE_wkspace_sample.getRun().getLogData("Ei").value)
print 'Incident energy found for sample run: ',ei,' meV'
end_time=time.time()
print 'Elapsed time =',end_time-start_time, 's'
if mtd.doesExist('_wksp.spe-white')==True:
DeleteWorkspace(Workspace='_wksp.spe-white')
# Hack for multirep mode?
if mtd.doesExist('hard_mask_ws') == True:
DeleteWorkspace(Workspace='hard_mask_ws')
return deltaE_wkspace_sample
def op_wrapper(self, other):
# Get the result variable to know what to call the output
result_info = lhs_info()
# Pass off to helper
return _do_binary_operation(algorithm, self, other, result_info,
inplace, reverse)
def op_wrapper(self):
# Get the result variable to know what to call the output
result_info = lhs_info()
# Pass off to helper
return _do_unary_operation(algorithm, self, result_info)
def cutMatplotlib(self, direction, intMin, intMax, minX, delX, maxX,
**kwargs):
#keywords, wkspout= name of output
# wkspIn= input data
#deal with the call type and where to get the output workspace name from
if kwargs.has_key('wkspout'):
cut_name = kwargs.get('wkspout')
else:
n, r = funcreturns.lhs_info('both')
cut_name = r[0]
if direction == 'x':
if kwargs.has_key('wkspIn'):
wkspinName = kwargs.get('wkspIn')
Rebin2D(InputWorkspace=wkspinName,
OutputWorkspace=cut_name,
Axis1Binning=str(minX) + ',' + str(delX) + ',' +
str(maxX),
Axis2Binning=str(intMin) + ',' + str(intMax - intMin) +
',' + str(intMax),
UseFractionalArea='1')
else:
Rebin2D(InputWorkspace=self.data,
OutputWorkspace=cut_name,
Axis1Binning=str(minX) + ',' + str(delX) + ',' +
str(maxX),
Axis2Binning=str(intMin) + ',' + str(intMax - intMin) +
',' + str(intMax),
UseFractionalArea='1')
if direction == 'y':
if kwargs.has_key('wkspin'):
wkspinName = kwargs.get('wkspin')
Rebin2D(InputWorkspace=wkspinName,
OutputWorkspace=cut_name,
Axis1Binning=str(intMin) + ',' + str(intMax - intMin) +
',' + str(intMax),
Axis2Binning=str(minX) + ',' + str(delX) + ',' +
str(maxX),
UseFractionalArea='1')
Transpose(InputWorkspace=cut_name, OutputWorkspace=cut_name)
else:
Rebin2D(InputWorkspace=self.data,
OutputWorkspace=cut_name,
Axis1Binning=str(intMin) + ',' + str(intMax - intMin) +
',' + str(intMax),
Axis2Binning=str(minX) + ',' + str(delX) + ',' +
str(maxX),
UseFractionalArea='1')
Transpose(InputWorkspace=cut_name, OutputWorkspace=cut_name)
ReplaceSpecialValues(InputWorkspace=cut_name,
OutputWorkspace=cut_name,
NaNValue='0',
InfinityValue='0')
cutDataWorkSpace = mtd[cut_name]
evals = cutDataWorkSpace.extractE()
xvals = cutDataWorkSpace.extractX()
yvals = cutDataWorkSpace.extractY()
NewXvals = (xvals +
numpy.roll(xvals, -1)) / 2 # calculate the bin center
NewXvals = numpy.delete(NewXvals,
-1) # remove the last element which is junk
yvals = yvals[0]
evals = evals[0]
output = [NewXvals, yvals, evals]
return output
def abs_units(wb_for_run,sample_run,monovan_run,wb_for_monovanadium,samp_rmm,samp_mass,ei_guess,rebin,map_file='default',monovan_mapfile='default',**kwargs):
"""
dgreduce.abs_units(wb_run Whitebeam run number or file name or workspace
sample_run Sample run run number or file name or workspace
monovan_run Monochromatic run run number or file name or workspace
wb_mono White beam for Monochromatic run run number or file name or workspace
samp_rmm Mass of formula unit of sample
samp_mass Actual sample mass
ei_guess Ei guess of run
rebin Rebin parameters for output data
map_file Mapfile for sample run
monovan_mapfile Mapfile for mono van run
keyword arguments Any specified additional keyword arguments
Example with run numbers
abs_units(11001,11002,11003,10098,250.1,5.2,80,'-10,.1,75','mari_res','mari_res')
A detector calibration file must be specified if running the reduction with workspace inputs
Example with workspace inputs
abs_units('wb_run','sam_run','mono_run','wb_for_mono',250.1,5.2,80,'-10,.1,75','mari_res','mari_res',
det_cal_file=10001,diag_remove_zero=False,norm_method='current')
A detector calibration file must be specified if running the reduction with workspace inputs
Available keywords
norm_method =[monitor-1],[monitor-2][Current]
background =False , True
fixei =False , True
save_format =['.spe'],['.nxspe'],'none'
detector_van_range =[20,40] in mev
bkgd_range =[15000,19000] :integration range for background tests
second_white - If provided an additional set of tests is performed on this. (default = None)
hard_mask_file - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
diag_van_median_rate_limit_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
diag_van_median_rate_limit_hi - Upper bound defining outliers as fraction of median value (default = 100.)
diag_van_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
diag_van_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
diag_van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars (default=0.0)
diag_remove_zero - If true then zeros in the vanadium data will count as failed (default = True)
diag_samp_samp_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
diag_samp_samp_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
diag_samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the"
difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation -The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum frame rate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
diag_remove_zero =True, False (default):Diag zero counts in background range
bleed=True , turn bleed correction on and off on by default for Merlin and LET
sum =True,False(default) , sum multiple files
det_cal_file= a valid detector block file and path or a raw file. Setting this
will use the detector calibration from the specified file NOT the
input raw file
mask_run = RunNumber to use for diag instead of the input run number
one2one =True, False :Reduction will not use a mapping file
hardmaskPlus=Filename :load a hardmarkfile and apply together with diag mask
hardmaskOnly=Filename :load a hardmask and use as only mask
use_sam_msk_on_monovan=False This will set the total mask to be that of the sample run
abs_units_van_range=[-40,40] integral range for absolute vanadium data
mono_correction_factor=float User specified correction factor for absolute units normalization
"""
kwargs['monovan_mapfile'] = monovan_mapfile
kwargs['sample_mass'] = samp_mass
kwargs['sample_rmm'] = samp_rmm
if sample_run:
Reducer.sample_run = sample_run
try:
n,r=funcreturns.lhs_info('both')
results_name=r[0]
except:
results_name = Reducer.prop_man.get_sample_ws_name()
if wb_for_run == wb_for_monovanadium: # wb_for_monovanadium property does not accept duplicated workspace
wb_for_monovanadium = None # if this value is none, it is constructed to be equal to wb_for_run
wksp_out = arb_units(wb_for_run,sample_run,ei_guess,rebin,map_file,monovan_run,wb_for_monovanadium,**kwargs)
if results_name != wksp_out.getName():
RenameWorkspace(InputWorkspace=wksp_out,OutputWorkspace=results_name)
return wksp_out
def abs_units(wb_for_run,sample_run,monovan_run,wb_for_monovanadium,samp_rmm,samp_mass,ei_guess,rebin,map_file='default',monovan_mapfile='default',**kwargs):
"""
dgreduce.abs_units(wb_run Whitebeam run number or file name or workspace
sample_run Sample run run number or file name or workspace
monovan_run Monochromatic run run number or file name or workspace
wb_mono White beam for Monochromatic run run number or file name or workspace
samp_rmm Mass of formula unit of sample
samp_mass Actual sample mass
ei_guess Ei guess of run
rebin Rebin parameters for output data
map_file Mapfile for sample run
monovan_mapfile Mapfile for mono van run
keyword arguments Any specified additional keyword arguments
Example with run numbers
abs_units(11001,11002,11003,10098,250.1,5.2,80,'-10,.1,75','mari_res','mari_res')
A detector calibration file must be specified if running the reduction with workspace inputs
Example with workspace inputs
abs_units('wb_run','sam_run','mono_run','wb_for_mono',250.1,5.2,80,'-10,.1,75','mari_res','mari_res',
det_cal_file=10001,diag_remove_zero=False,norm_method='current')
A detector calibration file must be specified if running the reduction with workspace inputs
Available keywords
norm_method =[monitor-1],[monitor-2][Current]
background =False , True
fixei =False , True
save_format =['.spe'],['.nxspe'],'none'
detector_van_range =[20,40] in mev
bkgd_range =[15000,19000] :integration range for background tests
second_white - If provided an additional set of tests is performed on this. (default = None)
hard_mask_file - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
diag_van_median_rate_limit_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
diag_van_median_rate_limit_hi - Upper bound defining outliers as fraction of median value (default = 100.)
diag_van_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
diag_van_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
diag_van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars (default=0.0)
diag_remove_zero - If true then zeros in the vanadium data will count as failed (default = True)
diag_samp_samp_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
diag_samp_samp_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
diag_samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the"
difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation -The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum frame rate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
diag_remove_zero =True, False (default):Diag zero counts in background range
bleed=True , turn bleed correction on and off on by default for Merlin and LET
sum =True,False(default) , sum multiple files
det_cal_file= a valid detector block file and path or a raw file. Setting this
will use the detector calibration from the specified file NOT the
input raw file
mask_run = RunNumber to use for diag instead of the input run number
one2one =True, False :Reduction will not use a mapping file
hardmaskPlus=Filename :load a hardmarkfile and apply together with diag mask
hardmaskOnly=Filename :load a hardmask and use as only mask
use_sam_msk_on_monovan=False This will set the total mask to be that of the sample run
abs_units_van_range=[-40,40] integral range for absolute vanadium data
mono_correction_factor=float User specified correction factor for absolute units normalization
"""
kwargs['monovan_mapfile'] = monovan_mapfile
kwargs['sample_mass'] = samp_mass
kwargs['sample_rmm'] = samp_rmm
if sample_run:
Reducer.sample_run = sample_run
try:
n,r=funcreturns.lhs_info('both')
results_name=r[0]
except:
results_name = Reducer.prop_man.get_sample_ws_name()
if wb_for_run == wb_for_monovanadium: # wb_for_monovanadium property does not accept duplicated workspace
wb_for_monovanadium = None # if this value is none, it is constructed to be equal to wb_for_run
wksp_out = arb_units(wb_for_run,sample_run,ei_guess,rebin,map_file,monovan_run,wb_for_monovanadium,**kwargs)
if results_name != wksp_out.getName():
RenameWorkspace(InputWorkspace=wksp_out,OutputWorkspace=results_name)
return wksp_out
def arb_units(wb_run,sample_run,ei_guess,rebin,map_file='default',monovan_run=None,second_wb=None,**kwargs):
""" One step conversion of run into workspace containing information about energy transfer
Usage:
>>arb_units(wb_run,sample_run,ei_guess,rebin)
>>arb_units(wb_run,sample_run,ei_guess,rebin,**arguments)
>>arb_units(wb_run,sample_run,ei_guess,rebin,mapfile,**arguments)
>>arb_units(wb_run Whitebeam run number or file name or workspace
sample_run sample run number or file name or workspace
ei_guess Ei guess
rebin Rebin parameters
mapfile Mapfile -- if absent/'default' the defaults from IDF are used
monovan_run If present will do the absolute units normalization. Number of additional parameters
specified in **kwargs is usually requested for this. If they are absent, program uses defaults,
but the defaults (e.g. sample_mass or sample_rmm ) are usually incorrect for a particular run.
arguments The dictionary containing additional keyword arguments.
The list of allowed additional arguments is defined in InstrName_Parameters.xml file, located in
MantidPlot->View->Preferences->Mantid->Directories->Parameter Definitions
with run numbers as input:
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70]) # will run on default instrument
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70],'mari_res', additional keywords as required)
>>dgreduce.arb_units(1000,10001,80,'-10,.1,70','mari_res',fixei=True)
A detector calibration file must be specified if running the reduction with workspaces as input
namely:
>>w2=iliad("wb_wksp","run_wksp",ei,rebin_params,mapfile,det_cal_file=cal_file
,diag_remove_zero=False,norm_method='current')
type help() for the list of all available keywords. All availible keywords are provided in InstName_Parameters.xml file
Some samples are:
norm_method =[monitor-1],[monitor-2][Current]
background =False , True
fixei =False , True
save_format =['.spe'],['.nxspe'],'none'
detector_van_range =[20,40] in mev
bkgd_range =[15000,19000] :integration range for background tests
second_white - If provided an additional set of tests is performed on this. (default = None)
hardmaskPlus - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
diag_van_median_rate_limit_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
diag_van_median_rate_limit_hi - Upper bound defining outliers as fraction of median value (default = 100.)
diag_van_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
diag_van_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
diag_van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars (default=0.0)
diag_remove_zero - If true then zeroes in the vanadium data will count as failed (default = True)
diag_samp_samp_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
diag_samp_samp_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
diag_samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the"
difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation -The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum framerate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
diag_remove_zero =True, False (default):Diag zero counts in background range
bleed=True , turn bleed correction on and off on by default for Merlin and LET
sum =True,False(default) , sum multiple files
det_cal_file= a valid detector block file and path or a raw file. Setting this
will use the detector calibraion from the specified file NOT the
input raw file
mask_run = RunNumber to use for diag instead of the input run number
one2one =True, False :Reduction will not use a mapping file
hardmaskPlus=Filename :load a hardmarkfile and apply together with diag mask
hardmaskOnly=Filename :load a hardmask and use as only mask
"""
global Reducer
if Reducer is None or Reducer.instrument is None:
raise ValueError("instrument has not been defined, call setup(instrument_name) first.")
# --------------------------------------------------------------------------------------------------------
# Deal with mandatory parameters for this and may be some top level procedures
# --------------------------------------------------------------------------------------------------------
if sample_run:
Reducer.sample_run = sample_run
try:
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
except:
wksp_out = Reducer.prop_man.get_sample_ws_name()
#
res = Reducer.convert_to_energy(wb_run,sample_run,ei_guess,rebin,map_file,monovan_run,second_wb,**kwargs)
#
results_name = res.name()
if results_name != wksp_out:
RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)
return res
def op_wrapper(self, other):
# Get the result variable to know what to call the output
result_info = lhs_info()
# Pass off to helper
return _do_binary_operation(algorithm, self, other, result_info,
inplace, reverse)
def arb_units(wb_run,sample_run,ei_guess,rebin,map_file='default',monovan_run=None,second_wb=None,**kwargs):
""" One step conversion of run into workspace containing information about energy transfer
Usage:
>>arb_units(wb_run,sample_run,ei_guess,rebin)
>>arb_units(wb_run,sample_run,ei_guess,rebin,**arguments)
>>arb_units(wb_run,sample_run,ei_guess,rebin,mapfile,**arguments)
>>arb_units(wb_run Whitebeam run number or file name or workspace
sample_run sample run number or file name or workspace
ei_guess Ei guess
rebin Rebin parameters
mapfile Mapfile -- if absent/'default' the defaults from IDF are used
monovan_run If present will do the absolute units normalization. Number of additional parameters
specified in **kwargs is usually requested for this. If they are absent, program uses defaults,
but the defaults (e.g. sample_mass or sample_rmm ) are usually incorrect for a particular run.
arguments The dictionary containing additional keyword arguments.
The list of allowed additional arguments is defined in InstrName_Parameters.xml file, located in
MantidPlot->View->Preferences->Mantid->Directories->Parameter Definitions
with run numbers as input:
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70]) # will run on default instrument
>>dgreduce.arb_units(1000,10001,80,[-10,.1,70],'mari_res', additional keywords as required)
>>dgreduce.arb_units(1000,10001,80,'-10,.1,70','mari_res',fixei=True)
A detector calibration file must be specified if running the reduction with workspaces as input
namely:
>>w2=iliad("wb_wksp","run_wksp",ei,rebin_params,mapfile,det_cal_file=cal_file
,diag_remove_zero=False,norm_method='current')
type help() for the list of all available keywords. All availible keywords are provided in InstName_Parameters.xml file
Some samples are:
norm_method =[monitor-1],[monitor-2][Current]
background =False , True
fixei =False , True
save_format =['.spe'],['.nxspe'],'none'
detector_van_range =[20,40] in mev
bkgd_range =[15000,19000] :integration range for background tests
second_white - If provided an additional set of tests is performed on this. (default = None)
hardmaskPlus - A file specifying those spectra that should be masked without testing (default=None)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
bkgd_range - A list of two numbers indicating the background range (default=instrument defaults)
diag_van_median_rate_limit_lo - Lower bound defining outliers as fraction of median value (default = 0.01)
diag_van_median_rate_limit_hi - Upper bound defining outliers as fraction of median value (default = 100.)
diag_van_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0.1)
diag_van_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 1.5)
diag_van_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the
difference with respect to the median value must also exceed this number of error bars (default=0.0)
diag_remove_zero - If true then zeroes in the vanadium data will count as failed (default = True)
diag_samp_samp_median_sigma_lo - Fraction of median to consider counting low for the white beam diag (default = 0)
diag_samp_samp_median_sigma_hi - Fraction of median to consider counting high for the white beam diag (default = 2.0)
diag_samp_sig - Error criterion as a multiple of error bar i.e. to fail the test, the magnitude of the"
difference with respect to the median value must also exceed this number of error bars (default=3.3)
variation -The number of medians the ratio of the first/second white beam can deviate from
the average by (default=1.1)
bleed_test - If true then the CreatePSDBleedMask algorithm is run
bleed_maxrate - If the bleed test is on then this is the maximum framerate allowed in a tube
bleed_pixels - If the bleed test is on then this is the number of pixels ignored within the
bleed test diagnostic
print_results - If True then the results are printed to the screen
diag_remove_zero =True, False (default):Diag zero counts in background range
bleed=True , turn bleed correction on and off on by default for Merlin and LET
sum =True,False(default) , sum multiple files
det_cal_file= a valid detector block file and path or a raw file. Setting this
will use the detector calibraion from the specified file NOT the
input raw file
mask_run = RunNumber to use for diag instead of the input run number
one2one =True, False :Reduction will not use a mapping file
hardmaskPlus=Filename :load a hardmarkfile and apply together with diag mask
hardmaskOnly=Filename :load a hardmask and use as only mask
"""
global Reducer
if Reducer is None or Reducer.instrument is None:
raise ValueError("instrument has not been defined, call setup(instrument_name) first.")
# --------------------------------------------------------------------------------------------------------
# Deal with mandatory parameters for this and may be some top level procedures
# --------------------------------------------------------------------------------------------------------
if sample_run:
Reducer.sample_run = sample_run
try:
n,r=funcreturns.lhs_info('both')
wksp_out=r[0]
except:
wksp_out = Reducer.prop_man.get_sample_ws_name();
#
res = Reducer.convert_to_energy(wb_run,sample_run,ei_guess,rebin,map_file,monovan_run,second_wb,**kwargs)
#
results_name = res.name();
if results_name != wksp_out:
RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)
return res
def QCut(self, Emin, Emax, Qmin, delQ, Qmax, **kwargs):
intmin = Emin
intmax = Emax
cutmin = Qmin
delcut = delQ
cutmax = Qmax
try:
n, r = funcreturns.lhs_info('both')
name = r[0]
if kwargs.has_key('shoelace'):
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle = kwargs.get_key('Handle')
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
cutName=name,
shoelace=True,
Handle=fighandle)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
cutName=name,
shoelace=True)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
cutName=name,
shoelace=True)
else:
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle = kwargs.get('Handle')
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
cutName=name,
Handle=fighandle)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
cutName=name)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
cutName=name)
except:
if kwargs.has_key('shoelace'):
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle = kwargs.get('Handle')
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
shoelace=True,
Handle=fighandle)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
shoelace=True)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
shoelace=True)
else:
if kwargs.has_key('over'):
if kwargs.has_key('Handle'):
fighandle = kwargs.get('Handle')
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True,
Handle=fighandle)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q',
over=True)
else:
self.cut(self.data,
intmin,
intmax,
cutmin,
delcut,
cutmax,
along='q')
def iliad_wrapper(*args):
#seq = inspect.stack()
# output workspace name.
try:
_, r = funcreturns.lhs_info('both')
out_ws_name = r[0]
# no-exception-type(s) specified. Who knows what exception this internal procedure rises...
#pylint: disable=W0702
except:
out_ws_name = None
host = args[0]
if len(args) > 1:
input_file = args[1]
if len(args) > 2:
output_directory = args[2]
else:
output_directory = None
else:
input_file = None
output_directory = None
# add input file folder to data search directory if file has it
if input_file and isinstance(input_file, str):
data_path = os.path.dirname(input_file)
if len(data_path) > 0:
try:
config.appendDataSearchDir(str(data_path))
args[1] = os.path.basename(input_file)
#pylint: disable=bare-except
except: # if mantid is not available, this should ignore config
pass
if output_directory:
config['defaultsave.directory'] = str(output_directory)
#pylint: disable=protected-access
if host._run_from_web:
#pylint: disable=protected-access
web_vars = host._wvs.get_all_vars()
host.reducer.prop_man.set_input_parameters(**web_vars)
else:
pass # we should set already set up variables using
custom_print_function = host.set_custom_output_filename()
if not custom_print_function is None:
PropertyManager.save_file_name.set_custom_print(
custom_print_function)
#
rez = reduce(*args)
# prohibit returning workspace to web services.
#pylint: disable=protected-access
if host._run_from_web and not isinstance(rez, str):
rez = ""
else:
if isinstance(rez, list):
# multirep run, just return as it is
return rez
if not (rez is None) and out_ws_name and rez.name() != out_ws_name:
# the function does not return None, pylint is wrong
#pylint: disable=W1111
rez = RenameWorkspace(InputWorkspace=rez,
OutputWorkspace=out_ws_name)
return rez
def op_wrapper(self):
# Get the result variable to know what to call the output
result_info = lhs_info()
# Pass off to helper
return _do_unary_operation(algorithm, self, result_info)
def run_reduction(self):
"""" Reduces runs one by one or sum all them together and reduce after this
if wait_for_file time is > 0, it will until missing files appear on the
data search path
"""
try:
n,r = funcreturns.lhs_info('both')
out_ws_name = r[0]
except:
out_ws_name = None
# if this is not None, we want to run validation not reduction
if self.validate_run_number:
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
rez,mess=self.build_or_validate_result()
if rez:
self.reducer.prop_man.log("*** SUCCESS! {0}".format(mess))
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
else:
self.reducer.prop_man.log("*** VALIDATION FAILED! {0}".format(mess))
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
raise RuntimeError("Validation against old data file failed")
self.validate_run_number=None
return rez,mess
if self.reducer.sum_runs:
# --------### sum runs provided ------------------------------------###
if out_ws_name is None:
self.sum_and_reduce()
return None
else:
red_ws = self.sum_and_reduce()
RenameWorkspace(InputWorkspace=red_ws,OutputWorkspace=out_ws_name)
return mtd[out_ws_name]
else:
# --------### reduce list of runs one by one ----------------------------###
runfiles = PropertyManager.sample_run.get_run_file_list()
if out_ws_name is None:
for file in runfiles:
self.reduce(file)
return None
else:
results = []
nruns = len(runfiles)
for num,file in enumerate(runfiles):
red_ws = self.reduce(file)
if isinstance(red_ws,list):
for ws in red_ws:
results.append(ws)
else:
if nruns == 1:
if red_ws.name() != out_ws_name:
RenameWorkspace(InputWorkspace=red_ws,OutputWorkspace=out_ws_name)
results.append(mtd[out_ws_name])
else:
OutWSName = '{0}#{1}of{2}'.format(out_ws_name,num+1,nruns)
if red_ws.name() != out_ws_name:
RenameWorkspace(InputWorkspace=red_ws,OutputWorkspace=OutWSName)
results.append(mtd[OutWSName])
#end
if len(results) == 1:
return results[0]
else:
return results
def run_reduction(self):
"""" Reduces runs one by one or sum all them together and reduce after this
if wait_for_file time is > 0, it will until missing files appear on the
data search path
"""
try:
_, r = funcreturns.lhs_info('both')
out_ws_name = r[0]
# no-exception-type(s) specified. Who knows what exception this internal procedure rises...
#pylint: disable=W0702
except:
out_ws_name = None
# if this is not None, we want to run validation not reduction
if self.validate_run_number:
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
rez, mess = self.build_or_validate_result()
if rez:
self.reducer.prop_man.log("*** SUCCESS! {0}".format(mess))
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
else:
self.reducer.prop_man.log(
"*** VALIDATION FAILED! {0}".format(mess))
self.reducer.prop_man.log\
("**************************************************************************************",'warning')
raise RuntimeError("Validation against old data file failed")
self.validate_run_number = None
return rez, mess
if self.reducer.sum_runs:
# --------### sum runs provided ------------------------------------###
if out_ws_name is None:
self.sum_and_reduce()
return None
else:
red_ws = self.sum_and_reduce()
RenameWorkspace(InputWorkspace=red_ws,
OutputWorkspace=out_ws_name)
return mtd[out_ws_name]
else:
# --------### reduce list of runs one by one ----------------------------###
runfiles = PropertyManager.sample_run.get_run_file_list()
if out_ws_name is None:
for file_name in runfiles:
self.reduce(file_name)
return None
else:
results = []
nruns = len(runfiles)
for num, file_name in enumerate(runfiles):
red_ws = self.reduce(file_name)
if isinstance(red_ws, list):
for ws in red_ws:
results.append(ws)
else:
if nruns == 1:
if red_ws.name() != out_ws_name:
RenameWorkspace(InputWorkspace=red_ws,
OutputWorkspace=out_ws_name)
results.append(mtd[out_ws_name])
else:
OutWSName = '{0}#{1}of{2}'.format(
out_ws_name, num + 1, nruns)
if red_ws.name() != out_ws_name:
RenameWorkspace(InputWorkspace=red_ws,
OutputWorkspace=OutWSName)
results.append(mtd[OutWSName])
#end
if len(results) == 1:
return results[0]
else:
return results
