def calc_average(self, data_ws):
"""
Compute the average Y value of a workspace.
The average is computed by collapsing the workspace to a single bin per spectra then masking
masking out detectors given by the FindDetectorsOutsideLimits and MedianDetectorTest algorithms.
The average is then the computed as the using the remainder and factoring in their errors as weights, i.e.
average = sum(Yvalue[i]*weight[i]) / sum(weights)
where only those detectors that are unmasked are used and the weight[i] = 1/errorValue[i].
"""
e_low = self.monovan_integr_range[0]
e_upp = self.monovan_integr_range[1]
if e_low > e_upp:
raise ValueError("Inconsistent mono-vanadium integration range defined!")
data_ws=Rebin(InputWorkspace=data_ws,OutputWorkspace=data_ws,Params= [e_low, 2.*(e_upp-e_low), e_upp])
data_ws=ConvertToMatrixWorkspace(InputWorkspace=data_ws,OutputWorkspace= data_ws)
args = {}
args['tiny'] = self.diag_tiny
args['huge'] = self.diag_huge
args['van_out_lo'] = self.monovan_lo_bound
args['van_out_hi'] = self.monovan_hi_bound
args['van_lo'] = self.monovan_lo_frac
args['van_hi'] = self.monovan_hi_frac
args['van_sig'] = self.diag_samp_sig
diagnostics.diagnose(data_ws, **args)
monovan_masks,det_ids = ExtractMask(InputWorkspace=data_ws,OutputWorkspace='monovan_masks')
MaskDetectors(Workspace=data_ws, MaskedWorkspace=monovan_masks)
DeleteWorkspace(Workspace=monovan_masks)
ConvertFromDistribution(Workspace=data_ws)
nhist = data_ws.getNumberHistograms()
average_value = 0.0
weight_sum = 0.0
for i in range(nhist):
try:
det = data_ws.getDetector(i)
except Exception:
continue
if det.isMasked():
continue
y_value = data_ws.readY(i)[0]
if y_value != y_value:
continue
weight = 1.0/data_ws.readE(i)[0]
average_value += y_value * weight
weight_sum += weight
return average_value / weight_sum
def diagnose(self, white_run, sample_run=None, other_white=None, remove_zero=None,
tiny=None, large=None, median_lo=None, median_hi=None, signif=None,
bkgd_threshold=None, bkgd_range=None, variation=None,
bleed_test=False, bleed_maxrate=None, bleed_pixels=None,
hard_mask=None, print_results=False):
"""
A pass through method to the 'real' one in diagnostics.py
Run diagnostics on the provided run and white beam files.
There are 4 possible tests, depending on the input given:
White beam diagnosis
Background tests
Second white beam
PSD bleed test
Required inputs:
white_run - The run number or filepath of the white beam run
Optional inputs:
sample_run - The run number or filepath of the sample run for the background test (default = None)
other_white - If provided an addional set of tests is performed on this file. (default = None)
remove_zero - If true then zeroes in the data will count as failed (default = False)
tiny - Minimum threshold for acceptance (default = 1e-10)
large - Maximum threshold for acceptance (default = 1e10)
median_lo - Fraction of median to consider counting low (default = 0.1)
median_hi - Fraction of median to consider counting high (default = 3.0)
signif - Counts within this number of multiples of the
standard dev will be kept (default = 3.3)
bkgd_threshold - High threshold for background removal in multiples of median (default = 5.0)
bkgd_range - The background range as a list of 2 numbers: [min,max].
If not present then they are taken from the parameter file. (default = None)
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
hard_mask - A file specifying those spectra that should be masked without testing
print_results - If True then the results are printed to std out
inst_name - The name of the instrument to perform the diagnosis.
If it is not provided then the default instrument is used (default = None)
"""
lhs_names = lhs_info('names')
if len(lhs_names) > 0:
var_name = lhs_names[0]
else:
var_name = None
__diagnostic_mask = diagnostics.diagnose(white_run, sample_run, other_white, remove_zero,
tiny, large, median_lo, median_hi, signif,
bkgd_threshold, bkgd_range, variation,
bleed_test, bleed_maxrate, bleed_pixels,
hard_mask, print_results, self.instr_name)
if var_name is not None:
result = RenameWorkspace(str(__diagnostic_mask), var_name).workspace()
else:
result = __diagnostic_mask
self.spectra_masks = result
return result
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
