def PyExec(self):
input_ws = self.getProperty("InputWorkspace").value
eff_ws = self.getProperty("DetectorEfficiencyWorkspace").value
transposed = Transpose(InputWorkspace=eff_ws, StoreInADS=False)
efficiencies = transposed.extractY().flatten()
errors = transposed.extractE().flatten()
n_hist = input_ws.getNumberHistograms()
if n_hist % efficiencies.size != 0:
raise ValueError(
'Number of histograms in input workspace is not a multiple of number of entries in detector efficiency '
'workspace.')
n_time_indexes = n_hist / efficiencies.size
to_multiply = CreateWorkspace(DataY=np.repeat(efficiencies,
n_time_indexes),
DataE=np.repeat(errors, n_time_indexes),
DataX=np.zeros(n_hist),
NSpec=n_hist,
StoreInADS=False)
output = Multiply(
LHSWorkspace=input_ws,
RHSWorkspace=to_multiply,
OutputWorkspace=self.getPropertyValue("OutputWorkspace"))
# In the output we should mask the detectors where calibration constant is masked
det_IDs = ''
n_pixels_per_tube = eff_ws.getNumberHistograms()
for spectrum in range(n_pixels_per_tube):
if eff_ws.hasMaskedBins(spectrum):
masked = eff_ws.maskedBinsIndices(spectrum)
for bin in masked:
det_IDs += str(bin * n_pixels_per_tube + spectrum +
1) + ','
if det_IDs:
MaskDetectors(Workspace=output, DetectorList=det_IDs[:-1])
self.setProperty("OutputWorkspace", output)
def PyExec(self):
input_ws = self.getProperty("InputWorkspace").value
eff_ws = self.getProperty("DetectorEfficiencyWorkspace").value
transposed = Transpose(InputWorkspace=eff_ws, StoreInADS=False)
efficiencies = transposed.extractY().flatten()
errors = transposed.extractE().flatten()
n_hist = input_ws.getNumberHistograms()
if n_hist % efficiencies.size != 0:
raise ValueError('Number of histograms in input workspace is not a multiple of number of entries in detector efficiency '
'workspace.')
n_time_indexes = n_hist / efficiencies.size
to_multiply = CreateWorkspace(DataY=np.repeat(efficiencies, n_time_indexes), DataE=np.repeat(errors, n_time_indexes),
DataX=np.zeros(n_hist), NSpec=n_hist, StoreInADS=False)
output = Multiply(LHSWorkspace=input_ws, RHSWorkspace=to_multiply, OutputWorkspace=self.getPropertyValue("OutputWorkspace"))
# In the output we should mask the detectors where calibration constant is masked
det_IDs = ''
n_pixels_per_tube = eff_ws.getNumberHistograms()
for spectrum in range(n_pixels_per_tube):
if eff_ws.hasMaskedBins(spectrum):
masked = eff_ws.maskedBinsIndices(spectrum)
for bin in masked:
det_IDs += str(bin * n_pixels_per_tube + spectrum + 1) + ','
if det_IDs:
MaskDetectors(Workspace=output, DetectorList=det_IDs[:-1])
self.setProperty("OutputWorkspace", output)
def test_2d_scanning_workspace(self):
input_ws = CreateSampleWorkspace(NumMonitors=0,
NumBanks=3,
BankPixelWidth=2,
XMin=0,
XMax=5,
BinWidth=1,
NumScanPoints=7)
calibration_x = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])
calibration_y = np.arange(12)
calibration_ws = CreateWorkspace(DataX=calibration_x,
DataY=calibration_y,
Nspec=4)
calibrated_ws = ApplyDetectorScanEffCorr(input_ws, calibration_ws)
tmp = Transpose(calibration_ws)
tmp = tmp.extractY().flatten()
to_multiply = np.repeat(tmp, 5 * 7)
to_multiply = np.reshape(to_multiply, [7 * 12, 5])
for det in range(7 * 12):
for bin in range(5):
self.assertEquals(
calibrated_ws.readY(det)[bin],
input_ws.readY(det)[bin] * to_multiply[det][bin])
def PyExec(self):
input_ws = self.getProperty("InputWorkspace").value
eff_ws = self.getProperty("DetectorEfficiencyWorkspace").value
transposed = Transpose(InputWorkspace=eff_ws, StoreInADS=False)
efficiencies = transposed.extractY().flatten()
errors = transposed.extractE().flatten()
n_hist = input_ws.getNumberHistograms()
if n_hist % efficiencies.size != 0:
raise ValueError('Number of histograms in input workspace is not a multiple of number of entries in detector efficiency '
'workspace.')
n_time_indexes = n_hist / efficiencies.size
to_multiply = CreateWorkspace(DataY=np.repeat(efficiencies, n_time_indexes),DataE=np.repeat(errors, n_time_indexes),
DataX=np.zeros(n_hist), NSpec=n_hist, StoreInADS=False)
output = Multiply(LHSWorkspace=input_ws, RHSWorkspace=to_multiply, OutputWorkspace=self.getPropertyValue("OutputWorkspace"))
self.setProperty("OutputWorkspace", output)
def test_2d_scanning_workspace(self):
input_ws = CreateSampleWorkspace(NumMonitors=0, NumBanks=3, BankPixelWidth=2, XMin=0, XMax=5, BinWidth=1, NumScanPoints=7)
calibration_x = np.array([0,1,2,0,1,2,0,1,2,0,1,2])
calibration_y = np.arange(12)
calibration_ws = CreateWorkspace(DataX=calibration_x, DataY=calibration_y, Nspec=4)
calibrated_ws = ApplyDetectorScanEffCorr(input_ws, calibration_ws)
tmp = Transpose(calibration_ws)
tmp = tmp.extractY().flatten()
to_multiply = np.repeat(tmp, 5*7)
to_multiply = np.reshape(to_multiply, [7*12,5])
for det in range(7*12):
for bin in range(5):
self.assertEquals(calibrated_ws.readY(det)[bin], input_ws.readY(det)[bin] * to_multiply[det][bin])
def PyExec(self):
input_ws = self.getProperty("InputWorkspace").value
eff_ws = self.getProperty("DetectorEfficiencyWorkspace").value
transposed = Transpose(InputWorkspace=eff_ws, StoreInADS=False)
efficiencies = transposed.extractY().flatten()
errors = transposed.extractE().flatten()
n_hist = input_ws.getNumberHistograms()
if n_hist % efficiencies.size != 0:
raise ValueError(
'Number of histograms in input workspace is not a multiple of number of entries in detector efficiency '
'workspace.')
n_time_indexes = n_hist / efficiencies.size
to_multiply = CreateWorkspace(DataY=np.repeat(efficiencies,
n_time_indexes),
DataE=np.repeat(errors, n_time_indexes),
DataX=np.zeros(n_hist),
NSpec=n_hist,
StoreInADS=False)
output = Multiply(
LHSWorkspace=input_ws,
RHSWorkspace=to_multiply,
OutputWorkspace=self.getPropertyValue("OutputWorkspace"))
self.setProperty("OutputWorkspace", output)