def PyExec(self):
""" Main execution body
"""
inputws = self.getProperty("InputWorkspace").value
epptable = self.getProperty("EPPTable").value
outws_name = self.getPropertyValue("OutputWorkspace")
run = inputws.getRun()
tof1 = float(run.getLogData('TOF1').value)
wavelength = float(run.getLogData('wavelength').value)
velocity = sp.constants.h / (sp.constants.m_n * wavelength * 1e-10
) # m/s
instrument = inputws.getInstrument()
sample = instrument.getSample()
t_fit = np.array(epptable.column('PeakCentre')) - tof1
outws = CloneWorkspace(inputws, OutputWorkspace=outws_name)
# mask detectors with EPP=0
bad_data = np.where(t_fit <= 0)[0]
if len(bad_data) > 0:
self.log().warning("Detectors " + str(bad_data) +
" have EPP=0 and will be masked.")
MaskDetectors(outws, DetectorList=bad_data)
for idx in range(outws.getNumberHistograms()):
# correct TOF only if fit of EPP was succesful
if t_fit[idx] > 0:
det = instrument.getDetector(
outws.getSpectrum(idx).getDetectorIDs()[0])
sdd = det.getDistance(sample)
t2_el = sdd * 1.0e+6 / velocity # microseconds
newX = inputws.readX(idx) + t2_el - t_fit[idx]
outws.setX(idx, newX)
self.setProperty("OutputWorkspace", outws)
def _generateNormalization(self, WS, normType, normWS):
if normType == 'None':
return None
elif normType == "Extracted from Data":
window = self.getProperty("PeakClippingWindowSize").value
smooth_range = self.getProperty("SmoothingRange").value
peak_clip_WS = CloneWorkspace(InputWorkspace=WS,
OutputWorkspace='peak_clip_WS')
n_histo = peak_clip_WS.getNumberHistograms()
x = peak_clip_WS.extractX()
y = peak_clip_WS.extractY()
e = peak_clip_WS.extractE()
for h in range(n_histo):
peak_clip_WS.setX(h, x[h])
peak_clip_WS.setY(
h,
self.peak_clip(y[h],
win=window,
decrese=True,
LLS=True,
smooth_window=smooth_range))
peak_clip_WS.setE(h, e[h])
return 'peak_clip_WS'
else: # other values are already held in normWS
return normWS
def PyExec(self):
""" Main execution body
"""
inputws = self.getProperty("InputWorkspace").value
epptable = self.getProperty("EPPTable").value
outws_name = self.getPropertyValue("OutputWorkspace")
run = inputws.getRun()
tof1 = float(run.getLogData('TOF1').value)
wavelength = float(run.getLogData('wavelength').value)
velocity = sp.constants.h/(sp.constants.m_n*wavelength*1e-10) # m/s
instrument = inputws.getInstrument()
sample = instrument.getSample()
t_fit = np.array(epptable.column('PeakCentre') ) - tof1
outws = CloneWorkspace(inputws, OutputWorkspace=outws_name)
# mask detectors with EPP=0
bad_data = np.where(t_fit <= 0)[0]
if len(bad_data) > 0:
self.log().warning("Detectors " + str(bad_data) + " have EPP=0 and will be masked.")
MaskDetectors(outws, DetectorList=bad_data)
for idx in range(outws.getNumberHistograms()):
# correct TOF only if fit of EPP was succesful
if t_fit[idx] > 0:
det = instrument.getDetector(outws.getSpectrum(idx).getDetectorIDs()[0])
sdd = det.getDistance(sample)
t2_el = sdd*1.0e+6/velocity # microseconds
newX = inputws.readX(idx) + t2_el - t_fit[idx]
outws.setX(idx, newX)
self.setProperty("OutputWorkspace", outws)
def _generateNormalization(self, WS, normType, normWS):
if normType == 'None':
return None
elif normType == "Extracted from Data":
window = self.getProperty("PeakClippingWindowSize").value
smooth_range = self.getProperty("SmoothingRange").value
peak_clip_WS = str(WS).replace('_red', '_normalizer')
peak_clip_WS = CloneWorkspace(InputWorkspace=WS, OutputWorkspace=peak_clip_WS)
n_histo = peak_clip_WS.getNumberHistograms()
for h in range(n_histo):
peak_clip_WS.setY(h, self.peak_clip(peak_clip_WS.readY(h), win=window, decrese=True,
LLS=True, smooth_window=smooth_range))
return str(peak_clip_WS)
else: # other values are already held in normWS
return normWS
def _generateNormalization(self, WS, normType, normWS):
if normType == 'None':
return None
elif normType == "Extracted from Data":
window = self.getProperty("PeakClippingWindowSize").value
smooth_range = self.getProperty("SmoothingRange").value
peak_clip_WS = str(WS).replace('_red', '_normalizer')
peak_clip_WS = CloneWorkspace(InputWorkspace=WS, OutputWorkspace=peak_clip_WS)
n_histo = peak_clip_WS.getNumberHistograms()
for h in range(n_histo):
peak_clip_WS.setY(h, self.peak_clip(peak_clip_WS.readY(h), win=window, decrese=True,
LLS=True, smooth_window=smooth_range))
return str(peak_clip_WS)
else: # other values are already held in normWS
return normWS
def convert_to_2theta(ws_name, num_bins=1000):
"""
"""
# duplicate for vanadium
vanadium = CloneWorkspace(InputWorkspace=ws_name,
OutputWorkspace='vanadium')
# transfer to 2theta for data
ConvertSpectrumAxis(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='Theta')
Transpose(InputWorkspace=ws_name, OutputWorkspace=ws_name)
ResampleX(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
NumberBins=num_bins,
PreserveEvents=False)
# vanadium: set to 1 for now
time_van_start = time.time()
for iws in range(vanadium.getNumberHistograms()):
vanadium.dataY(iws)[0] = 1.
time_van_stop = time.time()
ConvertSpectrumAxis(InputWorkspace='vanadium',
OutputWorkspace='vanadium',
Target='Theta')
Transpose(InputWorkspace='vanadium', OutputWorkspace='vanadium')
ResampleX(InputWorkspace='vanadium',
OutputWorkspace='vanadium',
NumberBins=num_bins,
PreserveEvents=False)
norm_ws_name = ws_name + '_normalized'
Divide(LHSWorkspace=ws_name,
RHSWorkspace='vanadium',
OutputWorkspace=norm_ws_name)
print('Create vanadium workspace : {} seconds'.format(time_van_stop -
time_van_start))
return norm_ws_name
def _generateNormalization(self, WS, normType, normWS):
if normType == 'None':
return None
elif normType == "Extracted from Data":
window = self.getProperty("PeakClippingWindowSize").value
smooth_range = self.getProperty("SmoothingRange").value
peak_clip_WS = CloneWorkspace(WS)
n_histo = peak_clip_WS.getNumberHistograms()
x = peak_clip_WS.extractX()
y = peak_clip_WS.extractY()
e = peak_clip_WS.extractE()
for h in range(n_histo):
peak_clip_WS.setX(h, x[h])
peak_clip_WS.setY(h, self.peak_clip(y[h], win=window, decrese=True,
LLS=True, smooth_window=smooth_range))
peak_clip_WS.setE(h, e[h])
return peak_clip_WS
else: # other values are already held in normWS
return normWS
