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):
# get input
inws = self.getProperty("InputWorkspace").value
niter = self.getProperty("NIterations").value
xwindow = self.getProperty("XWindow").value
doSGfilter = self.getProperty('ApplyFilterSG').value
# make output workspace
outws = CloneWorkspace(inws, OutputWorkspace=self.getProperty("OutputWorkspace").value)
# loop over all spectra
nbins = inws.blocksize()
nspec = inws.getNumberHistograms()
prog_reporter = Progress(self, start=0.0, end=1.0, nreports=nspec)
for ispec in range(0, nspec):
prog_reporter.report()
# get n points in convolution window
binwidth = np.mean(np.diff(inws.readX(ispec)))
nwindow = int(np.ceil(xwindow / binwidth))
if not nwindow % 2:
nwindow += 1
if nwindow < self.MIN_WINDOW_SIZE:
raise RuntimeError('Convolution window must have at least three points')
elif not nwindow < nbins:
# not effective due to edge effects of the convolution
raise RuntimeError("Data has must have at least the number of points as the convolution window")
# do initial filter to remove very high intensity points
if doSGfilter:
ybg = savgol_filter(inws.readY(ispec), nwindow, polyorder=1)
else:
ybg = np.copy(inws.readY(ispec))
Ibar = np.mean(ybg)
Imin = np.min(ybg)
ybg[ybg > (Ibar + 2 * (Ibar - Imin))] = (Ibar + 2 * (Ibar - Imin))
# perform iterative smoothing
ybg = self.doIterativeSmoothing(ybg, nwindow, niter)
# replace intensity in output spectrum with background
outws.setY(ispec, ybg)
outws.setE(ispec, np.zeros(ybg.shape))
# set output
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(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
