def remove_baseline(self,function="gaussian",redo=False):
"""
Attempts to remove the baseline amplitude from the dynamic spectrum
"""
if redo == False and self.baseline_removed == True:
return self
flatdata = self.data.flatten()
interval = np.power(10,np.floor(np.log10(np.ptp(flatdata/100)))) #~100 divisions, but bins to an even power of 10
center,hist = u.histogram(flatdata,interval=interval)
if function == "gaussian":
p1,err = ffit.gaussianfit(center,hist)
y = ffit.funcgaussian(p1,center)
peak = center[np.argmax(y)]
elif function == "simple_DISS":
area = np.trapz(hist,x=center)
shift = -np.min(center)+1.0
x = center + shift
y = np.array(hist,dtype=np.float)/area
p1,err = ffit.simpleDISSpdffit(x,y)
y1 = ffit.funcsimpleDISSpdf(p1,x)*area
peak = center[np.argmax(y1)]
else:
peak = 0.0
self.data -= peak
self.baseline_removed = True
return self
def remove_baseline(self, function="gaussian", redo=False):
"""
Attempts to remove the baseline amplitude from the dynamic spectrum
"""
if redo == False and self.baseline_removed == True:
return self
flatdata = self.data.flatten()
interval = np.power(10, np.floor(np.log10(
np.ptp(flatdata /
100)))) #~100 divisions, but bins to an even power of 10
center, hist = u.histogram(flatdata, interval=interval)
if function == "gaussian":
p1, err = ffit.gaussianfit(center, hist)
y = ffit.funcgaussian(p1, center)
peak = center[np.argmax(y)]
elif function == "simple_DISS":
area = np.trapz(hist, x=center)
shift = -np.min(center) + 1.0
x = center + shift
y = np.array(hist, dtype=np.float) / area
p1, err = ffit.simpleDISSpdffit(x, y)
y1 = ffit.funcsimpleDISSpdf(p1, x) * area
peak = center[np.argmax(y1)]
else:
peak = 0.0
self.data -= peak
self.baseline_removed = True
return self