def add_fit(self,parameters):
if not isinstance(parameters,ndarray) or len(parameters)!=4:
raise "parameters must be a ndarray of length 4"
self.fits.append(parameters)
self.c_amplitude -= fitting.lorenzian(parameters,self.frequency,{'baseline':0})
self.mean = self.c_amplitude.mean()
self.std = self.c_amplitude.std()
self.target_std = 100.0*sqrt(self.mean*(100.0-self.mean)/(10000.0*self.stats))
def remove_fit(self,index):
fit = self.fits[index]
self.c_amplitude += fitting.lorenzian(fit,self.frequency,{'baseline':0})
self.mean = self.c_amplitude.mean()
self.std = self.c_amplitude.std()
self.target_std = 100.0*sqrt(self.mean*(100.0-self.mean)/(10000.0*self.stats))
self.fits.pop(index)
return fit
def add_fit(self, parameters):
if not isinstance(parameters, ndarray) or len(parameters) != 4:
raise "parameters must be a ndarray of length 4"
self.fits.append(parameters)
self.c_amplitude -= fitting.lorenzian(parameters, self.frequency,
{'baseline': 0})
self.mean = self.c_amplitude.mean()
self.std = self.c_amplitude.std()
self.target_std = 100.0 * sqrt(self.mean * (100.0 - self.mean) /
(10000.0 * self.stats))
def remove_fit(self, index):
fit = self.fits[index]
self.c_amplitude += fitting.lorenzian(fit, self.frequency,
{'baseline': 0})
self.mean = self.c_amplitude.mean()
self.std = self.c_amplitude.std()
self.target_std = 100.0 * sqrt(self.mean * (100.0 - self.mean) /
(10000.0 * self.stats))
self.fits.pop(index)
return fit
