def test_gaussian_fit_length_minimize_trust_constr(self):
fitOpt = FitOptions(fittingRoutine='minimize', method='trust-constr')
t0 = time.perf_counter()
rms_gauss = gaussian_fit(self.time_array,
self.gaussian_dist,
fitOpt=fitOpt)[2]
t1 = time.perf_counter()
runtime = t1 - t0
result = (rms_gauss - self.length_gauss) / self.length_gauss
return runtime, result
def test_binomial_amplitudeN_fit_length_minimize_trust_constr(self):
fitOpt = FitOptions(fittingRoutine='minimize', method='trust-constr')
t0 = time.perf_counter()
full_binom, exponent = binomial_amplitudeN_fit(self.time_array,
self.binom_dist,
fitOpt=fitOpt)[2:]
t1 = time.perf_counter()
runtime = t1 - t0
rms_binom = _binomial_full_to_rms(full_binom, exponent)
result = (rms_binom - self.sigma_binom) / self.sigma_binom
return runtime, result
plotOpt = PlotOptions(figname='FWHM', clf=False)
fwhm_gauss = FWHM(time_array, gaussian_dist, plotOpt=plotOpt)
fwXm_parabamp = FWHM(time_array, parabamp_dist, level=0.8, plotOpt=plotOpt)
fwXm_binom = FWHM(time_array, binom_dist, level=0.2, plotOpt=plotOpt)
# In[6]:
# The FWHM can be obtained, note that the level can be manually set
# The bunchLengthFactor option can be used to rescale the FWHM to another value
# e.g. : to 4sigma assuming Gaussian, or parabolic_line, or parabolic_amplitude
from blond_common.fitting.profile import FWHM, FitOptions
fitOpt = FitOptions(bunchLengthFactor='gaussian')
fwhm_gauss = FWHM(time_array, gaussian_dist, fitOpt=fitOpt)
fitOpt = FitOptions(bunchLengthFactor='parabolic_amplitude')
fwhm_parabamp = FWHM(time_array, parabamp_dist, level=0.2, fitOpt=fitOpt)
print('Gauss: Input ->', [initial_params_gauss[1], sigma_gauss * 4],
'/ Output ->', fwhm_gauss[0:2])
print('Parab. amp.: Input ->',
[initial_params_parabamp[1], sigma_parabamp * 4], '/ Output ->',
fwhm_parabamp[0:2])
# In[7]:
# The width at 2 different levels can give all needed information on the binomial parameters
# The binomialParametersFromRatio function can be used directly