def test_binomial_amplitudeN_fit_length_default(self):
t0 = time.perf_counter()
full_binom, exponent = binomial_amplitudeN_fit(self.time_array,
self.binom_dist)[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
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
ratio_LUT=new_LUT)
print('Gauss: Initial ->', initial_params_gauss, '/ Final ->',
_binomial_full_to_rms(*binom_params_gauss[-2:]))
# ## 3. Distribution fitting routines
# In[11]:
from blond_common.fitting.profile import gaussian_fit, parabolic_amplitude_fit, binomial_amplitudeN_fit, PlotOptions
fitparams_gauss = gaussian_fit(time_array, gaussian_dist)
fitparams_parabamp = parabolic_amplitude_fit(time_array, parabamp_dist)
fitparams_binom = binomial_amplitudeN_fit(time_array, binom_dist)
print('Gauss: Initial ->', initial_params_gauss, '/ Final ->', fitparams_gauss)
print('Parab. amp.: Initial ->', initial_params_parabamp, '/ Final ->',
fitparams_parabamp)
print('Binomial: Initial ->', initial_params_binom, '/ Final ->',
fitparams_binom)
# In[12]:
plotOpt = PlotOptions(figname='Fit-1', clf=False)
fitparams_binom = binomial_amplitudeN_fit(time_array,
binom_dist,
plotOpt=plotOpt)
