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)