diff --git a/user/sumlab_auto.py b/user/sumlab_auto.py index 36666ec87c5a7eeaf60af72d7d0b0c2d94ecd15b..1db86beeb75273412ae1459185c11507db4a7459 100644 --- a/user/sumlab_auto.py +++ b/user/sumlab_auto.py @@ -69,7 +69,7 @@ print("=======================================\033[0m") # Loop thorough SORTED files in alphabetical order! files_to_fit = sorted(files_to_fit, key=lambda x: x[0]) -G = []; d_G = []; G_prefit = []; d_G_prefit = [] +G = []; d_G = []; mu=[]; d_mu=[]; G_prefit = []; d_G_prefit = []; mu_prefit=[]; d_mu_prefit=[] V_bias = [] t_gates=[] for i, (file, path) in enumerate(files_to_fit): @@ -102,6 +102,8 @@ for i, (file, path) in enumerate(files_to_fit): prefit_val, prefit_err = f_data.GetPrefitResults(bin_units=False) print("\033[95m"+rf"Prefit: G = {prefit_val.get('G') } d_G = {prefit_err.get('G')}"+"\033[0m") + print("\033[95m"+rf"Prefit: mu = {prefit_val.get('mu') + } d_mu = {prefit_err.get('mu')}"+"\033[0m") for key, value in prefit_val.items(): fit_out["prefit_{:s}".format(key)] = value for key, value in prefit_err.items(): @@ -111,6 +113,8 @@ for i, (file, path) in enumerate(files_to_fit): fit_val, fit_err = f_data.GetFitResults(bin_units=False) print("\033[95m"+rf"Fit: G = {fit_val.get('G') } d_G = {fit_err.get('G')}"+"\033[0m") + print("\033[95m"+rf"Fit: mu = {fit_val.get('mu') + } d_mu = {fit_err.get('mu')}"+"\033[0m") for key, value in fit_val.items(): fit_out["{:s}".format(key)] = value for key, value in fit_err.items(): @@ -121,27 +125,42 @@ for i, (file, path) in enumerate(files_to_fit): df = pd.DataFrame.from_dict([fit_out]) df.to_csv("{}/fit_results_{:s}.csv".format(folder, file[:-4])) - if not prefit_only and 'G' in fit_out and 'd_G' in fit_out: + if not prefit_only and 'G' in fit_out and 'd_G' in fit_out and 'mu' in fit_out and 'd_mu' in fit_out: G.append(fit_out['G']) d_G.append(fit_out['d_G']) + mu.append(fit_out['mu']) + d_mu.append(fit_out['d_mu']) else: G.append(0) d_G.append(0) + mu.append(0) + d_mu.append(0) + G_prefit.append(fit_out['prefit_G']) d_G_prefit.append(fit_out['prefit_d_G']) + mu_prefit.append(fit_out['prefit_mu']) + d_mu_prefit.append(fit_out['prefit_d_mu']) G = np.array(G) d_G = np.array(d_G) +mu=np.array(mu) +d_mu= np.array(d_mu) G_prefit = np.array(G_prefit) d_G_prefit = np.array(d_G_prefit) +mu_prefit=np.array(mu_prefit) +d_mu_prefit=np.array(d_mu_prefit) V_bias = np.array(V_bias) t_gates = np.array(t_gates) with h5py.File(f"{folder}/{os.path.basename(folder)}.h5", 'w') as f: f.create_dataset('G', data=G) f.create_dataset('d_G', data=d_G) + f.create_dataset('mu',data=mu) + f.create_dataset('d_mu',data=d_mu) f.create_dataset('G_prefit', data=G_prefit) f.create_dataset('d_G_prefit', data=d_G_prefit) + f.create_dataset('mu_prefit', data=mu_prefit) + f.create_dataset('d_mu_prefit', data=d_mu_prefit) f.create_dataset('V_bias', data=V_bias) f.create_dataset('integral_length', data=t_gates)