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| def obtain_metrics_hgcal(sd, matched, ms): | |
| true_e = matched.truthHitAssignedEnergies | |
| bins = np.arange(0, 51, 2) | |
| eff = [] | |
| fake_rate = [] | |
| energy_eff = [] | |
| for i in range(len(bins) - 1): | |
| bin_i = bins[i] | |
| bin_i1 = bins[i + 1] | |
| mask_above = sd.truthHitAssignedEnergies.values <= bin_i1 | |
| mask_below = sd.truthHitAssignedEnergies.values > bin_i | |
| mask = mask_below * mask_above | |
| number_of_non_reconstructed_showers = np.sum( | |
| np.isnan(sd.pred_energy_hits_raw.values)[mask] | |
| ) | |
| total_showers = len(sd.t_rec_energy.values[mask]) | |
| if total_showers > 0: | |
| eff.append( | |
| (total_showers - number_of_non_reconstructed_showers) / total_showers | |
| ) | |
| energy_eff.append((bin_i1 + bin_i) / 2) | |
| # fake rate per energy with a binning of 1 | |
| true_e = matched.truthHitAssignedEnergies | |
| bins_fakes = np.arange(0, 51, 2) | |
| fake_rate = [] | |
| energy_fakes = [] | |
| total_true_showers = np.sum( | |
| ~np.isnan(sd.truthHitAssignedEnergies.values) | |
| ) # the ones where truthHitAssignedEnergies is not nan | |
| for i in range(len(bins_fakes) - 1): | |
| bin_i = bins_fakes[i] | |
| bin_i1 = bins_fakes[i + 1] | |
| mask_above = sd.pred_energy_hits_raw.values <= bin_i1 | |
| mask_below = sd.pred_energy_hits_raw.values > bin_i | |
| mask = mask_below * mask_above | |
| fakes = np.sum(np.isnan(sd.truthHitAssignedEnergies)[mask]) | |
| total_showers = len(sd.pred_energy_hits_raw.values[mask]) | |
| if total_showers > 0: | |
| # print(fakes, np.mean(sd.pred_energy_hits_raw[mask])) | |
| fake_rate.append((fakes) / total_true_showers) | |
| energy_fakes.append((bin_i1 + bin_i) / 2) | |
| # plot 2 for each energy bin calculate the mean and the variance of the distribution | |
| mean = [] | |
| variance_om = [] | |
| mean_true_rec = [] | |
| variance_om_true_rec = [] | |
| energy_resolutions = [] | |
| for i in range(len(bins) - 1): | |
| bin_i = bins[i] | |
| bin_i1 = bins[i + 1] | |
| mask_above = ms["e_truth"] <= bin_i1 | |
| mask_below = ms["e_truth"] > bin_i | |
| mask = mask_below * mask_above | |
| pred_e = matched.pred_energy_hits_raw[mask] | |
| true_e = matched.truthHitAssignedEnergies[mask] | |
| true_rec = ms.e_truth[mask] | |
| if np.sum(mask) > 0: | |
| mean_predtotrue = np.mean(pred_e / true_e) | |
| mean_predtored = np.mean(pred_e / true_rec) | |
| var_predtotrue = np.var(pred_e / true_e) / mean_predtotrue | |
| variance_om_true_rec_ = np.var(pred_e / true_rec) / mean_predtored | |
| mean.append(mean_predtotrue) | |
| mean_true_rec.append(mean_predtored) | |
| variance_om.append(var_predtotrue) | |
| variance_om_true_rec.append(variance_om_true_rec_) | |
| energy_resolutions.append((bin_i1 + bin_i) / 2) | |
| bins = np.arange(0, 51, 2) | |
| fce_energy = [] | |
| fce_var_energy = [] | |
| energy_ms = [] | |
| purity_energy = [] | |
| purity_var_energy = [] | |
| fce = ms["e_pred_and_truth"] / ms["e_truth"] | |
| purity = ms["e_pred_and_truth"] / ms["e_pred"] | |
| for i in range(len(bins) - 1): | |
| bin_i = bins[i] | |
| bin_i1 = bins[i + 1] | |
| mask_above = ms["e_truth"] <= bin_i1 | |
| mask_below = ms["e_truth"] > bin_i | |
| mask = mask_below * mask_above | |
| fce_e = np.mean(fce[mask]) | |
| fce_var = np.var(fce[mask]) | |
| purity_e = np.mean(purity[mask]) | |
| purity_var = np.var(purity[mask]) | |
| if np.sum(mask) > 0: | |
| fce_energy.append(fce_e) | |
| fce_var_energy.append(fce_var) | |
| energy_ms.append((bin_i1 + bin_i) / 2) | |
| purity_energy.append(purity_e) | |
| purity_var_energy.append(purity_var) | |
| dict = { | |
| "energy_eff": energy_eff, | |
| "eff": eff, | |
| "energy_fakes": energy_fakes, | |
| "fake_rate": fake_rate, | |
| "mean_true_rec": mean, | |
| "variance_om_true_rec": variance_om, | |
| "fce_energy": fce_energy, | |
| "fce_var_energy": fce_var_energy, | |
| "energy_ms": energy_ms, | |
| "purity_energy": purity_energy, | |
| "purity_var_energy": purity_var_energy, | |
| "energy_resolutions": energy_resolutions, | |
| } | |
| return dict | |