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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
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