diff options
Diffstat (limited to 'utils')
| -rwxr-xr-x | utils/chi2 | 24 | ||||
| -rwxr-xr-x | utils/plot-michels | 6 |
2 files changed, 16 insertions, 14 deletions
@@ -489,17 +489,19 @@ def correct_energy_bias(df): Corrects for the energy bias of the reconstruction relative to the true Monte Carlo energy. """ - # Note: We subtract here since the values in the energy_bias array are MC - # reconstruction relative to truth. So, for example if we have an energy - # bias of -2% at 100 MeV, then the array will contain -0.02. In this case, - # our reconstruction is low relative to the truth, so we need to *increase* - # our estimate. - df.loc[df['id1'] == 20,'energy1'] -= df.loc[df['id1'] == 20,'energy1']*np.interp(df.loc[df['id1'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id2'] == 20,'energy2'] -= df.loc[df['id2'] == 20,'energy2']*np.interp(df.loc[df['id2'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id3'] == 20,'energy3'] -= df.loc[df['id3'] == 20,'energy3']*np.interp(df.loc[df['id3'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id1'] == 22,'energy1'] -= df.loc[df['id1'] == 22,'energy1']*np.interp(df.loc[df['id1'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) - df.loc[df['id2'] == 22,'energy2'] -= df.loc[df['id2'] == 22,'energy2']*np.interp(df.loc[df['id2'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) - df.loc[df['id3'] == 22,'energy3'] -= df.loc[df['id3'] == 22,'energy3']*np.interp(df.loc[df['id3'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) + # Note: We divide here since we define the bias as: + # + # bias = (T_reco - T_true)/T_true + # + # Therefore, + # + # T_true = T_reco/(1+bias) + df.loc[df['id1'] == 20,'energy1'] /= (1+np.interp(df.loc[df['id1'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id2'] == 20,'energy2'] /= (1+np.interp(df.loc[df['id2'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id3'] == 20,'energy3'] /= (1+np.interp(df.loc[df['id3'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id1'] == 22,'energy1'] /= (1+np.interp(df.loc[df['id1'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) + df.loc[df['id2'] == 22,'energy2'] /= (1+np.interp(df.loc[df['id2'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) + df.loc[df['id3'] == 22,'energy3'] /= (1+np.interp(df.loc[df['id3'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) df['ke'] = df['energy1'].fillna(0) + df['energy2'].fillna(0) + df['energy3'].fillna(0) return df diff --git a/utils/plot-michels b/utils/plot-michels index bec9e17..b313c3e 100755 --- a/utils/plot-michels +++ b/utils/plot-michels @@ -65,16 +65,16 @@ def print_particle_probs(data): print("P(%s) = %.2f +/- %.2f" % (particle_id_str,mode[i]*100,std[i]*100)) def make_nll(data, mc, bins, print_nll=False): + data_hist = np.histogram(data.ke.values,bins=bins)[0] + def nll(x, grad=None): if any(x[i] < 0 for i in (0,2)): return np.inf - data_hist = np.histogram(data.ke.values*(1+x[1]),bins=bins)[0] - # Get the Monte Carlo histograms. We need to do this within the # likelihood function since we apply the energy resolution parameters # to the Monte Carlo. - cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values,mc.ke.values*x[2]) + cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values*(1+x[1]),mc.ke.values*x[2]) mc_hist = np.sum(cdf[1:] - cdf[:-1],axis=-1)*x[0] # Calculate the negative log of the likelihood of observing the data |