get_center_of_mass_x: {"image_shape": image_shape}, get_center_of_mass_y: {"image_shape": image_shape},
get_std_energy: {"energy_scaler": 1}, get_energy_resolution: {"real_ET": tracker_real_ET/1000, "energy_scaler": 1}}
colnames = ["Enery", "MaxEnergy", "Cells", "X CoM", "Y CoM", "StdEnergy", "Resolution", "dN/dE", "(dN/dE) / (dN/dE)"]
htos_calo_images_real = crop_images(calo_images, **padding)
htos_calo_images_fake = crop_images(generated_images, **padding)
for func_idx, (func, params) in enumerate(use_functions.items()):
if func.__name__ in ["get_number_of_activated_cells", "get_max_energy"]:
build_histogram(true=htos_calo_images_real, fake=htos_calo_images_fake, function=func,
name=colnames[func_idx], epoch="", folder=None, ax=axes[func_idx], **params)
else:
build_histogram(true=calo_images, fake=generated_images, function=func,
name=colnames[func_idx], epoch="", folder=None, ax=axes[func_idx], **params)
build_histogram_HTOS(true=htos_calo_images_real, fake=htos_calo_images_fake,
energy_scaler=1, threshold=3.6, real_ET=tracker_real_ET,
ax1=axes[7], ax2=axes[8])
figs.append(fig2)
for idx in range(10):
print(idx+1, "/", 10)
use_functions = {
get_energies: {"energy_scaler": 1}, get_max_energy: {"energy_scaler": 1, "maxclip": 6.120},
get_number_of_activated_cells: {"threshold": 5/1000},
get_center_of_mass_x: {"image_shape": image_shape}, get_center_of_mass_y: {"image_shape": image_shape},
get_std_energy: {"energy_scaler": scaler["Calo"]}, get_energy_resolution: {"real_ET": tracker_real_ET[idx]/1000, "energy_scaler": 1}
}
figs.append(Generator.build_simulated_events(condition=tracker_events[idx],
tracker_image=[tracker_images[idx]],
calo_image=np.array([calo_images[idx]]),
folder=None,
ax=axes[func_idx],
**params)
else:
build_histogram(true=calo_images,
fake=generated_images,
function=func,
name=colnames[func_idx],
epoch="",
folder=None,
ax=axes[func_idx],
**params)
build_histogram_HTOS(true=calo_images,
fake=generated_images,
energy_scaler=1,
threshold=3.6,
real_ET=tracker_et,
ax1=axes[7],
ax2=axes[8])
axes[-1].scatter(tracker_et,
get_energies(calo_images),
label="true",
alpha=0.1)
axes[-1].scatter(tracker_et,
get_energies(generated_images),
label="fake",
alpha=0.1)
axes[-1].legend()
axes[-1].set_xlim(0, np.max(tracker_et))
axes[-1].set_ylim(0, np.max(tracker_et))
axes[-1].set_xlabel("Tracker")
alpha=0.05)
axes[model_idx,
-1].scatter(tracker_real_ET / 1000,
get_energies(generated_images_from_tracker,
energy_scaler=calo_scaler / 1000),
label="Tracker",
alpha=0.05)
axes[model_idx, -1].set_xlabel("Tracker [GeV]")
axes[model_idx, -1].set_ylabel("Reconstructed [GeV]")
axes[model_idx, -1].legend()
build_histogram_HTOS(true=mc_data_images_m,
fake=generated_images_from_cgan,
fake2=generated_images_from_tracker,
energy_scaler=calo_scaler,
threshold=3600,
real_ET=tracker_real_ET,
labels=["Geant4", "CGAN", "Tracker"],
ax1=axes[model_idx, -3],
ax2=axes[model_idx, -2])
# Resolution Geant vs Generated
resolution_geant_nn = (get_energies(mc_data_images_m) -
get_energies(generated_images_from_cgan))
is_small_discrepancy = np.abs(resolution_geant_nn) < 5
resolution_geant_nn_moderate = resolution_geant_nn[
is_small_discrepancy]
axes[model_idx, -4].hist(resolution_geant_nn_moderate,
bins=40,
histtype="step")
axes[model_idx, -4].set_title(
**params)
else:
build_histogram(true=mc_data_images_m,
fake=generated_images,
function=func,
name=colnames[func_idx],
epoch="",
folder=None,
ax=axes[func_idx],
labels=["Geant4", "Im2Im"],
**params)
build_histogram_HTOS(true=mc_data_images_m,
fake=generated_images,
energy_scaler=calo_scaler,
threshold=3600,
real_ET=tracker_real_ET,
labels=["Geant4", "Im2Im"],
ax1=axes[-3],
ax2=axes[-2])
axes[-1].scatter(tracker_real_ET,
get_energies(mc_data_images_m),
label="Geant4",
alpha=0.05)
axes[-1].scatter(tracker_real_ET,
get_energies(generated_images),
label="Im2Im",
alpha=0.05)
axes[-1].legend()
figs.append(fig2)
get_max_energy: {
"energy_scaler": calo_scaler
},
get_std_energy: {
"energy_scaler": calo_scaler
},
get_energy_resolution: {
"real_ET": tracker_real_ET,
"energy_scaler": calo_scaler
}
}
build_histogram_HTOS(true=calo_images,
fake=None,
energy_scaler=calo_scaler,
threshold=3600,
real_ET=tracker_real_ET,
ax1=axes[7],
ax2=axes[8])
for func_idx, (func, params) in enumerate(use_functions.items()):
build_histogram(true=calo_images,
fake=None,
function=func,
name=colnames[func_idx],
epoch="",
folder=None,
ax=axes[func_idx],
use_legend=True,
**params)