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Remove try for debugging
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@jacobbieker
jacobbieker committed Jul 5, 2020
1 parent 639741c commit 4668117
Showing 1 changed file with 83 additions and 83 deletions.
166 changes: 83 additions & 83 deletions factnn/data/preprocess/simulation_preprocessors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -588,92 +588,92 @@ def event_processor(self, directory, clean_images=False, only_core=True, clump_s
for index, file in enumerate(self.paths):
mc_truth = file.split(".phs")[0] + ".ch.gz"
file_name = file.split("/")[-1].split(".phs")[0]
try:
sim_reader = ps.SimulationReader(
photon_stream_path=file,
mmcs_corsika_path=mc_truth
)
counter = 0
for event in sim_reader:
df_event = self.dl2_file.loc[(np.isclose(self.dl2_file['corsika_event_header_total_energy'],
event.simulation_truth.air_shower.energy)) &
(self.dl2_file['run_id'] == event.simulation_truth.run)]
counter += 1
if not df_event.empty:
#try:
sim_reader = ps.SimulationReader(
photon_stream_path=file,
mmcs_corsika_path=mc_truth
)
counter = 0
for event in sim_reader:
df_event = self.dl2_file.loc[(np.isclose(self.dl2_file['corsika_event_header_total_energy'],
event.simulation_truth.air_shower.energy)) &
(self.dl2_file['run_id'] == event.simulation_truth.run)]
counter += 1
if not df_event.empty:

if os.path.isfile(os.path.join(directory, "clump"+str(clump_size), str(file_name) + "_" + str(counter))) \
and os.path.isfile(os.path.join(directory, "core"+str(clump_size), str(file_name) + "_" + str(counter))):
print("True: " + str(file_name) + "_" + str(counter))
continue
if os.path.isfile(os.path.join(directory, "clump"+str(clump_size), str(file_name) + "_" + str(counter))) \
and os.path.isfile(os.path.join(directory, "core"+str(clump_size), str(file_name) + "_" + str(counter))):
print("True: " + str(file_name) + "_" + str(counter))
continue

if clean_images:
all_photons, clump_photons, core_photons = self.clean_image(event, only_core=only_core,min_samples=clump_size)
if core_photons is None:
print("No Clumps, skip")
continue
else:
for key, photon_set in {"no_clean": all_photons, "clump": clump_photons, "core": core_photons}.items():
event.photon_stream.raw = photon_set
# Now extract parameters from the available photons and save them to a file
features = extract_single_simulation_features(event, min_samples=1)
# In the event chosen from the file
# Each event is the same as each line below
cog_x = df_event['cog_x'].values[0]
cog_y = df_event['cog_y'].values[0]
act_sky_source_zero = df_event['source_position_x'].values[0]
act_sky_source_one = df_event['source_position_y'].values[0]
event_photons = event.photon_stream.list_of_lists
zd_deg = event.zd
az_deg = event.az
delta = df_event['delta'].values[0]
energy = event.simulation_truth.air_shower.energy
sky_source_zd = df_event['source_position_zd'].values[0]
sky_source_az = df_event['source_position_az'].values[0]
zd_deg1 = df_event['aux_pointing_position_az'].values[0]
az_deg1 = df_event['aux_pointing_position_zd'].values[0]
data_dict = [[event_photons, act_sky_source_zero, act_sky_source_one,
cog_x, cog_y, zd_deg, az_deg, sky_source_zd, sky_source_az, delta,
energy, zd_deg1, az_deg1],
{'Image': 0, 'Source_X': 1, 'Source_Y': 2, 'COG_X': 3, 'COG_Y': 4, 'Zd_Deg': 5,
'Az_Deg': 6,
'Source_Zd': 7, 'Source_Az': 8, 'Delta': 9, 'Energy': 10, 'Pointing_Zd': 11,
'Pointing_Az': 12}, features]
if key != "no_clean":
with open(os.path.join(directory, key+str(clump_size), str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
else:
if not os.path.isfile(os.path.join(directory, key, str(file_name) + "_" + str(counter))):
with open(os.path.join(directory, key, str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
if clean_images:
all_photons, clump_photons, core_photons = self.clean_image(event, only_core=only_core,min_samples=clump_size)
if core_photons is None:
print("No Clumps, skip")
continue
else:
# In the event chosen from the file
# Each event is the same as each line below
features = extract_single_simulation_features(event)
cog_x = df_event['cog_x'].values[0]
cog_y = df_event['cog_y'].values[0]
act_sky_source_zero = df_event['source_position_x'].values[0]
act_sky_source_one = df_event['source_position_y'].values[0]
event_photons = event.photon_stream.list_of_lists
zd_deg = event.zd
az_deg = event.az
delta = df_event['delta'].values[0]
energy = event.simulation_truth.air_shower.energy
sky_source_zd = df_event['source_position_zd'].values[0]
sky_source_az = df_event['source_position_az'].values[0]
zd_deg1 = df_event['aux_pointing_position_az'].values[0]
az_deg1 = df_event['aux_pointing_position_zd'].values[0]
data_dict = [[event_photons, act_sky_source_zero, act_sky_source_one,
cog_x, cog_y, zd_deg, az_deg, sky_source_zd, sky_source_az, delta,
energy, zd_deg1, az_deg1],
{'Image': 0, 'Source_X': 1, 'Source_Y': 2, 'COG_X': 3, 'COG_Y': 4, 'Zd_Deg': 5,
'Az_Deg': 6,
'Source_Zd': 7, 'Source_Az': 8, 'Delta': 9, 'Energy': 10, 'Pointing_Zd': 11,
'Pointing_Az': 12}, features]
with open(os.path.join(directory, str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
except Exception as e:
print(str(e))
pass
for key, photon_set in {"no_clean": all_photons, "clump": clump_photons, "core": core_photons}.items():
event.photon_stream.raw = photon_set
# Now extract parameters from the available photons and save them to a file
features = extract_single_simulation_features(event, min_samples=1)
# In the event chosen from the file
# Each event is the same as each line below
cog_x = df_event['cog_x'].values[0]
cog_y = df_event['cog_y'].values[0]
act_sky_source_zero = df_event['source_position_x'].values[0]
act_sky_source_one = df_event['source_position_y'].values[0]
event_photons = event.photon_stream.list_of_lists
zd_deg = event.zd
az_deg = event.az
delta = df_event['delta'].values[0]
energy = event.simulation_truth.air_shower.energy
sky_source_zd = df_event['source_position_zd'].values[0]
sky_source_az = df_event['source_position_az'].values[0]
zd_deg1 = df_event['aux_pointing_position_az'].values[0]
az_deg1 = df_event['aux_pointing_position_zd'].values[0]
data_dict = [[event_photons, act_sky_source_zero, act_sky_source_one,
cog_x, cog_y, zd_deg, az_deg, sky_source_zd, sky_source_az, delta,
energy, zd_deg1, az_deg1],
{'Image': 0, 'Source_X': 1, 'Source_Y': 2, 'COG_X': 3, 'COG_Y': 4, 'Zd_Deg': 5,
'Az_Deg': 6,
'Source_Zd': 7, 'Source_Az': 8, 'Delta': 9, 'Energy': 10, 'Pointing_Zd': 11,
'Pointing_Az': 12}, features]
if key != "no_clean":
with open(os.path.join(directory, key+str(clump_size), str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
else:
if not os.path.isfile(os.path.join(directory, key, str(file_name) + "_" + str(counter))):
with open(os.path.join(directory, key, str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
else:
# In the event chosen from the file
# Each event is the same as each line below
features = extract_single_simulation_features(event)
cog_x = df_event['cog_x'].values[0]
cog_y = df_event['cog_y'].values[0]
act_sky_source_zero = df_event['source_position_x'].values[0]
act_sky_source_one = df_event['source_position_y'].values[0]
event_photons = event.photon_stream.list_of_lists
zd_deg = event.zd
az_deg = event.az
delta = df_event['delta'].values[0]
energy = event.simulation_truth.air_shower.energy
sky_source_zd = df_event['source_position_zd'].values[0]
sky_source_az = df_event['source_position_az'].values[0]
zd_deg1 = df_event['aux_pointing_position_az'].values[0]
az_deg1 = df_event['aux_pointing_position_zd'].values[0]
data_dict = [[event_photons, act_sky_source_zero, act_sky_source_one,
cog_x, cog_y, zd_deg, az_deg, sky_source_zd, sky_source_az, delta,
energy, zd_deg1, az_deg1],
{'Image': 0, 'Source_X': 1, 'Source_Y': 2, 'COG_X': 3, 'COG_Y': 4, 'Zd_Deg': 5,
'Az_Deg': 6,
'Source_Zd': 7, 'Source_Az': 8, 'Delta': 9, 'Energy': 10, 'Pointing_Zd': 11,
'Pointing_Az': 12}, features]
with open(os.path.join(directory, str(file_name) + "_" + str(counter)), "wb") as event_file:
pickle.dump(data_dict, event_file)
#except Exception as e:
# print(str(e))
# pass

def batch_processor(self, clean_images=False):
for index, file in enumerate(self.paths):
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