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UC Davis OptiGAN Bug Fix #574

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UC Davis OptiGAN Bug Fix #574

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28a8e8a
Fixed OptiGAN optical photon count error
guneet19 Nov 19, 2024
38ae6df
Revert changes
guneet19 Nov 19, 2024
e8dc78c
Added comment for clarity
guneet19 Nov 19, 2024
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[pre-commit.ci] Automatic python and c++ formatting
pre-commit-ci[bot] Nov 19, 2024
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162 changes: 63 additions & 99 deletions opengate/contrib/optical/optigan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,72 +78,6 @@ def _setter_hook_input_phsp_actor(self, input_phsp_actor):
return input_phsp_actor


def process_root_output_into_events(df_combined):
"""
This method takes the filtered root output (df_combined) and divides them
into a list elements based on gamma event.

All the info about one gamma event (gamma, electron, and optical photon info)
is saved in one list element.
"""

position_x = df_combined["Position_X"].to_numpy()
position_y = df_combined["Position_Y"].to_numpy()
position_z = df_combined["Position_Z"].to_numpy()
particle_types = df_combined["ParticleType"].to_numpy()

events = {} # will store all the events
current_event = []
event_id = 0

# flag to check if gamma is followed by electrons or photons
gamma_has_electrons_or_photons = False

# counts the occurrence of optical photons in current event
optical_photon_count = 0

# process each particle and segregate into events
for index, (ptype, x, y, z) in enumerate(
zip(particle_types, position_x, position_y, position_z)
):
if ptype == "gamma":
# store the previous event if it started with a gamma
# and is followed by electrons or photons
if current_event and gamma_has_electrons_or_photons:
current_event.append(
{
"type": "opticalphoton",
"optical_photon_count": optical_photon_count,
}
)
events[event_id] = current_event
event_id += 1
optical_photon_count = 0
# if it is a new event, add the gamma particle to it
current_event = [{"index": index, "type": ptype, "x": x, "y": y, "z": z}]
gamma_has_electrons_or_photons = False
elif ptype == "opticalphoton":
# if the particle is optical photon, just increment the count
optical_photon_count += 1
gamma_has_electrons_or_photons = True
elif ptype == "e-":
# Only add e- if there is an ongoing event (started by gamma)
if current_event:
current_event.append(
{"index": index, "type": ptype, "x": x, "y": y, "z": z}
)
gamma_has_electrons_or_photons = True

# Store the last event if it is not empty
if len(current_event) > 1:
current_event.append(
{"type": "opticalphoton", "optical_photon_count": optical_photon_count}
)
events[event_id] = current_event

return events


class OptiGAN(GateObject):
"""
Class Responsibilities:
Expand Down Expand Up @@ -459,8 +393,8 @@ def generate_and_save_optigan_output(self):
# Runs all the methods of OptiGAN
def run_optigan(self, create_output_graphs):
self.initialize()
df_combined = self.get_dataframe_from_root_file()
self.events = process_root_output_into_events(df_combined)
df = self.get_dataframe_from_root_file()
self.events = self.process_root_output_into_events(df)
self.extracted_events_details = self.extract_event_details()
self.save_optigan_inputs()
self.generator = self.create_generator()
Expand All @@ -472,50 +406,80 @@ def run_optigan(self, create_output_graphs):
def get_dataframe_from_root_file(self):
"""
First step in the pipeline to extract OptiGAN input from the root output.
Filters the raw root output into a format that is appropriate for OptiGAN.
"""

with uproot.open(self.root_file_path) as file:
root_tree = file["Phase"]
df = root_tree.arrays(library="pd")

# Save the particle co-ordinates and other information
position_x = root_tree["Position_X"].array(library="np")
position_y = root_tree["Position_Y"].array(library="np")
position_z = root_tree["Position_Z"].array(library="np")
particle_types = root_tree["ParticleName"].array(library="np")
track_ids = root_tree["TrackID"].array(library="np")
return df

# Create a DataFrame from the final arrays.
df = pd.DataFrame(
{
"TrackID": track_ids,
"ParticleType": particle_types,
"Position_X": position_x,
"Position_Y": position_y,
"Position_Z": position_z,
}
)
def process_root_output_into_events(self, df):
"""
This method takes the root output dataframe and divides them
into a list elements based on gamma event.

All the info about one gamma event (gamma, electron, and optical photon info)
is saved in one list element.
"""

df["OriginalIndex"] = df.index
position_x = df["Position_X"].to_numpy()
position_y = df["Position_Y"].to_numpy()
position_z = df["Position_Z"].to_numpy()
particle_types = df["ParticleName"].to_numpy()

# Step 1: Filter out rows where ParticleType is "opticalphoton".
opticalphoton_df = df[df["ParticleType"] == "opticalphoton"]
events = {} # will store all the events
current_event = []
event_id = 0

# Step 2: Drop duplicates based on TrackID, keeping the first occurrence.
opticalphoton_df_unique = opticalphoton_df.loc[
~opticalphoton_df.duplicated(subset="TrackID", keep="first")
]
# flag to check if gamma is followed by electrons or photons
gamma_has_electrons_or_photons = False

# Step 3: Filter out rows where ParticleType is NOT "opticalphoton" (e.g., "gamma").
non_opticalphoton_df = df[df["ParticleType"] != "opticalphoton"]
# counts the occurrence of optical photons in current event
optical_photon_count = 0

# Step 4: Concatenate both DataFrames to keep the structure intact.
df_combined = pd.concat([opticalphoton_df_unique, non_opticalphoton_df])
# process each particle and segregate into events
for index, (ptype, x, y, z) in enumerate(
zip(particle_types, position_x, position_y, position_z)
):
if ptype == "gamma":
# store the previous event if it started with a gamma
# and is followed by electrons or photons
if current_event and gamma_has_electrons_or_photons:
current_event.append(
{
"type": "opticalphoton",
"optical_photon_count": optical_photon_count,
}
)
events[event_id] = current_event
event_id += 1
optical_photon_count = 0
# if it is a new event, add the gamma particle to it
current_event = [
{"index": index, "type": ptype, "x": x, "y": y, "z": z}
]
gamma_has_electrons_or_photons = False
elif ptype == "opticalphoton":
# if the particle is optical photon, just increment the count
optical_photon_count += 1
gamma_has_electrons_or_photons = True
elif ptype == "e-":
# Only add e- if there is an ongoing event (started by gamma)
if current_event:
current_event.append(
{"index": index, "type": ptype, "x": x, "y": y, "z": z}
)
gamma_has_electrons_or_photons = True

# Step 5: Sort the DataFrame based on index to restore the original order if needed.
df_combined = df_combined.sort_index()
# Store the last event if it is not empty
if len(current_event) > 1:
current_event.append(
{"type": "opticalphoton", "optical_photon_count": optical_photon_count}
)
events[event_id] = current_event

return df_combined
return events

def extract_event_details(self):
"""
Expand Down
9 changes: 7 additions & 2 deletions opengate/tests/src/test081_simulation_optigan_with_random_seed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
sim = gate.Simulation()
sim.g4_verbose = True
sim.output_dir = paths.output
# sim.random_seed = 600
sim.random_seed = 600

# units
m = gate.g4_units.m
Expand Down Expand Up @@ -76,7 +76,12 @@
"TrackID",
]

phsp_actor.output_filename = "test075_simulation_optigan_with_random_seed_600.root"
# IMPORTANT
# When using OptiGAN, always set steps_to_store = "first"
phsp_actor.steps_to_store = "first"
phsp_actor.output_filename = (
"test075_simulation_optigan_with_random_seed_600_first.root"
)

# add a kill actor to the crystal
ka = sim.add_actor("KillActor", "kill_actor2")
Expand Down
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