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Merged
TjarkMiener merged 2 commits into from
Nov 12, 2025
Merged

Skip stereo combiner when only one telescope is selected #267

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510fcb5
fix subarray prediction when only one tel is selected
TjarkMiener Nov 4, 2025
10f6739
fix tel mask for the single telescope
TjarkMiener Nov 4, 2025
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255 changes: 175 additions & 80 deletions ctlearn/tools/predict_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -471,7 +471,9 @@ def _predict_with_model(self, model_path):
# Load the model from the specified path
model = keras.saving.load_model(model_path)
prediction_colname = (
"type" if isinstance(model.layers[-1], keras.layers.Softmax) else model.layers[-1].name
"type"
if isinstance(model.layers[-1], keras.layers.Softmax)
else model.layers[-1].name
)
backbone_model, feature_vectors = None, None
if self.dl1_features:
Expand Down Expand Up @@ -1190,38 +1192,67 @@ def start(self):
)
if self.dl2_subarray:
self.log.info("Processing and storing the subarray type prediction...")
# Combine the telescope predictions to the subarray prediction using the stereo combiner
classification_subarray_table = self.type_stereo_combiner.predict_table(
classification_table
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if (
classification_subarray_table[f"{self.prefix}_telescopes"].dtype
!= np.bool_
):
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(
len(classification_subarray_table),
len(self.dl1dh_reader.tel_ids),
),
dtype=bool,
# If only one telescope is used, copy the classification table
# and modify it to subarray format
if len(self.dl1dh_reader.tel_ids) == 1:
classification_subarray_table = classification_table.copy()
telescope_mask = (
classification_subarray_table["tel_id"]
== self.dl1dh_reader.tel_ids[0]
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
classification_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
classification_subarray_table[f"{self.prefix}_telescopes"] = (
reco_telescopes
classification_subarray_table = classification_subarray_table[
telescope_mask
]
classification_subarray_table.remove_column("tel_id")
for colname in classification_subarray_table.colnames:
if "_tel_" in colname:
classification_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
classification_subarray_table.add_column(
[
[val]
for val in classification_subarray_table[
f"{self.prefix}_is_valid"
]
],
name=f"{self.prefix}_telescopes",
)
else:
# Combine the telescope predictions to the subarray prediction using the stereo combiner
classification_subarray_table = (
self.type_stereo_combiner.predict_table(classification_table)
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if (
classification_subarray_table[f"{self.prefix}_telescopes"].dtype
!= np.bool_
):
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(
len(classification_subarray_table),
len(self.dl1dh_reader.tel_ids),
),
dtype=bool,
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
classification_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(
tel_id
)
] = True
# Overwrite the column with the boolean mask with fix length
classification_subarray_table[f"{self.prefix}_telescopes"] = (
reco_telescopes
)
# Sort the subarray classification table
classification_subarray_table.sort(SUBARRAY_EVENT_KEYS)
# Save the prediction to the output file
Expand Down Expand Up @@ -1294,30 +1325,62 @@ def start(self):
self.log.info(
"Processing and storing the subarray energy prediction..."
)
# Combine the telescope predictions to the subarray prediction using the stereo combiner
energy_subarray_table = self.energy_stereo_combiner.predict_table(
energy_table
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if energy_subarray_table[f"{self.prefix}_telescopes"].dtype != np.bool_:
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(len(energy_subarray_table), len(self.dl1dh_reader.tel_ids)),
dtype=bool,
# If only one telescope is used, copy the classification table
# and modify it to subarray format
if len(self.dl1dh_reader.tel_ids) == 1:
energy_subarray_table = energy_table.copy()
telescope_mask = (
energy_subarray_table["tel_id"] == self.dl1dh_reader.tel_ids[0]
)
energy_subarray_table = energy_subarray_table[telescope_mask]
energy_subarray_table.remove_column("tel_id")
for colname in energy_subarray_table.colnames:
if "_tel_" in colname:
energy_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
energy_subarray_table.add_column(
[
[val]
for val in energy_subarray_table[f"{self.prefix}_is_valid"]
],
name=f"{self.prefix}_telescopes",
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
energy_subarray_table[f"{self.prefix}_telescopes"]
else:
# Combine the telescope predictions to the subarray prediction using the stereo combiner
energy_subarray_table = self.energy_stereo_combiner.predict_table(
energy_table
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if (
energy_subarray_table[f"{self.prefix}_telescopes"].dtype
!= np.bool_
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
energy_subarray_table[f"{self.prefix}_telescopes"] = reco_telescopes
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(
len(energy_subarray_table),
len(self.dl1dh_reader.tel_ids),
),
dtype=bool,
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
energy_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(
tel_id
)
] = True
# Overwrite the column with the boolean mask with fix length
energy_subarray_table[f"{self.prefix}_telescopes"] = (
reco_telescopes
)
# Sort the subarray energy table
energy_subarray_table.sort(SUBARRAY_EVENT_KEYS)
# Save the prediction to the output file
Expand Down Expand Up @@ -1413,35 +1476,67 @@ def start(self):
direction_tel_tables = vstack(direction_tel_tables)
# Sort the table by the telescope event keys
direction_tel_tables.sort(TELESCOPE_EVENT_KEYS)
# Combine the telescope predictions to the subarray prediction using the stereo combiner
direction_subarray_table = self.geometry_stereo_combiner.predict_table(
direction_tel_tables
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if (
direction_subarray_table[f"{self.prefix}_telescopes"].dtype
!= np.bool_
):
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(len(direction_subarray_table), len(self.dl1dh_reader.tel_ids)),
dtype=bool,
# If only one telescope is used, copy the classification table
# and modify it to subarray format
if len(self.dl1dh_reader.tel_ids) == 1:
direction_subarray_table = direction_tel_tables.copy()
telescope_mask = (
direction_subarray_table["tel_id"]
== self.dl1dh_reader.tel_ids[0]
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
direction_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
direction_subarray_table[f"{self.prefix}_telescopes"] = (
reco_telescopes
direction_subarray_table = direction_subarray_table[telescope_mask]
direction_subarray_table.remove_column("tel_id")
for colname in direction_subarray_table.colnames:
if "_tel_" in colname:
direction_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
direction_subarray_table.add_column(
[
[val]
for val in direction_subarray_table[
f"{self.prefix}_is_valid"
]
],
name=f"{self.prefix}_telescopes",
)
else:
# Combine the telescope predictions to the subarray prediction using the stereo combiner
direction_subarray_table = (
self.geometry_stereo_combiner.predict_table(
direction_tel_tables
)
)
# TODO: Remove temporary fix once the stereo combiner returns correct table
# Check if the table has to be converted to a boolean mask
if (
direction_subarray_table[f"{self.prefix}_telescopes"].dtype
!= np.bool_
):
# Create boolean mask for telescopes that participate in the stereo reconstruction combination
reco_telescopes = np.zeros(
(
len(direction_subarray_table),
len(self.dl1dh_reader.tel_ids),
),
dtype=bool,
)
# Loop over the table and set the boolean mask for the telescopes
for index, tel_id_mask in enumerate(
direction_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
for tel_id in tel_id_mask:
reco_telescopes[index][
self.dl1dh_reader.subarray.tel_ids_to_indices(
tel_id
)
] = True
# Overwrite the column with the boolean mask with fix length
direction_subarray_table[f"{self.prefix}_telescopes"] = (
reco_telescopes
)
# Sort the subarray geometry table
direction_subarray_table.sort(SUBARRAY_EVENT_KEYS)
# Save the prediction to the output file
Expand Down