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TjarkMiener merged 2 commits into from
Oct 22, 2025
Merged

sorting subarray table before writing #263

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c438114
sorting subarray table before writing
TjarkMiener Oct 22, 2025
840d5fc
also rename tables in LST1 prediction tool
TjarkMiener Oct 22, 2025
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88 changes: 44 additions & 44 deletions ctlearn/tools/predict_LST1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -556,8 +556,8 @@ def start(self):
feature_vector_table = example_identifiers.copy()
fvs_columns_list, fvs_shapes_list = [], []
if self.load_type_model_from is not None:
classification_table = example_identifiers.copy()
classification_table.add_column(
classification_tel_table = example_identifiers.copy()
classification_tel_table.add_column(
prediction, name=f"{self.prefix}_tel_prediction"
)
# Produce output table with NaNs for missing predictions
Expand All @@ -567,24 +567,24 @@ def start(self):
columns=[f"{self.prefix}_tel_prediction"],
shapes=[(len(nonexample_identifiers),)],
)
classification_table = vstack([classification_table, nan_table])
classification_table.sort(TELESCOPE_EVENT_KEYS)
classification_is_valid = ~np.isnan(classification_table[f"{self.prefix}_tel_prediction"].data, dtype=bool)
classification_table.add_column(
classification_tel_table = vstack([classification_tel_table, nan_table])
classification_tel_table.sort(TELESCOPE_EVENT_KEYS)
classification_is_valid = ~np.isnan(classification_tel_table[f"{self.prefix}_tel_prediction"].data, dtype=bool)
classification_tel_table.add_column(
classification_is_valid,
name=f"{self.prefix}_tel_is_valid",
)
# Add the default values and meta data to the table
add_defaults_and_meta(
classification_table,
classification_tel_table,
ParticleClassificationContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
# Save the prediction to the output file
if self.dl2_telescope:
write_table(
classification_table,
classification_tel_table,
self.output_path,
f"{DL2_TELESCOPE_GROUP}/classification/{self.prefix}/tel_{self.tel_id:03d}",
overwrite=self.overwrite,
Expand All @@ -596,19 +596,19 @@ def start(self):
)
# Write the mono telescope prediction to the subarray prediction table
if self.dl2_subarray:
subarray_classification_table = classification_table.copy()
subarray_classification_table.remove_column("tel_id")
for colname in subarray_classification_table.colnames:
classification_subarray_table = classification_tel_table.copy()
classification_subarray_table.remove_column("tel_id")
for colname in classification_subarray_table.colnames:
if "_tel_" in colname:
subarray_classification_table.rename_column(
classification_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
subarray_classification_table.add_column(
classification_subarray_table.add_column(
classification_is_valid[np.newaxis], name=f"{self.prefix}_telescopes"
)
# Save the prediction to the output file
write_table(
subarray_classification_table,
classification_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}",
overwrite=self.overwrite,
Expand Down Expand Up @@ -636,36 +636,36 @@ def start(self):
)
)
if self.load_energy_model_from is not None:
energy_table = example_identifiers.copy()
energy_tel_table = example_identifiers.copy()
# Convert the reconstructed energy from log10(TeV) to TeV
reco_energy = u.Quantity(np.power(10, np.squeeze(energy)), unit=u.TeV)
# Add the reconstructed energy to the prediction table
energy_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy")
energy_tel_table.add_column(reco_energy, name=f"{self.prefix}_tel_energy")
# Produce output table with NaNs for missing predictions
if len(nonexample_identifiers) > 0:
nan_table = self._create_nan_table(
nonexample_identifiers,
columns=[f"{self.prefix}_tel_energy"],
shapes=[(len(nonexample_identifiers),)],
)
energy_table = vstack([energy_table, nan_table])
energy_table.sort(TELESCOPE_EVENT_KEYS)
energy_is_valid = ~np.isnan(energy_table[f"{self.prefix}_tel_energy"].data, dtype=bool)
energy_table.add_column(
energy_tel_table = vstack([energy_tel_table, nan_table])
energy_tel_table.sort(TELESCOPE_EVENT_KEYS)
energy_is_valid = ~np.isnan(energy_tel_table[f"{self.prefix}_tel_energy"].data, dtype=bool)
energy_tel_table.add_column(
energy_is_valid,
name=f"{self.prefix}_tel_is_valid",
)
# Add the default values and meta data to the table
add_defaults_and_meta(
energy_table,
energy_tel_table,
ReconstructedEnergyContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
# Save the prediction to the output file
if self.dl2_telescope:
write_table(
energy_table,
energy_tel_table,
self.output_path,
f"{DL2_TELESCOPE_GROUP}/energy/{self.prefix}/tel_{self.tel_id:03d}",
overwrite=self.overwrite,
Expand All @@ -677,19 +677,19 @@ def start(self):
)
# Write the mono telescope prediction to the subarray prediction table
if self.dl2_subarray:
subarray_energy_table = energy_table.copy()
subarray_energy_table.remove_column("tel_id")
for colname in subarray_energy_table.colnames:
energy_subarray_table = energy_tel_table.copy()
energy_subarray_table.remove_column("tel_id")
for colname in energy_subarray_table.colnames:
if "_tel_" in colname:
subarray_energy_table.rename_column(
energy_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
subarray_energy_table.add_column(
energy_subarray_table.add_column(
energy_is_valid[np.newaxis], name=f"{self.prefix}_telescopes"
)
# Save the prediction to the output file
write_table(
subarray_energy_table,
energy_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}",
overwrite=self.overwrite,
Expand Down Expand Up @@ -717,7 +717,7 @@ def start(self):
)
)
if self.load_cameradirection_model_from is not None:
direction_table = example_identifiers.copy()
direction_tel_table = example_identifiers.copy()
# Set the telescope position
tel_ground_frame = self.subarray.tel_coords[
self.subarray.tel_ids_to_indices(self.tel_id)
Expand Down Expand Up @@ -749,10 +749,10 @@ def start(self):
# Transform the true Alt/Az coordinates to camera coordinates
reco_direction = cam_coord_offset.transform_to(altaz)
# Add the reconstructed direction (az, alt) to the prediction table
direction_table.add_column(
direction_tel_table.add_column(
reco_direction.az.to(u.deg), name=f"{self.prefix}_tel_az"
)
direction_table.add_column(
direction_tel_table.add_column(
reco_direction.alt.to(u.deg), name=f"{self.prefix}_tel_alt"
)
# Produce output table with NaNs for missing predictions
Expand All @@ -762,28 +762,28 @@ def start(self):
columns=[f"{self.prefix}_tel_az", f"{self.prefix}_tel_alt"],
shapes=[(len(nonexample_identifiers),), (len(nonexample_identifiers),)],
)
direction_table = vstack([direction_table, nan_table])
direction_table.keep_columns(
direction_tel_table = vstack([direction_tel_table, nan_table])
direction_tel_table.keep_columns(
TELESCOPE_EVENT_KEYS
+ [f"{self.prefix}_tel_az", f"{self.prefix}_tel_alt"]
)
direction_table.sort(TELESCOPE_EVENT_KEYS)
direction_is_valid = ~np.isnan(direction_table[f"{self.prefix}_tel_az"].data, dtype=bool)
direction_table.add_column(
direction_tel_table.sort(TELESCOPE_EVENT_KEYS)
direction_is_valid = ~np.isnan(direction_tel_table[f"{self.prefix}_tel_az"].data, dtype=bool)
direction_tel_table.add_column(
direction_is_valid,
name=f"{self.prefix}_tel_is_valid",
)
# Add the default values and meta data to the table
add_defaults_and_meta(
direction_table,
direction_tel_table,
ReconstructedGeometryContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
# Save the prediction to the output file
if self.dl2_telescope:
write_table(
direction_table,
direction_tel_table,
self.output_path,
f"{DL2_TELESCOPE_GROUP}/geometry/{self.prefix}/tel_{self.tel_id:03d}",
overwrite=self.overwrite,
Expand All @@ -795,19 +795,19 @@ def start(self):
)
# Write the mono telescope prediction to the subarray prediction table
if self.dl2_subarray:
subarray_direction_table = direction_table.copy()
subarray_direction_table.remove_column("tel_id")
for colname in subarray_direction_table.colnames:
direction_subarray_table = direction_tel_table.copy()
direction_subarray_table.remove_column("tel_id")
for colname in direction_subarray_table.colnames:
if "_tel_" in colname:
subarray_direction_table.rename_column(
direction_subarray_table.rename_column(
colname, colname.replace("_tel", "")
)
subarray_direction_table.add_column(
direction_subarray_table.add_column(
direction_is_valid[np.newaxis], name=f"{self.prefix}_telescopes"
)
# Save the prediction to the output file
write_table(
subarray_direction_table,
direction_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}",
overwrite=self.overwrite,
Expand Down
42 changes: 24 additions & 18 deletions ctlearn/tools/predict_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1191,26 +1191,26 @@ 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
subarray_classification_table = self.type_stereo_combiner.predict_table(
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 (
subarray_classification_table[f"{self.prefix}_telescopes"].dtype
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(subarray_classification_table),
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(
subarray_classification_table[f"{self.prefix}_telescopes"]
classification_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
Expand All @@ -1219,12 +1219,14 @@ def start(self):
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
subarray_classification_table[f"{self.prefix}_telescopes"] = (
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
write_table(
subarray_classification_table,
classification_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/classification/{self.prefix}",
overwrite=self.overwrite_tables,
Expand Down Expand Up @@ -1293,20 +1295,20 @@ def start(self):
"Processing and storing the subarray energy prediction..."
)
# Combine the telescope predictions to the subarray prediction using the stereo combiner
subarray_energy_table = self.energy_stereo_combiner.predict_table(
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 subarray_energy_table[f"{self.prefix}_telescopes"].dtype != np.bool_:
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(subarray_energy_table), len(self.dl1dh_reader.tel_ids)),
(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(
subarray_energy_table[f"{self.prefix}_telescopes"]
energy_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
Expand All @@ -1315,10 +1317,12 @@ def start(self):
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
subarray_energy_table[f"{self.prefix}_telescopes"] = reco_telescopes
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
write_table(
subarray_energy_table,
energy_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/energy/{self.prefix}",
overwrite=self.overwrite_tables,
Expand Down Expand Up @@ -1410,23 +1414,23 @@ def start(self):
# 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
subarray_direction_table = self.geometry_stereo_combiner.predict_table(
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 (
subarray_direction_table[f"{self.prefix}_telescopes"].dtype
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(subarray_direction_table), len(self.dl1dh_reader.tel_ids)),
(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(
subarray_direction_table[f"{self.prefix}_telescopes"]
direction_subarray_table[f"{self.prefix}_telescopes"]
):
if not tel_id_mask:
continue
Expand All @@ -1435,12 +1439,14 @@ def start(self):
self.dl1dh_reader.subarray.tel_ids_to_indices(tel_id)
] = True
# Overwrite the column with the boolean mask with fix length
subarray_direction_table[f"{self.prefix}_telescopes"] = (
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
write_table(
subarray_direction_table,
direction_subarray_table,
self.output_path,
f"{DL2_SUBARRAY_GROUP}/geometry/{self.prefix}",
overwrite=self.overwrite_tables,
Expand Down
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