Source code for

Generate DL1 (a or b) output files in HDF5 format from {R0,R1,DL0} inputs.
# pylint: disable=W0201
import sys

from import tqdm

from ..calib import CameraCalibrator, GainSelector
from ..core import QualityQuery, Tool
from ..core.traits import Bool, classes_with_traits, flag
from ..image import ImageCleaner, ImageModifier, ImageProcessor
from ..image.extractor import ImageExtractor
from ..instrument import SoftwareTrigger
from import (
from ..reco import Reconstructor, ShowerProcessor
from ..utils import EventTypeFilter


__all__ = ["ProcessorTool"]

[docs]class ProcessorTool(Tool): """ Process data from lower-data levels up to DL1, including both image extraction and optinally image parameterization """ name = "ctapipe-process" description = ( __doc__ + f" This currently uses data model version {DATA_MODEL_VERSION}" ) examples = """ To process data with all default values: > ctapipe-process --input events.simtel.gz --output events.dl1.h5 --progress Or use an external configuration file, where you can specify all options: > ctapipe-process --config stage1_config.json --progress The config file should be in JSON or python format (see traitlets docs). For an example, see ctapipe/examples/stage1_config.json in the main code repo. """ progress_bar = Bool( help="show progress bar during processing", default_value=False ).tag(config=True) force_recompute_dl1 = Bool( help="Enforce dl1 recomputation even if already present in the input file", default_value=False, ).tag(config=True) force_recompute_dl2 = Bool( help="Enforce dl2 recomputation even if already present in the input file", default_value=False, ).tag(config=True) aliases = { ("i", "input"): "EventSource.input_url", ("o", "output"): "DataWriter.output_path", ("t", "allowed-tels"): "EventSource.allowed_tels", ("m", "max-events"): "EventSource.max_events", "energy-regressor": "ShowerProcessor.EnergyRegressor.load_path", "particle-classifier": "ShowerProcessor.ParticleClassifier.load_path", "image-cleaner-type": "ImageProcessor.image_cleaner_type", } flags = { "f": ( {"DataWriter": {"overwrite": True}}, "Overwrite output file if it exists", ), **flag( "overwrite", "DataWriter.overwrite", "Overwrite output file if it exists", "Don't overwrite output file if it exists", ), **flag( "progress", "ProcessorTool.progress_bar", "show a progress bar during event processing", "don't show a progress bar during event processing", ), **flag( "recompute-dl1", "ProcessorTool.force_recompute_dl1", "Enforce DL1 recomputation even if already present in the input file", "Only compute DL1 if there are no DL1b parameters in the file", ), **flag( "recompute-dl2", "ProcessorTool.force_recompute_dl2", "Enforce DL2 recomputation even if already present in the input file", "Only compute DL2 if there is no shower reconstruction in the file", ), **flag( "write-images", "DataWriter.write_images", "store DL1/Event/Telescope images in output", "don't store DL1/Event/Telescope images in output", ), **flag( "write-parameters", "DataWriter.write_parameters", "store DL1/Event/Telescope parameters in output", "don't store DL1/Event/Telescope parameters in output", ), **flag( "write-showers", "DataWriter.write_showers", "store DL2/Event parameters in output", "don't DL2/Event parameters in output", ), **flag( "write-index-tables", "DataWriter.write_index_tables", "generate PyTables index tables for the parameter and image datasets", ), "camera-frame": ( {"ImageProcessor": {"use_telescope_frame": False}}, "Use camera frame for image parameters instead of telescope frame", ), } classes = ( [ CameraCalibrator, DataWriter, ImageProcessor, ShowerProcessor, metadata.Instrument, metadata.Contact, SoftwareTrigger, ] + classes_with_traits(EventSource) + classes_with_traits(ImageCleaner) + classes_with_traits(ImageExtractor) + classes_with_traits(GainSelector) + classes_with_traits(QualityQuery) + classes_with_traits(ImageModifier) + classes_with_traits(EventTypeFilter) + classes_with_traits(Reconstructor) )
[docs] def setup(self): # setup components: self.event_source = EventSource(parent=self) if not self.event_source.has_any_datalevel(COMPATIBLE_DATALEVELS): self.log.critical( "%s needs the EventSource to provide either R1 or DL0 or DL1A data" ", %s provides only %s",, self.event_source, self.event_source.datalevels, ) sys.exit(1) subarray = self.event_source.subarray self.software_trigger = SoftwareTrigger(parent=self, subarray=subarray) self.calibrate = CameraCalibrator(parent=self, subarray=subarray) self.process_images = ImageProcessor(subarray=subarray, parent=self) self.process_shower = ShowerProcessor(subarray=subarray, parent=self) self.write = DataWriter(event_source=self.event_source, parent=self) # add ml reco classes if model paths were supplied via cli and not already configured reco_aliases = { "--energy-regressor": "EnergyRegressor", "--particle-classifier": "ParticleClassifier", } for alias, name in reco_aliases.items(): has_alias = any(arg.startswith(alias) for arg in self.argv) if has_alias and name not in self.process_shower.reconstructor_types: "Adding %s to ShowerProcesser because path was given on cli", name ) reconstructor = Reconstructor.from_name( name, parent=self.process_shower, subarray=subarray, ) self.process_shower.reconstructors.append(reconstructor) self.process_shower.reconstructor_types.append(name) self.write.write_showers = True self.event_type_filter = EventTypeFilter(parent=self) # warn if max_events prevents writing the histograms if ( isinstance(self.event_source, SimTelEventSource) and self.event_source.max_events and self.event_source.max_events > 0 ): self.log.warning( "No Simulated shower distributions will be written because " "EventSource.max_events is set to a non-zero number (and therefore " "shower distributions read from the input Simulation file are invalid)." )
@property def should_compute_dl2(self): """returns true if we should compute DL2 info""" if self.force_recompute_dl2: return True return self.write.write_showers @property def should_compute_dl1(self): """returns true if we should compute DL1 info""" if self.force_recompute_dl1: return True if DataLevel.DL1_PARAMETERS in self.event_source.datalevels: return False return self.write.write_parameters or self.should_compute_dl2 @property def should_calibrate(self): if self.force_recompute_dl1: return True if ( self.write.write_images and DataLevel.DL1_IMAGES not in self.event_source.datalevels ): return True if self.should_compute_dl1: return DataLevel.DL1_IMAGES not in self.event_source.datalevels return False def _write_processing_statistics(self): """write out the event selection stats, etc.""" # NOTE: don't remove this, not part of DataWriter if self.should_compute_dl1: image_stats = self.process_images.check_image.to_table(functions=True) write_table( image_stats, self.write.output_path, path="/dl1/service/image_statistics", append=True, ) if self.should_compute_dl2: reconstructors = self.process_shower.reconstructors reconstructor_names = self.process_shower.reconstructor_types for reconstructor_name, reconstructor in zip( reconstructor_names, reconstructors ): write_table( reconstructor.quality_query.to_table(functions=True), self.write.output_path, f"/dl2/service/tel_event_statistics/{reconstructor_name}", append=True, )
[docs] def start(self): """ Process events """"(re)compute DL1: %s", self.should_compute_dl1)"(re)compute DL2: %s", self.should_compute_dl2) for event in tqdm( self.event_source, desc=self.event_source.__class__.__name__, total=self.event_source.max_events, unit="ev", disable=not self.progress_bar, ): self.log.debug("Processessing event_id=%s", event.index.event_id) if not self.event_type_filter(event): continue if not self.software_trigger(event): self.log.debug( "Skipping event %i due to software trigger", event.index.event_id ) continue if self.should_calibrate: self.calibrate(event) if self.should_compute_dl1: self.process_images(event) if self.should_compute_dl2: self.process_shower(event) self.write(event)
[docs] def finish(self): """ Last steps after processing events. """ self.write.write_simulation_histograms(self.event_source) self.write.finish() self.event_source.close() self._write_processing_statistics()
def main(): """run the tool""" tool = ProcessorTool() if __name__ == "__main__": main()