Source code for jwst.pipeline.calwebb_image3

from ..stpipe import Pipeline
from .. import datamodels
from ..lib.exposure_types import is_moving_target

from ..assign_mtwcs import assign_mtwcs_step
from ..tweakreg import tweakreg_step
from ..skymatch import skymatch_step
from ..resample import resample_step
from ..outlier_detection import outlier_detection_step
from ..source_catalog import source_catalog_step

__all__ = ['Image3Pipeline']

[docs]class Image3Pipeline(Pipeline): """ Image3Pipeline: Applies level 3 processing to imaging-mode data from any JWST instrument. Included steps are: assign_mtwcs tweakreg skymatch outlier_detection resample source_catalog """ class_alias = "calwebb_image3" spec = """ """ # Define alias to steps step_defs = { 'assign_mtwcs': assign_mtwcs_step.AssignMTWcsStep, 'tweakreg': tweakreg_step.TweakRegStep, 'skymatch': skymatch_step.SkyMatchStep, 'outlier_detection': outlier_detection_step.OutlierDetectionStep, 'resample': resample_step.ResampleStep, 'source_catalog': source_catalog_step.SourceCatalogStep }
[docs] def process(self, input_data): """ Run the Image3Pipeline Parameters ---------- input_data: Level3 Association, or ~jwst.datamodels.ModelContainer The exposures to process """'Starting calwebb_image3 ...') # Only load science and background members from input ASN asn_exptypes = ['science', 'background'] # Configure settings for saving results files self.outlier_detection.suffix = 'crf' self.outlier_detection.save_results = self.save_results self.resample.suffix = 'i2d' self.resample.save_results = self.save_results self.source_catalog.save_results = self.save_results with, asn_exptypes=asn_exptypes) as input_models: # If input is an association, set the output to the product name. if self.output_file is None: try: self.output_file = input_models.meta.asn_table.products[0].name except (AttributeError, IndexError): pass # Check if input is single or multiple exposures try: has_groups = len(input_models.group_names) > 1 except (AttributeError, TypeError, KeyError): has_groups = False if isinstance(input_models, datamodels.ModelContainer) and has_groups: if is_moving_target(input_models): input_models = self.assign_mtwcs(input_models) else: input_models = self.tweakreg(input_models) input_models = self.skymatch(input_models) input_models = self.outlier_detection(input_models) elif self.skymatch.skymethod == 'match': self.log.warning("Turning 'skymatch' step off for a single " "input image when 'skymethod' is 'match'") else: input_models = self.skymatch(input_models) result = self.resample(input_models) if isinstance(result, datamodels.ImageModel) and result.meta.cal_step.resample == 'COMPLETE': self.source_catalog(result)