Source code for jwst.outlier_detection.outlier_detection_stack_step

"""Step defined for outlier detection for stacked observations."""

from ..stpipe import Step
from .. import datamodels
from . import outlier_detection

[docs]class OutlierDetectionStackStep(Step): """Class definition for stacked outlier detection. Flag outlier bad pixels and cosmic rays in the DQ array of each input image of a stack of exposures, which in the case of TSO data are from the same data cube. Input images can listed in an input association file or already opened with a ModelContainer. DQ arrays are modified in place. By default, resampling has been disabled. The 'resample_data' attribute can be reset to 'True' to turn on resampling if desired for the data. Parameters ----------- input : asn file or ModelContainer Single filename association table, or a datamodels.ModelContainer. """ spec = """ weight_type = option('exptime','error',None,default='exptime') pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel fillval = string(default='INDEF') nlow = integer(default=0) nhigh = integer(default=0) maskpt = float(default=0.7) grow = integer(default=1) snr = string(default='4.0 3.0') scale = string(default='0.5 0.4') backg = float(default=0.0) save_intermediate_results = boolean(default=False) resample_data = boolean(default=False) good_bits = string(default="~DO_NOT_USE") # DQ flags to allow """
[docs] def process(self, input): """Step interface for performing outlier_detection processing.""" with as input_models: if not isinstance(input_models, datamodels.ModelContainer): self.log.warning("Input is not a ModelContainer.") self.log.warning("Outlier detection stack step will \ be skipped.") result = input_models.copy() result.meta.cal_step.outlier_detection = "SKIPPED" return result"Performing outlier detection on stack of \ {} inputs".format(len(input_models))) self.input_models = input_models reffiles = {} pars = { 'weight_type': self.weight_type, 'pixfrac': self.pixfrac, 'kernel': self.kernel, 'fillval': self.fillval, 'nlow': self.nlow, 'nhigh': self.nhigh, 'maskpt': self.maskpt, 'grow': self.grow, 'snr': self.snr, 'scale': self.scale, 'backg': self.backg, 'save_intermediate_results': self.save_intermediate_results, 'resample_data': self.resample_data, 'good_bits': self.good_bits } # Set up outlier detection, then do detection step = outlier_detection.OutlierDetection(self.input_models, reffiles=reffiles, **pars) step.do_detection() for model in self.input_models: model.meta.cal_step.outlier_detection = 'COMPLETE' return self.input_models
def _build_reffile_container(self, reftype): """Return a ModelContainer of reference file models. Parameters ---------- input_models: ModelContainer the science data, ImageModels in a ModelContainer reftype: string type of reference file Returns ------- a ModelContainer with corresponding reference files for each input model """ reffile_to_model = {'gain': datamodels.GainModel, 'readnoise': datamodels.ReadnoiseModel} reffiles = [im.meta.ref_file.instance[reftype]['name'] for im in self.input_models] self.log.debug("Using {} reffile(s):".format(reftype.upper())) for r in set(reffiles): self.log.debug(" {}".format(r)) # Check if all the ref files are the same. If so build it by reading # the reference file just once. if len(set(reffiles)) <= 1: length = len(self.input_models) # This call to reference_uri_to_cache_path expects a reference # filename as a URI(crds://), not a file path(/path/to/file) ref_list = [reffile_to_model[reftype]( self.reference_uri_to_cache_path(reffiles[0]) )] * length else: ref_list = [reffile_to_model[reftype]( self.get_reference_file(im, reftype)) for im in self.input_models] return datamodels.ModelContainer(ref_list)