Source code for liger_iris_pipeline.flatfield.flat_field_step

#! /usr/bin/env python

import liger_iris_pipeline
from jwst.stpipe import Step
from jwst import datamodels
from . import flat_field

# For the following types of data, it is OK -- and in some cases
# required -- for the extract_2d step to have been run.  For all
# other types of data, the extract_2d step must not have been run.
EXTRACT_2D_IS_OK = []

__all__ = ["FlatFieldStep"]


[docs] class FlatFieldStep(Step): """Flat-field a science image using a flatfield reference image. """ reference_file_types = ["flat"]
[docs] def process(self, input): input_model = datamodels.open(input) exposure_type = input_model.meta.exposure.type.upper() # Figure out what kind of input data model is in use. self.log.debug("Input is {}".format(input_model.__class__.__name__)) # Check whether extract_2d has been run. if ( input_model.meta.cal_step.extract_2d == "COMPLETE" and not exposure_type in EXTRACT_2D_IS_OK ): self.log.warning( "The extract_2d step has been run, but for " "%s data it should not have been run, so ...", exposure_type, ) self.log.warning("flat fielding will be skipped.") return self.skip_step(input_model) self.flat_filename = self.get_reference_file(input_model, "flat") self.log.debug("Using FLAT reference file: %s", self.flat_filename) # Check for a valid reference file missing = False if self.flat_filename == "N/A": self.log.warning("No FLAT reference file found") missing = True if missing: self.log.warning("Flat-field step will be skipped") return self.skip_step(input_model) self.log.debug("Opening flat as FlatModel") flat_model = liger_iris_pipeline.datamodels.FlatModel(self.flat_filename) # Do the flat-field correction output_model = flat_field.do_correction( input_model, flat_model, ) # Close the inputs input_model.close() flat_model.close() return output_model
[docs] def skip_step(self, input_model): """Set the calibration switch to SKIPPED. This method makes a copy of input_model, sets the calibration switch for the flat_field step to SKIPPED in the copy, closes input_model, and returns the copy. """ result = input_model.copy() result.meta.cal_step.flat_field = "SKIPPED" input_model.close() return result