import logging
from stdatamodels.jwst import datamodels
from jwst.gain_scale import gain_scale
from jwst.stpipe import Step
__all__ = ["GainScaleStep"]
log = logging.getLogger(__name__)
[docs]
class GainScaleStep(Step):
"""Rescale all integrations in an exposure by a gain factor."""
class_alias = "gain_scale"
spec = """
""" # noqa: E501
reference_file_types = ["gain"]
[docs]
def process(self, step_input):
"""
Perform gain scale step.
Rescales countrate data to account for use of a non-standard
gain value. All integrations are multiplied by the factor GAINFACT.
Parameters
----------
step_input : datamodel
Input datamodel on which to perform gain scale step.
Returns
-------
result : datamodel
Output datamodel on which the gain scale step has been performed.
"""
# Open the input data model
with datamodels.open(step_input) as input_model:
# Work on a copy
result = input_model.copy()
# Is the gain_factor already populated in the input model?
if result.meta.exposure.gain_factor is None:
# Try to get the gain factor from the gain reference file
gain_filename = self.get_reference_file(result, "gain")
if gain_filename != "N/A":
log.info("Using GAIN reference file: %s", gain_filename)
with datamodels.GainModel(gain_filename) as gain_model:
gain_factor = gain_model.meta.exposure.gain_factor
else:
gain_factor = None
# Try to read the GAINFACT keyword value
if gain_factor is None:
log.info("GAINFACT not found in gain reference file")
log.info("Step will be skipped")
result.meta.cal_step.gain_scale = "SKIPPED"
return result
else:
gain_factor = input_model.meta.exposure.gain_factor
# Do the scaling
result = gain_scale.do_correction(result, gain_factor)
return result