import logging
from stdatamodels.jwst import datamodels
from jwst.guider_cds import guider_cds
from jwst.stpipe import Step
__all__ = ["GuiderCdsStep"]
log = logging.getLogger(__name__)
[docs]
class GuiderCdsStep(Step):
"""Calculate the count rate for each pixel for FGS modes."""
class_alias = "guider_cds"
reference_file_types = ["gain", "readnoise"]
[docs]
def process(self, input_data):
"""
Execute the step.
Parameters
----------
input_data : datamodel, str
The input GuiderRawModel or filename containing
a GuiderRawModel.
Returns
-------
JWST GuiderCalModel or GuiderRawModel
This will be a GuiderRawModel if the step was skipped; otherwise,
it will be a GuiderCalModel containing calibrated count rates.
"""
with datamodels.GuiderRawModel(input_data) as input_model:
# Get the gain reference file
gain_filename = self.get_reference_file(input_model, "gain")
if gain_filename == "N/A":
log.warning("No GAIN reference file found!")
log.warning("guider_cds step will be skipped.")
out_model = input_model.copy()
out_model.meta.cal_step.guider_cds = "SKIPPED"
return out_model
log.info("Using GAIN reference file: %s", gain_filename)
gain_model = datamodels.GainModel(gain_filename)
# Get the readnoise reference file
readnoise_filename = self.get_reference_file(input_model, "readnoise")
if readnoise_filename == "N/A":
log.warning("No READNOISE reference file found!")
log.warning("guider_cds step will be skipped.")
out_model = input_model.copy()
out_model.meta.cal_step.guider_cds = "SKIPPED"
return out_model
log.info("Using READNOISE reference file: %s", readnoise_filename)
readnoise_model = datamodels.ReadnoiseModel(readnoise_filename)
out_model = guider_cds.guider_cds(input_model, gain_model, readnoise_model)
out_model.meta.cal_step.guider_cds = "COMPLETE"
return out_model