"""Barshadow correction step."""
import logging
from stdatamodels.jwst import datamodels
from jwst.barshadow import bar_shadow
from jwst.stpipe import Step
__all__ = ["BarShadowStep"]
log = logging.getLogger(__name__)
[docs]
class BarShadowStep(Step):
"""
Correct NIRSpec MOS data for barshadow effects.
Bar shadow correction depends on the position of a pixel along the slit
and the wavelength. It is only applied to uniform sources and only for
NRS MSA data.
"""
class_alias = "barshadow"
spec = """
inverse = boolean(default=False) # Invert the operation
source_type = string(default=None) # Process as specified source type.
""" # noqa: E501
reference_file_types = ["barshadow"]
[docs]
def process(self, input_data):
"""
Perform the barshadow correction step.
Parameters
----------
input_data : str or `~stdatamodels.jwst.datamodels.MultiSlitModel`
Input JWST file name or datamodel object.
Returns
-------
output_model : `~stdatamodels.jwst.datamodels.MultiSlitModel`
JWST datamodel object with corrected science data and barshadow
extension(s) added.
"""
# Open the input data model
output_model = self.prepare_output(input_data)
# Check for MOS data
exp_type = output_model.meta.exposure.type
if exp_type != "NRS_MSASPEC":
output_model.meta.cal_step.barshadow = "SKIPPED"
return output_model
if self.use_correction_pars:
correction_pars = self.correction_pars
barshadow_model = None
else:
correction_pars = None
# Get the name of the bar shadow reference file to use
barshadow_name = self.get_reference_file(output_model, "barshadow")
log.info(f"Using BARSHADOW reference file {barshadow_name}")
# Check for a valid reference file
if barshadow_name == "N/A":
log.warning("No BARSHADOW reference file found")
log.warning("Bar shadow step will be skipped")
output_model.meta.cal_step.barshadow = "SKIPPED"
return output_model
# Open the barshadow ref file data model
barshadow_model = datamodels.BarshadowModel(barshadow_name)
# Do the bar shadow correction
output_model, self.correction_pars = bar_shadow.do_correction(
output_model,
barshadow_model,
inverse=self.inverse,
source_type=self.source_type,
correction_pars=correction_pars,
)
if barshadow_model:
barshadow_model.close()
output_model.meta.cal_step.barshadow = "COMPLETE"
return output_model