"""Correct NIRSpec ramp or rate data for thermal effects."""
import logging
from stdatamodels.jwst import datamodels
from jwst.picture_frame.picture_frame import correct_picture_frame
from jwst.stpipe import Step
__all__ = ["PictureFrameStep"]
log = logging.getLogger(__name__)
[docs]
class PictureFrameStep(Step):
"""Perform picture frame correction."""
class_alias = "picture_frame"
spec = """
mask_science_regions = boolean(default=True) # Mask known science regions.
n_sigma = float(default=2.0) # Clipping level for non-background signal.
save_mask = boolean(default=False) # Save the created background mask.
save_correction = boolean(default=False) # Save the thermal correction data.
skip = boolean(default=True) # By default, skip the step.
""" # noqa: E501
reference_file_types = ["pictureframe"]
[docs]
def process(self, input_data):
"""
Scale and subtract the thermal "picture frame" effect from a ramp data set.
First, a draft rate image is created if needed from the input ramp data.
A background mask is created from the rate image by masking any known science
regions, then iteratively sigma-clipping. Then, the median levels are computed
from background data in a central region and from the edges of each group image.
These levels are used to scale and offset the correction image in the
PICTUREFRAME reference file, then the scaled image is subtracted from the
input data in each group.
Input data is expected to be a NIRSpec FULL frame ramp file (RampModel),
in between jump and ramp fitting steps (before 1/f noise cleaning),
or a rate file (ImageModel or CubeModel).
Alternately, a rate image or rateints cube may be provided. In that case,
no draft rate image is necessary, and the correction is directly applied
to the rate image.
Parameters
----------
input_data : str, `~stdatamodels.jwst.datamodels.RampModel`, \
`~stdatamodels.jwst.datamodels.ImageModel`, or \
`~stdatamodels.jwst.datamodels.CubeModel`
Filename or input datamodel to be corrected. Must be NIRSpec full-frame.
Returns
-------
output_model : `~stdatamodels.jwst.datamodels.RampModel`, \
`~stdatamodels.jwst.datamodels.ImageModel`, or \
`~stdatamodels.jwst.datamodels.CubeModel`
The corrected datamodel, matching the type of the input.
"""
# Open the input data model
# Note: this step is incorporated in the detector1 pipeline, but it
# can be run standalone on either ramp-type or rate-type data, so it's
# important not to provide a specific datamodel to prepare_output.
output_model = self.prepare_output(input_data)
pictureframe_file = self.get_reference_file(output_model, "pictureframe")
exp_type = output_model.meta.exposure.type
if pictureframe_file == "N/A":
log.warning(f"Picture frame correction is not available for exposure type {exp_type}.")
output_model.meta.cal_step.picture_frame = "SKIPPED"
return output_model
log.info(f"Using PICTUREFRAME reference file: {pictureframe_file}")
pictureframe_model = datamodels.PictureFrameModel(pictureframe_file)
# Correct for the picture frame effect.
# The output model is updated in place.
output_model, mask_model, correction_model, status = correct_picture_frame(
output_model,
pictureframe_model,
mask_science_regions=self.mask_science_regions,
n_sigma=self.n_sigma,
input_dir=self.input_dir,
save_mask=self.save_mask,
save_correction=self.save_correction,
)
# Save the mask, if requested
if self.save_mask and mask_model is not None:
mask_path = self.make_output_path(
basepath=output_model.meta.filename, suffix="pctfrm_mask"
)
log.info(f"Saving mask file {mask_path}")
mask_model.save(mask_path)
mask_model.close()
# Save the correction, if requested
if self.save_correction and correction_model is not None:
correction_path = self.make_output_path(
basepath=output_model.meta.filename, suffix="pctfrm_correction"
)
log.info(f"Saving correction file {correction_path}")
correction_model.save(correction_path)
correction_model.close()
# Set the step completion status
output_model.meta.cal_step.picture_frame = status
return output_model