from stdatamodels.jwst import datamodels
from jwst.clean_flicker_noise import clean_flicker_noise
from jwst.stpipe import Step
__all__ = ["NSCleanStep"]
[docs]
class NSCleanStep(Step):
"""
Perform 1/f noise correction.
NOTE: This step is a deprecated alias to the ``clean_flicker_noise`` step.
"""
class_alias = "nsclean"
spec = """
fit_method = option('fft', 'median', default='fft') # Noise fitting algorithm
fit_by_channel = boolean(default=False) # Fit noise separately by amplifier
background_method = option('median', 'model', None, default=None) # Background fit
background_box_size = int_list(min=2, max=2, default=None) # Background box size
mask_spectral_regions = boolean(default=True) # Mask WCS-defined spectral regions
n_sigma = float(default=5.0) # Clipping level for outliers
fit_histogram = boolean(default=False) # Fit a value histogram to derive sigma
single_mask = boolean(default=False) # Make a single mask for all integrations
user_mask = string(default=None) # Path to user-supplied mask
save_mask = boolean(default=False) # Save the created mask
save_background = boolean(default=False) # Save the fit background
save_noise = boolean(default=False) # Save the fit noise
skip = boolean(default=True) # By default, skip the step
""" # noqa: E501
[docs]
def process(self, input_data):
"""
Fit and subtract 1/f background noise from a NIRSpec image.
Parameters
----------
input_data : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`
Input datamodel to be corrected.
Returns
-------
output_model : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`
The 1/f corrected datamodel.
"""
message = (
"The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' "
"and will be removed in future builds."
)
self.log.warning(message)
# Open the input data model
with datamodels.open(input_data) as input_model:
# clean_flicker_noise allows flat handling, but NIRSpec is
# not supported via CRDS files, since it does not have a full
# frame flat. Since this step is for NIRSpec only, don't allow
# flat handling here.
flat_filename = None
# Do the NSClean correction
result = clean_flicker_noise.do_correction(
input_model,
input_dir=self.input_dir,
fit_method=self.fit_method,
fit_by_channel=self.fit_by_channel,
background_method=self.background_method,
background_box_size=self.background_box_size,
mask_science_regions=self.mask_spectral_regions,
flat_filename=flat_filename,
n_sigma=self.n_sigma,
fit_histogram=self.fit_histogram,
single_mask=self.single_mask,
user_mask=self.user_mask,
save_mask=self.save_mask,
save_background=self.save_background,
save_noise=self.save_noise,
)
output_model, mask_model, background_model, noise_model, status = result
# Save the mask, if requested
if self.save_mask and mask_model is not None:
mask_path = self.make_output_path(basepath=input_model.meta.filename, suffix="mask")
self.log.info(f"Saving mask file {mask_path}")
mask_model.save(mask_path)
mask_model.close()
# Save the background, if requested
if self.save_background and background_model is not None:
bg_path = self.make_output_path(
basepath=input_model.meta.filename, suffix="flicker_bkg"
)
self.log.info(f"Saving background file {bg_path}")
background_model.save(bg_path)
background_model.close()
# Save the noise, if requested
if self.save_noise and noise_model is not None:
noise_path = self.make_output_path(
basepath=input_model.meta.filename, suffix="flicker_noise"
)
self.log.info(f"Saving noise file {noise_path}")
noise_model.save(noise_path)
noise_model.close()
# Set the step completion status
output_model.meta.cal_step.nsclean = status
return output_model