#! /usr/bin/env python
from stdatamodels.jwst import datamodels
from ..stpipe import Step
from . import background_sub
import numpy as np
__all__ = ["BackgroundStep"]
[docs]
class BackgroundStep(Step):
"""
BackgroundStep: Subtract background exposures from target exposures.
"""
class_alias = "background"
spec = """
save_combined_background = boolean(default=False) # Save combined background image
sigma = float(default=3.0) # Clipping threshold
maxiters = integer(default=None) # Number of clipping iterations
wfss_mmag_extract = float(default=None) # WFSS minimum abmag to extract
"""
# These reference files are only used for WFSS/GRISM data.
reference_file_types = ["wfssbkg", "wavelengthrange"]
# Define a suffix for optional saved output of the combined background
bkg_suffix = 'combinedbackground'
[docs]
def process(self, input, bkg_list):
"""
Subtract the background signal from target exposures by subtracting
designated background images from them.
Parameters
----------
input: JWST data model
input target data model to which background subtraction is applied
bkg_list: filename list
list of background exposure file names
Returns
-------
result: JWST data model
the background-subtracted target data model
"""
# Load the input data model
with datamodels.open(input) as input_model:
if input_model.meta.exposure.type in ["NIS_WFSS", "NRC_WFSS"]:
# Get the reference file names
bkg_name = self.get_reference_file(input_model, "wfssbkg")
wlrange_name = self.get_reference_file(input_model,
"wavelengthrange")
self.log.info('Using WFSSBKG reference file %s', bkg_name)
self.log.info('Using WavelengthRange reference file %s',
wlrange_name)
# Do the background subtraction for WFSS/GRISM data
result = background_sub.subtract_wfss_bkg(
input_model, bkg_name, wlrange_name, self.wfss_mmag_extract)
if result is None:
result = input_model
result.meta.cal_step.back_sub = 'SKIPPED'
else:
result.meta.cal_step.back_sub = 'COMPLETE'
else:
# check if input data is NRS_IFU
tolerance = 1.0e-8
do_sub = True
if input_model.meta.instrument.name in ["NIRSPEC"]:
# check if GWA_XTIL & GWA_YTIL values of source
# background are the same. If not skip step
input_xtilt = input_model.meta.instrument.gwa_xtilt
input_ytilt = input_model.meta.instrument.gwa_ytilt
for bkg_file in bkg_list:
with datamodels.open(bkg_file) as bkg_model:
bkg_xtilt = bkg_model.meta.instrument.gwa_xtilt
bkg_ytilt = bkg_model.meta.instrument.gwa_ytilt
if np.allclose((input_xtilt, input_ytilt),
(bkg_xtilt, bkg_ytilt), atol=tolerance, rtol=0):
pass
else:
do_sub = False
break
# Do the background subtraction
if do_sub:
bkg_model, result = background_sub.background_sub(input_model,
bkg_list,
self.sigma,
self.maxiters)
result.meta.cal_step.back_sub = 'COMPLETE'
if self.save_combined_background:
comb_bkg_path = self.save_model(bkg_model, suffix=self.bkg_suffix, force=True)
self.log.info(f'Combined background written to "{comb_bkg_path}".')
else:
result = input_model.copy()
result.meta.cal_step.back_sub = 'SKIPPED'
self.log.warning('Skipping background subtraction')
self.log.warning('GWA_XTIL and GWA_YTIL source values '
'are not the same as bkg values')
return result