#! /usr/bin/env python
from stdatamodels.jwst import datamodels
from ..stpipe import Step
from . import dq_initialization
__all__ = ["DQInitStep"]
[docs]
class DQInitStep(Step):
"""Initialize the Data Quality extension from the
mask reference file.
The dq_init step initializes the pixeldq attribute of the
input datamodel using the MASK reference file. For some
FGS exp_types, initialize the dq attribute of the input model
instead. The dq attribute of the MASK model is bitwise OR'd
with the pixeldq (or dq) attribute of the input model.
"""
class_alias = "dq_init"
reference_file_types = ['mask']
[docs]
def process(self, input):
"""Perform the dq_init calibration step
Parameters
----------
input : JWST datamodel
input jwst datamodel
Returns
-------
output_model : JWST datamodel
result JWST datamodel
"""
# Try to open the input as a regular RampModel
try:
input_model = datamodels.RampModel(input)
# Check to see if it's Guider raw data
if input_model.meta.exposure.type in dq_initialization.guider_list:
# Reopen as a GuiderRawModel
input_model.close()
input_model = datamodels.GuiderRawModel(input)
self.log.info("Input opened as GuiderRawModel")
except (TypeError, ValueError):
# If the initial open attempt fails,
# try to open as a GuiderRawModel
try:
input_model = datamodels.GuiderRawModel(input)
self.log.info("Input opened as GuiderRawModel")
except (TypeError, ValueError):
self.log.error("Unexpected or unknown input model type")
except Exception:
self.log.error("Can't open input")
raise
# Retrieve the mask reference file name
self.mask_filename = self.get_reference_file(input_model, 'mask')
self.log.info('Using MASK reference file %s', self.mask_filename)
# Check for a valid reference file
if self.mask_filename == 'N/A':
self.log.warning('No MASK reference file found')
self.log.warning('DQ initialization step will be skipped')
result = input_model.copy()
result.meta.cal_step.dq_init = 'SKIPPED'
return result
# Load the reference file
mask_model = datamodels.MaskModel(self.mask_filename)
# Apply the step
result = dq_initialization.correct_model(input_model, mask_model)
# Close the data models for the input and ref file
input_model.close()
mask_model.close()
return result