Source code for jwst.dq_init.dq_init_step

#! /usr/bin/env python

from ..stpipe import Step
from .. import datamodels
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. """ 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)"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)"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')'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