#!/usr/bin/env python
from ..stpipe import Pipeline
from .. import datamodels
# step imports
from ..group_scale import group_scale_step
from ..dq_init import dq_init_step
from ..saturation import saturation_step
from ..ipc import ipc_step
from ..superbias import superbias_step
from ..refpix import refpix_step
from ..rscd import rscd_step
from ..firstframe import firstframe_step
from ..lastframe import lastframe_step
from ..linearity import linearity_step
from ..dark_current import dark_current_step
from ..persistence import persistence_step
from ..jump import jump_step
from ..ramp_fitting import ramp_fit_step
from ..gain_scale import gain_scale_step
__all__ = ['Detector1Pipeline']
# Define logging
import logging
log = logging.getLogger()
log.setLevel(logging.DEBUG)
[docs]class Detector1Pipeline(Pipeline):
"""
Detector1Pipeline: Apply all calibration steps to raw JWST
ramps to produce a 2-D slope product. Included steps are:
group_scale, dq_init, saturation, ipc, superbias, refpix, rscd,
lastframe, linearity, dark_current, persistence, jump detection,
ramp_fit, and gain_scale.
"""
spec = """
save_calibrated_ramp = boolean(default=False)
"""
# Define aliases to steps
step_defs = {'group_scale': group_scale_step.GroupScaleStep,
'dq_init': dq_init_step.DQInitStep,
'saturation': saturation_step.SaturationStep,
'ipc': ipc_step.IPCStep,
'superbias': superbias_step.SuperBiasStep,
'refpix': refpix_step.RefPixStep,
'rscd': rscd_step.RSCD_Step,
'firstframe': firstframe_step.FirstFrameStep,
'lastframe': lastframe_step.LastFrameStep,
'linearity': linearity_step.LinearityStep,
'dark_current': dark_current_step.DarkCurrentStep,
'persistence': persistence_step.PersistenceStep,
'jump': jump_step.JumpStep,
'ramp_fit': ramp_fit_step.RampFitStep,
'gain_scale': gain_scale_step.GainScaleStep,
}
# start the actual processing
[docs] def process(self, input):
log.info('Starting calwebb_detector1 ...')
# open the input as a RampModel
input = datamodels.RampModel(input)
# propagate output_dir to steps that might need it
self.dark_current.output_dir = self.output_dir
self.ramp_fit.output_dir = self.output_dir
if input.meta.instrument.name == 'MIRI':
# process MIRI exposures;
# the steps are in a different order than NIR
log.debug('Processing a MIRI exposure')
input = self.group_scale(input)
input = self.dq_init(input)
input = self.saturation(input)
input = self.ipc(input)
input = self.linearity(input)
input = self.rscd(input)
input = self.firstframe(input)
input = self.lastframe(input)
input = self.dark_current(input)
input = self.refpix(input)
# skip until MIRI team has figured out an algorithm
#input = self.persistence(input)
else:
# process Near-IR exposures
log.debug('Processing a Near-IR exposure')
input = self.group_scale(input)
input = self.dq_init(input)
input = self.saturation(input)
input = self.ipc(input)
input = self.superbias(input)
input = self.refpix(input)
input = self.linearity(input)
# skip persistence for NIRSpec
if input.meta.instrument.name != 'NIRSPEC':
input = self.persistence(input)
input = self.dark_current(input)
# apply the jump step
input = self.jump(input)
# save the corrected ramp data, if requested
if self.save_calibrated_ramp:
self.save_model(input, 'ramp')
# apply the ramp_fit step
# This explicit test on self.ramp_fit.skip is a temporary workaround
# to fix the problem that the ramp_fit step ordinarily returns two
# objects, but when the step is skipped due to `skip = True` in a
# cfg file, only the input is returned when the step is invoked.
if self.ramp_fit.skip:
input = self.ramp_fit(input)
ints_model = None
else:
input, ints_model = self.ramp_fit(input)
# apply the gain_scale step to the exposure-level product
self.gain_scale.suffix = 'gain_scale'
input = self.gain_scale(input)
# apply the gain scale step to the multi-integration product,
# if it exists, and then save it
if ints_model is not None:
self.gain_scale.suffix = 'gain_scaleints'
ints_model = self.gain_scale(ints_model)
self.save_model(ints_model, 'rateints')
# setup output_file for saving
self.setup_output(input)
log.info('... ending calwebb_detector1')
return input
[docs] def setup_output(self, input):
# Determine the proper file name suffix to use later
if input.meta.cal_step.ramp_fit == 'COMPLETE':
self.suffix = 'rate'
else:
self.suffix = 'ramp'