#!/usr/bin/env python
import logging
import os.path as op
from .. import datamodels
from ..stpipe import Pipeline
# step imports
from ..ami import ami_analyze_step
from ..ami import ami_average_step
from ..ami import ami_normalize_step
from ..model_blender import blendmeta
__all__ = ['Ami3Pipeline']
# Define logging
log = logging.getLogger()
log.setLevel(logging.DEBUG)
[docs]class Ami3Pipeline(Pipeline):
"""
Ami3Pipeline: Apply all level-3 calibration steps to an
association of level-2b AMI exposures. Included steps are:
ami_analyze (fringe detection)
ami_average (average results of fringe detection)
ami_normalize (normalize results by reference target)
"""
spec = """
save_averages = boolean(default=False)
"""
# Define aliases to steps
step_defs = {'ami_analyze': ami_analyze_step.AmiAnalyzeStep,
'ami_average': ami_average_step.AmiAverageStep,
'ami_normalize': ami_normalize_step.AmiNormalizeStep
}
[docs] def process(self, input):
log.info('Starting calwebb_ami3')
# Load the input association table
asn = self.load_as_level3_asn(input)
# We assume there's one final product defined by the
# association
asn_id = asn['asn_id']
prod = asn['products'][0]
self.output_file = prod.get('name', self.output_file)
# Construct lists of all the PSF and science target members
# so that we know what we've been given to work with
psf_files = [
m['expname']
for m in prod['members']
if m['exptype'].upper() == 'PSF'
]
targ_files = [
m['expname']
for m in prod['members']
if m['exptype'].upper() == 'SCIENCE'
]
# Make sure we found some science target members
if len(targ_files) == 0:
log.error('No science target members found in association table')
log.error('Calwebb_ami3 processing will be aborted')
return
# If there aren't any PSF images, we can't do the normalize step
if len(psf_files) == 0:
log.info('No PSF reference members found in association table')
log.info('ami_normalize step will be skipped')
# Loop over all the images in the assocation
psf_files = []
targ_files = []
for member in prod['members']:
input_file = member['expname']
# Do the LG analysis for this image
log.debug('Do LG processing for member %s', input_file)
result = self.ami_analyze(input_file)
# Save the LG analysis results to a file
result.meta.asn.pool_name = asn['asn_pool']
result.meta.asn.table_name = op.basename(asn.filename)
self.save_model(result, output_file=input_file, suffix='ami', asn_id=asn_id)
# Save the result file name for input to ami_average
if member['exptype'].upper() == 'PSF':
psf_files.append(result)
if member['exptype'].upper() == 'SCIENCE':
targ_files.append(result)
# Average the reference PSF image results
psf_avg = None
if len(psf_files) > 0:
self.log.debug('Calling ami_average for PSF results ...')
psf_avg = self.ami_average(psf_files)
# Save the results to a file, if requested
if self.save_averages:
psf_avg.meta.asn.pool_name = asn['asn_pool']
psf_avg.meta.asn.table_name = op.basename(asn.filename)
# Perform blending of metadata for all inputs to this
# output file
self.log.info(
'Blending metadata for averaged psf...'
)
self.log.info(
"INPUT WCS: {}".format(
hasattr(datamodels.open(psf_files[0]), 'meta.wcs')
)
)
self.log.info("PSF_AVG type: {}".format(psf_avg.meta.model_type))
self.log.info(
"PSF_AVG WCS: {}".format(
hasattr(psf_avg, 'meta.wcs')
)
)
blendmeta.blendmodels(psf_avg, inputs=psf_files)
self.save_model(psf_avg, suffix='psf-amiavg')
# Average the science target image results
if len(targ_files) > 0:
self.log.debug('Calling ami_average for target results ...')
targ_avg = self.ami_average(targ_files)
# Save the results to a file, if requested
if self.save_averages:
targ_avg.meta.asn.pool_name = asn['asn_pool']
targ_avg.meta.asn.table_name = op.basename(asn.filename)
# Perform blending of metadata for all inputs to this
# output file
self.log.info(
'Blending metadata for averaged target'
)
blendmeta.blendmodels(targ_avg, inputs=targ_files)
self.save_model(targ_avg, suffix='amiavg')
# Now that all LGAVG products have been produced, do
# normalization of the target results by the reference
# results, if reference results exist
if psf_avg is not None:
result = self.ami_normalize(targ_avg, psf_avg)
# Save the result
result.meta.asn.pool_name = asn['asn_pool']
result.meta.asn.table_name = op.basename(asn.filename)
# Perform blending of metadata for all inputs to this output file
self.log.info(
'Blending metadata for PSF normalized target...'
)
input_list = [targ_avg, psf_avg]
blendmeta.blendmodels(result, inputs=input_list)
self.save_model(result, suffix='aminorm')
# We're done
log.info('... ending calwebb_ami3')
return