#! /usr/bin/env python
"""
JWST pipeline step for sky matching.
:Authors: Mihai Cara
"""
from copy import deepcopy
import logging
import numpy as np
from astropy.nddata.bitmask import (
bitfield_to_boolean_mask,
interpret_bit_flags,
)
from stdatamodels.jwst.datamodels.dqflags import pixel
from stdatamodels.jwst.datamodels.util import (
open as datamodel_open,
is_association
)
from jwst.datamodels import ModelContainer
from ..stpipe import Step
# LOCAL:
from .skymatch import match
from .skyimage import SkyImage, SkyGroup
from .skystatistics import SkyStats
__all__ = ['SkyMatchStep']
[docs]
class SkyMatchStep(Step):
"""
SkyMatchStep: Subtraction or equalization of sky background in science
images.
"""
class_alias = "skymatch"
spec = """
# General sky matching parameters:
skymethod = option('local', 'global', 'match', 'global+match', default='match') # sky computation method
match_down = boolean(default=True) # adjust sky to lowest measured value?
subtract = boolean(default=False) # subtract computed sky from image data?
# Image's bounding polygon parameters:
stepsize = integer(default=None) # Max vertex separation
# Sky statistics parameters:
skystat = option('median', 'midpt', 'mean', 'mode', default='mode') # sky statistics
dqbits = string(default='~DO_NOT_USE+NON_SCIENCE') # "good" DQ bits
lower = float(default=None) # Lower limit of "good" pixel values
upper = float(default=None) # Upper limit of "good" pixel values
nclip = integer(min=0, default=5) # number of sky clipping iterations
lsigma = float(min=0.0, default=4.0) # Lower clipping limit, in sigma
usigma = float(min=0.0, default=4.0) # Upper clipping limit, in sigma
binwidth = float(min=0.0, default=0.1) # Bin width for 'mode' and 'midpt' `skystat`, in sigma
""" # noqa: E501
reference_file_types = []
def __init__(self, *args, **kwargs):
minimize_memory = kwargs.pop('minimize_memory', False)
super().__init__(*args, **kwargs)
self.minimize_memory = minimize_memory
[docs]
def process(self, input):
self.log.setLevel(logging.DEBUG)
# for now turn off memory optimization until we have better machinery
# to handle outputs in a consistent way.
if hasattr(self, 'minimize_memory') and self.minimize_memory:
self._is_asn = (
is_association(input) or isinstance(input, str)
)
else:
self._is_asn = False
img = ModelContainer(
input,
save_open=not self._is_asn,
return_open=not self._is_asn
)
self._dqbits = interpret_bit_flags(self.dqbits, flag_name_map=pixel)
# set sky statistics:
self._skystat = SkyStats(
skystat=self.skystat,
lower=self.lower,
upper=self.upper,
nclip=self.nclip,
lsig=self.lsigma,
usig=self.usigma,
binwidth=self.binwidth
)
# group images by their "group id":
grp_img = img.models_grouped
# create a list of "Sky" Images and/or Groups:
images = []
grp_id = 1
for g in grp_img:
if len(g) > 1:
images.append(
SkyGroup(
list(map(self._imodel2skyim, g)),
id=grp_id
)
)
grp_id += 1
elif len(g) == 1:
images.append(self._imodel2skyim(g[0]))
else:
raise AssertionError("Logical error in the pipeline code.")
# match/compute sky values:
match(images, skymethod=self.skymethod, match_down=self.match_down,
subtract=self.subtract)
# set sky background value in each image's meta:
for im in images:
if isinstance(im, SkyImage):
self._set_sky_background(
im,
"COMPLETE" if im.is_sky_valid else "SKIPPED"
)
else:
for gim in im:
self._set_sky_background(
gim,
"COMPLETE" if gim.is_sky_valid else "SKIPPED"
)
return input if self._is_asn else img
def _imodel2skyim(self, image_model):
input_image_model = image_model
if self._is_asn:
image_model = datamodel_open(image_model)
if self._dqbits is None:
dqmask = np.isfinite(image_model.data).astype(dtype=np.uint8)
else:
dqmask = bitfield_to_boolean_mask(
image_model.dq,
self._dqbits,
good_mask_value=1,
dtype=np.uint8
) * np.isfinite(image_model.data)
# see if 'skymatch' was previously run and raise an exception
# if 'subtract' mode has changed compared to the previous pass:
if image_model.meta.background.subtracted is None:
if image_model.meta.background.level is not None:
if self._is_asn:
image_model.close()
# report inconsistency:
raise ValueError("Background level was set but the "
"'subtracted' property is undefined (None).")
level = 0.0
else:
level = image_model.meta.background.level
if level is None:
# NOTE: In principle we could assume that level is 0 and
# possibly add a log entry documenting this, however,
# at this moment I think it is saver to quit and...
#
# report inconsistency:
if self._is_asn:
image_model.close()
raise ValueError("Background level was subtracted but the "
"'level' property is undefined (None).")
if image_model.meta.background.subtracted != self.subtract:
# cannot run 'skymatch' step on already "skymatched" images
# when 'subtract' spec is inconsistent with
# meta.background.subtracted:
if self._is_asn:
image_model.close()
raise ValueError("'subtract' step's specification is "
"inconsistent with background info already "
"present in image '{:s}' meta."
.format(image_model.meta.filename))
wcs = deepcopy(image_model.meta.wcs)
sky_im = SkyImage(
image=image_model.data,
wcs_fwd=wcs.__call__,
wcs_inv=wcs.invert,
pix_area=1.0, # TODO: pixel area
convf=1.0, # TODO: conv. factor to brightness
mask=dqmask,
id=image_model.meta.filename, # file name?
skystat=self._skystat,
stepsize=self.stepsize,
reduce_memory_usage=self._is_asn,
meta={'image_model': input_image_model}
)
if self._is_asn:
image_model.close()
if self.subtract:
sky_im.sky = level
return sky_im
def _set_sky_background(self, sky_image, step_status):
image = sky_image.meta['image_model']
sky = sky_image.sky
if self._is_asn:
dm = datamodel_open(image)
else:
dm = image
if step_status == "COMPLETE":
dm.meta.background.method = str(self.skymethod)
dm.meta.background.level = sky
dm.meta.background.subtracted = self.subtract
if self.subtract:
dm.data[...] = sky_image.image[...]
dm.meta.cal_step.skymatch = step_status
if self._is_asn:
dm.save(image)
dm.close()