"""Primary code for performing outlier detection on JWST observations."""
from functools import partial
import logging
import warnings
import numpy as np
from astropy.stats import sigma_clip
from scipy import ndimage
from drizzle.cdrizzle import tblot
from stdatamodels.jwst.datamodels.util import open as datamodel_open
from stdatamodels.jwst import datamodels
from jwst.datamodels import ModelContainer
from jwst.resample import resample
from jwst.resample.resample_utils import build_driz_weight, calc_gwcs_pixmap
from jwst.stpipe import Step
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE']
OUTLIER = datamodels.dqflags.pixel['OUTLIER']
__all__ = ["OutlierDetection", "flag_cr", "abs_deriv"]
[docs]
class OutlierDetection:
"""Main class for performing outlier detection.
This is the controlling routine for the outlier detection process.
It loads and sets the various input data and parameters needed by
the various functions and then controls the operation of this process
through all the steps used for the detection.
Notes
-----
This routine performs the following operations::
1. Extracts parameter settings from input model and merges
them with any user-provided values
2. Resamples all input images into grouped observation mosaics.
3. Creates a median image from all grouped observation mosaics.
4. Blot median image to match each original input image.
5. Perform statistical comparison between blotted image and original
image to identify outliers.
6. Updates input data model DQ arrays with mask of detected outliers.
"""
default_suffix = 'i2d'
def __init__(self, input_models, reffiles=None, **pars):
"""
Initialize the class with input ModelContainers.
Parameters
----------
input_models : list of DataModels, str
list of data models as ModelContainer or ASN file,
one data model for each input image
pars : dict, optional
Optional user-specified parameters to modify how outlier_detection
will operate. Valid parameters include:
- resample_suffix
"""
self.inputs = input_models
self.reffiles = reffiles
self.outlierpars = {}
if 'outlierpars' in reffiles:
self._get_outlier_pars()
self.outlierpars.update(pars)
# Insure that self.input_models always refers to a ModelContainer
# representation of the inputs
# Define how file names are created
self.make_output_path = pars.get(
'make_output_path',
partial(Step._make_output_path, None)
)
def _convert_inputs(self):
"""Convert input into datamodel required for processing.
This method converts `self.inputs` into a version of
`self.input_models` suitable for processing by the class.
This base class works on imaging data, and relies on use of the
ModelContainer class as the format needed for processing. However,
the input may not always be a ModelContainer object, so this method
will convert the input to a ModelContainer object for processing.
Additionally, sub-classes may redefine this to set up the input as
whatever format the sub-class needs for processing.
"""
bits = self.outlierpars['good_bits']
if isinstance(self.inputs, ModelContainer):
self.input_models = self.inputs
self.converted = False
else:
self.input_models = ModelContainer()
num_inputs = self.inputs.data.shape[0]
log.debug("Converting CubeModel to ModelContainer with {} images".
format(num_inputs))
for i in range(self.inputs.data.shape[0]):
image = datamodels.ImageModel(data=self.inputs.data[i],
err=self.inputs.err[i],
dq=self.inputs.dq[i])
image.meta = self.inputs.meta
image.wht = build_driz_weight(image,
weight_type=self.outlierpars['weight_type'],
good_bits=bits)
self.input_models.append(image)
self.converted = True
def _get_outlier_pars(self):
"""Extract outlier detection parameters from reference file."""
# start by interpreting input data models to define selection criteria
input_dm = self.input_models[0]
filtname = input_dm.meta.instrument.filter
if hasattr(self.input_models, 'group_names'):
num_groups = len(self.input_models.group_names)
else:
num_groups = 1
ref_model = datamodels.OutlierParsModel(self.reffiles['outlierpars'])
# look for row that applies to this set of input data models
# NOTE:
# This logic could be replaced by a method added to the DrizParsModel
# object to select the correct row based on a set of selection
# parameters
row = None
outlierpars = ref_model.outlierpars_table
# flag to support wild-card rows in outlierpars table
filter_match = False
for n, filt, num in zip(range(1, outlierpars.numimages.shape[0] + 1),
outlierpars.filter, outlierpars.numimages):
# only remember this row if no exact match has already been made
# for the filter. This allows the wild-card row to be anywhere in
# the table; since it may be placed at beginning or end of table.
if filt == "ANY" and not filter_match and num_groups >= num:
row = n
# always go for an exact match if present, though...
if filtname == filt and num_groups >= num:
row = n
filter_match = True
# With presence of wild-card rows, code should never trigger this logic
if row is None:
log.error("No row found in %s that matches input data.",
self.reffiles)
raise ValueError
# read in values from that row for each parameter
for kw in list(self.outlierpars.keys()):
self.outlierpars[kw] = \
ref_model['outlierpars_table.{0}'.format(kw)]
[docs]
def build_suffix(self, **pars):
"""Build suffix.
Class-specific method for defining the resample_suffix attribute
using a suffix specific to the sub-class.
"""
# Parse any user-provided filename suffix for resampled products
self.resample_suffix = '_outlier_{}.fits'.format(
pars.get('resample_suffix', self.default_suffix))
if 'resample_suffix' in pars:
del pars['resample_suffix']
log.debug("Defined output product suffix as: {}".format(
self.resample_suffix))
[docs]
def do_detection(self):
"""Flag outlier pixels in DQ of input images."""
self._convert_inputs()
self.build_suffix(**self.outlierpars)
pars = self.outlierpars
if pars['resample_data']:
# Start by creating resampled/mosaic images for
# each group of exposures
resamp = resample.ResampleData(self.input_models, single=True,
blendheaders=False, **pars)
drizzled_models = resamp.do_drizzle()
else:
# for non-dithered data, the resampled image is just the original image
drizzled_models = self.input_models
for i in range(len(self.input_models)):
drizzled_models[i].wht = build_driz_weight(
self.input_models[i],
weight_type=pars['weight_type'],
good_bits=pars['good_bits'])
# Initialize intermediate products used in the outlier detection
with datamodel_open(drizzled_models[0]) as dm0:
median_model = datamodels.ImageModel(dm0.data.shape)
median_model.update(dm0)
median_model.meta.wcs = dm0.meta.wcs
# Perform median combination on set of drizzled mosaics
median_model.data = self.create_median(drizzled_models)
if pars['save_intermediate_results']:
if self.outlierpars.get('asn_id', None) is None:
suffix_to_remove = self.resample_suffix
else:
suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}"
median_model_output_path = self.make_output_path(
basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'),
suffix='median')
median_model.save(median_model_output_path)
log.info(f"Saved model in {median_model_output_path}")
if pars['resample_data']:
# Blot the median image back to recreate each input image specified
# in the original input list/ASN/ModelContainer
blot_models = self.blot_median(median_model)
else:
# Median image will serve as blot image
blot_models = ModelContainer(open_models=False)
for i in range(len(self.input_models)):
blot_models.append(median_model)
# Perform outlier detection using statistical comparisons between
# each original input image and its blotted version of the median image
self.detect_outliers(blot_models)
# clean-up (just to be explicit about being finished with
# these results)
del median_model, blot_models
[docs]
def detect_outliers(self, blot_models):
"""Flag DQ array for cosmic rays in input images.
The science frame in each ImageModel in input_models is compared to
the corresponding blotted median image in blot_models. The result is
an updated DQ array in each ImageModel in input_models.
Parameters
----------
input_models: JWST ModelContainer object
data model container holding science ImageModels, modified in place
blot_models : JWST ModelContainer object
data model container holding ImageModels of the median output frame
blotted back to the wcs and frame of the ImageModels in
input_models
Returns
-------
None
The dq array in each input model is modified in place
"""
log.info("Flagging outliers")
for image, blot in zip(self.input_models, blot_models):
blot = datamodel_open(blot)
flag_cr(image, blot, **self.outlierpars)
blot.close()
del blot
if self.converted:
# Make sure actual input gets updated with new results
for i in range(len(self.input_models)):
self.inputs.dq[i, :, :] = self.input_models[i].dq
[docs]
def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0,
resample_data=True, **kwargs):
"""Masks outliers in science image by updating DQ in-place
Mask blemishes in dithered data by comparing a science image
with a model image and the derivative of the model image.
Parameters
----------
sci_image : ~jwst.datamodels.ImageModel
the science data
blot_image : ~jwst.datamodels.ImageModel
the blotted median image of the dithered science frames
snr : str
Signal-to-noise ratio
scale : str
scaling factor applied to the derivative
backg : float
Background value (scalar) to subtract
resample_data : bool
Boolean to indicate whether blot_image is created from resampled,
dithered data or not
"""
snr1, snr2 = [float(val) for val in snr.split()]
scale1, scale2 = [float(val) for val in scale.split()]
# Get background level of science data if it has not been subtracted, so it
# can be added into the level of the blotted data, which has been
# background-subtracted
if (sci_image.meta.background.subtracted is False and
sci_image.meta.background.level is not None):
subtracted_background = sci_image.meta.background.level
log.debug(f"Adding background level {subtracted_background} to blotted image")
else:
# No subtracted background. Allow user-set value, which defaults to 0
subtracted_background = backg
sci_data = sci_image.data
blot_data = blot_image.data
blot_deriv = abs_deriv(blot_data)
err_data = np.nan_to_num(sci_image.err)
# create the outlier mask
if resample_data: # dithered outlier detection
blot_data += subtracted_background
diff_noise = np.abs(sci_data - blot_data)
# Create a boolean mask based on a scaled version of
# the derivative image (dealing with interpolating issues?)
# and the standard n*sigma above the noise
threshold1 = scale1 * blot_deriv + snr1 * err_data
mask1 = np.greater(diff_noise, threshold1)
# Smooth the boolean mask with a 3x3 boxcar kernel
kernel = np.ones((3, 3), dtype=int)
mask1_smoothed = ndimage.convolve(mask1, kernel, mode='nearest')
# Create a 2nd boolean mask based on the 2nd set of
# scale and threshold values
threshold2 = scale2 * blot_deriv + snr2 * err_data
mask2 = np.greater(diff_noise, threshold2)
# Final boolean mask
cr_mask = mask1_smoothed & mask2
else: # stack outlier detection
diff_noise = np.abs(sci_data - blot_data)
# straightforward detection of outliers for non-dithered data since
# err_data includes all noise sources (photon, read, and flat for baseline)
cr_mask = np.greater(diff_noise, snr1 * err_data)
# Count existing DO_NOT_USE pixels
count_existing = np.count_nonzero(sci_image.dq & DO_NOT_USE)
# Update the DQ array in the input image.
sci_image.dq = np.bitwise_or(sci_image.dq, cr_mask * (DO_NOT_USE | OUTLIER))
# Report number (and percent) of new DO_NOT_USE pixels found
count_outlier = np.count_nonzero(sci_image.dq & DO_NOT_USE)
count_added = count_outlier - count_existing
percent_cr = count_added / (sci_image.shape[0] * sci_image.shape[1]) * 100
log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)")
[docs]
def abs_deriv(array):
"""Take the absolute derivate of a numpy array."""
tmp = np.zeros(array.shape, dtype=np.float64)
out = np.zeros(array.shape, dtype=np.float64)
tmp[1:, :] = array[:-1, :]
tmp, out = _absolute_subtract(array, tmp, out)
tmp[:-1, :] = array[1:, :]
tmp, out = _absolute_subtract(array, tmp, out)
tmp[:, 1:] = array[:, :-1]
tmp, out = _absolute_subtract(array, tmp, out)
tmp[:, :-1] = array[:, 1:]
tmp, out = _absolute_subtract(array, tmp, out)
return out
def _absolute_subtract(array, tmp, out):
tmp = np.abs(array - tmp)
out = np.maximum(tmp, out)
tmp = tmp * 0.
return tmp, out
def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
median_model : `~stdatamodels.jwst.datamodels.JwstDataModel`
blot_img : datamodel
Datamodel containing header and WCS to define the 'blotted' image
interp : str, optional
The type of interpolation used in the resampling. The
possible values are "nearest" (nearest neighbor interpolation),
"linear" (bilinear interpolation), "poly3" (cubic polynomial
interpolation), "poly5" (quintic polynomial interpolation),
"sinc" (sinc interpolation), "lan3" (3rd order Lanczos
interpolation), and "lan5" (5th order Lanczos interpolation).
sincscl : float, optional
The scaling factor for sinc interpolation.
"""
blot_wcs = blot_img.meta.wcs
# Compute the mapping between the input and output pixel coordinates
pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs, blot_img.data.shape)
log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape))
log.debug("Sci shape: {}".format(blot_img.data.shape))
pix_ratio = 1
log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape))
outsci = np.zeros(blot_img.shape, dtype=np.float32)
# Currently tblot cannot handle nans in the pixmap, so we need to give some
# other value. -1 is not optimal and may have side effects. But this is
# what we've been doing up until now, so more investigation is needed
# before a change is made. Preferably, fix tblot in drizzle.
pixmap[np.isnan(pixmap)] = -1
tblot(median_model.data, pixmap, outsci, scale=pix_ratio, kscale=1.0,
interp=interp, exptime=1.0, misval=0.0, sinscl=sinscl)
return outsci