"""Primary code for performing outlier detection on JWST observations."""
from functools import partial
import numpy as np
from stsci.image import median
from astropy.stats import sigma_clipped_stats
from scipy import ndimage
from .. import datamodels
from ..resample import resample, gwcs_blot
from ..resample.resample_utils import build_driz_weight
from ..stpipe.step import Step
import logging
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
CRBIT = np.uint32(datamodels.dqflags.pixel['JUMP_DET'])
__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, datamodels.ModelContainer):
self.input_models = self.inputs
self.converted = False
else:
self.input_models = datamodels.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='exptime',
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
save_intermediate_results = pars['save_intermediate_results']
if pars['resample_data']:
# Start by creating resampled/mosaic images for
# each group of exposures
sdriz = resample.ResampleData(self.input_models, single=True,
blendheaders=False, **pars)
sdriz.do_drizzle()
drizzled_models = sdriz.output_models
for model in drizzled_models:
if save_intermediate_results:
log.info("Writing out resampled exposures...")
self.save_model(
model,
output_file=model.meta.filename,
suffix=self.resample_suffix
)
else:
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='exptime',
good_bits=pars['good_bits'])
# Initialize intermediate products used in the outlier detection
median_model = datamodels.ImageModel(
init=drizzled_models[0].data.shape)
median_model.update(drizzled_models[0])
median_model.meta.wcs = drizzled_models[0].meta.wcs
# Perform median combination on set of drizzled mosaics
median_model.data = self.create_median(drizzled_models)
if save_intermediate_results:
median_output_path = self.make_output_path(
basepath=self.input_models[0].meta.filename,
suffix='median'
)
log.info("Writing out MEDIAN image to: {}".format(
median_output_path
))
median_model.save(median_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)
if save_intermediate_results:
log.info("Writing out BLOT images...")
for model in blot_models:
model_path = self.make_output_path(
basename=model.meta.filename,
suffix='blot'
)
model.save(model_path)
else:
# Median image will serve as blot image
blot_models = datamodels.ModelContainer()
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
"""
for image, blot in zip(self.input_models, blot_models):
flag_cr(image, blot, **self.outlierpars)
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, **pars):
"""Masks outliers in science image.
Mask blemishes in dithered data by comparing a science image
with a model image and the derivative of the model image.
Parameters
----------
sci_image : ImageModel
the science data
blot_image : ImageModel
the blotted median image of the dithered science frames
pars : dict
the user parameters for Outlier Detection
Default parameters:
grow = 1 # Radius to mask [default=1 for 3x3]
ctegrow = 0 # Length of CTE correction to be applied
snr = "5.0 4.0" # Signal-to-noise ratio
scale = "1.2 0.7" # scaling factor applied to the derivative
backg = 0 # Background value
"""
grow = pars.get('grow', 1)
ctegrow = pars.get('ctegrow', 0) # not provided by outlierpars
backg = pars.get('backg', 0)
snr1, snr2 = [float(val) for val in pars.get('snr', '5.0 4.0').split()]
scl1, scl2 = [float(val) for val in pars.get('scale', '1.2 0.7').split()]
if not sci_image.meta.background.subtracted:
# Include background back into blotted image for comparison
subtracted_background = sci_image.meta.background.level
log.debug("Subtracted background: {}".format(subtracted_background))
if subtracted_background is None:
subtracted_background = backg
exptime = sci_image.meta.exposure.exposure_time
sci_data = sci_image.data * exptime
blot_data = blot_image.data * exptime
blot_deriv = abs_deriv(blot_data)
err_data = np.nan_to_num(sci_image.err)
# Define output cosmic ray mask to populate
cr_mask = np.zeros(sci_image.shape, dtype=np.uint8)
#
#
# COMPUTATION PART I
#
#
# Model the noise and create a CR mask
diff_noise = np.abs(sci_data - blot_data)
# ta = np.sqrt(np.abs(blot_data + subtracted_background) + rn ** 2)
ta = np.sqrt(np.abs(blot_data + subtracted_background) + err_data ** 2)
t2 = scl1 * blot_deriv + snr1 * ta
tmp1 = np.logical_not(np.greater(diff_noise, t2))
# Convolve mask with 3x3 kernel
kernel = np.ones((3, 3), dtype=np.uint8)
tmp2 = np.zeros(tmp1.shape, dtype=np.int32)
ndimage.convolve(tmp1, kernel, output=tmp2, mode='nearest', cval=0)
#
#
# COMPUTATION PART II
#
#
# Create a second CR Mask
xt2 = scl2 * blot_deriv + snr2 * ta
np.logical_not(np.greater(diff_noise, xt2) & np.less(tmp2, 9), cr_mask)
#
#
# COMPUTATION PART III
#
#
# Flag additional cte 'radial' and 'tail' pixels surrounding CR
# pixels as CRs
# In both the 'radial' and 'length' kernels below, 0=good and
# 1=bad, so that upon convolving the kernels with cr_mask, the
# convolution output will have low->bad and high->good from which
# 2 new arrays are created having 0->bad and 1->good. These 2 new
# arrays are then AND'ed to create a new cr_mask.
# recast cr_mask to int for manipulations below; will recast to
# Bool at end
cr_mask_orig_bool = cr_mask.copy()
cr_mask = cr_mask_orig_bool.astype(np.int8)
# make radial convolution kernel and convolve it with original cr_mask
cr_grow_kernel = np.ones((grow, grow))
cr_grow_kernel_conv = cr_mask.copy()
ndimage.convolve(cr_mask, cr_grow_kernel, output=cr_grow_kernel_conv)
# make tail convolution kernel and (shortly) convolve it with
# original cr_mask
cr_ctegrow_kernel = np.zeros((2 * ctegrow + 1, 2 * ctegrow + 1))
cr_ctegrow_kernel_conv = cr_mask.copy()
# which pixels are masked by tail kernel depends on readout direction
# We could put useful info in here for CTE masking if needed. Code
# remains below. For now, we set to zero, which turns off CTE masking.
ctedir = 0
if (ctedir == 1):
cr_ctegrow_kernel[0:ctegrow, ctegrow] = 1
if (ctedir == -1):
cr_ctegrow_kernel[ctegrow + 1:2 * ctegrow + 1, ctegrow] = 1
if (ctedir == 0):
pass
# finally do the tail convolution
ndimage.convolve(cr_mask, cr_ctegrow_kernel, output=cr_ctegrow_kernel_conv)
# select high pixels from both convolution outputs; then 'and' them to
# create new cr_mask
where_cr_grow_kernel_conv = np.where(cr_grow_kernel_conv < grow * grow,
0, 1)
where_cr_ctegrow_kernel_conv = np.where(cr_ctegrow_kernel_conv < ctegrow,
0, 1)
# combine masks and cast back to Bool
np.logical_and(where_cr_ctegrow_kernel_conv,
where_cr_grow_kernel_conv, cr_mask)
cr_mask = cr_mask.astype(bool)
count_sci = np.count_nonzero(sci_image.dq)
count_cr = np.count_nonzero(cr_mask)
log.debug("Pixels in input DQ: {}".format(count_sci))
log.debug("Pixels in cr_mask: {}".format(count_cr))
# Update the DQ array in the input image in place
np.bitwise_or(sci_image.dq, np.invert(cr_mask) * CRBIT, sci_image.dq)
[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