Source code for jwst.outlier_detection.outlier_detection

"""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_clip
from scipy import ndimage
from drizzle.cdrizzle import tblot

from .. import datamodels
from ..resample import resample
from ..resample.resample_utils import build_driz_weight, calc_gwcs_pixmap
from ..stpipe.step import Step

import logging
log = logging.getLogger(__name__)

CRBIT = np.uint32(
    datamodels.dqflags.pixel['DO_NOT_USE'] + 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, datamodels.ModelContainer): self.input_models = self.inputs self.converted = False else: self.input_models = datamodels.ModelContainer() num_inputs =[0] log.debug("Converting CubeModel to ModelContainer with {} images". format(num_inputs)) for i in range([0]): image = datamodels.ImageModel([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:"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 = 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' )"Writing out MEDIAN image to: {}".format( 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:"Writing out BLOT images...") for model in blot_models: model_path = self.make_output_path( basename=model.meta.filename, suffix='blot' ) 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 create_median(self, resampled_models): """Create a median image from the singly resampled images. NOTES ----- This version is simplified from astrodrizzle's version in the following ways: - type of combination: fixed to 'median' - 'minmed' not implemented as an option """ resampled_sci = [ for i in resampled_models] resampled_weight = [i.wht for i in resampled_models] nlow = self.outlierpars.get('nlow', 0) nhigh = self.outlierpars.get('nhigh', 0) maskpt = self.outlierpars.get('maskpt', 0.7) # Create a mask for each input image, masking out areas where there is # no data or the data has very low weight badmasks = [] for weight in resampled_weight: # Create boolean masks for weight being zero or NaN mask_zero_weight = np.equal(weight, 0.) mask_nans = np.isnan(weight) # Combine the masks weight_masked =, mask=np.logical_or( mask_zero_weight, mask_nans)) # Sigma-clip the unmasked data weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) mean_weight = np.mean(weight_masked) # Mask pixels where weight falls below maskpt percent weight_threshold = mean_weight * maskpt badmask = np.less(weight, weight_threshold) log.debug("Percentage of pixels with low weight: {}".format( np.sum(badmask) / len(weight.flat) * 100)) badmasks.append(badmask) # Compute median of stack of images using `badmasks` to remove # low-weight values. In the future we should use a masked array # and np.median median_image = median(resampled_sci, nlow=nlow, nhigh=nhigh, badmasks=badmasks) return median_image
[docs] def blot_median(self, median_model): """Blot resampled median image back to the detector images.""" interp = self.outlierpars.get('interp', 'poly5') sinscl = self.outlierpars.get('sinscl', 1.0) # Initialize container for output blot images blot_models = datamodels.ModelContainer()"Blotting median...") for model in self.input_models: blotted_median = model.copy() blot_root = '_'.join(model.meta.filename.replace( '.fits', '').split('_')[:-1]) blotted_median.meta.filename = '{}_blot.fits'.format(blot_root) # clean out extra data not related to blot result blotted_median.err = None blotted_median.dq = None # apply blot to re-create from median image = gwcs_blot(median_model, model, interp=interp, sinscl=sinscl) blot_models.append(blotted_median) return 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 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 : 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()] # Get background level if it has been subtracted if (sci_image.meta.background.subtracted is True and sci_image.meta.background.level is not None): subtracted_background = sci_image.meta.background.level log.debug(f"Including subtracted background ({subtracted_background}) " "back into blotted image") else: # No subtracted background. Allow user-set value, which defaults to 0 subtracted_background = backg exptime = sci_image.meta.exposure.exposure_time sci_data = * exptime blot_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 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 : ~jwst.datamodels.DataModel 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, log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) log.debug("Sci shape: {}".format( # median_model_pscale = median_model.meta.wcsinfo.cdelt1 # blot_pscale = blot_img.meta.wcsinfo.cdelt1 pix_ratio = 1'Blotting {} <-- {}'.format(, outsci = np.zeros(blot_img.shape, dtype=np.float32) tblot(, pixmap, outsci, scale=pix_ratio, kscale=1.0, interp=interp, exptime=1.0, misval=0.0, sinscl=sinscl) return outsci