Source code for jwst.assign_wcs.niriss

import logging

import asdf
from astropy import coordinates as coord
from astropy import units as u
from astropy.modeling.models import Const1D, Mapping, Identity
import gwcs.coordinate_frames as cf
from gwcs import wcs

from .util import (not_implemented_mode, subarray_transform,
                   velocity_correction, bounding_box_from_subarray, transform_bbox_from_shape)
from . import pointing
from ..transforms.models import (NirissSOSSModel,
                                 NIRISSForwardRowGrismDispersion,
                                 NIRISSBackwardGrismDispersion,
                                 NIRISSForwardColumnGrismDispersion)
from ..datamodels import ImageModel, NIRISSGrismModel, DistortionModel
from ..lib import s3_utils

log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)

__all__ = ["create_pipeline", "imaging", "niriss_soss", "niriss_soss_set_input", "wfss"]


def create_pipeline(input_model, reference_files):
    """Create the WCS pipeline based on EXP_TYPE.

    Parameters
    ----------
    input_model : `~jwst.datamodel.DataModel`
        Input datamodel for processing
    reference_files : dict
        The dictionary of reference file names and their associated files
        {reftype: reference file name}.

    Returns
    -------
    pipeline : list
        The pipeline list that is returned is suitable for
        input into  gwcs.wcs.WCS to create a GWCS object.
    """

    exp_type = input_model.meta.exposure.type.lower()
    pipeline = exp_type2transform[exp_type](input_model, reference_files)

    return pipeline


[docs]def niriss_soss_set_input(model, order_number): """ Extract a WCS fr a specific spectral order. Parameters ---------- model : `~jwst.datamodels.ImageModel` An instance of an ImageModel order_number : int the spectral order Returns ------- WCS - the WCS corresponding to the spectral order. """ # Make sure the spectral order is available. if order_number < 1 or order_number > 3: raise ValueError('Order must be between 1 and 3') # Return the correct transform based on the order_number obj = model.meta.wcs.forward_transform.get_model(order_number) # use the size of the input subarray detector = cf.Frame2D(name='detector', axes_order=(0, 1), unit=(u.pix, u.pix)) spec = cf.SpectralFrame(name='spectral', axes_order=(2,), unit=(u.micron,), axes_names=('wavelength',)) sky = cf.CelestialFrame(reference_frame=coord.ICRS(), axes_names=('ra', 'dec'), axes_order=(0, 1), unit=(u.deg, u.deg), name='sky') world = cf.CompositeFrame([sky, spec], name='world') pipeline = [(detector, obj), (world, None) ] return wcs.WCS(pipeline)
def niriss_soss(input_model, reference_files): """ The NIRISS SOSS WCS pipeline. Parameters ---------- input_model : `~jwst.datamodel.DataModel` Input datamodel for processing reference_files : dict The dictionary of reference file names and their associated files {reftype: reference file name}. Returns ------- pipeline : list The pipeline list that is returned is suitable for input into gwcs.wcs.WCS to create a GWCS object. Notes ----- It includes tWO coordinate frames - "detector" and "world". It uses the "specwcs" reference file. """ # Get the target RA and DEC, they will be used for setting the WCS RA # and DEC based on a conversation with Kevin Volk. try: target_ra = float(input_model.meta.target.ra) target_dec = float(input_model.meta.target.dec) except TypeError: # There was an error getting the target RA and DEC, so we are not going to continue. raise ValueError('Problem getting the TARG_RA or TARG_DEC from input model {}'.format(input_model)) # Define the frames detector = cf.Frame2D(name='detector', axes_order=(0, 1), unit=(u.pix, u.pix)) spec = cf.SpectralFrame(name='spectral', axes_order=(2,), unit=(u.micron,), axes_names=('wavelength',)) sky = cf.CelestialFrame(reference_frame=coord.ICRS(), axes_names=('ra', 'dec'), axes_order=(0, 1), unit=(u.deg, u.deg), name='sky') world = cf.CompositeFrame([sky, spec], name='world') try: # We'd like to open this file as a DataModel, so we can consolidate # the S3 URI handling to one place. The S3-related code here can # be removed once that has been changed. if s3_utils.is_s3_uri(reference_files['specwcs']): wl = asdf.open(s3_utils.get_object(reference_files['specwcs'])) else: wl = asdf.open(reference_files['specwcs']) with wl: wl1 = wl.tree[1].copy() wl2 = wl.tree[2].copy() wl3 = wl.tree[3].copy() except Exception: raise IOError('Error reading wavelength correction from {}'.format(reference_files['specwcs'])) try: velosys = input_model.meta.wcsinfo.velosys except AttributeError: pass else: if velosys is not None: velocity_corr = velocity_correction(input_model.meta.wcsinfo.velosys) wl1 = wl1 | velocity_corr wl2 = wl2 | velocity_corr wl2 = wl3 | velocity_corr log.info("Applied Barycentric velocity correction: {}".format(velocity_corr[1].amplitude.value)) # Reverse the order of inputs passed to Tabular because it's in python order in modeling. # Consider changing it in modelng ? cm_order1 = (Mapping((0, 1, 1, 0)) | \ (Const1D(target_ra) & Const1D(target_dec) & wl1) ).rename('Order1') cm_order2 = (Mapping((0, 1, 1, 0)) | \ (Const1D(target_ra) & Const1D(target_dec) & wl2) ).rename('Order2') cm_order3 = (Mapping((0, 1, 1, 0)) | \ (Const1D(target_ra) & Const1D(target_dec) & wl3) ).rename('Order3') subarray2full = subarray_transform(input_model) if subarray2full is not None: cm_order1 = subarray2full | cm_order1 cm_order2 = subarray2full | cm_order2 cm_order3 = subarray2full | cm_order3 bbox = ((-0.5, input_model.meta.subarray.ysize - 0.5), (-0.5, input_model.meta.subarray.xsize - 0.5)) cm_order1.bounding_box = bbox cm_order2.bounding_box = bbox cm_order3.bounding_box = bbox # Define the transforms, they should accept (x,y) and return (ra, dec, lambda) soss_model = NirissSOSSModel([1, 2, 3], [cm_order1, cm_order2, cm_order3] ).rename('3-order SOSS Model') # Define the pipeline based on the frames and models above. pipeline = [(detector, soss_model), (world, None) ] return pipeline def imaging(input_model, reference_files): """ The NIRISS imaging WCS pipeline. Parameters ---------- input_model : `~jwst.datamodel.DataModel` Input datamodel for processing reference_files : dict The dictionary of reference file names and their associated files {reftype: reference file name}. Returns ------- pipeline : list The pipeline list that is returned is suitable for input into gwcs.wcs.WCS to create a GWCS object. Notes ----- It includes three coordinate frames - "detector" "v2v3" and "world". It uses the "distortion" reference file. """ detector = cf.Frame2D(name='detector', axes_order=(0, 1), unit=(u.pix, u.pix)) v2v3 = cf.Frame2D(name='v2v3', axes_order=(0, 1), unit=(u.arcsec, u.arcsec)) world = cf.CelestialFrame(reference_frame=coord.ICRS(), name='world') distortion = imaging_distortion(input_model, reference_files) subarray2full = subarray_transform(input_model) if subarray2full is not None: distortion = subarray2full | distortion distortion.bounding_box = bounding_box_from_subarray(input_model) tel2sky = pointing.v23tosky(input_model) pipeline = [(detector, distortion), (v2v3, tel2sky), (world, None)] return pipeline def imaging_distortion(input_model, reference_files): """ Create the transform from "detector" to "v2v3". Parameters ---------- input_model : `~jwst.datamodel.DataModel` Input datamodel for processing reference_files : dict The dictionary of reference file names and their associated files. Returns ------- The transform model """ dist = DistortionModel(reference_files['distortion']) distortion = dist.model try: # Check if the model has a bounding box. distortion.bounding_box except NotImplementedError: distortion.bounding_box = transform_bbox_from_shape(input_model.data.shape) dist.close() return distortion def wfss(input_model, reference_files): """ Create the WCS pipeline for a NIRISS grism observation. Parameters ---------- input_model: `~jwst.datamodels.ImagingModel` The input datamodel, derived from datamodels reference_files: dict Dictionary specifying reference file names Returns ------- pipeline : list The pipeline list that is returned is suitable for input into gwcs.wcs.WCS to create a GWCS object. Notes ----- reference_files = { "specwcs": 'GR150C_F090W.asdf' "distortion": 'NRCA1_FULL_distortion.asdf' } The tree in the grism reference file has a section for each order/beam as well as the link to the filter data file, not sure if there will be a separate passband reference file needed for the wavelength scaling or the wedge offsets. This file is currently created in jwreftools/niriss/niriss_reftools. The direct image the catalog has been created from was corrected for distortion, but the dispersed images have not. This is OK if the trace and dispersion solutions are defined with respect to the distortion-corrected image. The catalog from the combined direct image has object locations in in detector space and the RA DEC of the object on sky. The WCS information for the grism image plus the observed filter will be used to translate these to pixel locations for each of the objects. The grism images will then use their grism trace information to translate to detector space. The translation is assumed to be one-to-one for purposes of identifying the center of the object trace. The extent of the trace for each object can then be calculated based on the grism in use (row or column). Where the left/bottom of the trace starts at t = 0 and the right/top of the trace ends at t = 1, as long as they have been defined as such by th team. The extraction box is calculated to be the minimum bounding box of the object extent in the segmentation map associated with the direct image. The values of the min and max corners are saved in the photometry catalog in units of RA,DEC so they can be translated to pixels by the dispersed image's imaging wcs. The sensitivity information from the original aXe style configuration file needs to be modified by the passband of the filter used for the direct image to get the min and max wavelengths which correspond to t=0 and t=1, this currently has been done by the team and the min and max wavelengths to use to calculate t are stored in the grism reference file as wrange, which can be selected by wrange_selector which contains the filter names. Source catalog use moved to extract_2d. """ # The input is the grism image if not isinstance(input_model, ImageModel): raise TypeError('The input data model must be an ImageModel.') # make sure this is a grism image if "NIS_WFSS" != input_model.meta.exposure.type: raise ValueError('The input exposure is not NIRISS grism') # Create the empty detector as a 2D coordinate frame in pixel units gdetector = cf.Frame2D(name='grism_detector', axes_order=(0, 1), unit=(u.pix, u.pix)) # translate the x,y detector-in to x,y detector out coordinates # Get the disperser parameters which are defined as a model for each # spectral order with NIRISSGrismModel(reference_files['specwcs']) as f: dispx = f.dispx dispy = f.dispy displ = f.displ invdispl = f.invdispl orders = f.orders fwcpos_ref = f.fwcpos_ref # This is the actual rotation from the input model fwcpos = input_model.meta.instrument.filter_position if fwcpos is None: raise ValueError('FWCPOS keyword value not found in input image') # sep the row and column grism models # In "DMS" orientation (same parity as the sky), the GR150C spectra # are aligned more closely with the rows, and the GR150R spectra are # aligned more closely with the columns. if input_model.meta.instrument.filter.endswith('C'): det2det = NIRISSForwardRowGrismDispersion(orders, lmodels=displ, xmodels=dispx, ymodels=dispy, theta=fwcpos_ref - fwcpos) elif input_model.meta.instrument.filter.endswith('R'): det2det = NIRISSForwardColumnGrismDispersion(orders, lmodels=displ, xmodels=dispx, ymodels=dispy, theta=fwcpos_ref - fwcpos) else: raise ValueError("FILTER keyword {} is not valid." .format(input_model.meta.instrument.filter)) backward = NIRISSBackwardGrismDispersion(orders, lmodels=invdispl, xmodels=dispx, ymodels=dispy, theta=fwcpos_ref - fwcpos) det2det.inverse = backward # Add in the wavelength shift from the velocity dispersion try: velosys = input_model.meta.wcsinfo.velosys except AttributeError: pass if velosys is not None: velocity_corr = velocity_correction(input_model.meta.wcsinfo.velosys) log.info("Added Barycentric velocity correction: {}".format(velocity_corr[1].amplitude.value)) det2det = det2det | Mapping((0, 1, 2, 3)) | Identity(2) & velocity_corr & Identity(1) # create the pipeline to construct a WCS object for the whole image # which can translate ra,dec to image frame reference pixels # it also needs to be part of the grism image wcs pipeline to # go from detector to world coordinates. However, the grism image # will be effectively translating pixel->world coordinates in a # manner that gives you the originating pixels ra and dec, not the # pure ra/dec on the sky from the pointing wcs. # use the imaging_distortion reference file here image_pipeline = imaging(input_model, reference_files) # forward input is (x,y,lam,order) -> x, y # backward input needs to be the same ra, dec, lam, order -> x, y grism_pipeline = [(gdetector, det2det)] # pass through the wave, beam and theta in the pipeline # Theta is a constant for each grism exposure and is in the # meta information for the input_model, pass it to the model # so the user doesn't have to imagepipe = [] world = image_pipeline.pop() for cframe, trans in image_pipeline: trans = trans & (Identity(2)) imagepipe.append((cframe, trans)) imagepipe.append((world)) grism_pipeline.extend(imagepipe) return grism_pipeline exp_type2transform = {'nis_image': imaging, 'nis_wfss': wfss, 'nis_soss': niriss_soss, 'nis_ami': imaging, 'nis_tacq': imaging, 'nis_taconfirm': imaging, 'nis_focus': imaging, 'nis_dark': not_implemented_mode, 'nis_lamp': not_implemented_mode, }