def ifu_msa_to_oteip(reference_files):
"""
Transform from the MSA frame to the OTEIP frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from MSA to OTEIP.
"""
with AsdfFile.open(reference_files['fore']) as f:
fore = f.tree['model'].copy()
with AsdfFile.open(reference_files['ifufore']) as f:
ifufore = f.tree['model'].copy()
msa2fore_mapping = Mapping((0, 1, 2, 2))
msa2fore_mapping.inverse = Identity(3)
ifu_fore_transform = ifufore & Identity(1)
ifu_fore_transform.inverse = Mapping(
(0, 1, 2, 2)) | ifufore.inverse & Identity(1)
fore_transform = msa2fore_mapping | fore & Identity(1)
return msa2fore_mapping | ifu_fore_transform | fore_transform
def compute_spec_transform(fiducial, refwcs):
"""
Compute a simple transform given a fidicial point in a spatial-spectral wcs.
"""
cdelt1 = refwcs.wcsinfo.cdelt1 / 3600.
cdelt2 = refwcs.wcsinfo.cdelt2 / 3600.
cdelt3 = refwcs.wcsinfo.cdelt3
roll_ref = refwcs.wcsinfo.roll_ref
y, x = grid_from_spec_domain(refwcs)
ra, dec, lam = refwcs(x, y)
min_lam = np.nanmin(lam)
offset = Shift(0.) & Shift(0.)
rot = Rotation2D(roll_ref)
scale = Scale(cdelt1) & Scale(cdelt2)
tan = Pix2Sky_TAN()
skyrot = RotateNative2Celestial(fiducial[0][0], fiducial[0][1], 180.0)
spatial = offset | rot | scale | tan | skyrot
spectral = Scale(cdelt3) | Shift(min_lam)
mapping = Mapping((1, 1, 0), )
mapping.inverse = Mapping((2, 1))
transform = mapping | spatial & spectral
transform.outputs = ('ra', 'dec', 'lamda')
return transform
def setup_class(cls):
cls.model1D = Identity(n_inputs=1)
cls.model2D = Identity(n_inputs=2) | Mapping((0, ), n_inputs=2)
cls.model3D = Identity(n_inputs=3) | Mapping((0, ), n_inputs=3)
cls.data = cls.x = cls.y = cls.z = np.linspace(0, 10, num=100)
cls.lsq_exp = 0
def ifu(input_model, reference_files):
"""
IFU pipeline
"""
slits = np.arange(30)
# Get the corrected disperser model
disperser = get_disperser(input_model, reference_files['disperser'])
# Get the default spectral order and wavelength range and record them in the model.
sporder, wrange = get_spectral_order_wrange(
input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = sporder
# DMS to SCA transform
dms2detector = dms_to_sca(input_model)
# DETECTOR to GWA transform
det2gwa = Identity(2) & detector_to_gwa(
reference_files, input_model.meta.instrument.detector, disperser)
# GWA to SLIT
gwa2slit = gwa_to_ifuslit(slits, disperser, wrange, sporder,
reference_files)
# SLIT to MSA transform
slit2msa = ifuslit_to_msa(slits, reference_files)
det, sca, gwa, slit_frame, msa_frame, oteip, v2v3, world = create_frames()
if input_model.meta.instrument.filter != 'OPAQUE':
# MSA to OTEIP transform
msa2oteip = ifu_msa_to_oteip(reference_files)
# OTEIP to V2,V3 transform
# This includes a wavelength unit conversion from meters to microns.
oteip2v23 = oteip_to_v23(reference_files)
# Create coordinate frames in the NIRSPEC WCS pipeline"
#
# The oteip2v2v3 transform converts the wavelength from meters (which is assumed
# in the whole pipeline) to microns (which is the expected output)
#
# "detector", "gwa", "slit_frame", "msa_frame", "oteip", "v2v3", "world"
pipeline = [(det, dms2detector), (sca, det2gwa.rename('detector2gwa')),
(gwa, gwa2slit.rename('gwa2slit')),
(slit_frame, (Mapping(
(0, 1, 2, 3, 4)) | slit2msa).rename('slit2msa')),
(msa_frame, msa2oteip.rename('msa2oteip')),
(oteip, oteip2v23.rename('oteip2v23')), (v2v3, None)]
else:
pipeline = [(det, dms2detector), (sca, det2gwa.rename('detector2gwa')),
(gwa, gwa2slit.rename('gwa2slit')),
(slit_frame, (Mapping(
(0, 1, 2, 3, 4)) | slit2msa).rename('slit2msa')),
(msa_frame, None)]
return pipeline
def pcf_forward(pcffile, outname):
"""
Create the **IDT** forward transform from collimator to gwa.
"""
with open(pcffile) as f:
lines = [l.strip() for l in f.readlines()]
factors = lines[lines.index('*Factor 2') + 1].split()
# factor==1/factor in backward msa2ote direction and factor==factor in sky2detector direction
scale = models.Scale(float(factors[0]), name="x_scale") & \
models.Scale(float(factors[1]), name="y_scale")
rotation_angle = lines[lines.index('*Rotation') + 1]
# The minius sign here is because astropy.modeling has the opposite direction of rotation than the idl implementation
rotation = models.Rotation2D(-float(rotation_angle), name='rotation')
# Here the model is called "output_shift" but in the team version it is the "input_shift".
input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
input_rot_shift = models.Shift(-float(input_rot_center[0]), name='input_x_shift') & \
models.Shift(-float(input_rot_center[1]), name='input_y_shift')
# Here the model is called "input_shift" but in the team version it is the "output_shift".
output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
output_rot_shift = models.Shift(float(output_rot_center[0]), name='output_x_shift') & \
models.Shift(float(output_rot_center[1]), name='output_y_shift')
degree = int(lines[lines.index('*FitOrder') + 1])
xcoeff_index = lines.index('*xForwardCoefficients 21 2')
xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
xcoeff_forward = coeffs_from_pcf(degree, xlines)
x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_forward)
ycoeff_index = lines.index('*yForwardCoefficients 21 2')
ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_forward)
xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_backward)
ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_backward)
x_poly_forward.inverse = x_poly_backward
y_poly_forward.inverse = y_poly_backward
poly_mapping1 = Mapping((0, 1, 0, 1))
poly_mapping1.inverse = Identity(2)
poly_mapping2 = Identity(2)
poly_mapping2.inverse = Mapping((0, 1, 0, 1))
model = input_rot_shift | rotation | scale | output_rot_shift | \
poly_mapping1 | x_poly_forward & y_poly_forward | poly_mapping2
f = AsdfFile()
f.tree = {'model': model}
f.write_to(outname)
def gwa_to_ifuslit(slits, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits : list
A list of slit IDs for all IFU slits 0-29.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst_lib.pipeline_models.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
agreq = AngleFromGratingEquation(disperser['groove_density'],
order,
name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'],
order,
name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
lam = (wrange[1] - wrange[0]) / 2 + wrange[0]
ifuslicer = AsdfFile.open(reference_files['ifuslicer'])
ifupost = AsdfFile.open(reference_files['ifupost'])
slit_models = {}
ifuslicer_model = ifuslicer.tree['model']
for slit in slits:
slitdata = ifuslicer.tree['data'][slit]
slitdata_model = get_slit_location_model(slitdata)
ifuslicer_transform = (slitdata_model | ifuslicer_model)
ifupost_transform = ifupost.tree[slit]['model']
msa2gwa = ifuslicer_transform | ifupost_transform | collimator2gwa
gwa2msa = gwa_to_ymsa(msa2gwa) # TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7)| Identity(2) & lgreq
# msa to before_gwa
#msa2bgwa = Mapping((0, 1, 2, 2)) | msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
msa2bgwa = msa2gwa & Identity(1) | Mapping((3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
slit_models[slit] = bgwa2msa
ifuslicer.close()
ifupost.close()
return Gwa2Slit(slit_models)
def _make_reference_gwcs_wcs(fits_hdr):
hdr = fits.Header.fromfile(get_pkg_data_filename(fits_hdr))
fw = fitswcs.WCS(hdr)
unit_conv = Scale(1.0 / 3600.0, name='arcsec_to_deg_1D')
unit_conv = unit_conv & unit_conv
unit_conv.name = 'arcsec_to_deg_2D'
unit_conv_inv = Scale(3600.0, name='deg_to_arcsec_1D')
unit_conv_inv = unit_conv_inv & unit_conv_inv
unit_conv_inv.name = 'deg_to_arcsec_2D'
c2s = CartesianToSpherical(name='c2s', wrap_lon_at=180)
s2c = SphericalToCartesian(name='s2c', wrap_lon_at=180)
c2tan = ((Mapping((0, 1, 2), name='xyz') / Mapping(
(0, 0, 0), n_inputs=3, name='xxx')) | Mapping((1, 2), name='xtyt'))
c2tan.name = 'Cartesian 3D to TAN'
tan2c = (Mapping((0, 0, 1), n_inputs=2, name='xtyt2xyz') |
(Const1D(1, name='one') & Identity(2, name='I(2D)')))
tan2c.name = 'TAN to cartesian 3D'
tan2c.inverse = c2tan
c2tan.inverse = tan2c
aff = AffineTransformation2D(matrix=fw.wcs.cd)
offx = Shift(-fw.wcs.crpix[0])
offy = Shift(-fw.wcs.crpix[1])
s = 5e-6
scale = Scale(s) & Scale(s)
det2tan = (offx & offy) | scale | tan2c | c2s | unit_conv_inv
taninv = s2c | c2tan
tan = Pix2Sky_TAN()
n2c = RotateNative2Celestial(fw.wcs.crval[0], fw.wcs.crval[1], 180)
wcslin = unit_conv | taninv | scale.inverse | aff | tan | n2c
sky_frm = cf.CelestialFrame(reference_frame=coord.ICRS())
det_frm = cf.Frame2D(name='detector')
v2v3_frm = cf.Frame2D(name="v2v3",
unit=(u.arcsec, u.arcsec),
axes_names=('x', 'y'),
axes_order=(0, 1))
pipeline = [(det_frm, det2tan), (v2v3_frm, wcslin), (sky_frm, None)]
gw = gwcs.WCS(input_frame=det_frm,
output_frame=sky_frm,
forward_transform=pipeline)
gw.crpix = fw.wcs.crpix
gw.crval = fw.wcs.crval
gw.bounding_box = ((-0.5, fw.pixel_shape[0] - 0.5),
(-0.5, fw.pixel_shape[1] - 0.5))
return gw
def gwa_to_slit(slits_id, disperser, wrange, order, reference_files):
"""
GWA to SLIT transform.
Parameters
----------
slits_id : list
A list of slit IDs for all open shutters/slitlets.
disperser : dict
A corrected disperser ASDF object.
filter : str
The filter used.
grating : str
The grating used in the observation.
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~jwst_lib.pipeline_models.Gwa2Slit` model.
Transform from GWA frame to SLIT frame.
"""
agreq = AngleFromGratingEquation(disperser['groove_density'],
order,
name='alpha_from_greq')
lgreq = WavelengthFromGratingEquation(disperser['groove_density'],
order,
name='lambda_from_greq')
collimator2gwa = collimator_to_gwa(reference_files, disperser)
msa = AsdfFile.open(reference_files['msa'])
slit_models = {}
for i in range(1, 6):
slit_names = slits_id[slits_id[:, 0] == i]
if slit_names.any():
msa_model = msa.tree[i]['model']
for slit in slit_names:
index = slit[1] - 1
slitdata = msa.tree[slit[0]]['data'][index]
slitdata_model = get_slit_location_model(slitdata)
msa_transform = slitdata_model | msa_model
msa2gwa = (msa_transform | collimator2gwa)
gwa2msa = gwa_to_ymsa(msa2gwa) # TODO: Use model sets here
bgwa2msa = Mapping((0, 1, 0, 1), n_inputs=3) | \
Const1D(0) * Identity(1) & Const1D(-1) * Identity(1) & Identity(2) | \
Identity(1) & gwa2msa & Identity(2) | \
Mapping((0, 1, 0, 1, 2, 3)) | Identity(2) & msa2gwa & Identity(2) | \
Mapping((0, 1, 2, 5), n_inputs=7)| Identity(2) & lgreq
# msa to before_gwa
msa2bgwa = msa2gwa & Identity(1) | Mapping(
(3, 0, 1, 2)) | agreq
bgwa2msa.inverse = msa2bgwa
s = slitid_to_slit(np.array([slit]))[0]
slit_models[s] = bgwa2msa
msa.close()
return Gwa2Slit(slit_models)
def imaging(input_model, reference_files):
"""
Imaging pipeline
frames : detector, gwa, msa, sky
"""
# Get the corrected disperser model
disperser = get_disperser(input_model, reference_files['disperser'])
# DETECTOR to GWA transform
det2gwa = detector_to_gwa(reference_files,
input_model.meta.instrument.detector, disperser)
gwa_through = Const1D(-1) * Identity(1) & Const1D(-1) * Identity(
1) & Identity(1)
angles = [
disperser['theta_x'], disperser['theta_y'], disperser['theta_z'],
disperser['tilt_y']
]
rotation = Rotation3DToGWA(angles, axes_order="xyzy",
name='rotaton').inverse
dircos2unitless = DirCos2Unitless(name='directional_cosines2unitless')
col = AsdfFile.open(reference_files['collimator']).tree['model']
# Get the default spectral order and wavelength range and record them in the model.
sporder, wrange = get_spectral_order_wrange(
input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = sporder
lam = wrange[0] + (wrange[1] - wrange[0]) * .5
lam_model = Mapping((0, 1, 1)) | Identity(2) & Const1D(lam)
gwa2msa = gwa_through | rotation | dircos2unitless | col | lam_model
gwa2msa.inverse = col.inverse | dircos2unitless.inverse | rotation.inverse | gwa_through
# MSA to OTEIP transform
msa2ote = msa_to_oteip(reference_files)
msa2oteip = msa2ote | Mapping((0, 1), n_inputs=3)
msa2oteip.inverse = Mapping((0, 1, 0, 1)) | msa2ote.inverse | Mapping(
(0, 1), n_inputs=3)
# OTEIP to V2,V3 transform
with AsdfFile.open(reference_files['ote']) as f:
oteip2v23 = f.tree['model'].copy()
# Create coordinate frames in the NIRSPEC WCS pipeline
# "detector", "gwa", "msa", "oteip", "v2v3", "world"
det, gwa, msa_frame, oteip, v2v3 = create_imaging_frames()
imaging_pipeline = [(det, det2gwa), (gwa, gwa2msa), (msa_frame, msa2oteip),
(oteip, oteip2v23), (v2v3, None)]
return imaging_pipeline
def test_regions_selector(tmpdir):
m1 = Mapping([0, 1, 1]) | Shift(1) & Shift(2) & Shift(3)
m2 = Mapping([0, 1, 1]) | Scale(2) & Scale(3) & Scale(3)
sel = {1: m1, 2: m2}
a = np.zeros((5, 6), dtype=np.int32)
a[:, 1:3] = 1
a[:, 4:5] = 2
mask = selector.LabelMapperArray(a)
rs = selector.RegionsSelector(inputs=('x', 'y'), outputs=('ra', 'dec', 'lam'),
selector=sel, label_mapper=mask)
assert_selector_roundtrip(rs, tmpdir)
def test_regions_selector(tmpdir):
m1 = Mapping([0, 1, 1]) | Shift(1) & Shift(2) & Shift(3)
m2 = Mapping([0, 1, 1]) | Scale(2) & Scale(3) & Scale(3)
sel = {1:m1, 2:m2}
a = np.zeros((5,6), dtype=np.int32)
a[:, 1:3] = 1
a[:, 4:5] = 2
mask = selector.LabelMapperArray(a)
rs = selector.RegionsSelector(inputs=('x', 'y'), outputs=('ra', 'dec', 'lam'),
selector=sel, label_mapper=mask)
tree = {'model': rs}
helpers.assert_roundtrip_tree(tree, tmpdir, extensions=GWCSExtension())
def _v2v3_to_tpcorr_from_full(tpcorr):
s2c = tpcorr['s2c']
c2s = tpcorr['c2s']
# unit_conv = _get_submodel(tpcorr, 'arcsec_to_deg_2D')
# unit_conv_inv = _get_submodel(tpcorr, 'deg_to_arcsec_2D')
#
# The code below is a work-around to the code above not working.
# TODO: understand why _get_submodel is unable to retrieve
# some submodels in a compound model.
#
unit_conv = _get_submodel(tpcorr, 'arcsec_to_deg_1D')
unit_conv = unit_conv & unit_conv
unit_conv.name = 'arcsec_to_deg_2D'
unit_conv_inv = _get_submodel(tpcorr, 'deg_to_arcsec_1D')
unit_conv_inv = unit_conv_inv & unit_conv_inv
unit_conv_inv.name = 'deg_to_arcsec_2D'
affine = tpcorr['tp_affine']
affine_inv = affine.inverse
affine_inv.name = 'tp_affine_inv'
rot = tpcorr['det_to_optic_axis']
rot_inv = rot.inverse
rot_inv.name = 'optic_axis_to_det'
# c2tan = _get_submodel(tpcorr, 'Cartesian 3D to TAN')
# tan2c = _get_submodel(tpcorr, 'TAN to cartesian 3D')
#
# The code below is a work-around to the code above not working.
# TODO: understand why _get_submodel is unable to retrieve
# some submodels in a compound model.
#
c2tan = ((Mapping((0, 1, 2), name='xyz') / Mapping(
(0, 0, 0), n_inputs=3, name='xxx')) | Mapping((1, 2), name='xtyt'))
c2tan.name = 'Cartesian 3D to TAN'
tan2c = (Mapping((0, 0, 1), n_inputs=2, name='xtyt2xyz') |
(Const1D(1, name='one') & Identity(2, name='I(2D)')))
tan2c.name = 'TAN to cartesian 3D'
v2v3_to_tpcorr = unit_conv | s2c | rot | c2tan | affine
v2v3_to_tpcorr.name = 'jwst_v2v3_to_tpcorr'
tpcorr_to_v2v3 = affine_inv | tan2c | rot_inv | c2s | unit_conv_inv
tpcorr_to_v2v3.name = 'jwst_tpcorr_to_v2v3'
v2v3_to_tpcorr.inverse = tpcorr_to_v2v3
tpcorr_to_v2v3.inverse = v2v3_to_tpcorr
return v2v3_to_tpcorr
def test_slicing_on_instance_with_parameterless_model():
"""
Regression test to fix an issue where the indices attached to parameter
names on a compound model were not handled properly when one or more
submodels have no parameters. This was especially evident in slicing.
"""
p2 = Polynomial2D(1, c0_0=1, c1_0=2, c0_1=3)
p1 = Polynomial2D(1, c0_0=1, c1_0=2, c0_1=3)
mapping = Mapping((0, 1, 0, 1))
offx = Shift(-2, name='x_translation')
offy = Shift(-1, name='y_translation')
aff = AffineTransformation2D(matrix=[[1, 2], [3, 4]], name='rotation')
model = mapping | (p1 & p2) | (offx & offy) | aff
assert model.param_names == ('c0_0_1', 'c1_0_1', 'c0_1_1',
'c0_0_2', 'c1_0_2', 'c0_1_2',
'offset_3', 'offset_4',
'matrix_5', 'translation_5')
assert model(1, 2) == (23.0, 53.0)
m = model[3:]
assert m.param_names == ('offset_3', 'offset_4', 'matrix_5',
'translation_5')
assert m(1, 2) == (1.0, 1.0)
def oteip_to_v23(reference_files):
"""
Transform from the OTEIP frame to the V2V3 frame.
Parameters
----------
reference_files: dict
Dictionary with reference files returned by CRDS.
Returns
-------
model : `~astropy.modeling.core.Model` model.
Transform from OTEIP to V2V3.
"""
with AsdfFile.open(reference_files['ote']) as f:
ote = f.tree['model'].copy()
fore2ote_mapping = Identity(3, name='fore2ote_mapping')
fore2ote_mapping.inverse = Mapping((0, 1, 2, 2))
# Create the transform to v2/v3/lambda. The wavelength units up to this point are
# meters as required by the pipeline but the desired output wavelength units is microns.
# So we are going to Scale the spectral units by 1e6 (meters -> microns)
# The spatial units are currently in deg. Convertin to arcsec.
oteip_to_xyan = fore2ote_mapping | (ote & Scale(1e6))
# Add a shift for the aperture.
oteip2v23 = oteip_to_xyan | Identity(1) & (Shift(468 / 3600) | Scale(-1)) & Identity(1)
return oteip2v23
def test_LabelMapperDict(tmpdir):
dmapper = create_scalar_mapper()
sel = selector.LabelMapperDict(('x', 'y'),
dmapper,
inputs_mapping=Mapping((0, ), n_inputs=2),
atol=1e-3)
tree = {'model': sel}
helpers.assert_roundtrip_tree(tree, tmpdir, extensions=GWCSExtension())
def niriss_soss(input_model, reference_files):
"""
The NIRISS SOSS pipeline includes 3 coordinate frames -
detector, focal plane and sky
reference_files={'specwcs': 'soss_wavelengths_configuration.asdf'}
"""
# 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:
# 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:
with AsdfFile.open(reference_files['specwcs']) as wl:
wl1 = wl.tree[1].copy()
wl2 = wl.tree[2].copy()
wl3 = wl.tree[3].copy()
except Exception as e:
raise IOError('Error reading wavelength correction from {}'.format(reference_files['specwcs']))
cm_order1 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl1)).rename('Order1')
cm_order2 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl2)).rename('Order2')
cm_order3 = (Mapping((0, 1, 0, 1)) | (Const1D(target_ra) & Const1D(target_dec) & wl3)).rename('Order3')
# 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 _tpcorr_init(v2_ref, v3_ref, roll_ref):
s2c = SphericalToCartesian(name='s2c', wrap_lon_at=180)
c2s = CartesianToSpherical(name='c2s', wrap_lon_at=180)
unit_conv = Scale(1.0 / 3600.0, name='arcsec_to_deg_1D')
unit_conv = unit_conv & unit_conv
unit_conv.name = 'arcsec_to_deg_2D'
unit_conv_inv = Scale(3600.0, name='deg_to_arcsec_1D')
unit_conv_inv = unit_conv_inv & unit_conv_inv
unit_conv_inv.name = 'deg_to_arcsec_2D'
affine = AffineTransformation2D(name='tp_affine')
affine_inv = AffineTransformation2D(name='tp_affine_inv')
rot = RotationSequence3D([v2_ref, -v3_ref, roll_ref],
'zyx',
name='det_to_optic_axis')
rot_inv = rot.inverse
rot_inv.name = 'optic_axis_to_det'
# projection submodels:
c2tan = ((Mapping((0, 1, 2), name='xyz') / Mapping(
(0, 0, 0), n_inputs=3, name='xxx')) | Mapping((1, 2), name='xtyt'))
c2tan.name = 'Cartesian 3D to TAN'
tan2c = (Mapping((0, 0, 1), n_inputs=2, name='xtyt2xyz') |
(Const1D(1, name='one') & Identity(2, name='I(2D)')))
tan2c.name = 'TAN to cartesian 3D'
total_corr = (unit_conv | s2c | rot | c2tan | affine | tan2c | rot_inv
| c2s | unit_conv_inv)
total_corr.name = 'JWST tangent-plane linear correction. v1'
inv_total_corr = (unit_conv | s2c | rot | c2tan | affine_inv | tan2c
| rot_inv | c2s | unit_conv_inv)
inv_total_corr.name = 'Inverse JWST tangent-plane linear correction. v1'
# TODO
# re-enable circular inverse definitions once
# https://github.com/spacetelescope/asdf/issues/744 or
# https://github.com/spacetelescope/asdf/issues/745 are resolved.
#
# inv_total_corr.inverse = total_corr
total_corr.inverse = inv_total_corr
return total_corr
def test_swap_axes():
x = np.zeros((2, 3))
y = np.ones((2, 3))
mapping = Mapping((1, 0))
assert mapping(1, 2) == (2.0, 1.0)
assert mapping.inverse(2, 1) == (1, 2)
assert_array_equal(mapping(x, y), (y, x))
assert_array_equal(mapping.inverse(y, x), (x, y))
def test_mapping_basic_permutations():
"""
Tests a couple basic examples of the Mapping model--specifically examples
that merely permute the outputs.
"""
x, y = Rotation2D(90)(1, 2)
rs = Rotation2D(90) | Mapping((1, 0))
x_prime, y_prime = rs(1, 2)
assert_allclose((x, y), (y_prime, x_prime))
# A more complicated permutation
m = Rotation2D(90) & Scale(2)
x, y, z = m(1, 2, 3)
ms = m | Mapping((2, 0, 1))
x_prime, y_prime, z_prime = ms(1, 2, 3)
assert_allclose((x, y, z), (y_prime, z_prime, x_prime))
def from_tree_transform(cls, node, ctx):
mapping = node['mapping']
n_inputs = node.get('n_inputs')
if all([isinstance(x, int) for x in mapping]):
return Mapping(tuple(mapping), n_inputs)
if n_inputs is None:
n_inputs = max([x for x in mapping if isinstance(x, int)]) + 1
transform = Identity(n_inputs)
new_mapping = []
i = n_inputs
for entry in mapping:
if isinstance(entry, int):
new_mapping.append(entry)
else:
new_mapping.append(i)
transform = transform & Const1D(entry.value)
i += 1
return transform | Mapping(new_mapping)
def test_cdot():
result = _cdot(sh1, scl1)
assert_allclose(result, np.array([[1]]))
result = _cdot(rot, p2)
assert_allclose(result, np.array([[2, 2]]))
result = _cdot(rot, rot)
assert_allclose(result, np.array([[2, 2], [2, 2]]))
result = _cdot(Mapping((0, 0)), rot)
assert_allclose(result, np.array([[2], [2]]))
def test_fittable_compound():
m = Identity(1) | Mapping((0, )) | Gaussian1D(1, 5, 4)
x = np.arange(10)
y_real = m(x)
dy = 0.005
with NumpyRNGContext(1234567):
n = np.random.normal(0., dy, x.shape)
y_noisy = y_real + n
pfit = LevMarLSQFitter()
new_model = pfit(m, x, y_noisy)
y_fit = new_model(x)
assert_allclose(y_fit, y_real, atol=dy)
def test_mapping_inverse():
"""Tests inverting a compound model that includes a `Mapping`."""
rs1 = Rotation2D(12.1) & Scale(13.2)
rs2 = Rotation2D(14.3) & Scale(15.4)
# Rotates 2 of the coordinates and scales the third--then rotates on a
# different axis and scales on the axis of rotation. No physical meaning
# here just a simple test
m = rs1 | Mapping([2, 0, 1]) | rs2
assert_allclose((0, 1, 2), m.inverse(*m(0, 1, 2)), atol=1e-08)
def mask_slit(ymin=-.5, ymax=.5):
"""
Returns a model which masks out pixels in a NIRSpec cutout outside the slit.
Uses ymin, ymax for the slit and the wavelength range to define the location of the slit.
Parameters
----------
ymin, ymax : float
ymin and ymax relative boundary of a slit.
Returns
-------
model : `~astropy.modeling.core.Model`
A model which takes x_slit, y_slit, lam inputs and substitutes the
values outside the slit with NaN.
"""
greater_than_ymax = Logical(condition='GT', compareto=ymax, value=np.nan)
less_than_ymin = Logical(condition='LT', compareto=ymin, value=np.nan)
model = Mapping((0, 1, 2, 1)) | Identity(3) & (greater_than_ymax | less_than_ymin | models.Scale(0)) | \
Mapping((0, 1, 3, 2, 3)) | Identity(1) & Mapping((0,), n_inputs=2) + Mapping((1,)) & \
Mapping((0,), n_inputs=2) + Mapping((1,))
model.inverse = Identity(3)
return model
def slits_wcs(input_model, reference_files):
"""
Create the WCS pipeline for observations using the MSA shutter array or fixed slits.
Parameters
----------
input_model : `~jwst_lib.models.ImageModel`
The input data model.
reference_files : dict
Dictionary with reference files supplied by CRDS.
"""
open_slits_id = get_open_slits(input_model)
n_slits = len(open_slits_id)
log.info("Computing WCS for {0} open slitlets".format(n_slits))
# Get the corrected disperser model
disperser = get_disperser(input_model, reference_files['disperser'])
# Get the default spectral order and wavelength range and record them in the model.
sporder, wrange = get_spectral_order_wrange(
input_model, reference_files['wavelengthrange'])
input_model.meta.wcsinfo.waverange_start = wrange[0]
input_model.meta.wcsinfo.waverange_end = wrange[1]
input_model.meta.wcsinfo.spectral_order = sporder
# DETECTOR to GWA transform
#det2gwa = Identity(2) & detector_to_gwa(reference_files, input_model.meta.instrument.detector, disperser)
det2gwa = detector_to_gwa(reference_files,
input_model.meta.instrument.detector, disperser)
# GWA to SLIT
gwa2slit = gwa_to_slit(open_slits_id, disperser, wrange, sporder,
reference_files)
# SLIT to MSA transform
slit2msa = slit_to_msa(open_slits_id, reference_files['msa'])
# MSA to OTEIP transform
msa2oteip = msa_to_oteip(reference_files)
# OTEIP to V2,V3 transform
oteip2v23 = oteip_to_v23(reference_files)
# Create coordinate frames in the NIRSPEC WCS pipeline"
# "detector", "gwa", "slit_frame", "msa_frame", "oteip", "v2v3", "world"
det, gwa, slit_frame, msa_frame, oteip, v2v3 = create_frames()
msa_pipeline = [(det, det2gwa), (gwa, gwa2slit),
(slit_frame, Mapping((0, 1, 2, 3, 4)) | slit2msa),
(msa_frame, msa2oteip), (oteip, oteip2v23), (v2v3, None)]
return msa_pipeline
def test_mapping_inverse():
"""Tests inverting a compound model that includes a `Mapping`."""
RS = Rotation2D & Scale
# Rotates 2 of the coordinates and scales the third--then rotates on a
# different axis and scales on the axis of rotation. No physical meaning
# here just a simple test
M = RS | Mapping([2, 0, 1]) | RS
m = M(12.1, 13.2, 14.3, 15.4)
assert_allclose((0, 1, 2), m.inverse(*m(0, 1, 2)), atol=1e-08)
def test_bad_inputs(name):
mapping = Mapping((1, 0), name=name)
if name is None:
name = "Mapping"
x = [np.ones((2, 3)) * idx for idx in range(5)]
for idx in range(1, 6):
if idx == 2:
continue
with pytest.raises(TypeError) as err:
mapping.evaluate(*x[:idx])
assert str(err.value) == f"{name} expects 2 inputs; got {idx}"
def from_tree_transform(cls, node, ctx):
from astropy.modeling.models import Identity, Mapping
mapping = node['mapping']
n_inputs = node.get('n_inputs')
if all([isinstance(x, six.integer_types) for x in mapping]):
return Mapping(tuple(mapping), n_inputs)
if n_inputs is None:
n_inputs = max([x for x in mapping
if isinstance(x, six.integer_types)]) + 1
transform = Identity(n_inputs)
new_mapping = []
i = n_inputs
for entry in mapping:
if isinstance(entry, six.integer_types):
new_mapping.append(entry)
else:
new_mapping.append(i)
transform = transform & ConstantType.from_tree(
{'value': int(entry.value)}, ctx)
i += 1
return transform | Mapping(new_mapping)
def create_slit(model, x0, y0, order):
""" Create a SlitModel representing a grism slit."""
ymin = 0
xmin = 0
# ymax = 58
# xmax = 1323
model = Mapping((0, 1, 0, 0, 0)) | (Shift(xmin) & Shift(ymin) & Const1D(x0)
& Const1D(y0) & Const1D(order)) | model
wcsobj = wcs.WCS([('det', model), ('world', None)])
wcsobj.bounding_box = ((20, 25), (800, 805))
slit = SlitModel()
slit.meta.wcs = wcsobj
slit.source_xpos = x0
slit.source_ypos = y0
return slit
def test_LabelMapperRange(tmpdir):
m = []
for i in np.arange(9) * .1:
c0_0, c1_0, c0_1, c1_1 = np.ones((4, )) * i
m.append(Polynomial2D(2, c0_0=c0_0, c1_0=c1_0, c0_1=c0_1, c1_1=c1_1))
keys = np.array([[4.88, 5.64], [5.75, 6.5], [6.67, 7.47], [7.7, 8.63],
[8.83, 9.96], [10.19, 11.49], [11.77, 13.28],
[13.33, 15.34], [15.56, 18.09]])
rmapper = {}
for k, v in zip(keys, m):
rmapper[tuple(k)] = v
sel = selector.LabelMapperRange(('x', 'y'),
rmapper,
inputs_mapping=Mapping((0, ), n_inputs=2))
tree = {'model': sel}
helpers.assert_roundtrip_tree(tree, tmpdir, extensions=GWCSExtension())