def test_compound_model_with_nonstandard_broadcasting():
"""
Ensure that the ``standard_broadcasting`` flag is properly propagated when
creating compound models.
See the commit message for the commit in which this was added for more
details.
"""
offx = Shift(1)
offy = Shift(2)
rot = AffineTransformation2D([[0, -1], [1, 0]])
m = (offx & offy) | rot
x, y = m(0, 0)
assert x == -2
assert y == 1
# make sure conversion back to scalars is working properly
assert isinstance(x, float)
assert isinstance(y, float)
x, y = m([0, 1, 2], [0, 1, 2])
assert np.all(x == [-2, -3, -4])
assert np.all(y == [1, 2, 3])
def compute_output_transform(refwcs, filename, fiducial):
"""Compute a simple FITS-type WCS transform
"""
x0, y0 = refwcs.backward_transform(*fiducial)
x1 = x0 + 1
y1 = y0 + 1
ra0, dec0 = refwcs(x0, y0)
ra_xdir, dec_xdir = refwcs(x1, y0)
ra_ydir, dec_ydir = refwcs(x0, y1)
position0 = SkyCoord(ra=ra0, dec=dec0, unit='deg')
position_xdir = SkyCoord(ra=ra_xdir, dec=dec_xdir, unit='deg')
position_ydir = SkyCoord(ra=ra_ydir, dec=dec_ydir, unit='deg')
offset_xdir = position0.spherical_offsets_to(position_xdir)
offset_ydir = position0.spherical_offsets_to(position_ydir)
xscale = np.abs(position0.separation(position_xdir).value)
yscale = np.abs(position0.separation(position_ydir).value)
scale = np.sqrt(xscale * yscale)
c00 = offset_xdir[0].value / scale
c01 = offset_xdir[1].value / scale
c10 = offset_ydir[0].value / scale
c11 = offset_ydir[1].value / scale
pc_matrix = AffineTransformation2D(matrix=[[c00, c01], [c10, c11]])
cdelt = Scale(scale) & Scale(scale)
return pc_matrix | cdelt
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 _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 _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 _tpcorr_combine_affines(tpcorr, matrix, shift):
AffineTransformation2D(matrix, shift) # check input parameters are OK
m = np.dot(matrix, tpcorr['tp_affine'].matrix.value)
t = np.dot(matrix, tpcorr['tp_affine'].translation.value) + shift
tpcorr['tp_affine'].matrix = m
tpcorr['tp_affine'].translation = t
# update the affine transformation of the inverse model as well:
invm = np.linalg.inv(m)
tpcorr.inverse['tp_affine_inv'].matrix = invm
tpcorr.inverse['tp_affine_inv'].translation = -np.dot(invm, t)
def create_DetToV2V3(v2ref=0.0,
v3ref=0.0,
roll=0.0,
cd=[[1.0, 0.0], [0.0, 1.0]],
crpix=[0, 0]):
tpcorr = JWSTgWCS._tpcorr_init(v2_ref=v2ref, v3_ref=v3ref, roll_ref=roll)
afinv = AffineTransformation2D(cd, -np.dot(cd, crpix)).inverse
JWSTgWCS._tpcorr_combine_affines(tpcorr, afinv.matrix.value,
afinv.translation.value)
p = JWSTgWCS._v2v3_to_tpcorr_from_full(tpcorr)
partial_tpcorr = p.inverse
partial_tpcorr.inverse = p
return partial_tpcorr
def map_to_transform(smap):
# crval1u, crval2u = smap.reference_coordinate.Tx, smap.reference_coordinate.Ty
# cdelt1u, cdelt2u = smap.scale
# pcu = smap.rotation_matrix * u.arcsec
# # First, shift the reference pixel from FITS (1) to Python (0) indexing
# crpix1, crpix2 = u.Quantity(smap.reference_pixel) - 1 * u.pixel
# # Then FITS WCS uses the negative of this value as the shift.
# shiftu = Shift(-crpix1) & Shift(-crpix2)
# # Next we define the Affine Transform.
# # This also includes the pixel scale operation by using equivalencies
# scale_e = {a: u.pixel_scale(scale) for a, scale in zip('xy', smap.scale)}
# rotu = AffineTransformation2D(pcu, translation=(0, 0) * u.arcsec)
# rotu.input_units_equivalencies = scale_e
# # Handle the projection
# tanu = Pix2Sky_TAN()
# # Rotate from native spherical to celestial spherical coordinates
# skyrotu = RotateNative2Celestial(crval1u, crval2u, 180 * u.deg)
# # Combine the whole pipeline into one compound model
# transu = shiftu | rotu | tanu | skyrotu
crpix1u, crpix2u = u.Quantity(smap.reference_pixel) - 1 * u.pixel
crval1u, crval2u = smap.reference_coordinate.Tx, smap.reference_coordinate.Ty
cdelt1u, cdelt2u = smap.scale
pcu = smap.rotation_matrix * u.arcsec
shiftu = Shift(-crpix1u) & Shift(-crpix2u)
scaleu = Multiply(cdelt1u) & Multiply(cdelt2u)
rotu = AffineTransformation2D(pcu, translation=(0, 0) * u.arcsec)
tanu = Pix2Sky_TAN()
skyrotu = RotateNative2Celestial(crval1u, crval2u, 180 * u.deg)
transu = shiftu | scaleu | rotu | tanu | skyrotu
transu.rename("spatial")
return transu
def spatial_model_from_quantity(crpix1,
crpix2,
cdelt1,
cdelt2,
pc,
crval1,
crval2,
projection='TAN'):
"""
Given quantity representations of a HPLx FITS WCS return a model for the
spatial transform.
The ordering of ctype1 and ctype2 should be LON, LAT
"""
# TODO: Find this from somewhere else or extend it or something
projections = {'TAN': Pix2Sky_TAN()}
shiftu = Shift(-crpix1) & Shift(-crpix2)
scale = Multiply(cdelt1) & Multiply(cdelt2)
rotu = AffineTransformation2D(pc, translation=(0, 0) * u.arcsec)
tanu = projections[projection]
skyrotu = RotateNative2Celestial(crval1, crval2, 180 * u.deg)
return shiftu | scale | rotu | tanu | skyrotu
def _make_gwcs_wcs(fits_hdr):
hdr = fits.Header.fromfile(get_pkg_data_filename(fits_hdr))
fw = fitswcs.WCS(hdr)
a_order = hdr['A_ORDER']
a_coeff = {}
for i in range(a_order + 1):
for j in range(a_order + 1 - i):
key = 'A_{:d}_{:d}'.format(i, j)
if key in hdr:
a_coeff[key] = hdr[key]
b_order = hdr['B_ORDER']
b_coeff = {}
for i in range(b_order + 1):
for j in range(b_order + 1 - i):
key = 'B_{:d}_{:d}'.format(i, j)
if key in hdr:
b_coeff[key] = hdr[key]
cx = {"c" + k[2:]: v for k, v in a_coeff.items()}
cy = {"c" + k[2:]: v for k, v in b_coeff.items()}
sip_distortion = ((Shift(-fw.wcs.crpix[0]) & Shift(-fw.wcs.crpix[1]))
| Mapping((0, 1, 0, 1))
| (polynomial.Polynomial2D(a_order, **cx, c1_0=1)
& polynomial.Polynomial2D(b_order, **cy, c0_1=1))
| (Shift(fw.wcs.crpix[0]) & Shift(fw.wcs.crpix[1])))
y, x = np.indices(fw.array_shape)
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)
sip_distortion |= (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, sip_distortion), (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))
# sanity check:
for _ in range(100):
x = np.random.randint(1, fw.pixel_shape[0])
y = np.random.randint(1, fw.pixel_shape[1])
assert np.allclose(gw(x, y),
fw.all_pix2world(x, y, 1),
rtol=0,
atol=1e-11)
return gw
