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 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 run_test(model):
wcsobj = model.meta.wcs
for ch in model.meta.instrument.channel:
ref_data = mrs_ref_data[ch + band_mapping[model.meta.instrument.band]]
detector_to_alpha_beta = wcsobj.get_transform('detector', 'alpha_beta')
#ab_to_xan_yan = wcsobj.get_transform('alpha_beta', 'Xan_Yan').set_input(int(ch))
ab_to_v2v3 = wcsobj.get_transform('alpha_beta',
'v2v3').set_input(int(ch))
ab_to_xan_yan = ab_to_v2v3 | Scale(1 / 60) & Scale(1 / 60) & Identity(
1) | Identity(1) & (Scale(-1) | Shift(-7.8)) & Identity(1)
ref_alpha = ref_data['alpha']
ref_beta = ref_data['beta']
ref_lam = ref_data['lam']
x, y = ref_data['x'], ref_data['y']
for i, s in enumerate(ref_data['s']):
sl = int(ch) * 100 + s
alpha, beta, lam = detector_to_alpha_beta.set_input(sl)(x[i], y[i])
utils.assert_allclose(alpha, ref_alpha[i], atol=10**-4)
utils.assert_allclose(beta, ref_beta[i], atol=10**-4)
utils.assert_allclose(lam, ref_lam[i], atol=10**-4)
xan, yan, lam = ab_to_xan_yan(ref_alpha, ref_beta, ref_lam)
utils.assert_allclose(xan, ref_data['v2'], atol=10**-4)
utils.assert_allclose(yan, ref_data['v3'], atol=10**-4)
utils.assert_allclose(lam, ref_data['lam'], atol=10**-4)
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_instances_3():
"""
Like `test_slicing_on_instances_2` but uses a compound model that does not
have any invalid slices due to the resulting model being invalid
(originally test_slicing_on_instances_2 passed without any
ModelDefinitionErrors being raised, but that was before we prevented
invalid models from being created).
"""
model_a = Shift(1, name='a')
model_b = Shift(2, name='b')
model_c = Gaussian1D(3, 0, 0.1, name='c')
model_d = Scale(2, name='d')
model_e = Scale(3, name='e')
m = (model_a + model_b) | model_c | (model_d + model_e)
assert m[1:].submodel_names == ('b', 'c', 'd', 'e')
assert m[:].submodel_names == ('a', 'b', 'c', 'd', 'e')
assert m['a':].submodel_names == ('a', 'b', 'c', 'd', 'e')
assert m['c':'d'].submodel_names == ('c', 'd')
assert m[1:2].name == 'b'
assert m[2:7].submodel_names == ('c', 'd', 'e')
with pytest.raises(IndexError):
m['x']
with pytest.raises(IndexError):
m['a':'r']
assert m[-4:4].submodel_names == ('b', 'c', 'd')
assert m[-4:-2].submodel_names == ('b', 'c')
def test_basic_compound_inverse():
"""
Test basic inversion of compound models in the limited sense supported for
models made from compositions and joins only.
"""
t = (Shift(2) & Shift(3)) | (Scale(2) & Scale(3)) | Rotation2D(90)
assert_allclose(t.inverse(*t(0, 1)), (0, 1))
def dva_corr_model(va_scale, v2_ref, v3_ref):
"""
Create transformation that accounts for differential velocity aberration
(scale).
Parameters
----------
va_scale : float, None
Ratio of the apparent plate scale to the true plate scale. When
``va_scale`` is `None`, it is assumed to be identical to ``1`` and
an ``astropy.modeling.models.Identity`` model will be returned.
v2_ref : float, None
Telescope ``v2`` coordinate of the reference point in ``arcsec``. When
``v2_ref`` is `None`, it is assumed to be identical to ``0``.
v3_ref : float, None
Telescope ``v3`` coordinate of the reference point in ``arcsec``. When
``v3_ref`` is `None`, it is assumed to be identical to ``0``.
Returns
-------
va_corr : astropy.modeling.CompoundModel, astropy.modeling.models.Identity
A 2D compound model that corrects DVA. If ``va_scale`` is `None` or 1
then `astropy.modeling.models.Identity` will be returned.
"""
if va_scale is None or va_scale == 1:
return Identity(2)
if va_scale <= 0:
raise ValueError(
"'Velocity aberration scale must be a positive number.")
va_corr = Scale(va_scale, name='dva_scale_v2') & Scale(va_scale,
name='dva_scale_v3')
if v2_ref is None:
v2_ref = 0
if v3_ref is None:
v3_ref = 0
if v2_ref == 0 and v3_ref == 0:
return va_corr
# NOTE: it is assumed that v2, v3 angles and va scale are small enough
# so that for expected scale factors the issue of angle wrapping
# (180 degrees) can be neglected.
v2_shift = (1 - va_scale) * v2_ref
v3_shift = (1 - va_scale) * v3_ref
va_corr |= Shift(v2_shift, name='dva_v2_shift') & Shift(
v3_shift, name='dva_v3_shift')
va_corr.name = 'DVA_Correction'
return va_corr
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_name():
offx = Shift(1)
scl = Scale(2)
m = offx | scl
scl.name = "scale"
assert m.submodel_names == ('None_0', 'scale')
assert m.name is None
m.name = "M"
assert m.name == "M"
m1 = m.rename("M1")
assert m.name == "M1"
assert m1.name == "M1"
def test_name():
offx = Shift(1)
scl = Scale(2)
m = offx | scl
scl.name = "scale"
assert m._submodel_names == ('None_0', 'None_1')
assert m.name is None
m.name = "M"
assert m.name == "M"
m1 = m.rename("M1")
assert m.name == "M"
assert m1.name == "M1"
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 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 imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
# Convert to deg - output of distortion models is in arcsec.
transform = distortion | Scale(1 / 3600) & Scale(1 / 3600)
try:
bb = transform.bounding_box
except NotImplementedError:
shape = input_model.data.shape
# Note: Since bounding_box is attached to the model here it's in reverse order.
transform.bounding_box = ((-0.5, shape[0] - 0.5), (-0.5,
shape[1] - 0.5))
return transform
def test_compound_custom_inverse():
"""
Test that a compound model with a custom inverse has that inverse applied
when the inverse of another model, of which it is a component, is computed.
Regression test for https://github.com/astropy/astropy/issues/3542
"""
poly = Polynomial1D(1, c0=1, c1=2)
scale = Scale(1)
shift = Shift(1)
model1 = poly | scale
model1.inverse = poly
# model1 now has a custom inverse (the polynomial itself, ignoring the
# trivial scale factor)
model2 = shift | model1
assert_allclose(model2.inverse(1), (poly | shift.inverse)(1))
# Make sure an inverse is not allowed if the models were combined with the
# wrong operator, or if one of the models doesn't have an inverse defined
with pytest.raises(NotImplementedError):
(shift + model1).inverse
with pytest.raises(NotImplementedError):
(model1 & poly).inverse
def test_compound_input_units_strict():
"""
Test setting input_units_strict on one of the models.
"""
class ScaleDegrees(Scale):
input_units = {'x': u.deg}
s1 = ScaleDegrees(2)
s2 = Scale(2)
cs = s1 | s2
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
assert out.unit is u.deg # important since this tests input_units_strict
cs = s2 | s1
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
assert out.unit is u.deg # important since this tests input_units_strict
cs = s1 & s2
out = cs(10 * u.arcsec, 10 * u.arcsec)
assert_quantity_allclose(out, 20 * u.arcsec)
assert out[0].unit is u.deg
assert out[1].unit is u.arcsec
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 test_v2v3todet_roundtrips():
s2c = (Scale(1.0 / 3600.0)
& Scale(1.0 / 3600.0)) | SphericalToCartesian(wrap_lon_at=180)
s = 1.0e-5
crpix = np.random.random(2)
alpha = 0.25 * np.pi * np.random.random()
x, y = 1024 * np.random.random(2)
v2, v3 = 45 * np.random.random(2)
cd = [[s * np.cos(alpha), -s * np.sin(alpha)],
[s * np.sin(alpha), s * np.cos(alpha)]]
d2v = create_DetToV2V3(v2ref=123.0,
v3ref=500.0,
roll=15.0,
crpix=crpix,
cd=cd)
v2d = create_V2V3ToDet(v2ref=123.0,
v3ref=500.0,
roll=15.0,
crpix=crpix,
cd=cd)
assert np.allclose(d2v.inverse(*d2v(x, y)), (x, y),
rtol=1e3 * _ATOL,
atol=1e3 * _ATOL)
assert (np.allclose(s2c(*v2d.inverse(*v2d(v2, v3))),
s2c(v2, v3),
rtol=1e3 * _ATOL,
atol=_ATOL)
or np.allclose(-np.asanyarray(s2c(*v2d.inverse(*v2d(v2, v3)))),
s2c(v2, v3),
rtol=1e3 * _ATOL,
atol=_ATOL))
assert np.allclose(v2d(*d2v(x, y)), (x, y),
rtol=1e5 * _ATOL,
atol=1e3 * _ATOL)
assert (np.allclose(s2c(*d2v(*v2d(v2, v3))),
s2c(v2, v3),
rtol=1e3 * _ATOL,
atol=1e3 * _ATOL)
or np.allclose(-np.asanyarray(s2c(*d2v(*v2d(v2, v3)))),
s2c(v2, v3),
rtol=1e3 * _ATOL,
atol=1e3 * _ATOL))
def v23tosky(input_model, wrap_v2_at=180, wrap_lon_at=360):
v2_ref = input_model.meta.wcsinfo.v2_ref / 3600
v3_ref = input_model.meta.wcsinfo.v3_ref / 3600
roll_ref = input_model.meta.wcsinfo.roll_ref
ra_ref = input_model.meta.wcsinfo.ra_ref
dec_ref = input_model.meta.wcsinfo.dec_ref
angles = np.array([v2_ref, -v3_ref, roll_ref, dec_ref, -ra_ref])
axes = "zyxyz"
rot = RotationSequence3D(angles, axes_order=axes)
# The sky rotation expects values in deg.
# This should be removed when models work with quantities.
m = ((Scale(1 / 3600) & Scale(1 / 3600)) | SphericalToCartesian(wrap_lon_at=wrap_v2_at)
| rot | CartesianToSpherical(wrap_lon_at=wrap_lon_at))
m.name = 'v23tosky'
return m
def test_simple_two_model_compose_1d():
"""
Shift and Scale are two of the simplest models to test model composition
with.
"""
S1 = Shift(2) | Scale(3) # First shift then scale
assert isinstance(S1, CompoundModel)
assert S1.n_inputs == 1
assert S1.n_outputs == 1
assert S1(1) == 9.0
S2 = Scale(2) | Shift(3) # First scale then shift
assert isinstance(S2, CompoundModel)
assert S2.n_inputs == 1
assert S2.n_outputs == 1
assert S2(1) == 5.0
# Test with array inputs
assert_array_equal(S2([1, 2, 3]), [5.0, 7.0, 9.0])
def test_update_parameters():
offx = Shift(1)
scl = Scale(2)
m = offx | scl
assert (m(1) == 4)
offx.offset = 42
assert (m(1) == 4)
m.factor_1 = 100
assert (m(1) == 200)
m2 = m | offx
assert (m2(1) == 242)
def test_update_parameters():
offx = Shift(1)
scl = Scale(2)
m = offx | scl
assert m(1) == 4
offx.offset = 42
assert m(1) == 86
m.factor_1 = 100
assert m(1) == 4300
m2 = m | offx
assert m2(1) == 4342
def test_slicing_on_instances_2():
"""
More slicing tests.
Regression test for https://github.com/embray/astropy/pull/10
"""
model_a = Shift(1, name='a')
model_b = Shift(2, name='b')
model_c = Rotation2D(3, name='c')
model_d = Scale(2, name='d')
model_e = Scale(3, name='e')
m = (model_a & model_b) | model_c | (model_d & model_e)
with pytest.raises(ModelDefinitionError):
# The slice can't actually be taken since the resulting model cannot be
# evaluated
assert m[1:].submodel_names == ('b', 'c', 'd', 'e')
assert m[:].submodel_names == ('a', 'b', 'c', 'd', 'e')
assert m['a':].submodel_names == ('a', 'b', 'c', 'd', 'e')
with pytest.raises(ModelDefinitionError):
assert m['c':'d'].submodel_names == ('c', 'd')
assert m[1:2].name == 'b'
assert m[2:7].submodel_names == ('c', 'd', 'e')
with pytest.raises(IndexError):
m['x']
with pytest.raises(IndexError):
m['a':'r']
with pytest.raises(ModelDefinitionError):
assert m[-4:4].submodel_names == ('b', 'c', 'd')
with pytest.raises(ModelDefinitionError):
assert m[-4:-2].submodel_names == ('b', 'c')
def __mul__(self, other):
"""Multiply self and other."""
self._validate_other_mul_div(other)
if isinstance(other, (u.Quantity, numbers.Number)):
newcls = self.__class__(modelclass=self.model | Scale(other))
elif isinstance(other, BaseUnitlessSpectrum):
newcls = self.__class__(modelclass=self.model * other.model)
else: # Source spectrum
raise exceptions.IncompatibleSources(
'Cannot multiply two source spectra together')
self._merge_meta(self, other, newcls)
return newcls
def test_replace_submodel():
"""
Replace a model in a Compound model
"""
S1 = Shift(2, name='shift2') | Scale(
3, name='scale3') # First shift then scale
S2 = Scale(2, name='scale2') | Shift(
3, name='shift3') # First scale then shift
m = S1 & S2
assert m(1, 2) == (9, 7)
m2 = m.replace_submodel('scale3', Scale(4, name='scale4'))
assert m2(1, 2) == (12, 7)
assert m(1, 2) == (9, 7)
# Check the inverse has been updated
assert m2.inverse(12, 7) == (1, 2)
# Produce the same result by replacing a single model with a compound
m3 = m.replace_submodel('shift2', Shift(2) | Scale(2))
assert m(1, 2) == (9, 7)
assert m3(1, 2) == (18, 7)
# Check the inverse has been updated
assert m3.inverse(18, 7) == (1, 2)
# Test with arithmetic model compunding operator
m = S1 + S2
assert m(1) == 14
m2 = m.replace_submodel('scale2', Scale(4, name='scale4'))
assert m2(1) == 16
# Test with fix_inputs()
R = fix_inputs(Rotation2D(angle=90, name='rotate'), {0: 1})
m4 = S1 | R
assert_allclose(m4(0), (-6, 1))
m5 = m4.replace_submodel('rotate', Rotation2D(180))
assert_allclose(m5(0), (-1, -6))
# Check we get a value error when model name doesn't exist
with pytest.raises(ValueError):
m2 = m.replace_submodel('not_there', Scale(2))
# And now a model set
P = Polynomial1D(degree=1, n_models=2, name='poly')
S = Shift([1, 2], n_models=2)
m = P | S
assert_array_equal(m([0, 1]), (1, 2))
with pytest.raises(ValueError):
m2 = m.replace_submodel('poly', Polynomial1D(degree=1, c0=1))
m2 = m.replace_submodel('poly',
Polynomial1D(degree=1, c0=[1, 2], n_models=2))
assert_array_equal(m2([0, 1]), (2, 4))
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 _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 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)
return fore2ote_mapping | (ote & Scale(1e6))
def test_compound_input_units_allow_dimensionless():
"""
Test setting input_units_allow_dimensionless on one of the models.
"""
class ScaleDegrees(Scale):
input_units = {'x': u.deg}
s1 = ScaleDegrees(2)
s1._input_units_allow_dimensionless = True
s2 = Scale(2)
cs = s1 | s2
cs = cs.rename('TestModel')
out = cs(10)
assert_quantity_allclose(out, 40 * u.one)
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10 * u.m)
assert exc.value.args[
0] == "TestModel: Units of input 'x', m (length), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = False
cs = s1 | s2
cs = cs.rename('TestModel')
with pytest.raises(UnitsError) as exc:
out = cs(10)
assert exc.value.args[
0] == "TestModel: Units of input 'x', (dimensionless), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = True
cs = s2 | s1
cs = cs.rename('TestModel')
out = cs(10)
assert_quantity_allclose(out, 40 * u.one)
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10 * u.m)
assert exc.value.args[
0] == "ScaleDegrees: Units of input 'x', m (length), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = False
cs = s2 | s1
with pytest.raises(UnitsError) as exc:
out = cs(10)
assert exc.value.args[
0] == "ScaleDegrees: Units of input 'x', (dimensionless), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = True
s1 = ScaleDegrees(2)
s1._input_units_allow_dimensionless = True
s2 = ScaleDegrees(2)
s2._input_units_allow_dimensionless = False
cs = s1 & s2
cs = cs.rename('TestModel')
out = cs(10, 10 * u.arcsec)
assert_quantity_allclose(out[0], 20 * u.one)
assert_quantity_allclose(out[1], 20 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10, 10)
assert exc.value.args[
0] == "TestModel: Units of input 'x1', (dimensionless), could not be converted to required input units of deg (angle)"
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
def build_nirspec_output_wcs(self, refwcs=None):
"""
Create a simple output wcs covering footprint of the input datamodels
"""
# TODO: generalize this for more than one input datamodel
# TODO: generalize this for imaging modes with distorted wcs
input_model = self.input_models[0]
if refwcs == None:
refwcs = input_model.meta.wcs
# Generate grid of sky coordinates for area within bounding box
bb = refwcs.bounding_box
det = x, y = wcstools.grid_from_bounding_box(bb,
step=(1, 1),
center=True)
sky = ra, dec, lam = refwcs(*det)
x_center = int((bb[0][1] - bb[0][0]) / 2)
y_center = int((bb[1][1] - bb[1][0]) / 2)
log.debug("Center of bounding box: {} {}".format(x_center, y_center))
# Compute slit angular size, slit center sky coords
xpos = []
sz = 3
for row in lam:
if np.isnan(row[x_center]):
xpos.append(np.nan)
else:
f = interpolate.interp1d(row[x_center - sz + 1:x_center + sz],
x[y_center,
x_center - sz + 1:x_center + sz],
bounds_error=False,
fill_value='extrapolate')
xpos.append(f(lam[y_center, x_center]))
x_arg = np.array(xpos)[~np.isnan(lam[:, x_center])]
y_arg = y[~np.isnan(lam[:, x_center]), x_center]
# slit_coords, spect0 = refwcs(x_arg, y_arg, output='numericals_plus')
slit_ra, slit_dec, slit_spec_ref = refwcs(x_arg, y_arg)
slit_coords = SkyCoord(ra=slit_ra, dec=slit_dec, unit=u.deg)
pix_num = np.flipud(np.arange(len(slit_ra)))
# pix_num = np.arange(len(slit_ra))
interpol_ra = interpolate.interp1d(pix_num, slit_ra)
interpol_dec = interpolate.interp1d(pix_num, slit_dec)
slit_center_pix = len(slit_spec_ref) / 2. - 1
log.debug('Slit center pix: {0}'.format(slit_center_pix))
slit_center_sky = SkyCoord(ra=interpol_ra(slit_center_pix),
dec=interpol_dec(slit_center_pix),
unit=u.deg)
log.debug('Slit center: {0}'.format(slit_center_sky))
log.debug('Fiducial: {0}'.format(
resample_utils.compute_spec_fiducial([refwcs])))
angular_slit_size = np.abs(slit_coords[0].separation(slit_coords[-1]))
log.debug('Slit angular size: {0}'.format(angular_slit_size.arcsec))
dra, ddec = slit_coords[0].spherical_offsets_to(slit_coords[-1])
offset_up_slit = (dra.to(u.arcsec), ddec.to(u.arcsec))
log.debug('Offset up the slit: {0}'.format(offset_up_slit))
# Compute spatial and spectral scales
xposn = np.array(xpos)[~np.isnan(xpos)]
dx = xposn[-1] - xposn[0]
slit_npix = np.sqrt(dx**2 + np.array(len(xposn) - 1)**2)
spatial_scale = angular_slit_size / slit_npix
log.debug('Spatial scale: {0}'.format(spatial_scale.arcsec))
spectral_scale = lam[y_center, x_center] - lam[y_center, x_center - 1]
# Compute slit angle relative (clockwise) to y axis
slit_rot_angle = (np.arcsin(dx / slit_npix) * u.radian).to(u.degree)
slit_rot_angle = slit_rot_angle.value
log.debug('Slit rotation angle: {0}'.format(slit_rot_angle))
# Compute transform for output frame
roll_ref = input_model.meta.wcsinfo.roll_ref
min_lam = np.nanmin(lam)
offset = Shift(-slit_center_pix) & Shift(-slit_center_pix)
# TODO: double-check the signs on the following rotation angles
rot = Rotation2D(roll_ref + slit_rot_angle)
scale = Scale(spatial_scale.value) & Scale(spatial_scale.value)
tan = Pix2Sky_TAN()
lon_pole = _compute_lon_pole(slit_center_sky, tan)
skyrot = RotateNative2Celestial(slit_center_sky.ra.value,
slit_center_sky.dec.value,
lon_pole.value)
spatial_trans = offset | rot | scale | tan | skyrot
spectral_trans = Scale(spectral_scale) | Shift(min_lam)
mapping = Mapping((1, 1, 0))
mapping.inverse = Mapping((2, 1))
transform = mapping | spatial_trans & spectral_trans
transform.outputs = ('ra', 'dec', 'lamda')
# Build the output wcs
input_frame = refwcs.input_frame
output_frame = refwcs.output_frame
wnew = WCS(output_frame=output_frame, forward_transform=transform)
# Build the bounding_box in the output frame wcs object
bounding_box_grid = wnew.backward_transform(ra, dec, lam)
bounding_box = []
for axis in input_frame.axes_order:
axis_min = np.nanmin(bounding_box_grid[axis])
axis_max = np.nanmax(bounding_box_grid[axis])
bounding_box.append((axis_min, axis_max))
wnew.bounding_box = tuple(bounding_box)
# Update class properties
self.output_spatial_scale = spatial_scale
self.output_spectral_scale = spectral_scale
self.output_wcs = wnew
def build_miri_output_wcs(self, refwcs=None):
"""
Create a simple output wcs covering footprint of the input datamodels
"""
# TODO: generalize this for more than one input datamodel
# TODO: generalize this for imaging modes with distorted wcs
input_model = self.input_models[0]
if refwcs == None:
refwcs = input_model.meta.wcs
x, y = wcstools.grid_from_bounding_box(refwcs.bounding_box,
step=(1, 1),
center=True)
ra, dec, lam = refwcs(x.flatten(), y.flatten())
# TODO: once astropy.modeling._Tabular is fixed, take out the
# flatten() and reshape() code above and below
ra = ra.reshape(x.shape)
dec = dec.reshape(x.shape)
lam = lam.reshape(x.shape)
# Find rotation of the slit from y axis from the wcs forward transform
# TODO: figure out if angle is necessary for MIRI. See for discussion
# https://github.com/STScI-JWST/jwst/pull/347
rotation = [m for m in refwcs.forward_transform if \
isinstance(m, Rotation2D)]
if rotation:
rot_slit = functools.reduce(lambda x, y: x | y, rotation)
rot_angle = rot_slit.inverse.angle.value
unrotate = rot_slit.inverse
refwcs_minus_rot = refwcs.forward_transform | \
unrotate & Identity(1)
# Correct for this rotation in the wcs
ra, dec, lam = refwcs_minus_rot(x.flatten(), y.flatten())
ra = ra.reshape(x.shape)
dec = dec.reshape(x.shape)
lam = lam.reshape(x.shape)
# Get the slit size at the center of the dispersion
sky_coords = SkyCoord(ra=ra, dec=dec, unit=u.deg)
slit_coords = sky_coords[int(sky_coords.shape[0] / 2)]
slit_angular_size = slit_coords[0].separation(slit_coords[-1])
log.debug('Slit angular size: {0}'.format(slit_angular_size.arcsec))
# Compute slit center from bounding_box
dx0 = refwcs.bounding_box[0][0]
dx1 = refwcs.bounding_box[0][1]
dy0 = refwcs.bounding_box[1][0]
dy1 = refwcs.bounding_box[1][1]
slit_center_pix = (dx1 - dx0) / 2
dispersion_center_pix = (dy1 - dy0) / 2
slit_center = refwcs_minus_rot(dx0 + slit_center_pix,
dy0 + dispersion_center_pix)
slit_center_sky = SkyCoord(ra=slit_center[0],
dec=slit_center[1],
unit=u.deg)
log.debug('slit center: {0}'.format(slit_center))
# Compute spatial and spectral scales
spatial_scale = slit_angular_size / slit_coords.shape[0]
log.debug('Spatial scale: {0}'.format(spatial_scale.arcsec))
tcenter = int((dx1 - dx0) / 2)
trace = lam[:, tcenter]
trace = trace[~np.isnan(trace)]
spectral_scale = np.abs((trace[-1] - trace[0]) / trace.shape[0])
log.debug('spectral scale: {0}'.format(spectral_scale))
# Compute transform for output frame
log.debug('Slit center %s' % slit_center_pix)
offset = Shift(-slit_center_pix) & Shift(-slit_center_pix)
# TODO: double-check the signs on the following rotation angles
roll_ref = input_model.meta.wcsinfo.roll_ref * u.deg
rot = Rotation2D(roll_ref)
tan = Pix2Sky_TAN()
lon_pole = _compute_lon_pole(slit_center_sky, tan)
skyrot = RotateNative2Celestial(slit_center_sky.ra,
slit_center_sky.dec, lon_pole)
min_lam = np.nanmin(lam)
mapping = Mapping((0, 0, 1))
transform = Shift(-slit_center_pix) & Identity(1) | \
Scale(spatial_scale) & Scale(spectral_scale) | \
Identity(1) & Shift(min_lam) | mapping | \
(rot | tan | skyrot) & Identity(1)
transform.inputs = (x, y)
transform.outputs = ('ra', 'dec', 'lamda')
# Build the output wcs
input_frame = refwcs.input_frame
output_frame = refwcs.output_frame
wnew = WCS(output_frame=output_frame, forward_transform=transform)
# Build the bounding_box in the output frame wcs object
bounding_box_grid = wnew.backward_transform(ra, dec, lam)
bounding_box = []
for axis in input_frame.axes_order:
axis_min = np.nanmin(bounding_box_grid[axis])
axis_max = np.nanmax(bounding_box_grid[axis])
bounding_box.append((axis_min, axis_max))
wnew.bounding_box = tuple(bounding_box)
# Update class properties
self.output_spatial_scale = spatial_scale
self.output_spectral_scale = spectral_scale
self.output_wcs = wnew
def test_compound_input_units_allow_dimensionless():
"""
Test setting input_units_allow_dimensionless on one of the models.
"""
class ScaleDegrees(Scale):
input_units = {'x': u.deg}
s1 = ScaleDegrees(2)
s1._input_units_allow_dimensionless = True
s2 = Scale(2)
cs = s1 | s2
cs = cs.rename('TestModel')
out = cs(10)
assert_quantity_allclose(out, 40 * u.one)
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10 * u.m)
assert exc.value.args[0] == "TestModel: Units of input 'x', m (length), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = False
cs = s1 | s2
cs = cs.rename('TestModel')
with pytest.raises(UnitsError) as exc:
out = cs(10)
assert exc.value.args[0] == "TestModel: Units of input 'x', (dimensionless), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = True
cs = s2 | s1
cs = cs.rename('TestModel')
out = cs(10)
assert_quantity_allclose(out, 40 * u.one)
out = cs(10 * u.arcsec)
assert_quantity_allclose(out, 40 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10 * u.m)
assert exc.value.args[0] == "ScaleDegrees: Units of input 'x', m (length), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = False
cs = s2 | s1
with pytest.raises(UnitsError) as exc:
out = cs(10)
assert exc.value.args[0] == "ScaleDegrees: Units of input 'x', (dimensionless), could not be converted to required input units of deg (angle)"
s1._input_units_allow_dimensionless = True
s1 = ScaleDegrees(2)
s1._input_units_allow_dimensionless = True
s2 = ScaleDegrees(2)
s2._input_units_allow_dimensionless = False
cs = s1 & s2
cs = cs.rename('TestModel')
out = cs(10, 10 * u.arcsec)
assert_quantity_allclose(out[0], 20 * u.one)
assert_quantity_allclose(out[1], 20 * u.arcsec)
with pytest.raises(UnitsError) as exc:
out = cs(10, 10)
assert exc.value.args[0] == "TestModel: Units of input 'x1', (dimensionless), could not be converted to required input units of deg (angle)"
