def time_init_7_no_units():
m = (models.Shift(-10.5) & models.Shift(-13.2) |
models.AffineTransformation2D(matrix=[[1, 0], [0, 1]],
translation=[0, 0]) |
models.Scale(.01) & models.Scale(.04) |
models.Pix2Sky_TAN() |
models.RotateNative2Celestial(5.6, -72.05, 180))
def miri_old_model():
d2c = fits.open('polyd2c_1A_01.00.00.fits')
slices = d2c[1].data
rids = np.unique(slices).tolist()
rids.remove(0)
shiftx = models.Shift(-d2c[0].header['CENTERX'])
shifty = models.Shift(-d2c[0].header['CENTERY'])
scalex = models.Scale(1. / d2c[0].header['NORMX'])
scaley = models.Scale(1. / d2c[0].header['NORMY'])
b1 = d2c[1].header['B_DEL']
b0 = d2c[1].header['B_MIN']
coeffs = d2c[2].data
channel_selector = {}
for i in rids:
palpha = fits_column_to_model(coeffs.field("alpha_" + str(int(i))), 4,
4)
plam = fits_column_to_model(coeffs.field("lambda_" + str(int(i))), 4,
4)
malpha = (shiftx & shifty) | (scalex & scaley) | palpha
mlam = (shiftx & shifty) | (scalex & scaley) | plam
beta = models.Const1D(b0 + i * b1)
channel_selector[i] = models.Mapping(
(0, 1, 0, 0, 1)) | malpha & beta & mlam
return channel_selector
def from_fits(cls, header):
if not isinstance(header, fits.Header):
raise TypeError("expected a FIST Header")
fitswcs = util.read_wcs_from_header(header)
wcsaxes = fitswcs['WCSAXES']
if fitswcs['has_cd']:
pc = fitswcs['CD']
else:
pc = fitswcs['PC']
# get the part of the PC matrix corresponding to the imaging axes
sky_axes = None
if pc.shape != (2, 2):
sky_axes, _ = util.get_axes(fitswcs)
i, j = sky_axes
sky_pc = np.zeros((2,2))
sky_pc[0, 0] = pc[i, i]
sky_pc[0, 1] = pc[i, j]
sky_pc[1, 0] = pc[j, i]
sky_pc[1, 1] = pc[j, j]
pc = sky_pc.copy()
if sky_axes is not None:
crpix = []
cdelt = []
for i in sky_axes:
crpix.append(fitswcs['CRPIX'][i])
cdelt.append(fitswcs['CDELT'][i])
else:
cdelt = fitswcs['CDELT']
crpix = fitswcs['CRPIX']
translation = astmodels.Shift(-crpix[0], name='offset_x') & astmodels.Shift(-crpix[1], name='offset_y')
rotation = astmodels.AffineTransformation2D(matrix=pc, name='orient')
scale = astmodels.Scale(cdelt[0], name='scale_x') & astmodels.Scale(cdelt[1], name='scale_y')
return cls(translation, rotation, scale)
def test_bounding_box():
trans3 = models.Shift(10) & models.Scale(2) & models.Shift(-1)
pipeline = [('detector', trans3), ('sky', None)]
w = wcs.WCS(pipeline)
bb = ((-1, 10), (6, 15))
with pytest.raises(ValueError):
w.bounding_box = bb
trans2 = models.Shift(10) & models.Scale(2)
pipeline = [('detector', trans2), ('sky', None)]
w = wcs.WCS(pipeline)
w.bounding_box = bb
assert w.bounding_box == w.forward_transform.bounding_box[::-1]
pipeline = [("detector", models.Shift(2)), ("sky", None)]
w = wcs.WCS(pipeline)
w.bounding_box = (1, 5)
assert w.bounding_box == w.forward_transform.bounding_box
with pytest.raises(ValueError):
w.bounding_box = ((1, 5), (2, 6))
# Test that bounding_box with quantities can be assigned and evaluates
bb = ((1 * u.pix, 5 * u.pix), (2 * u.pix, 6 * u.pix))
trans = models.Shift(10 * u.pix) & models.Shift(2 * u.pix)
pipeline = [('detector', trans), ('sky', None)]
w = wcs.WCS(pipeline)
w.bounding_box = bb
assert_allclose(w(-1 * u.pix, -1 * u.pix), (np.nan, np.nan))
def setup(self):
aff = models.AffineTransformation2D(matrix=[[1, 0], [0, 1]],
translation=[0, 0])
self.model = (models.Shift(-10.5) & models.Shift(-13.2) | aff
| models.Scale(.01) & models.Scale(.04)
| models.Pix2Sky_TAN()
| models.RotateNative2Celestial(5.6, -72.05, 180))
def test_RegionsSelector():
labels = np.zeros((10, 10))
labels[1, 2] = 1
labels[2][2: 4] = 1
labels[3][1: 4] = 1
labels[4][: 4] = 1
labels[5][1: 4] = 1
labels[6][2: 7] = 1
labels[7][3: 6] = 1
labels[:, -2:] = 2
mapper = selector.LabelMapperArray(labels)
sel = {1: models.Shift(1) & models.Scale(1),
2: models.Shift(2) & models.Scale(2)
}
with pytest.raises(ValueError):
# 0 can't be a key in ``selector``
selector.RegionsSelector(inputs=('x', 'y'), outputs=('x', 'y'),
label_mapper=mapper,
selector={0: models.Shift(1) & models.Scale(1),
2: models.Shift(2) & models.Scale(2)
}
)
reg_selector = selector.RegionsSelector(inputs=('x', 'y'), outputs=('x', 'y'),
label_mapper=mapper,
selector=sel
)
with pytest.raises(NotImplementedError):
reg_selector.inverse
mapper.inverse = mapper.copy()
assert_allclose(reg_selector(2, 1), sel[1](2, 1))
assert_allclose(reg_selector(8, 2), sel[2](8, 2))
# test set_input
with pytest.raises(gwutils.RegionError):
reg_selector.set_input(3)
transform = reg_selector.set_input(2)
assert_equal(transform.parameters, [2, 2])
assert_allclose(transform(1, 1), sel[2](1, 1))
# test inverse
rsinv = reg_selector.inverse
# The label_mapper arays should be the same
assert_equal(reg_selector.label_mapper.mapper, rsinv.label_mapper.mapper)
# the transforms of the inverse ``RegionsSelector`` should be the inverse of the
# transforms of the ``RegionsSelector`` model.
x = np.linspace(-5, 5, 100)
assert_allclose(reg_selector.selector[1].inverse(x, x),
rsinv.selector[1](x, x))
assert_allclose(reg_selector.selector[2].inverse(x, x),
rsinv.selector[2](x, x))
assert np.isnan(reg_selector(0, 0)).all()
# Test setting ``undefined_transform_value`` to a non-default value.
reg_selector.undefined_transform_value = -100
assert_equal(reg_selector(0, 0), [-100, -100])
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 imaging_distortion(input_model, reference_files):
"""
Create pixe2sky and sky2pixel transformation for the MIRI imager.
Parameters
----------
model : jwst.datamodels.ImagingModel
input model
reference_files : dict
reference files from CRDS
using CDP 3 Reference distortion file
Old one: ~~MIRI_FM_MIRIMAGE_F1000W_PSF_03.01.00.fits~~
Current one: MIRI_FM_MIRIMAGE_DISTORTION_06.03.00.fits
reference files/corrections needed (pixel to sky):
1. Filter dependent shift in (x,y) (!with an oposite sign to that delievred by the IT)
2. Apply MI
3. Apply Ai and BI matrices
4. Apply the TI matrix (this gives V2/V3 coordinates)
5. Apply V2/V3 to sky transformation
ref_file: filter_offset.asdf - (1)
ref_file: distortion.asdf -(2,3,4)
"""
# Load the distortion and filter from the reference files.
# Load in the distortion file.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[
input_model.meta.instrument.filter]
# Now apply each of the models. The Scale(60) converts from arc-minutes to deg.
full_distortion = models.Shift(
filter_offset['column_offset']) & models.Shift(
filter_offset['row_offset']) | distortion | models.Scale(
1 / 60) & models.Scale(1 / 60)
# ToDo: This will likely have to change in the future, but the "filteroffset" file we have
# ToDo: currently does not contain that key.
filter_offset = None
if input_model.meta.instrument.filter in AsdfFile.open(
reference_files['filteroffset']).tree:
filter_offset = AsdfFile.open(reference_files['filteroffset']).tree[
input_model.meta.instrument.filter]
full_distortion = models.Shift(
filter_offset['row_offset']) & models.Shift(
filter_offset['column_offset']) | distortion
else:
full_distortion = distortion
full_distortion = full_distortion.rename('distortion')
return full_distortion
def ifu(input_model, reference_files):
"""
Create the WCS pipeline for a MIRI IFU observation.
"""
#reference_files = {'distortion': 'jwst_miri_distortion_00001.asdf', #files must hold 2 channels each
#'specwcs': 'jwst_miri_specwcs_00001.asdf',
#'regions': 'jwst_miri_regions_00001.asdf',
#'v2v3': 'jwst_miri_v2v3_00001.asdf'
#'wavelengthrange': 'jwst_miri_wavelengthrange_0001.asdf'}
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
alpha_beta = cf.Frame2D(name='alpha_beta_spatial',
axes_order=(0, 1),
unit=(u.arcsec, u.arcsec),
axes_names=('alpha', 'beta'))
spec_local = cf.SpectralFrame(name='alpha_beta_spectral',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
miri_focal = cf.CompositeFrame([alpha_beta, spec_local], name='alpha_beta')
xyan_spatial = cf.Frame2D(name='Xan_Yan_spatial',
axes_order=(0, 1),
unit=(u.arcmin, u.arcmin),
axes_names=('v2', 'v3'))
spec = cf.SpectralFrame(name='Xan_Yan_spectral',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
xyan = cf.CompositeFrame([xyan_spatial, spec], name='Xan_Yan')
v23_spatial = cf.Frame2D(name='V2_V3_spatial',
axes_order=(0, 1),
unit=(u.deg, u.deg),
axes_names=('v2', 'v3'))
spec = cf.SpectralFrame(name='spectral',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
v2v3 = cf.CompositeFrame([v23_spatial, spec], name='v2v3')
icrs = cf.CelestialFrame(name='icrs',
reference_frame=coord.ICRS(),
axes_order=(0, 1),
unit=(u.deg, u.deg),
axes_names=('RA', 'DEC'))
world = cf.CompositeFrame([icrs, spec], name='world')
det2alpha_beta = (detector_to_alpha_beta(
input_model, reference_files)).rename("detector_to_alpha_beta")
ab2xyan = (alpha_beta2XanYan(
input_model, reference_files)).rename("alpha_beta_to_Xan_Yan")
xyan2v23 = models.Identity(1) & (models.Shift(7.8) | models.Scale(-1)) & models.Identity(1) | \
models.Scale(1/60) & models.Scale(1/60) & models.Identity(1)
tel2sky = pointing.v23tosky(input_model) & models.Identity(1)
pipeline = [(detector, det2alpha_beta), (miri_focal, ab2xyan),
(xyan, xyan2v23), (v2v3, tel2sky), (world, None)]
return pipeline
def test_calculate_affine_matrices(angle, scale, xoffset, yoffset):
m = ((models.Scale(scale) & models.Scale(scale)) | models.Rotation2D(angle)
| (models.Shift(xoffset) & models.Shift(yoffset)))
affine = adwcs.calculate_affine_matrices(m, (100, 100))
assert_allclose(affine.offset, (yoffset, xoffset), atol=1e-10)
angle = math.radians(angle)
assert_allclose(affine.matrix,
((scale * math.cos(angle), scale * math.sin(angle)),
(-scale * math.sin(angle), scale * math.cos(angle))),
atol=1e-10)
def setup(self):
aff = models.AffineTransformation2D(matrix=[[1, 0], [0, 1]] * u.arcsec,
translation=[0, 0] * u.arcsec)
aff.input_units_equivalencies = {'x': u.pixel_scale(1 * u.arcsec/u.pix),
'y': u.pixel_scale(1 * u.arcsec/u.pix)}
self.model = (models.Shift(-10.5 * u.pix) & models.Shift(-13.2 * u.pix) |
aff |
models.Scale(.01 * u.arcsec) & models.Scale(.04 * u.deg) |
models.Pix2Sky_TAN() |
models.RotateNative2Celestial(5.6 * u.deg, -72.05 * u.deg, 180 * u.deg))
def test_ScaleModel():
# Scale by a scalar
m = models.Scale(42)
assert m(0) == 0
assert_equal(m([1, 2]), [42, 84])
# Scale by a list
m = models.Scale([42, 43], n_models=2)
assert_equal(m(0), [0, 0])
assert_equal(m([1, 2], model_set_axis=False), [[42, 84], [43, 86]])
def fpa2asdf(fpafile, outname, ref_kw):
"""
Create an asdf reference file with the FPA description.
The CDP2 delivery includes a fits file - "FPA.fpa" which is the
input to this function. This file is converted to asdf and is a
reference file of type "FPA".
nirspec_fs_ref_tools.fpa2asdf('Ref_Files/CoordTransform/Description/FPA.fpa', 'fpa.asdf')
Parameters
----------
fpafile : str
A fits file with FPA description (FPA.fpa)
outname : str
Name of output ASDF file.
"""
with open(fpafile) as f:
lines = [l.strip() for l in f.readlines()]
# NRS1
ind = lines.index("*SCA491_PitchX")
scalex_nrs1 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_x')
ind = lines.index("*SCA491_PitchY")
scaley_nrs1 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_y')
ind = lines.index("*SCA491_RotAngle")
rot_nrs1 = models.Rotation2D(np.rad2deg(-float(lines[ind + 1])),
name='fpa_rotation')
ind = lines.index("*SCA491_PosX")
shiftx_nrs1 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_x')
ind = lines.index("*SCA491_PosY")
shifty_nrs1 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_y')
# NRS2
ind = lines.index("*SCA492_PitchX")
scalex_nrs2 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_x')
ind = lines.index("*SCA492_PitchY")
scaley_nrs2 = models.Scale(1 / float(lines[ind + 1]), name='fpa_scale_y')
ind = lines.index("*SCA492_RotAngle")
rot_nrs2 = models.Rotation2D(np.rad2deg(float(lines[ind + 1])),
name='fpa_rotation')
ind = lines.index("*SCA492_PosX")
shiftx_nrs2 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_x')
ind = lines.index("*SCA492_PosY")
shifty_nrs2 = models.Shift(-float(lines[ind + 1]), name='fpa_shift_y')
tree = ref_kw.copy()
tree['NRS1'] = (shiftx_nrs1 & shifty_nrs1) | rot_nrs1 | (scalex_nrs1
& scaley_nrs1)
tree['NRS2'] = (shiftx_nrs2 & shifty_nrs2) | rot_nrs2 | (scalex_nrs2
& scaley_nrs2)
fasdf = AsdfFile()
fasdf.tree = tree
fasdf.write_to(outname)
return fasdf
def test_domain():
trans3 = models.Shift(10) & models.Scale(2) & models.Shift(-1)
pipeline = [('detector', trans3), ('sky', None)]
w = wcs.WCS(pipeline)
bb = ((-1, 10), (6, 15))
with pytest.raises(DimensionalityError):
w.bounding_box = bb
trans2 = models.Shift(10) & models.Scale(2)
pipeline = [('detector', trans2), ('sky', None)]
w = wcs.WCS(pipeline)
w.bounding_box = bb
assert w.bounding_box == w.forward_transform.bounding_box[::-1]
def test_coupled_compound_model_nested():
ccm = CoupledCompoundModel("&", m.Shift(5) & m.Scale(2), m.Scale(10) | m.Shift(3))
new = roundtrip_object(ccm)
assert isinstance(new, CoupledCompoundModel)
assert isinstance(new.left, CompoundModel)
assert isinstance(new.left.left, m.Shift)
assert isinstance(new.left.right, m.Scale)
assert isinstance(new.right, CompoundModel)
assert isinstance(new.right.left, m.Scale)
assert isinstance(new.right.right, m.Shift)
assert ccm.n_inputs == new.n_inputs
assert ccm.inputs == new.inputs
def v23tosky(input_model):
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 = [-v2_ref, v3_ref, -roll_ref, -dec_ref, ra_ref]
axes = "zyxyz"
sky_rotation = V23ToSky(angles, axes_order=axes, name="v23tosky")
# The sky rotation expects values in deg.
# This should be removed when models work with quantities.
return astmodels.Scale(1 / 3600) & astmodels.Scale(1 / 3600) | sky_rotation
def test_domain():
trans3 = models.Shift(10) & models.Scale(2) & models.Shift(-1)
pipeline = [('detector', trans3), ('sky', None)]
w = wcs.WCS(pipeline)
domain = [{'lower': -1, 'upper': 10, 'include_lower': True, 'include_upper': False, 'step': .1},
{'lower': 6, 'upper': 15, 'include_lower': False, 'include_upper': True, 'step': .5}]
with pytest.raises(ValueError):
w.domain = domain
trans2 = models.Shift(10) & models.Scale(2)
pipeline = [('detector', trans2), ('sky', None)]
w = wcs.WCS(pipeline)
w.domain = domain
assert w.domain == w.forward_transform.meta['domain']
def imaging_distortion(input_model, reference_files):
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
# Convert to deg
transform = distortion | models.Scale(1 / 3600) & models.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.
bb = ((-0.5, shape[0] - 0.5), (-0.5, shape[1] - 0.5))
transform.bounding_box = bb
return transform
def test_create_wcs(tmpdir):
m1 = models.Shift(12.4) & models.Shift(-2)
m2 = models.Scale(2) & models.Scale(-2)
icrs = cf.CelestialFrame(name='icrs', reference_frame=coord.ICRS())
det = cf.Frame2D(name='detector', axes_order=(0, 1))
gw1 = wcs.WCS(output_frame='icrs',
input_frame='detector',
forward_transform=m1)
gw2 = wcs.WCS(output_frame='icrs', forward_transform=m1)
gw3 = wcs.WCS(output_frame=icrs, input_frame=det, forward_transform=m1)
tree = {'gw1': gw1, 'gw2': gw2, 'gw3': gw3}
helpers.assert_roundtrip_tree(tree, tmpdir)
def test_backward_transform():
"""
Test backward transform raises an error when an analytical
inverse is not available.
"""
# Test that an error is raised when one of the models has not inverse.
poly = models.Polynomial1D(1, c0=4)
w = wcs.WCS(forward_transform=poly & models.Scale(2), output_frame='sky')
with pytest.raises(NotImplementedError):
w.backward_transform
# test backward transform
poly.inverse = models.Shift(-4)
w = wcs.WCS(forward_transform=poly & models.Scale(2), output_frame='sky')
assert_allclose(w.backward_transform(1, 2), (-3, 1))
def _generate_wcs_transform(dispaxis):
"""Create mock gwcs.WCS object for resampled s2d data"""
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
icrs = cf.CelestialFrame(name='icrs',
reference_frame=coord.ICRS(),
axes_order=(0, 1),
unit=(u.deg, u.deg),
axes_names=('RA', 'DEC'))
spec = cf.SpectralFrame(name='spec',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
world = cf.CompositeFrame(name="world", frames=[icrs, spec])
if dispaxis == 1:
mapping = models.Mapping((0, 1, 0))
if dispaxis == 2:
mapping = models.Mapping((0, 1, 1))
transform = mapping | (models.Const1D(42) & models.Const1D(42)
& (models.Shift(30) | models.Scale(0.1)))
pipeline = [(detector, transform), (world, None)]
wcs = WCS(pipeline)
return wcs
def test_footprint():
icrs = cf.CelestialFrame(name='icrs',
reference_frame=coord.ICRS(),
axes_order=(0, 1))
spec = cf.SpectralFrame(name='freq', unit=[
u.Hz,
], axes_order=(2, ))
world = cf.CompositeFrame([icrs, spec])
transform = (models.Shift(10) & models.Shift(-1)) & models.Scale(2)
pipe = [('det', transform), (world, None)]
w = wcs.WCS(pipe)
with pytest.raises(TypeError):
w.footprint()
w.bounding_box = ((1, 5), (1, 3), (1, 6))
assert_equal(
w.footprint(),
np.array([[11, 0, 2], [11, 0, 12], [11, 2, 2], [11, 2, 12], [15, 0, 2],
[15, 0, 12], [15, 2, 2], [15, 2, 12]]))
assert_equal(w.footprint(axis_type='spatial'),
np.array([[11., 0.], [11., 2.], [15., 2.], [15., 0.]]))
assert_equal(w.footprint(axis_type='spectral'), np.array([2, 12]))
def test_from_fiducial_composite():
sky = coord.SkyCoord(1.63 * u.radian, -72.4 * u.deg, frame='fk5')
tan = models.Pix2Sky_TAN()
spec = cf.SpectralFrame(unit=(u.micron, ), axes_order=(0, ))
celestial = cf.CelestialFrame(reference_frame=sky.frame,
unit=(sky.spherical.lon.unit,
sky.spherical.lat.unit),
axes_order=(1, 2))
coord_frame = cf.CompositeFrame([spec, celestial], name='cube_frame')
w = wcs_from_fiducial([.5, sky], coord_frame, projection=tan)
assert isinstance(w.cube_frame.frames[1].reference_frame, coord.FK5)
assert_allclose(w(1, 1, 1), (1.5, 96.52373368309931, -71.37420187296995))
# test returning coordinate objects with composite output_frame
res = w(1, 2, 2, with_units=True)
assert_allclose(res[0], u.Quantity(1.5 * u.micron))
assert isinstance(res[1], coord.SkyCoord)
assert_allclose(res[1].ra.value, 99.329496642319)
assert_allclose(res[1].dec.value, -70.30322020351122)
trans = models.Shift(10) & models.Scale(2) & models.Shift(-1)
w = wcs_from_fiducial([.5, sky],
coord_frame,
projection=tan,
transform=trans)
assert_allclose(w(1, 1, 1), (11.5, 99.97738475762152, -72.29039139739766))
# test coordinate object output
coord_result = w(1, 1, 1, with_units=True)
assert_allclose(coord_result[0], u.Quantity(11.5 * u.micron))
def test_high_level_api():
"""
Test WCS high level API.
"""
output_frame = cf.CompositeFrame(frames=[icrs, spec])
transform = m1 & models.Scale(1.5)
det = cf.CoordinateFrame(naxes=3,
unit=(u.pix, u.pix, u.pix),
axes_order=(0, 1, 2),
axes_type=('length', 'length', 'length'))
w = wcs.WCS(forward_transform=transform,
output_frame=output_frame,
input_frame=det)
r, d, lam = w(xv, yv, xv)
world_coord = w.pixel_to_world(xv, yv, xv)
assert isinstance(world_coord[0], coord.SkyCoord)
assert isinstance(world_coord[1], u.Quantity)
assert_allclose(world_coord[0].data.lon.value, r)
assert_allclose(world_coord[0].data.lat.value, d)
assert_allclose(world_coord[1].value, lam)
x1, y1, z1 = w.world_to_pixel(*world_coord)
assert_allclose(x1, xv)
assert_allclose(y1, yv)
assert_allclose(z1, xv)
def fitswcs_transform_from_model(wcsinfo, wavetable=None):
"""
Create a WCS object using from datamodel.meta.wcsinfo.
Transforms assume 0-based coordinates.
Parameters
----------
wcsinfo : dict-like
``~jwst.meta.wcsinfo`` structure.
Return
------
transform : `~astropy.modeling.core.Model`
WCS forward transform - from pixel to world coordinates.
"""
spatial_axes, spectral_axes, unknown = gwutils.get_axes(wcsinfo)
transform = gwutils.make_fitswcs_transform(wcsinfo)
if spectral_axes:
sp_axis = spectral_axes[0]
if wavetable is None:
# Subtract one from CRPIX which is 1-based.
spectral_transform = astmodels.Shift(-(wcsinfo['CRPIX'][sp_axis] - 1)) | \
astmodels.Scale(wcsinfo['CDELT'][sp_axis]) | \
astmodels.Shift(wcsinfo['CRVAL'][sp_axis])
else:
# Wave dimension is an array that needs to be converted to a table
waves = wavetable['wavelength'].flatten()
spectral_transform = astmodels.Tabular1D(lookup_table=waves)
transform = transform & spectral_transform
return transform
def fitswcs_transform_from_model(wcsinfo):
"""
Create a WCS object using from datamodel.meta.wcsinfo.
Transforms assume 0-based coordinates.
Parameters
----------
wcsinfo : dict-like
``~jwst.meta.wcsinfo`` structure.
Return
------
transform : `~astropy.modeling.core.Model`
WCS forward transform - from pixel to world coordinates.
"""
spatial_axes, spectral_axes, unknown = gwutils.get_axes(wcsinfo)
#sp_axis = spectral_axes[0]
transform = gwutils.make_fitswcs_transform(wcsinfo)
#if wcsinfo['WCSAXES'] == 3:
if spectral_axes:
sp_axis = spectral_axes[0]
# Subtract one from CRPIX which is 1-based.
spectral_transform = astmodels.Shift(-(wcsinfo['CRPIX'][sp_axis] - 1)) | \
astmodels.Scale(wcsinfo['CDELT'][sp_axis]) | \
astmodels.Shift(wcsinfo['CRVAL'][sp_axis])
transform = transform & spectral_transform
return transform
def test_custom_inverse_reset():
"""Test resetting a custom inverse to the model's default inverse."""
class TestModel(Model):
inputs = ()
outputs = ('y', )
@property
def inverse(self):
return models.Shift()
@staticmethod
def evaluate():
return 0
# The above test model has no meaning, nor does its inverse--this just
# tests that setting an inverse and resetting to the default inverse works
m = TestModel()
assert isinstance(m.inverse, models.Shift)
m.inverse = models.Scale()
assert isinstance(m.inverse, models.Scale)
del m.inverse
assert isinstance(m.inverse, models.Shift)
def from_tree_transform(self, node):
factor = node['factor']
if not isinstance(factor, u.Quantity) and not np.isscalar(factor):
raise NotImplementedError(
"Asdf currently only supports scalar inputs to Scale transform."
)
return models.Scale(factor)
def test_insert_transform():
""" Tests inserting a transform."""
m1 = models.Shift(12.4)
m2 = models.Scale(3.1)
gw = wcs.WCS(output_frame='icrs', forward_transform=m1)
assert (gw.forward_transform(1.2) == m1(1.2))
gw.insert_transform(frame='icrs', transform=m2)
assert (gw.forward_transform(1.2) == (m1 | m2)(1.2))
def test_Scale_inverse_bounding_box():
model = models.Scale(2)
model.bounding_box = (1, 5)
assert model.bounding_box == (1, 5)
inverse_model = model.inverse
assert inverse_model.bounding_box == (2, 10)
assert inverse_model(model(4, with_bounding_box=True), with_bounding_box=True) == 4.0
