def test_prepare_outputs_sparse_grid():
"""
Test to show that #11060 has been solved.
"""
shape = (3, 3)
data = np.arange(np.product(shape)).reshape(shape) * u.m / u.s
points_unit = u.pix
points = [np.arange(size) * points_unit for size in shape]
kwargs = {
'bounds_error': False,
'fill_value': np.nan,
'method': 'nearest',
}
transform = models.Tabular2D(points, data, **kwargs)
truth = np.array([[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]]) * u.m / u.s
points = np.meshgrid(np.arange(3), np.arange(3), indexing='ij', sparse=True)
x = points[0] * u.pix
y = points[1] * u.pix
value = transform(x, y)
assert (value == truth).all()
points = np.meshgrid(np.arange(3), np.arange(3), indexing='ij', sparse=False) * u.pix
value = transform(*points)
assert (value == truth).all()
def test_tabular_str():
points = np.arange(5)
lt = np.arange(5)
t = models.Tabular1D(points, lt)
assert str(t) == ("Model: Tabular1D\n"
"N_inputs: 1\n"
"N_outputs: 1\n"
"Parameters: \n"
" points: (array([0, 1, 2, 3, 4]),)\n"
" lookup_table: [0 1 2 3 4]\n"
" method: linear\n"
" fill_value: nan\n"
" bounds_error: True")
table = np.arange(5 * 5).reshape(5, 5)
points = np.arange(0, 5)
points = (points, points)
t = models.Tabular2D(points=points, lookup_table=table)
assert str(t) == (
"Model: Tabular2D\n"
"N_inputs: 2\n"
"N_outputs: 1\n"
"Parameters: \n"
" points: (array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4]))\n"
" lookup_table: [[ 0 1 2 3 4]\n"
" [ 5 6 7 8 9]\n"
" [10 11 12 13 14]\n"
" [15 16 17 18 19]\n"
" [20 21 22 23 24]]\n"
" method: linear\n"
" fill_value: nan\n"
" bounds_error: True")
def test_tabular1d_inverse():
"""Test that the Tabular1D inverse is defined"""
points = np.arange(5)
values = np.array([1.5, 3.4, 6.7, 7, 32])
t = models.Tabular1D(points, values)
result = t.inverse((3.4, 6.7))
assert_allclose(result, np.array((1., 2.)))
# Check that it works for descending values in lookup_table
t2 = models.Tabular1D(points, values[::-1])
assert_allclose(t2.inverse.points[0], t2.lookup_table[::-1])
result2 = t2.inverse((7, 6.7))
assert_allclose(result2, np.array((1., 2.)))
# Check that it errors on double-valued lookup_table
points = np.arange(5)
values = np.array([1.5, 3.4, 3.4, 32, 25])
t = models.Tabular1D(points, values)
with pytest.raises(NotImplementedError):
t.inverse((3.4, 7.))
# Check that Tabular2D.inverse raises an error
table = np.arange(5 * 5).reshape(5, 5)
points = np.arange(0, 5)
points = (points, points)
t3 = models.Tabular2D(points=points, lookup_table=table)
with pytest.raises(NotImplementedError):
t3.inverse((3, 3))
def test_tabular_grid_shape_mismatch_error():
points = np.arange(5)
lt = np.mgrid[0:5, 0:5][0]
with pytest.raises(ValueError) as err:
models.Tabular2D(points, lt)
assert str(err.value) ==\
"Expected grid points in 2 directions, got 5."
def test_tabular_2d_quantity():
shape = (3, 3)
data = np.arange(np.product(shape)).reshape(shape) * u.m / u.s
# The integer location is at the centre of the pixel.
points_unit = u.pix
points = [(np.arange(size) - 0) * points_unit for size in shape]
kwargs = {
'bounds_error': False,
'fill_value': np.nan,
'method': 'nearest',
}
forward = models.Tabular2D(points, data, **kwargs)
input_frame = cf.CoordinateFrame(2, ("PIXEL", "PIXEL"), (0, 1),
unit=(u.pix, u.pix),
name="detector")
output_frame = cf.CoordinateFrame(1, "CUSTOM", (0, ), unit=(u.m / u.s, ))
w = wcs.WCS(forward_transform=forward,
input_frame=input_frame,
output_frame=output_frame)
bb = w.bounding_box
assert all(u.allclose(u.Quantity(b), [0, 2] * u.pix) for b in bb)
def test_tabular_interp_2d():
table = np.array(
[[-0.04614432, -0.02512547, -0.00619557, 0.0144165, 0.0297525],
[-0.04510594, -0.03183369, -0.01118008, 0.01201388, 0.02496205],
[-0.05464094, -0.02804499, -0.00960086, 0.01134333, 0.02284104],
[-0.04879338, -0.02539565, -0.00440462, 0.01795145, 0.02122417],
[-0.03637372, -0.01630025, -0.00157902, 0.01649774, 0.01952131]])
points = np.arange(0, 5)
points = (points, points)
xnew = np.array([0., .7, 1.4, 2.1, 3.9])
LookupTable = models.tabular_model(2)
model = LookupTable(points, table)
znew = model(xnew, xnew)
result = np.array(
[-0.04614432, -0.03450009, -0.02241028, -0.0069727, 0.01938675])
assert_allclose(znew, result, atol=1e-7)
# test 2D arrays as input
a = np.arange(12).reshape((3, 4))
y, x = np.mgrid[:3, :4]
t = models.Tabular2D(lookup_table=a)
r = t(y, x)
assert_allclose(a, r)
with pytest.raises(ValueError):
model = LookupTable(points=([1.2, 2.3], [1.2, 6.7], [3, 4]))
with pytest.raises(ValueError):
model = LookupTable(lookup_table=[1, 2, 3])
with pytest.raises(NotImplementedError):
model = LookupTable(n_models=2)
with pytest.raises(ValueError):
model = LookupTable(([1, 2], [3, 4]), [5, 6])
with pytest.raises(ValueError):
model = LookupTable(([1, 2] * u.m, [3, 4]), [[5, 6], [7, 8]])
with pytest.raises(ValueError):
model = LookupTable(points,
table,
bounds_error=False,
fill_value=1 * u.Jy)
# Test unit support
points = points[0] * u.nm
points = (points, points)
xnew = xnew * u.nm
model = LookupTable(points, table * u.nJy)
result = result * u.nJy
assert_quantity_allclose(model(xnew, xnew), result, atol=1e-7 * u.nJy)
xnew = xnew.to(u.m)
assert_quantity_allclose(model(xnew, xnew), result, atol=1e-7 * u.nJy)
bbox = (0 * u.nm, 4 * u.nm)
bbox = (bbox, bbox)
assert model.bounding_box == bbox
def test_tabular_model(tmpdir):
points = np.arange(0, 5)
values = [1., 10, 2, 45, -3]
model = astmodels.Tabular1D(points=points, lookup_table=values)
tree = {'model': model}
helpers.assert_roundtrip_tree(tree, tmpdir)
table = np.array([[ 3., 0., 0.],
[ 0., 2., 0.],
[ 0., 0., 0.]])
points = ([1, 2, 3], [1, 2, 3])
model2 = astmodels.Tabular2D(points, lookup_table=table, bounds_error=False,
fill_value=None, method='nearest')
tree = {'model': model2}
helpers.assert_roundtrip_tree(tree, tmpdir)
def test_tabular_repr():
points = np.arange(5)
lt = np.arange(5)
t = models.Tabular1D(points, lt)
assert repr(t) ==\
"<Tabular1D(points=(array([0, 1, 2, 3, 4]),), lookup_table=[0 1 2 3 4])>"
table = np.arange(5 * 5).reshape(5, 5)
points = np.arange(0, 5)
points = (points, points)
t = models.Tabular2D(points=points, lookup_table=table)
assert repr(t) ==\
"<Tabular2D(points=(array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4])), " +\
"lookup_table=[[ 0 1 2 3 4]\n" +\
" [ 5 6 7 8 9]\n" +\
" [10 11 12 13 14]\n" +\
" [15 16 17 18 19]\n" +\
" [20 21 22 23 24]])>"
def _wzpc2asdf(wzpcfile, author, description, useafter):
f = fits.open(wzpcfile)
width = f[1].header['width']
name = ap_names_map[f[0].header['COMPNAME']]
data = f[1].data
var = f[2].data
w = wcs.WCS(f[1].header)
f.close()
y, x = np.mgrid[:data.shape[0], :data.shape[1]]
# The WCS in the current ref files is 0-based.
X, Y = w.all_pix2world(x, y, 0)
tab = models.Tabular2D(points=(X[0], Y[:, 0]), lookup_table=data)
aperture = {
'aperture_name': name,
'variance': var,
'zero_point_offset': tab,
'width': width
}
return aperture
result = model([1, 1], [2, 2])
assert_allclose(result, (np.array([1, 1]), np.array([2, 2])))
def test_format_input_arrays_transposed():
model = TInputFormatter()
input = np.array([[1, 1]]).T, np.array([[2, 2]]).T
result = model(*input)
assert_allclose(result, input)
@pytest.mark.parametrize('model', [
models.Gaussian2D(),
models.Polynomial2D(1, ),
models.Pix2Sky_TAN(),
models.Tabular2D(lookup_table=np.ones((4, 5)))
])
@pytest.mark.skipif('not HAS_SCIPY')
def test_call_keyword_args_2(model):
"""
Test calling a model with positional, keywrd and a mixture of both arguments.
"""
positional = model(1, 2)
assert_allclose(positional, model(x=1, y=2))
assert_allclose(positional, model(1, y=2))
model.inputs = ('r', 't')
assert_allclose(positional, model(r=1, t=2))
assert_allclose(positional, model(1, t=2))
assert_allclose(positional, model(1, 2))
def xytov2v3lam(xin, yin, stype):
# Open relevant distortion file
specfile = fits.open(get_fitsreffile())
# Convert input x,y vectors to numpy arrays
x = np.array(xin)
y = np.array(yin)
# Global header
hdr = specfile[0].header
# File data
lrsdata = np.array([l for l in specfile[1].data])
# Define zero points (in subarray frame), subarray location, subarray size
if (stype.lower() == 'slit'):
xsub0, ysub0 = 0, 0
xsub1, ysub1 = 1031, 1023
zero_point = hdr['imx'] - 1, hdr['imy'] - 1
elif (stype.lower() == 'slitless'):
xsub0, ysub0 = 0, 528
xsub1, ysub1 = 71, 415
zero_point = hdr['imxsltl'] - 1 - xsub0, hdr['imysltl'] - 1 - ysub0
else:
print('Invalid operation type: specify either slit or slitless')
# In the lrsdata reference table, X_center,y_center,wavelength describe the location of the
# centroid trace along the detector in pixels relative to nominal location.
# The box corners for this wavelength are x0,y0(ul) x1,y1 (ur) x2,y2(lr) x3,y3(ll)
# Use these to create the bounding box for all valid detector locations in units of subarray pixels
xcen = lrsdata[:, 0]
ycen = lrsdata[:, 1]
wavetab = lrsdata[:, 2]
x0 = lrsdata[:, 3]
y0 = lrsdata[:, 4]
x1 = lrsdata[:, 5]
y2 = lrsdata[:, 8]
bb = ((x0.min() - 0.5 + zero_point[0], x1.max() + 0.5 + zero_point[0]),
(y2.min() - 0.5 + zero_point[1], y0.max() + 0.5 + zero_point[1]))
# Find the ROW of the zero point
row_zero_point = zero_point[1]
# Make a vector of x,y locations for every pixel in the reference row
yrow, xrow = np.mgrid[row_zero_point:row_zero_point + 1, 0:xsub1 + 1]
# And compute the v2,v3 coordinates of pixels in this reference row
v2refrow, v3refrow = mt.xytov2v3(xrow + xsub0, yrow + ysub0, 'F770W')
# Now repeat the v2,v3, matrix from the central row so that it is copied to all of the other valid rows too
v2_full = mb.repmat(v2refrow, np.int(bb[1][1]) + 1 - np.int(bb[1][0]), 1)
v3_full = mb.repmat(v3refrow, np.int(bb[1][1]) + 1 - np.int(bb[1][0]), 1)
# v2_full and v3_full now have shape (e.g. for slitless) 391x72
# Now take these matrices and put them into tabular models that can be interpolated to find v2,v3 for arbitrary
# x,y pixel values in the valid region.
v2_t2d = models.Tabular2D(lookup_table=v2_full,
name='v2table',
bounds_error=False,
fill_value=np.nan)
v3_t2d = models.Tabular2D(lookup_table=v3_full,
name='v3table',
bounds_error=False,
fill_value=np.nan)
# Now deal with the fact that the spectral trace isn't perfectly up and down along detector.
# This information is contained in the xcenter/ycenter values in the CDP table, but we'll handle it
# as a simple rotation using a linear fit to this relation provided by the CDP.
z = np.polyfit(xcen, ycen, 1)
slope = 1. / z[0]
traceangle = np.arctan(slope) * 180. / np.pi # trace angle in degrees
rot = models.Rotation2D(traceangle) # Rotation model
# Now include this rotation in our overall transform
# First shift to a frame relative to the trace zeropoint, then apply the rotation
# to correct for the curved trace. End in a rotated frame relative to zero at the reference point
# and where yrot is aligned with the spectral trace)
xysubtoxyrot = models.Shift(-zero_point[0]) & models.Shift(
-zero_point[1]) | rot
# Next shift back to the subarray frame, and then map to v2v3
xyrottov2v3 = models.Shift(zero_point[0]) & models.Shift(
zero_point[1]) | models.Mapping((1, 0, 1, 0)) | v2_t2d & v3_t2d
# Compute the rotated x,y points for our inputs
xrot, yrot = xysubtoxyrot(x, y)
# Compute the v2,v3 points for our inputs
v2, v3 = xyrottov2v3(xrot, yrot)
# Work out the spectral component of the transform
# First compute the reference trace in the rotated-Y frame
xcenrot, ycenrot = rot(xcen, ycen)
# The input table of wavelengths isn't perfect, and the delta-wavelength steps show some unphysical behaviour
# Therefore fit with a spline for the ycenrot->wavelength transform
# Reverse vectors so that yinv is increasing (needed for spline fitting function)
wavetab = lrsdata[:, 2]
yrev = ycenrot[::-1]
wrev = wavetab[::-1]
# Spline fit with enforced smoothness
spl = UnivariateSpline(yrev, wrev, s=0.002)
# Evaluate the fit at the rotated-y reference points
waves = spl(yrot)
return v2, v3, waves
def test_format_input_arrays():
model = TInputFormatter()
result = model([1, 1], [2, 2])
assert_allclose(result, (np.array([1, 1]), np.array([2, 2])))
def test_format_input_arrays_transposed():
model = TInputFormatter()
input = np.array([[1, 1]]).T, np.array([[2, 2]]).T
result = model(*input)
assert_allclose(result, input)
@pytest.mark.parametrize('model',
[models.Gaussian2D(), models.Polynomial2D(1,),
models.Pix2Sky_TAN(), models.Tabular2D(lookup_table=np.ones((4,5)))])
@pytest.mark.skipif('not HAS_SCIPY')
def test_call_keyword_args_2(model):
"""
Test calling a model with positional, keywrd and a mixture of both arguments.
"""
positional = model(1, 2)
assert_allclose(positional, model(x=1, y=2))
assert_allclose(positional, model(1, y=2))
model.inputs = ('r', 't')
assert_allclose(positional, model(r=1, t=2))
assert_allclose(positional, model(1, t=2))
assert_allclose(positional, model(1, 2))
with pytest.raises(ValueError):
def lrs(input_model, reference_files):
"""
The LRS-FIXEDSLIT and LRS-SLITLESS WCS pipeline.
It has two coordinate frames: "detecor" and "world".
Uses the "specwcs" and "distortion" reference files.
"""
# Setup the frames.
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
spec = cf.SpectralFrame(name='wavelength',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
sky = cf.CelestialFrame(reference_frame=coord.ICRS(), name='sky')
world = cf.CompositeFrame(name="world", frames=[sky, spec])
# Determine the distortion model.
subarray2full = subarray_transform(input_model)
with DistortionModel(reference_files['distortion']) as dist:
distortion = dist.model
full_distortion = subarray2full | distortion
# Load and process the reference data.
with fits.open(reference_files['specwcs']) as ref:
lrsdata = np.array([l for l in ref[1].data])
# Get the zero point from the reference data.
# The zero_point is X, Y (which should be COLUMN, ROW)
# TODO: Are imx, imy 0- or 1-indexed? We are treating them here as
# 0-indexed. Since they are FITS, they are probably 1-indexed.
if input_model.meta.exposure.type.lower() == 'mir_lrs-fixedslit':
zero_point = ref[1].header['imx'], ref[1].header['imy']
elif input_model.meta.exposure.type.lower() == 'mir_lrs-slitless':
#zero_point = ref[1].header['imxsltl'], ref[1].header['imysltl']
zero_point = [35, 442] # [35, 763] # account for subarray
# Create the bounding_box
x0 = lrsdata[:, 3]
y0 = lrsdata[:, 4]
x1 = lrsdata[:, 5]
bb = ((x0.min() - 0.5 + zero_point[0], x1.max() + 0.5 + zero_point[0]),
(y0.min() - 0.5 + zero_point[1], y0.max() + 0.5 + zero_point[1]))
# Find the ROW of the zero point which should be the [1] of zero_point
row_zero_point = zero_point[1]
# Compute the v2v3 to sky.
tel2sky = pointing.v23tosky(input_model)
# Compute the V2/V3 for each pixel in this row
# x.shape will be something like (1, 388)
y, x = np.mgrid[row_zero_point:row_zero_point + 1,
0:input_model.data.shape[1]]
spatial_transform = full_distortion | tel2sky
radec = np.array(spatial_transform(x, y))[:, 0, :]
ra_full = np.matlib.repmat(radec[0],
_toindex(bb[1][1]) + 1 - _toindex(bb[1][0]), 1)
dec_full = np.matlib.repmat(radec[1],
_toindex(bb[1][1]) + 1 - _toindex(bb[1][0]), 1)
ra_t2d = models.Tabular2D(lookup_table=ra_full,
name='xtable',
bounds_error=False,
fill_value=np.nan)
dec_t2d = models.Tabular2D(lookup_table=dec_full,
name='ytable',
bounds_error=False,
fill_value=np.nan)
# Create the model transforms.
lrs_wav_model = jwmodels.LRSWavelength(lrsdata, zero_point)
# Incorporate the small rotation
angle = np.arctan(0.00421924)
rot = models.Rotation2D(angle)
radec_t2d = ra_t2d & dec_t2d | rot
# Account for the subarray when computing spatial coordinates.
xshift = -bb[0][0]
yshift = -bb[1][0]
det2world = models.Mapping((1, 0, 1, 0, 0, 1)) | models.Shift(yshift, name='yshift1') & \
models.Shift(xshift, name='xshift1') & \
models.Shift(yshift, name='yshift2') & models.Shift(xshift, name='xshift2') & \
models.Identity(2) | radec_t2d & lrs_wav_model
det2world.bounding_box = bb[::-1]
# Now the actual pipeline.
pipeline = [(detector, det2world), (world, None)]
return pipeline
def lrs(input_model, reference_files):
"""
Create the WCS pipeline for a MIRI fixed slit observation.
Parameters
----------
input_model : `jwst.datamodels.ImagingModel`
Data model.
reference_files : dict
Dictionary {reftype: reference file name}.
reference_files = {
"specwcs": 'MIRI_FM_MIRIMAGE_P750L_DISTORTION_04.02.00.fits'
}
"""
# Setup the frames.
detector = cf.Frame2D(name='detector',
axes_order=(0, 1),
unit=(u.pix, u.pix))
spec = cf.SpectralFrame(name='wavelength',
axes_order=(2, ),
unit=(u.micron, ),
axes_names=('lambda', ))
sky = cf.CelestialFrame(reference_frame=coord.ICRS(), name='sky')
world = cf.CompositeFrame(name="world", frames=[sky, spec])
# Determine the distortion model.
distortion = AsdfFile.open(reference_files['distortion']).tree['model']
# Distortion is in arcmin. Convert to degrees
full_distortion = distortion | models.Scale(1 / 60.) & models.Scale(
1 / 60.)
# Load and process the reference data.
with fits.open(reference_files['specwcs']) as ref:
lrsdata = np.array([l for l in ref[1].data])
# Get the zero point from the reference data.
# The zero_point is X, Y (which should be COLUMN, ROW)
# TODO: Are imx, imy 0- or 1-indexed? We are treating them here as
# 0-indexed. Since they are FITS, they are probably 1-indexed.
if input_model.meta.exposure.type.lower() == 'mir_lrs-fixedslit':
zero_point = ref[1].header['imx'], ref[1].header['imy']
elif input_model.meta.exposure.type.lower() == 'mir_lrs-slitless':
#zero_point = ref[1].header['imxsltl'], ref[1].header['imysltl']
zero_point = [35, 442] # [35, 763] # account for subarray
# Create the domain
x0 = lrsdata[:, 3]
y0 = lrsdata[:, 4]
x1 = lrsdata[:, 5]
domain = [{
'lower': x0.min() + zero_point[0],
'upper': x1.max() + zero_point[0]
}, {
'lower': (y0.min() + zero_point[1]),
'upper': (y0.max() + zero_point[1])
}]
# Find the ROW of the zero point which should be the [1] of zero_point
row_zero_point = zero_point[1]
# Compute the v2v3 to sky.
tel2sky = pointing.v23tosky(input_model)
# Compute the V2/V3 for each pixel in this row
# x.shape will be something like (1, 388)
y, x = np.mgrid[row_zero_point:row_zero_point + 1,
0:input_model.data.shape[1]]
spatial_transform = full_distortion | tel2sky
radec = np.array(spatial_transform(x, y))[:, 0, :]
ra_full = np.matlib.repmat(radec[0],
domain[1]['upper'] + 1 - domain[1]['lower'], 1)
dec_full = np.matlib.repmat(radec[1],
domain[1]['upper'] + 1 - domain[1]['lower'], 1)
ra_t2d = models.Tabular2D(lookup_table=ra_full,
name='xtable',
bounds_error=False,
fill_value=np.nan)
dec_t2d = models.Tabular2D(lookup_table=dec_full,
name='ytable',
bounds_error=False,
fill_value=np.nan)
# Create the model transforms.
lrs_wav_model = jwmodels.LRSWavelength(lrsdata, zero_point)
# Incorporate the small rotation
angle = np.arctan(0.00421924)
rot = models.Rotation2D(angle)
radec_t2d = ra_t2d & dec_t2d | rot
# Account for the subarray when computing spatial coordinates.
xshift = -domain[0]['lower']
yshift = -domain[1]['lower']
det2world = models.Mapping((1, 0, 1, 0, 0, 1)) | models.Shift(yshift, name='yshift1') & \
models.Shift(xshift, name='xshift1') & \
models.Shift(yshift, name='yshift2') & models.Shift(xshift, name='xshift2') & \
models.Identity(2) | radec_t2d & lrs_wav_model
det2world.meta['domain'] = domain
# Now the actual pipeline.
pipeline = [(detector, det2world), (world, None)]
return pipeline
astropy_models.RotateNative2Celestial(5.63, -72.5, 180),
astropy_models.RotateNative2Celestial(5.63 * u.deg, -72.5 * u.deg,
180 * u.deg),
astropy_models.Rotation2D(angle=1.51),
astropy_models.RotationSequence3D([1.2, 2.3, 3.4, .3], "xyzx"),
astropy_models.SphericalRotationSequence([1.2, 2.3, 3.4, .3], "xyzy"),
# astropy.modeling.tabular
astropy_models.Tabular1D(points=np.arange(0, 5),
lookup_table=[1., 10, 2, 45, -3]),
astropy_models.Tabular1D(points=np.arange(0, 5) * u.pix,
lookup_table=[1., 10, 2, 45, -3] * u.nm),
astropy_models.Tabular2D(
points=([1, 2, 3], [1, 2, 3]),
lookup_table=np.arange(0, 9).reshape(3, 3),
bounds_error=False,
fill_value=None,
method="nearest",
),
astropy_models.Tabular2D(
points=([1, 2, 3], [1, 2, 3]) * u.pix,
lookup_table=np.arange(0, 9).reshape(3, 3) * u.nm,
bounds_error=False,
fill_value=None,
method="nearest",
),
]
if astropy.__version__ >= "4.1":
SINGLE_MODELS.append(astropy_models.Plummer1D(mass=10.0, r_plum=5.0))
