def test_distortion_correlations():
filename = get_pkg_data_filename('../../tests/data/sip.fits')
with pytest.warns(FITSFixedWarning):
w = WCS(filename)
assert_equal(w.axis_correlation_matrix, True)
# Changing PC to an identity matrix doesn't change anything since
# distortions are still present.
w.wcs.pc = [[1, 0], [0, 1]]
assert_equal(w.axis_correlation_matrix, True)
# Nor does changing the name of the axes to make them non-celestial
w.wcs.ctype = ['X', 'Y']
assert_equal(w.axis_correlation_matrix, True)
# However once we turn off the distortions the matrix changes
w.sip = None
assert_equal(w.axis_correlation_matrix, [[True, False], [False, True]])
# If we go back to celestial coordinates then the matrix is all True again
w.wcs.ctype = ['RA---TAN', 'DEC--TAN']
assert_equal(w.axis_correlation_matrix, True)
# Or if we change to X/Y but have a non-identity PC
w.wcs.pc = [[0.9, -0.1], [0.1, 0.9]]
w.wcs.ctype = ['X', 'Y']
assert_equal(w.axis_correlation_matrix, True)
def test_slice_with_sip():
mywcs = WCS(naxis=2)
mywcs.wcs.crval = [1, 1]
mywcs.wcs.cdelt = [0.1, 0.1]
mywcs.wcs.crpix = [1, 1]
mywcs._naxis = [1000, 500]
mywcs.wcs.ctype = ['RA---TAN-SIP', 'DEC--TAN-SIP']
a = np.array([[0, 0, 5.33092692e-08, 3.73753773e-11, -2.02111473e-13],
[0, 2.44084308e-05, 2.81394789e-11, 5.17856895e-13, 0.0],
[-2.41334657e-07, 1.29289255e-10, 2.35753629e-14, 0.0, 0.0],
[-2.37162007e-10, 5.43714947e-13, 0.0, 0.0, 0.0],
[-2.81029767e-13, 0.0, 0.0, 0.0, 0.0]])
b = np.array([[0, 0, 2.99270374e-05, -2.38136074e-10, 7.23205168e-13],
[0, -1.71073858e-07, 6.31243431e-11, -5.16744347e-14, 0.0],
[6.95458963e-06, -3.08278961e-10, -1.75800917e-13, 0.0, 0.0],
[3.51974159e-11, 5.60993016e-14, 0.0, 0.0, 0.0],
[-5.92438525e-13, 0.0, 0.0, 0.0, 0.0]])
mywcs.sip = Sip(a, b, None, None, mywcs.wcs.crpix)
mywcs.wcs.set()
pscale = 0.1 # from cdelt
slice_wcs = mywcs.slice([slice(1, None), slice(0, None)])
# test that CRPIX maps to CRVAL:
assert_allclose(slice_wcs.all_pix2world(*slice_wcs.wcs.crpix, 1),
slice_wcs.wcs.crval,
rtol=0.0,
atol=1e-6 * pscale)
slice_wcs = mywcs.slice([slice(1, None, 2), slice(0, None, 4)])
# test that CRPIX maps to CRVAL:
assert_allclose(slice_wcs.all_pix2world(*slice_wcs.wcs.crpix, 1),
slice_wcs.wcs.crval,
rtol=0.0,
atol=1e-6 * pscale)
def test_non_convergence_warning():
"""Test case for issue #11446
Since we can't define a target accuracy when plotting a WCS `all_world2pix`
should not error but only warn when the default accuracy can't be reached.
"""
# define a minimal WCS where convergence fails for certain image positions
wcs = WCS(naxis=2)
crpix = [0, 0]
a = b = ap = bp = np.zeros((4, 4))
a[3, 0] = -1.20116753e-07
test_pos_x = [1000, 1]
test_pos_y = [0, 2]
wcs.sip = Sip(a, b, ap, bp, crpix)
# first make sure the WCS works when using a low accuracy
expected = wcs.all_world2pix(test_pos_x, test_pos_y, 0, tolerance=1e-3)
# then check that it fails when using the default accuracy
with pytest.raises(NoConvergence):
wcs.all_world2pix(test_pos_x, test_pos_y, 0)
# at last check that world_to_pixel_values raises a warning but returns
# the same 'low accuray' result
with pytest.warns(UserWarning):
assert_allclose(wcs.world_to_pixel_values(test_pos_x, test_pos_y),
expected)
def test_distortion_correlations():
filename = get_pkg_data_filename('../../tests/data/sip.fits')
w = WCS(filename)
assert_equal(w.axis_correlation_matrix, True)
# Changing PC to an identity matrix doesn't change anything since
# distortions are still present.
w.wcs.pc = [[1, 0], [0, 1]]
assert_equal(w.axis_correlation_matrix, True)
# Nor does changing the name of the axes to make them non-celestial
w.wcs.ctype = ['X', 'Y']
assert_equal(w.axis_correlation_matrix, True)
# However once we turn off the distortions the matrix changes
w.sip = None
assert_equal(w.axis_correlation_matrix, [[True, False], [False, True]])
# If we go back to celestial coordinates then the matrix is all True again
w.wcs.ctype = ['RA---TAN', 'DEC--TAN']
assert_equal(w.axis_correlation_matrix, True)
# Or if we change to X/Y but have a non-identity PC
w.wcs.pc = [[0.9, -0.1], [0.1, 0.9]]
w.wcs.ctype = ['X', 'Y']
assert_equal(w.axis_correlation_matrix, True)
def test_slice_with_sip():
mywcs = WCS(naxis=2)
mywcs.wcs.crval = [1, 1]
mywcs.wcs.cdelt = [0.1, 0.1]
mywcs.wcs.crpix = [1, 1]
mywcs._naxis = [1000, 500]
mywcs.wcs.ctype = ['RA---TAN-SIP', 'DEC--TAN-SIP']
a = np.array(
[[0, 0, 5.33092692e-08, 3.73753773e-11, -2.02111473e-13],
[0, 2.44084308e-05, 2.81394789e-11, 5.17856895e-13, 0.0],
[-2.41334657e-07, 1.29289255e-10, 2.35753629e-14, 0.0, 0.0],
[-2.37162007e-10, 5.43714947e-13, 0.0, 0.0, 0.0],
[ -2.81029767e-13, 0.0, 0.0, 0.0, 0.0]]
)
b = np.array(
[[0, 0, 2.99270374e-05, -2.38136074e-10, 7.23205168e-13],
[0, -1.71073858e-07, 6.31243431e-11, -5.16744347e-14, 0.0],
[6.95458963e-06, -3.08278961e-10, -1.75800917e-13, 0.0, 0.0],
[3.51974159e-11, 5.60993016e-14, 0.0, 0.0, 0.0],
[-5.92438525e-13, 0.0, 0.0, 0.0, 0.0]]
)
mywcs.sip = Sip(a, b, None, None, mywcs.wcs.crpix)
mywcs.wcs.set()
pscale = 0.1 # from cdelt
slice_wcs = mywcs.slice([slice(1, None), slice(0, None)])
# test that CRPIX maps to CRVAL:
assert_allclose(
slice_wcs.all_pix2world(*slice_wcs.wcs.crpix, 1),
slice_wcs.wcs.crval, rtol=0.0, atol=1e-6 * pscale
)
slice_wcs = mywcs.slice([slice(1, None, 2), slice(0, None, 4)])
# test that CRPIX maps to CRVAL:
assert_allclose(
slice_wcs.all_pix2world(*slice_wcs.wcs.crpix, 1),
slice_wcs.wcs.crval, rtol=0.0, atol=1e-6 * pscale
)
