def test_kernel_is_scale_properly_if_cdelts_differ(self, factor):
fov = _centre_fov(n=10)
fov.header["CDELT1"] *= factor
fov.header["CDELT2"] *= factor
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
kernels = fvpsf.get_kernel(fov)
psf_shape = np.array(fvpsf._file[2].data[0].shape)
kernel_shape = kernels[0][0].shape
assert all(kernel_shape == psf_shape * factor)
def test_convolution_with_central_psf_for_central_region(self, centre_fov):
nax1, nax2 = centre_fov.header["NAXIS1"], centre_fov.header["NAXIS2"]
centre_fov.hdu.data = np.zeros((nax2, nax1))
centre_fov.hdu.data[::3, ::3] = 1
sum_orig = np.sum(centre_fov.hdu.data)
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
fov_back = fvpsf.apply_to(centre_fov)
assert np.sum(fov_back.hdu.data) == sum_orig
if PLOTS:
plt.imshow(fov_back.hdu.data, origin="lower")
plt.show()
def test_convolution_with_fvpsfs_for_shifted_region(self, centre_fov):
nax1, nax2 = centre_fov.header["NAXIS1"], centre_fov.header["NAXIS2"]
centre_fov.hdu.data = np.zeros((nax2, nax1))
centre_fov.hdu.data[1::5, 1::5] = 1
centre_fov.fields = [1]
sum_orig = np.sum(centre_fov.hdu.data)
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
fov_back = fvpsf.apply_to(centre_fov)
assert np.sum(fov_back.hdu.data) == approx(sum_orig, rel=1E-2)
if PLOTS:
plt.imshow(fov_back.hdu.data, origin="lower")
plt.show()
def test_returns_four_arrays_when_fov_on_intersection(self):
fov = _centre_fov(n=20)
fov.header["CRVAL1"] -= 15/3600.
fov.header["CRVAL2"] -= 15/3600.
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
kernels = fvpsf.get_kernel(fov)
assert len(kernels) == 4
if PLOTS:
for ii, kernel_mask in enumerate(kernels):
plt.subplot(2, 4, ii+1)
plt.imshow(kernel_mask[0].T, origin="lower")
plt.subplot(2, 4, ii+5)
plt.imshow(kernel_mask[1].T, origin="lower")
plt.show()
def test_circular_fvpsf(self, basic_circular_fvpsf):
centre_fov = _centre_fov(n=62)
nax1, nax2 = centre_fov.header["NAXIS1"], centre_fov.header["NAXIS2"]
centre_fov.hdu.data = np.zeros((nax2, nax1))
x, y = np.random.randint(6, nax1-6, (2, 150))
centre_fov.hdu.data[x, y] = 1
# centre_fov.hdu.data[6:nax1-6:10, 6:nax1-6:10] = 1
centre_fov.fields = [1]
sum_orig = np.sum(centre_fov.hdu.data)
fvpsf = FieldVaryingPSF(filename="test_circular_fvpsf.fits")
fov_back = fvpsf.apply_to(centre_fov)
if PLOTS:
plt.imshow(fov_back.hdu.data, origin="lower", vmax=0.1)
plt.show()
# print(np.sum(fov_back.hdu.data), sum_orig)
assert np.sum(fov_back.hdu.data) == approx(sum_orig, rel=1E-2)
def test_returns_correct_section_of_strehl_map(self, scale):
centre_fov = _centre_fov(10)
centre_fov.header["CDELT1"] *= scale
centre_fov.header["CDELT2"] *= scale
centre_fov.header["CRVAL1"] -= 15/3600.
centre_fov.header["CRVAL2"] -= 15/3600.
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
strehl_hdu = scopesim.effects.psf_utils.get_strehl_cutout(centre_fov.header,
fvpsf.strehl_imagehdu)
if PLOTS:
plt.imshow(strehl_hdu.data, origin="lower")
plt.colorbar()
plt.show()
assert all(np.unique(strehl_hdu.data).astype(int) == [0, 1, 3, 4])
def test_returns_array_with_single_kernel_from_fov(self):
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
kernels = fvpsf.get_kernel(_centre_fov(n=10))
assert np.all(kernels[0][0] == fvpsf._file[2].data[4])
assert kernels[0][1] is None
def test_returns_the_correct_hdu(self):
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
strehl_imhdu = fvpsf.strehl_imagehdu
assert isinstance(strehl_imhdu, fits.ImageHDU)
def test_initialised_when_passed_fits_filename(self):
fvpsf = FieldVaryingPSF(filename="test_FVPSF.fits")
assert isinstance(fvpsf, FieldVaryingPSF)
def test_errors_when_initialised_with_nothing(self):
with pytest.raises(ValueError):
isinstance(FieldVaryingPSF(), FieldVaryingPSF)
