def test_fit2d(psf_model, fit_fkt, y, x, framesize, psfsize):
frame = np.zeros((framesize, framesize))
psf = create_synth_psf(psf_model, shape=(psfsize, psfsize))
inj_frame = put_center(frame, psf, y, x)
y_out, x_out = fit_fkt(inj_frame)
# correct "half-pixel centering", to make output of fit_2d* comparable
# with `put`.
if (
(framesize % 2 == 0 and psfsize % 2 == 0) or
(framesize % 2 == 1 and psfsize % 2 == 0)
):
y_exp = y - 0.5
x_exp = x - 0.5
else:
y_exp = y
x_exp = x
yx_real = np.unravel_index(inj_frame.argmax(), inj_frame.shape)
print("demanded injection: {}".format((y, x)))
print("brightes pixel: {}".format(yx_real))
print("fit should return: {}".format((y_exp, x_exp)))
print("fitted injection: {}".format((y_out, x_out)))
aarc((y_out, x_out), (y_exp, x_exp), atol=0.05)
def test_fit2d(psf_model, fit_fkt, y, x, framesize, psfsize):
frame = np.zeros((framesize, framesize))
psf = create_synth_psf(psf_model, shape=(psfsize, psfsize))
inj_frame = put_center(frame, psf, y, x)
y_out, x_out = fit_fkt(inj_frame)
# correct "half-pixel centering", to make output of fit_2d* comparable
# with `put`.
if ((framesize % 2 == 0 and psfsize % 2 == 0)
or (framesize % 2 == 1 and psfsize % 2 == 0)):
y_exp = y - 0.5
x_exp = x - 0.5
else:
y_exp = y
x_exp = x
yx_real = np.unravel_index(inj_frame.argmax(), inj_frame.shape)
print("demanded injection: {}".format((y, x)))
print("brightes pixel: {}".format(yx_real))
print("fit should return: {}".format((y_exp, x_exp)))
print("fitted injection: {}".format((y_out, x_out)))
aarc((y_out, x_out), (y_exp, x_exp), atol=0.05)
def test_find_indices_sdi(dist, index_ref, truth):
wl = np.arange(10) + 1
fwhm = 4
indices = _find_indices_sdi(wl, dist, index_ref, fwhm, debug=True)
aarc(indices, truth)
def test_matrix_scaling():
"""
The "truth" values were verified by hand.
"""
m = np.array([[6, 12, 18], [0, 0, 12]], dtype=float)
res = matrix_scaling(m, None)
truth = m
aarc(res, truth)
res = matrix_scaling(m, "temp-mean")
truth = np.array([[ 3, 6, 3],
[-3, -6, -3]])
aarc(res, truth)
res = matrix_scaling(m, "spat-mean")
truth = np.array([[-6, 0, 6],
[-4, -4, 8]])
aarc(res, truth)
res = matrix_scaling(m, "temp-standard")
truth = np.array([[ 1, 1, 1],
[-1, -1, -1]])
aarc(res, truth)
res = matrix_scaling(m, "spat-standard")
truth = np.array([[-np.sqrt(3/2), 0, np.sqrt(3/2)],
[-np.sqrt(1/2), -np.sqrt(1/2), np.sqrt(2)]])
aarc(res, truth)
def test_matrix_scaling():
"""
The "truth" values were verified by hand.
"""
m = np.array([[6, 12, 18], [0, 0, 12]], dtype=float)
res = matrix_scaling(m, None)
truth = m
aarc(res, truth)
res = matrix_scaling(m, "temp-mean")
truth = np.array([[3, 6, 3], [-3, -6, -3]])
aarc(res, truth)
res = matrix_scaling(m, "spat-mean")
truth = np.array([[-6, 0, 6], [-4, -4, 8]])
aarc(res, truth)
res = matrix_scaling(m, "temp-standard")
truth = np.array([[1, 1, 1], [-1, -1, -1]])
aarc(res, truth)
res = matrix_scaling(m, "spat-standard")
truth = np.array([[-np.sqrt(3 / 2), 0, np.sqrt(3 / 2)],
[-np.sqrt(1 / 2), -np.sqrt(1 / 2),
np.sqrt(2)]])
aarc(res, truth)
def test_find_indices_sdi(dist, index_ref, truth):
wl = np.arange(10) + 1
fwhm = 4
indices = _find_indices_sdi(wl, dist, index_ref, fwhm, debug=True)
aarc(indices, truth)
def test_cube_rescaling_wavelengths(imlib, interpolation):
scal_list = np.arange(10) + 1 # no zero
# === basic function ===
res1 = cube_rescaling_wavelengths(CUBE,
scal_list,
imlib=imlib,
interpolation=interpolation)
cube1, med1, y1, x1, cy1, cx1 = res1
assert cube1.shape == (10, 1000, 1000) # frame size x10 x10
for i in range(cube1.shape[0]):
aarc(cube1[i].mean() * scal_list[i]**2, 1)
# === undo ===
res2 = cube_rescaling_wavelengths(cube1,
scal_list,
imlib=imlib,
interpolation=interpolation,
inverse=True,
x_in=100,
y_in=100)
cube2, med2, y2, x2, cy2, cx2 = res2
aarc(cube2, CUBE)
def test_cube_inject_companions(dataset, branches, dists):
"""
Verify position of injected companions, for 3D and 4D cases.
"""
def _expected(branches, dists):
"""
Expected positions.
"""
if branches == 1 and dists == 2:
return [(2, 4)]
elif branches == 2 and dists == 2:
return [(2, 4), (2, 0)]
elif branches == 2 and dists == [1, 2]:
return [(2, 3), (2, 4), (2, 1), (2, 0)]
else:
raise ValueError("no expected result defined")
cube, psf, angles = dataset
c, yx = cube_inject_companions(cube,
psf_template=psf,
angle_list=angles,
rad_dists=dists,
n_branches=branches,
flevel=3,
full_output=True,
plsc=1,
verbose=True)
yx_expected = _expected(branches, dists)
aarc(yx, yx_expected)
def test_saveable_dataset():
"""
Test the HCIDataset.save() and .load() methods
"""
# build HCIDataset
cube = np.zeros((5, 10, 10))
angles = np.linspace(1, 2, 5)
fwhm = 4 # test non-numpy type saving/loading
ds = HCIDataset(cube=cube, angles=angles, fwhm=fwhm)
# save
fd, fn = tempfile.mkstemp(prefix="vip_")
ds.save(fn)
# restore
ds2 = HCIDataset.load(fn)
# compare
aarc(ds2.cube, cube)
aarc(ds2.angles, angles)
assert ds2.fwhm == fwhm
# cleanup
os.remove(fn)
def test_cube_inject_companions(dataset, branches, dists):
"""
Verify position of injected companions, for 3D and 4D cases.
"""
def _expected(branches, dists):
"""
Expected positions.
"""
if branches == 1 and dists == 2:
return [(2, 4)]
elif branches == 2 and dists == 2:
return [(2, 4), (2, 0)]
elif branches == 2 and dists == [1, 2]:
return [(2, 3), (2, 4), (2, 1), (2, 0)]
else:
raise ValueError("no expected result defined")
cube, psf, angles = dataset
c, yx = cube_inject_companions(cube, psf_template=psf, angle_list=angles,
rad_dists=dists, n_branches=branches,
flevel=3,
full_output=True,
plsc=1, verbose=True)
yx_expected = _expected(branches, dists)
aarc(yx, yx_expected)
def test_reshape_matrix():
vectorized_frames = np.array([[1, 1, 1, 2, 2, 2], [1, 2, 3, 4, 5, 6]])
cube = reshape_matrix(vectorized_frames, 2, 3)
assert cube.shape == (2, 2, 3) # 2 frames of 2x3
cube_truth = np.array([[[1, 1, 1], [2, 2, 2]], [[1, 2, 3], [4, 5, 6]]])
aarc(cube, cube_truth)
def test_reshape_matrix():
vectorized_frames = np.array([[1, 1, 1, 2, 2, 2], [1, 2, 3, 4, 5, 6]])
cube = reshape_matrix(vectorized_frames, 2, 3)
assert cube.shape == (2, 2, 3) # 2 frames of 2x3
cube_truth = np.array([[[1, 1, 1],
[2, 2, 2]],
[[1, 2, 3],
[4, 5, 6]]])
aarc(cube, cube_truth)
def test_mask_circle():
size = 5
radius = 2
ones = np.ones((size, size))
# "in" and "out" should be complementary
res_in = mask_circle(ones, radius=radius, mode="in")
res_out = mask_circle(ones, radius=radius, mode="out")
aarc(res_in+res_out, ones)
# radius=2 -> central region should be 3x3 pixels = 9 pixels
aarc(res_out.sum(), 9)
def test_mask_circle():
size = 5
radius = 2
ones = np.ones((size, size))
# "in" and "out" should be complementary
res_in = mask_circle(ones, radius=radius, mode="in")
res_out = mask_circle(ones, radius=radius, mode="out")
aarc(res_in + res_out, ones)
# radius=2 -> central region should be 3x3 pixels = 9 pixels
aarc(res_out.sum(), 9)
def test_check_scal_vector():
scal_vec = np.array([2, 8, 4])
# === basic function ===
res = check_scal_vector(scal_vec)
truth = np.array([4, 1, 2])
aarc(res, truth)
# === do nothing if min factor is already 1 ===
res2 = check_scal_vector(res)
aarc(res2, res)
# === wrong input value ===
with raises(TypeError):
check_scal_vector(42)
def test_check_scal_vector():
scal_vec = np.array([2, 8, 4])
# === basic function ===
res = check_scal_vector(scal_vec)
truth = np.array([4, 1, 2])
aarc(res, truth)
# === do nothing if min factor is already 1 ===
res2 = check_scal_vector(res)
aarc(res2, res)
# === wrong input value ===
with raises(TypeError):
check_scal_vector(42)
def test_get_circle():
ar = np.ones((10, 10), dtype=int)
aarc(
vip.var.get_circle(ar, radius=4, output_values=False),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0,
0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0,
0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1,
0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1,
0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
aarc(
vip.var.get_circle(pretty_odd, radius=4, output_values=True),
np.array([
1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 3, 2, 1, 1, 2, 2, 2, 1, 1, 1,
1, 1, 1
]))
aarc(
vip.var.get_circle(pretty_even, radius=4, output_values=True),
np.array([
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3, 3,
2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1
]))
def test_get_circle():
ar = np.ones((10, 10), dtype=int)
aarc(
get_circle(ar, radius=4),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0,
0], [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0,
0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1,
0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1,
0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
aarc(
get_circle(PRETTY_ODD, radius=4, mode="val"),
np.array([
1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 3, 2, 1, 1, 2, 2, 2, 1, 1, 1,
1, 1, 1
]))
aarc(
get_circle(PRETTY_EVEN, radius=4, mode="val"),
np.array([
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3, 3,
2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1
]))
def test_cube_rescaling_wavelengths(imlib, interpolation):
scal_list = np.arange(10) + 1 # no zero
# === basic function ===
res1 = cube_rescaling_wavelengths(CUBE, scal_list, imlib=imlib,
interpolation=interpolation)
cube1, med1, y1, x1, cy1, cx1 = res1
assert cube1.shape == (10, 1000, 1000) # frame size x10 x10
for i in range(cube1.shape[0]):
aarc(cube1[i].mean() * scal_list[i]**2, 1)
# === undo ===
res2 = cube_rescaling_wavelengths(cube1, scal_list, imlib=imlib,
interpolation=interpolation,
inverse=True, x_in=100, y_in=100)
cube2, med2, y2, x2, cy2, cx2 = res2
aarc(cube2, CUBE)
def test_get_circle():
ar = np.ones((10, 10), dtype=int)
aarc(get_circle(ar, radius=4),
np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
aarc(get_circle(PRETTY_ODD, radius=4, mode="val"),
np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 3, 2, 1, 1, 2, 2,
2, 1, 1, 1, 1, 1, 1]))
aarc(get_circle(PRETTY_EVEN, radius=4, mode="val"),
np.array([1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 3, 3, 2, 1,
1, 2, 3, 3, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1]))
def test_andromeda(example_dataset):
# TODO: test `fast`
# TODO: test `nsnmooth_snr`
# TODO: test `homogeneous_variance`
# TODO: test `filtering_fraction`
# TODO: test `oversampling_fact`
# TODO: test `min_sep`
# TODO: test `opt_method`
global IDL_DATA
out = andromeda(
example_dataset.cube,
angles=-example_dataset.angles,
# VIP uses other PA convention than IDL version.
# the IDL_DATA was created with the IDL version, so we need
# to match its PA convention.
psf=example_dataset.psf,
oversampling_fact=1,
filtering_fraction=1, # turn off high pass
min_sep=0.3,
opt_method="no",
nsmooth_snr=0, # turn off smoothing
homogeneous_variance=True,
)
contrast, snr, snr_norm, stdcontrast, stdcontrast_norm, likelihood, _ = out
aarc(contrast, IDL_DATA["andromeda_contrast_1"], atol=1e-2)
aarc(snr, IDL_DATA["andromeda_snr_1"], atol=1e-4)
aarc(snr_norm, IDL_DATA["andromeda_snr_norm_1"], atol=1e-4)
aarc(stdcontrast, IDL_DATA["andromeda_stdcontrast_1"])
aarc(stdcontrast_norm, IDL_DATA["andromeda_stdcontrast_norm_1"])
aarc(likelihood, IDL_DATA["andromeda_likelihood_1"], atol=1e-4)
def test_fft_ifft():
global FRAME
FRAME = np.arange(100).reshape((10, 10))
res = ifft(fft(FRAME))
aarc(res, FRAME)
def test_get_annulus_segments():
arr = np.ones((10, 10))
# single segment, like the old get_annulus. Note the ``[0]``.
res = get_annulus_segments(arr, 2, 3)[0]
truth = (np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6,
6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9,
9, 9]),
np.array([3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1,
2, 3, 6, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1, 2, 3,
6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 3, 4,
5, 6]))
aarc(res, truth)
res = get_annulus_segments(arr, 2, 3, mode="val")[0]
truth = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
aarc(res, truth)
# multiple segments:
res = get_annulus_segments(PRETTY_EVEN, 2, 3, nsegm=2)
truth = [(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5, 5]),
np.array([3, 4, 5, 4, 5, 5, 5, 4, 5, 3, 4, 5])),
(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5, 5]),
np.array([0, 1, 2, 0, 1, 0, 0, 0, 1, 0, 1, 2]))]
aarc(res, truth)
res = get_annulus_segments(PRETTY_EVEN, 2, 3, nsegm=3)
truth = [(np.array([2, 3, 4, 4, 5, 5, 5]),
np.array([5, 5, 4, 5, 3, 4, 5])),
(np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1]),
np.array([0, 1, 2, 3, 4, 5, 0, 1, 4, 5])),
(np.array([2, 3, 4, 4, 5, 5, 5]),
np.array([0, 0, 0, 1, 0, 1, 2]))]
assert repr(res) == repr(truth)
# TODO: cannot compare using `allclose`, as elements have variable length!
# tuple as input:
res = get_annulus_segments((6, 6), 2, 3, nsegm=3)
assert repr(res) == repr(truth)
# masked arr:
res = get_annulus_segments(arr, 2, 3, mode="mask")[0]
truth = np.array([[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.]])
aarc(res, truth)
# tuple as input:
res = get_annulus_segments((10, 10), 2, 3, mode="mask")[0]
# masking a zeros array -> only zeros left!
assert res.sum() == 0
def test_idl_where():
a = np.arange(3*3, dtype=int).reshape((3, 3))
aarc(idl_where(a <= 3), [0, 1, 2, 3]) # WHERE(a LE 3)
aarc(idl_where(a == 5), [5]) # WHERE(a EQ 5)
aarc(idl_where(a > 20), []) # WHERE(a GT 20) -> returns [-1]
def test_get_annulus_segments():
arr = np.ones((10, 10))
# single segment, like the old get_annulus. Note the ``[0]``.
res = vip.var.get_annulus_segments(arr, 2, 3)[0]
truth = (np.array([
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,
3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,
7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9
]),
np.array([
3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0,
1, 2, 3, 6, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1,
2, 3, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7,
8, 3, 4, 5, 6
]))
aarc(res, truth)
res = vip.var.get_annulus_segments(arr, 2, 3, mode="val")[0]
truth = np.array([
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.
])
aarc(res, truth)
# multiple segments:
res = vip.var.get_annulus_segments(pretty_even, 2, 3, nsegm=2)
truth = [(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5,
5]), np.array([3, 4, 5, 4, 5, 5, 5, 4, 5, 3, 4, 5])),
(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5,
5]), np.array([0, 1, 2, 0, 1, 0, 0, 0, 1, 0, 1, 2]))]
aarc(res, truth)
res = vip.var.get_annulus_segments(pretty_even, 2, 3, nsegm=3)
truth = [(np.array([2, 3, 4, 4, 5, 5, 5]), np.array([5, 5, 4, 5, 3, 4,
5])),
(np.array([0, 0, 0, 0, 0, 0, 1, 1, 1,
1]), np.array([0, 1, 2, 3, 4, 5, 0, 1, 4, 5])),
(np.array([2, 3, 4, 4, 5, 5, 5]), np.array([0, 0, 0, 1, 0, 1,
2]))]
assert repr(res) == repr(truth)
# TODO: cannot compare using `allclose`, as elements have variable length!
# masked arr
res = vip.var.get_annulus_segments(arr, 2, 3, mode="mask")[0]
truth = np.array([[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.]])
aarc(res, truth)
def test_andromeda(example_dataset):
# TODO: test `fast`
# TODO: test `nsnmooth_snr`
# TODO: test `homogeneous_variance`
# TODO: test `filtering_fraction`
# TODO: test `oversampling_fact`
# TODO: test `min_sep`
# TODO: test `opt_method`
global IDL_DATA
out = andromeda(example_dataset.cube,
angles=-example_dataset.angles,
# VIP uses other PA convention than IDL version.
# the IDL_DATA was created with the IDL version, so we need
# to match its PA convention.
psf=example_dataset.psf,
oversampling_fact=1,
filtering_fraction=1, # turn off high pass
min_sep=0.3,
opt_method="no",
nsmooth_snr=0, # turn off smoothing
homogeneous_variance=True,
)
contrast, snr, snr_norm, stdcontrast, stdcontrast_norm, likelihood, _ = out
aarc(contrast, IDL_DATA["andromeda_contrast_1"], atol=1e-2)
aarc(snr, IDL_DATA["andromeda_snr_1"], atol=1e-4)
aarc(snr_norm, IDL_DATA["andromeda_snr_norm_1"], atol=1e-4)
aarc(stdcontrast, IDL_DATA["andromeda_stdcontrast_1"])
aarc(stdcontrast_norm, IDL_DATA["andromeda_stdcontrast_norm_1"])
aarc(likelihood, IDL_DATA["andromeda_likelihood_1"], atol=1e-4)
def test_get_square():
aarc(vip.var.get_square(pretty_odd, size=3, x=2, y=2),
np.array([[2, 2, 2], [2, 3, 2], [2, 2, 2]]))
aarc(vip.var.get_square(pretty_odd, size=2, x=2, y=2),
np.array([[2, 2, 2], [2, 3, 2], [2, 2, 2]]))
# -> prints warning
aarc(vip.var.get_square(pretty_odd, size=2, x=2, y=2, force=True),
np.array([[2, 2], [2, 3]]))
aarc(vip.var.get_square(pretty_even, size=2, x=3, y=3),
np.array([[3, 3], [3, 3]]))
aarc(vip.var.get_square(pretty_even, size=3, x=3, y=3),
np.array([[2, 2, 2, 2], [2, 3, 3, 2], [2, 3, 3, 2], [2, 2, 2, 2]]))
# -> prints warning
aarc(vip.var.get_square(pretty_even, size=2, x=4, y=2, force=True),
np.array([[2, 2], [3, 2]]))
def test_get_square():
aarc(get_square(PRETTY_ODD, size=3, x=2, y=2),
np.array([[2, 2, 2], [2, 3, 2], [2, 2, 2]]))
aarc(get_square(PRETTY_ODD, size=2, x=2, y=2),
np.array([[2, 2, 2], [2, 3, 2], [2, 2, 2]]))
# -> prints warning
aarc(get_square(PRETTY_ODD, size=2, x=2, y=2, force=True),
np.array([[2, 2], [2, 3]]))
aarc(get_square(PRETTY_EVEN, size=2, x=3, y=3), np.array([[3, 3], [3, 3]]))
aarc(get_square(PRETTY_EVEN, size=3, x=3, y=3),
np.array([[2, 2, 2, 2], [2, 3, 3, 2], [2, 3, 3, 2], [2, 2, 2, 2]]))
# -> prints warning
aarc(get_square(PRETTY_EVEN, size=2, x=4, y=2, force=True),
np.array([[2, 2], [3, 2]]))
def test_fft_ifft():
global FRAME
FRAME = np.arange(100).reshape((10, 10))
res = ifft(fft(FRAME))
aarc(res, FRAME)
def test_idl_where():
a = np.arange(3 * 3, dtype=int).reshape((3, 3))
aarc(idl_where(a <= 3), [0, 1, 2, 3]) # WHERE(a LE 3)
aarc(idl_where(a == 5), [5]) # WHERE(a EQ 5)
aarc(idl_where(a > 20), []) # WHERE(a GT 20) -> returns [-1]
def test_get_square():
aarc(get_square(PRETTY_ODD, size=3, x=2, y=2),
np.array([[2, 2, 2],
[2, 3, 2],
[2, 2, 2]]))
aarc(get_square(PRETTY_ODD, size=2, x=2, y=2),
np.array([[2, 2, 2],
[2, 3, 2],
[2, 2, 2]]))
# -> prints warning
aarc(get_square(PRETTY_ODD, size=2, x=2, y=2, force=True),
np.array([[2, 2],
[2, 3]]))
aarc(get_square(PRETTY_EVEN, size=2, x=3, y=3),
np.array([[3, 3],
[3, 3]]))
aarc(get_square(PRETTY_EVEN, size=3, x=3, y=3),
np.array([[2, 2, 2, 2],
[2, 3, 3, 2],
[2, 3, 3, 2],
[2, 2, 2, 2]]))
# -> prints warning
aarc(get_square(PRETTY_EVEN, size=2, x=4, y=2, force=True),
np.array([[2, 2],
[3, 2]]))
def test_get_annulus_segments():
arr = np.ones((10, 10))
# single segment, like the old get_annulus. Note the ``[0]``.
res = get_annulus_segments(arr, 2, 3)[0]
truth = (np.array([
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,
3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,
7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9
]),
np.array([
3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 0,
1, 2, 3, 6, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1, 2, 7, 8, 9, 0, 1,
2, 3, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7,
8, 3, 4, 5, 6
]))
aarc(res, truth)
res = get_annulus_segments(arr, 2, 3, mode="val")[0]
truth = np.array([
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.
])
aarc(res, truth)
# multiple segments:
res = get_annulus_segments(PRETTY_EVEN, 2, 3, nsegm=2)
truth = [(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5,
5]), np.array([3, 4, 5, 4, 5, 5, 5, 4, 5, 3, 4, 5])),
(np.array([0, 0, 0, 1, 1, 2, 3, 4, 4, 5, 5,
5]), np.array([0, 1, 2, 0, 1, 0, 0, 0, 1, 0, 1, 2]))]
aarc(res, truth)
res = get_annulus_segments(PRETTY_EVEN, 2, 3, nsegm=3)
truth = [(np.array([2, 3, 4, 4, 5, 5, 5]), np.array([5, 5, 4, 5, 3, 4,
5])),
(np.array([0, 0, 0, 0, 0, 0, 1, 1, 1,
1]), np.array([0, 1, 2, 3, 4, 5, 0, 1, 4, 5])),
(np.array([2, 3, 4, 4, 5, 5, 5]), np.array([0, 0, 0, 1, 0, 1,
2]))]
assert repr(res) == repr(truth)
# TODO: cannot compare using `allclose`, as elements have variable length!
# tuple as input:
res = get_annulus_segments((6, 6), 2, 3, nsegm=3)
assert repr(res) == repr(truth)
# masked arr:
res = get_annulus_segments(arr, 2, 3, mode="mask")[0]
truth = np.array([[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 0., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 1., 0., 0., 1., 1., 1., 1.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[0., 0., 0., 1., 1., 1., 1., 0., 0., 0.]])
aarc(res, truth)
# tuple as input:
res = get_annulus_segments((10, 10), 2, 3, mode="mask")[0]
# masking a zeros array -> only zeros left!
assert res.sum() == 0