def dataset(request):
"""
Create 3D and 4D datasets for use with ``test_cube_inject_companions``.
"""
if request.param == "3D":
cube = np.zeros((3, 5, 5))
psf = np.ones((1, 1))
elif request.param == "4D":
cube = np.zeros((2, 3, 5, 5)) # lambda, frames, width, height
psf = np.ones((2, 1, 1))
angles = np.array([0, 90, 180])
return cube, psf, angles
def dataset(request):
"""
Create 3D and 4D datasets for use with ``test_cube_inject_companions``.
"""
if request.param == "3D":
cube = np.zeros((3, 5, 5))
psf = np.ones((1, 1))
elif request.param == "4D":
cube = np.zeros((2, 3, 5, 5)) # lambda, frames, width, height
psf = np.ones((2, 1, 1))
angles = np.array([0, 90, 180])
return cube, psf, angles
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_get_ellipse():
f = np.ones((6, 10))
# masked array:
fem = get_ellipse(f, 4, 2, 90, mode="mask")
assert fem.sum() == 28 # outer region masked, 28 pixels kept
# values:
fev = get_ellipse(f, 4, 2, 90, mode="val")
assert fev.sum() == 28
# indices:
fei = get_ellipse(f, 4, 2, 90, mode="ind")
assert fei[0].shape == fei[1].shape == (28, )
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_get_ellipse():
f = np.ones((6, 10))
# masked array:
fem = get_ellipse(f, 4, 2, 90, mode="mask")
assert fem.sum() == 28 # outer region masked, 28 pixels kept
# values:
fev = get_ellipse(f, 4, 2, 90, mode="val")
assert fev.sum() == 28
# indices:
fei = get_ellipse(f, 4, 2, 90, mode="ind")
assert fei[0].shape == fei[1].shape == (28,)
def test_normalize_psf_shapes():
"""
Test if normalize_psf produces the expected shapes.
"""
# `Force_odd` is True therefore `size` was set to 19
res_even = normalize_psf(np.ones((20, 20)), size=18)
res_odd = normalize_psf(np.ones((21, 21)), size=18)
assert res_even.shape == res_odd.shape == (19, 19)
res_even = normalize_psf(np.ones((20, 20)), size=18, force_odd=False)
res_odd = normalize_psf(np.ones((21, 21)), size=18, force_odd=False)
assert res_even.shape == res_odd.shape == (18, 18)
# set to odd size
res_even = normalize_psf(np.ones((20, 20)), size=19)
res_odd = normalize_psf(np.ones((21, 21)), size=19)
assert res_even.shape == res_odd.shape == (19, 19)
res_even = normalize_psf(np.ones((20, 20)), size=19, force_odd=False)
res_odd = normalize_psf(np.ones((21, 21)), size=19, force_odd=False)
assert res_even.shape == res_odd.shape == (19, 19)
def test_normalize_psf_shapes():
"""
Test if normalize_psf produces the expected shapes.
"""
# `Force_odd` is True therefore `size` was set to 19
res_even = normalize_psf(np.ones((20, 20)), size=18)
res_odd = normalize_psf(np.ones((21, 21)), size=18)
assert res_even.shape == res_odd.shape == (19, 19)
res_even = normalize_psf(np.ones((20, 20)), size=18, force_odd=False)
res_odd = normalize_psf(np.ones((21, 21)), size=18, force_odd=False)
assert res_even.shape == res_odd.shape == (18, 18)
# set to odd size
res_even = normalize_psf(np.ones((20, 20)), size=19)
res_odd = normalize_psf(np.ones((21, 21)), size=19)
assert res_even.shape == res_odd.shape == (19, 19)
res_even = normalize_psf(np.ones((20, 20)), size=19, force_odd=False)
res_odd = normalize_psf(np.ones((21, 21)), size=19, force_odd=False)
assert res_even.shape == res_odd.shape == (19, 19)
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]))
"""
from __future__ import division, print_function
__author__ = "Ralf Farkas"
from helpers import np, aarc, raises, parametrize
from vip_hci.preproc.rescaling import (cube_px_resampling,
frame_px_resampling,
cube_rescaling_wavelengths,
check_scal_vector,
_find_indices_sdi)
CUBE = np.ones((10, 100, 100))
FRAME = np.zeros((100, 100))
@parametrize("imlib", ["ndimage", "opencv"])
def test_cube_px_resampling(imlib):
# === enlargen ===
res = cube_px_resampling(CUBE, scale=2, imlib=imlib)
assert res.shape == (10, 200, 200)
# === shrink ===
res = cube_px_resampling(CUBE, scale=0.5, imlib=imlib)
assert res.shape == (10, 50, 50)
def test_get_ell_annulus():
f = np.ones((15, 30))
fa = get_ell_annulus(f, 8, 3, 90, 6, mode="mask")
assert fa.sum() == 124
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_get_ell_annulus():
f = np.ones((15, 30))
fa = get_ell_annulus(f, 8, 3, 90, 6, mode="mask")
assert fa.sum() == 124
"""
from __future__ import division, print_function, absolute_import
__author__ = "Ralf Farkas"
from helpers import aarc, np, param, parametrize
from vip_hci.var.filters import (fft, ifft,
cube_filter_iuwt,
cube_filter_highpass,
cube_filter_lowpass,
frame_filter_highpass,
frame_filter_lowpass)
CUBE = np.ones((5, 10, 10), dtype=float)
FRAME = np.arange(100, dtype=float).reshape((10, 10))
@parametrize("filter_mode",
[
"laplacian",
"laplacian-conv",
"median-subt",
"gauss-subt",
"fourier-butter",
"hann"
]
)
@parametrize("data, fkt",
[
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
"""
Tests for preproc/rescaling.py
"""
from __future__ import division, print_function
__author__ = "Ralf Farkas"
from helpers import np, aarc, raises, parametrize
from vip_hci.preproc.rescaling import (cube_px_resampling, frame_px_resampling,
cube_rescaling_wavelengths,
check_scal_vector, _find_indices_sdi)
CUBE = np.ones((10, 100, 100))
FRAME = np.zeros((100, 100))
@parametrize("imlib", ["ndimage", "opencv"])
def test_cube_px_resampling(imlib):
# === enlargen ===
res = cube_px_resampling(CUBE, scale=2, imlib=imlib)
assert res.shape == (10, 200, 200)
# === shrink ===
res = cube_px_resampling(CUBE, scale=0.5, imlib=imlib)
assert res.shape == (10, 50, 50)
test_iuwt) do NOT verify the actual filtering, as the results produced by the
various filtering algorithms are very diverse. Instead, it is ONLY tested IF the
algorithms actually run.
"""
from __future__ import division, print_function, absolute_import
__author__ = "Ralf Farkas"
from helpers import aarc, np, param, parametrize
from vip_hci.var.filters import (fft, ifft, cube_filter_iuwt,
cube_filter_highpass, cube_filter_lowpass,
frame_filter_highpass, frame_filter_lowpass)
CUBE = np.ones((5, 10, 10), dtype=float)
FRAME = np.arange(100, dtype=float).reshape((10, 10))
@parametrize("filter_mode", [
"laplacian", "laplacian-conv", "median-subt", "gauss-subt",
"fourier-butter", "hann"
])
@parametrize("data, fkt", [
param(CUBE, cube_filter_highpass, id="cube"),
param(FRAME, frame_filter_highpass, id="frame")
],
ids=lambda x: (x.__name__ if callable(x) else None))
def test_highpass(data, fkt, filter_mode):
res = fkt(data, mode=filter_mode)
assert res.shape == data.shape