def test_single_pixel_overlap_pillbox():
# NB Grid uv-coordinates are np.arange(n_image) - n_image/2, so e.g. if
# n_image = 8 then the co-ords in the u/x-direction are:
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
n_image = 8
support = 1
uv = np.array([(-2., 0), (-2., 0)])
# Real vis will be complex_, but we can substitute float_ for testing:
vis_amplitude = 42.123
vis = vis_amplitude * np.ones(len(uv), dtype=np.float_)
kernel_func = conv_funcs.Pillbox(0.5)
vis_grid, sampling_grid = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
oversampling=None)
assert vis_grid.sum() == vis.sum()
expected_result = np.array(
[[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 1., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ]]
)
assert (expected_result * vis.sum() == vis_grid).all()
assert (expected_result * len(uv) == sampling_grid).all()
def test_small_pillbox():
n_image = 8
support = 1
uv = np.array([(-1.5, 0.5)])
vis = np.ones(len(uv), dtype=np.float_)
kernel_func = conv_funcs.Pillbox(0.55)
grid, _ = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
oversampling=None)
assert grid.sum() == vis.sum()
# This time we're on a mid-point, with a smaller pillbox
# so we should get a 2x2 output
v = 1. / 4.
expected_result = np.array(
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
[[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., v, v, 0., 0., 0., 0., ],
[0., 0., v, v, 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ]]
)
assert (expected_result == grid).all()
def test_multi_pixel_pillbox():
n_image = 8
support = 1
uv = np.array([(-2., 0)])
vis = np.ones(len(uv), dtype=np.float_)
kernel_func = conv_funcs.Pillbox(1.1)
vis_grid, sampling_grid = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
oversampling=None)
assert vis_grid.sum() == vis.sum()
# Since uv is precisely on a sampling point, we'll get a
# 3x3 pillbox
v = 1. / 9.
expected_result = np.array(
[[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., v, v, v, 0., 0., 0., 0., ],
[0., v, v, v, 0., 0., 0., 0., ],
[0., v, v, v, 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ],
[0., 0., 0., 0., 0., 0., 0., 0., ]]
)
assert (expected_result == vis_grid).all()
# In this case we expect sampling == vis, since we only have one vis == 1.0
assert (expected_result == sampling_grid).all()
def test_tophat_func():
tophat3 = conv_funcs.Pillbox(half_base_width=3.0)
io_pairs = np.array([
[0.0, 1.0],
[2.5, 1.0],
[2.999, 1.0],
[3.0, 0.0],
[4.2, 0.],
])
check_function_results_close(tophat3, io_pairs)
def test_regular_sampling_pillbox():
testfunc = conv_funcs.Pillbox(half_base_width=1.1)
support = 2
oversampling = 1
# Map subpixel offset to expected results
expected_results = {}
# No offset - expect 1's for all central 3x3 pixels:
expected_results[(0., 0.)] = np.array([[0., 0., 0., 0., 0.],
[0., 1., 1., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.]])
# Tiny offset (less than pillbox overlap) - expect same:
expected_results[(0.05, 0.05)] = expected_results[(0., 0.)]
## Now shift the pillbox just enough to +ve x that we drop a column
expected_results[(0.15, 0.0)] = np.array([[0., 0., 0., 0., 0.],
[0., 0., 1., 1., 0.],
[0., 0., 1., 1., 0.],
[0., 0., 1., 1., 0.],
[0., 0., 0., 0., 0.]])
## And shift to -ve x:
expected_results[(-0.15, 0.0)] = np.array([[0., 0., 0., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 0., 0., 0., 0.]])
## Shift to +ve y:
expected_results[(0.0, 0.15)] = np.array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 1., 1., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.]])
## Shift to +ve y & +ve x:
expected_results[(0.15, 0.15)] = np.array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 1., 1., 0.],
[0., 0., 1., 1., 0.],
[0., 0., 0., 0., 0.]])
for offset, expected_array in expected_results.items():
k = Kernel(
kernel_func=testfunc,
support=support,
offset=offset,
oversampling=oversampling,
normalize=False,
)
assert (k.array == expected_array).all()
def test_bounds_checking():
n_image = 8
support = 2
# n_image = 8 then the co-ords in the u/x-direction are:
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
# Support = 2 takes this over the edge (since -3 -2 = -5):
bad_uv = np.array([(-3., 0)])
vis = np.ones(len(bad_uv), dtype=np.float_)
kernel_func = conv_funcs.Pillbox(1.5)
with pytest.raises(ValueError):
grid = convolve_to_grid(
kernel_func,
support=support,
image_size=n_image,
uv=bad_uv,
vis=vis,
vis_weights=np.ones_like(vis),
)
grid, _ = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=bad_uv,
vis=vis,
vis_weights=np.ones_like(vis),
raise_bounds=False)
assert grid.sum() == 0.
# Now check we're filtering indices in the correct order
# The mixed
good_uv = np.array([(0., 0.)])
mixed_uv = np.array([(-3., 0), (0., 0.)])
good_grid, _ = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=good_uv,
vis=np.ones(len(good_uv), dtype=np.float_),
vis_weights=np.ones(len(good_uv),
dtype=np.float_),
raise_bounds=False)
mixed_grid, _ = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=mixed_uv,
vis=np.ones(len(mixed_uv),
dtype=np.float_),
vis_weights=np.ones(len(mixed_uv),
dtype=np.float_),
raise_bounds=False)
assert (good_grid == mixed_grid).all()
def test_oversampled_pillbox_small():
testfunc = conv_funcs.Pillbox(half_base_width=0.25)
support = 1
oversampling = 5
# Map subpixel offset to expected results
expected_results = {}
expected_results[(0., 0.)] = np.array([
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
])
expected_results[(0.4, 0.0)] = np.array([
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 1., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
])
for offset, expected_array in expected_results.items():
k = Kernel(
kernel_func=testfunc,
support=support,
offset=offset,
oversampling=oversampling,
normalize=False,
)
assert (k.array == expected_array).all()
def test_oversampled_pillbox():
testfunc = conv_funcs.Pillbox(half_base_width=0.7)
support = 1
oversampling = 3
# Map subpixel offset to expected results
expected_results = {}
# No offset - expect 1's for all central 5x5 pixels, since cut-off is
# just above 2/3:
expected_results[(0., 0.)] = np.array([[0., 0., 0., 0., 0., 0., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[0., 0., 0., 0., 0., 0., 0.]])
# Same for tiny offset
expected_results[(0.01, 0.01)] = expected_results[(0.00, 0.00)]
# Displace towards -ve x a bit:
expected_results[(-.05, 0.)] = np.array([[0., 0., 0., 0., 0., 0., 0.],
[0., 1., 1., 1., 1., 0., 0.],
[0., 1., 1., 1., 1., 0., 0.],
[0., 1., 1., 1., 1., 0., 0.],
[0., 1., 1., 1., 1., 0., 0.],
[0., 1., 1., 1., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 0.]])
expected_results[(0.4, 0.)] = np.array([[0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 1.],
[0., 0., 0., 1., 1., 1., 1.],
[0., 0., 0., 0., 0., 0., 0.]])
for offset, expected_array in expected_results.items():
k = Kernel(
kernel_func=testfunc,
support=support,
offset=offset,
oversampling=oversampling,
normalize=False,
)
assert (k.array == expected_array).all()
def test_nearby_complex_vis():
# Quick sanity check for multiple visibilities, kernel footprints
# overlapping:
n_image = 8
support = 2
uv = np.array([
(-2., 1),
(0., -1),
])
# vis = np.ones(len(uv), dtype=np.float_)
vis = np.ones(len(uv), dtype=np.complex_)
vis_weights = np.ones_like(vis)
kernel_func = conv_funcs.Pillbox(1.1)
vis_grid, sampling_grid = convolve_to_grid(
kernel_func,
support=support,
image_size=n_image,
uv=uv,
vis=vis,
vis_weights=vis_weights,
)
# simplification true since weights are all 1:
assert vis_grid.sum() == vis.sum()
# Since uv is precisely on a sampling point, we'll get a
# 3x3 pillbox
v = 1. / 9. + 0j
expected_sample_grid = np.array(
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
[[0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., v, v, v, 0., 0.], [0., 0., 0., v, v, v, 0., 0.],
[0., v, v, 2. * v, v, v, 0., 0.], [0., v, v, v, 0., 0., 0., 0.],
[0., v, v, v, 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0.]])
# simplification true since weights are all 1:
assert (expected_sample_grid == vis_grid).all()
assert (expected_sample_grid == sampling_grid).all()
def test_bounds_checking():
n_image = 8
support = 2
# n_image = 8 then the co-ords in the u/x-direction are:
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
# Support = 2 takes this over the edge (since -3 -2 = -5):
uv = np.array([(-3., 0)])
vis = np.ones(len(uv), dtype=np.float_)
kernel_func = conv_funcs.Pillbox(1.5)
with pytest.raises(ValueError):
grid = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
oversampling=None)
grid, _ = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
raise_bounds=False
)
assert grid.sum() == 0.
def test_multiple_complex_vis():
# Quick sanity check for multiple visibilities, complex_ this time:
n_image = 8
support = 2
uv = np.array([(-2., 1),
(1., -1),
])
# vis = np.ones(len(uv), dtype=np.float_)
vis = np.ones(len(uv), dtype=np.complex_)
kernel_func = conv_funcs.Pillbox(1.1)
vis_grid, sampling_grid = convolve_to_grid(kernel_func,
support=support,
image_size=n_image,
uv=uv, vis=vis,
oversampling=None)
assert vis_grid.sum() == vis.sum()
# Since uv is precisely on a sampling point, we'll get a
# 3x3 pillbox
v = 1. / 9. + 0j
expected_result = np.array(
# [-4, -3, -2, -1, 0, 1, 2, 3 ]
[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., v, v, v, 0.],
[0., 0., 0., 0., v, v, v, 0.],
[0., v, v, v, v, v, v, 0.],
[0., v, v, v, 0., 0., 0., 0.],
[0., v, v, v, 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]]
)
assert (expected_result == vis_grid).all()
assert (expected_result == sampling_grid).all()
