def __init__(self,
regular,
sub,
blurring=None,
pix=None,
regular_to_nearest_pix=None):
self.regular = grids.RegularGrid(regular, mask=None)
self.sub = sub
self.blurring = grids.RegularGrid(
blurring, mask=None) if blurring is not None else None
self.pix = grids.PixGrid(
pix, regular_to_nearest_pix=regular_to_nearest_pix,
mask=None) if pix is not None else np.array([[0.0, 0.0]])
def test__3x3_simple_grid__include_mask__create_using_regular_grid(self):
regular_grid = np.array([[1.0, 0.0], [0.0, -1.0], [0.0, 0.0],
[0.0, 1.0], [-1.0, 0.0]])
mask = msk.Mask(array=np.array([[True, False, True],
[False, False, False],
[True, False, True]]),
pixel_scale=1.0)
sub_grid = np.array([[1.0, 0.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0],
[-1.0, 0.0]])
sub_to_regular = np.array([0, 1, 2, 3, 4])
regular_grid = grids.RegularGrid(arr=regular_grid, mask=mask)
sub_grid = MockSubGrid(sub_grid, sub_to_regular, sub_grid_size=1)
pix = pixelizations.AdaptiveMagnification(shape=(3, 3))
image_plane_pix = pix.image_plane_pix_grid_from_regular_grid(
regular_grid=regular_grid)
grid_stack = MockGridStack(
regular=regular_grid,
sub=sub_grid,
pix=image_plane_pix.sparse_grid,
regular_to_nearest_pix=image_plane_pix.regular_to_sparse)
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=None)
assert mapper.is_image_plane_pixelization == True
assert mapper.geometry.shape_arc_seconds == pytest.approx((2.0, 2.0),
1.0e-4)
assert (mapper.geometry.pixel_centres == image_plane_pix.sparse_grid
).all()
assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4)
assert isinstance(mapper, pm.VoronoiMapper)
assert (mapper.mapping_matrix == np.array([[1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0,
1.0]])).all()
reg = regularization.Constant(coefficients=(1.0, ))
regularization_matrix = reg.regularization_matrix_from_pixel_neighbors(
mapper.geometry.pixel_neighbors,
mapper.geometry.pixel_neighbors_size)
assert (regularization_matrix == np.array(
[[3.00000001, -1.0, -1.0, -1.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0],
[-1.0, -1.0, 4.00000001, -1.0, -1.0],
[-1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, -1.0, -1.0, -1.0, 3.00000001]])).all()
def test__ensure_index_of_plane_image_has_negative_arcseconds_at_start(
self, grid_stack_0):
# The grid coordinates -2.0 -> 2.0 mean a plane of shape (5,5) has arc second coordinates running over
# -1.6, -0.8, 0.0, 0.8, 1.6. The origin -1.6, -1.6 of the model_galaxy means its brighest pixel should be
# index 0 of the 1D grid and (0,0) of the 2d plane datas_.
msk = mask.Mask(array=np.full((5, 5), False), pixel_scale=1.0)
grid_stack_0.regular = grids.RegularGrid(np.array([[-2.0, -2.0],
[2.0, 2.0]]),
mask=msk)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(1.6, -1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert plane_stack.plane_images[0].shape == (5, 5)
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (0,
0)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(1.6, 1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (0,
4)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(
centre=(-1.6, -1.6), intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (4,
0)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(-1.6, 1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (4,
4)