def test__compare_to_imaging_util(self):
pix = mappers.RectangularMapper(pixels=9,
shape=(4, 3),
grid_stack=None,
border=None,
geometry=MockGeometry())
solution = np.array(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.0, 2.0, 3.0])
recon_pix = pix.reconstructed_pixelization_from_solution_vector(
solution_vector=solution)
recon_pix_util = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=solution, shape=(4, 3))
assert (recon_pix == recon_pix_util).all()
assert recon_pix.shape == (4, 3)
pix = mappers.RectangularMapper(pixels=9,
shape=(3, 4),
grid_stack=None,
border=None,
geometry=MockGeometry())
solution = np.array(
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 1.0, 2.0, 3.0])
recon_pix = pix.reconstructed_pixelization_from_solution_vector(
solution_vector=solution)
recon_pix_util = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=solution, shape=(3, 4))
assert (recon_pix == recon_pix_util).all()
assert recon_pix.shape == (3, 4)
def test__1d_array_in__can_map_it_to_3x2_2d_array(self):
array_1d = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
array_2d = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d, shape=(3, 2))
assert (array_2d == np.array([[1.0, 2.0], [3.0, 4.0], [5.0,
6.0]])).all()
def plane_image_of_galaxies_from_grid(shape, grid, galaxies, buffer=1.0e-2):
y_min = np.min(grid[:, 0]) - buffer
y_max = np.max(grid[:, 0]) + buffer
x_min = np.min(grid[:, 1]) - buffer
x_max = np.max(grid[:, 1]) + buffer
pixel_scales = (float(
(y_max - y_min) / shape[0]), float((x_max - x_min) / shape[1]))
origin = ((y_max + y_min) / 2.0, (x_max + x_min) / 2.0)
uniform_grid = grid_util.regular_grid_1d_masked_from_mask_pixel_scales_and_origin(
mask=np.full(shape=shape, fill_value=False),
pixel_scales=pixel_scales,
origin=origin)
image_1d = sum([
galaxy_util.intensities_of_galaxies_from_grid(grid=uniform_grid,
galaxies=[galaxy])
for galaxy in galaxies
])
image_2d = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=image_1d, shape=shape)
return pl.PlaneImage(array=image_2d,
pixel_scales=pixel_scales,
grid=grid,
origin=origin)
def reconstructed_pixelization_from_solution_vector(self, solution_vector):
"""Given the solution vector of an inversion (see *inversions.Inversion*), determine the reconstructed \
pixelization of the rectangular pixelization by using the mapper."""
recon = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=solution_vector, shape=self.shape)
return scaled_array.ScaledRectangularPixelArray(
array=recon,
pixel_scales=self.geometry.pixel_scales,
origin=self.geometry.origin)
def map_to_2d_keep_padded(self, padded_array_1d):
""" Map a padded 1D array of values to its padded 2D array.
Parameters
-----------
padded_array_1d : ndarray
A 1D array of values which were computed using the *PaddedRegularGrid*.
"""
return mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=padded_array_1d, shape=self.mask.shape)
def test__1d_array_in__maps_it_to_4x4_2d_array(self):
array_1d = np.array([
1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
13.0, 14.0, 15.0, 16.0
])
array_2d = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d, shape=(4, 4))
assert (array_2d == np.array([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0],
[13.0, 14.0, 15.0, 16.0]])).all()
def convolve_array_1d_with_psf(self, padded_array_1d, psf):
"""Convolve a 1d padded array of values (e.g. intensities before PSF blurring) with a PSF, and then trim \
the convolved array to its original 2D shape.
Parameters
-----------
padded_array_1d: ndarray
A 1D array of values which were computed using the *PaddedRegularGrid*.
psf : ndarray
An array describing the PSF kernel of the image.
"""
padded_array_2d = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=padded_array_1d, shape=self.mask.shape)
blurred_padded_array_2d = psf.convolve(array=padded_array_2d)
return mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
array_2d=blurred_padded_array_2d,
mask=np.full(self.mask.shape, False))
def test__3x2_grid__shape_change_correct_and_coordinates_shift(self):
galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=1.0))
grid = np.array([[-1.5, -1.5], [1.5, 1.5]])
plane_image = lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 2), grid=grid, galaxies=[galaxy], buffer=0.0)
plane_image_galaxy = galaxy.intensities_from_grid(
grid=np.array([[-1.0, -0.75], [-1.0, 0.75], [0.0, -0.75],
[0.0, 0.75], [1.0, -0.75], [1.0, 0.75]]))
plane_image_galaxy = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=plane_image_galaxy, shape=(3, 2))
assert (plane_image == plane_image_galaxy).all()
def test__3x3_grid__extracts_max_min_coordinates__creates_regular_grid_including_half_pixel_offset_from_edge(
self):
galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=1.0))
grid = np.array([[-1.5, -1.5], [1.5, 1.5]])
plane_image = lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0)
plane_image_galaxy = galaxy.intensities_from_grid(grid=np.array(
[[-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0],
[0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0]]))
plane_image_galaxy = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=plane_image_galaxy, shape=(3, 3))
assert (plane_image == plane_image_galaxy).all()
def test__3x3_grid__extracts_max_min_coordinates__ignores_other_coordinates_more_central(
self):
galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=1.0))
grid = np.array([[-1.5, -1.5], [1.5, 1.5], [0.1, -0.1], [-1.0, 0.6],
[1.4, -1.3], [1.5, 1.5]])
plane_image = lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0)
plane_image_galaxy = galaxy.intensities_from_grid(grid=np.array(
[[-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0],
[0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0]]))
plane_image_galaxy = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=plane_image_galaxy, shape=(3, 3))
assert (plane_image == plane_image_galaxy).all()
def test__3x3_grid__buffer_aligns_two_grids(self):
galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=1.0))
grid_without_buffer = np.array([[-1.48, -1.48], [1.48, 1.48]])
plane_image = lens_util.plane_image_of_galaxies_from_grid(
shape=(3, 3),
grid=grid_without_buffer,
galaxies=[galaxy],
buffer=0.02)
plane_image_galaxy = galaxy.intensities_from_grid(grid=np.array(
[[-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0],
[0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0]]))
plane_image_galaxy = mapping_util.map_unmasked_1d_array_to_2d_array_from_array_1d_and_shape(
array_1d=plane_image_galaxy, shape=(3, 3))
assert (plane_image == plane_image_galaxy).all()
