def mapper_from_grid_and_sparse_grid(self,
grid,
sparse_grid,
inversion_uses_border=False):
galaxies_with_pixelization = list(
filter(lambda galaxy: galaxy.pixelization is not None,
self.galaxies))
if len(galaxies_with_pixelization) == 0:
return None
if len(galaxies_with_pixelization) == 1:
pixelization = galaxies_with_pixelization[0].pixelization
return pixelization.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=sparse_grid,
inversion_uses_border=inversion_uses_border,
hyper_image=galaxies_with_pixelization[0].hyper_galaxy_image,
)
elif len(galaxies_with_pixelization) > 1:
raise exc.PixelizationException(
"The number of galaxies with pixelizations in one plane is above 1"
)
def regularization(self):
galaxies_with_regularization = list(filter(lambda galaxy: galaxy.has_regularization, self.galaxies))
if len(galaxies_with_regularization) == 0:
return None
if len(galaxies_with_regularization) == 1:
return galaxies_with_regularization[0].regularization
elif len(galaxies_with_regularization) > 1:
raise exc.PixelizationException('The number of galaxies with regularizations in one plane is above 1')
def pixelization(self):
if len(self.galaxies_with_pixelization) == 0:
return None
if len(self.galaxies_with_pixelization) == 1:
return self.galaxies_with_pixelization[0].pixelization
elif len(self.galaxies_with_pixelization) > 1:
raise exc.PixelizationException(
"The number of galaxies with pixelizations in one plane is above 1"
)
def mapper(self):
galaxies_with_pixelization = list(filter(lambda galaxy: galaxy.has_pixelization, self.galaxies))
if len(galaxies_with_pixelization) == 0:
return None
if len(galaxies_with_pixelization) == 1:
pixelization = galaxies_with_pixelization[0].pixelization
return pixelization.mapper_from_grid_stack_and_border(grid_stack=self.grid_stack, border=self.border)
elif len(galaxies_with_pixelization) > 1:
raise exc.PixelizationException('The number of galaxies with pixelizations in one plane is above 1')
def voronoi_from_pixel_centers(pixel_centers):
"""Compute the Voronoi grid of the pixelization, using the pixel centers.
Parameters
----------
pixel_centers : ndarray
The (y,x) centre of every Voronoi pixel.
"""
try:
return scipy.spatial.Voronoi(
np.asarray([pixel_centers[:, 1], pixel_centers[:, 0]]).T,
qhull_options="Qbb Qc Qx Qm",
)
except OverflowError or scipy.spatial.qhull.QhullError:
raise exc.PixelizationException()
def __init__(self, shape=(3, 3)):
"""A rectangular pixelization, where pixels are defined on a Cartesian and uniform grid of shape \
(rows, columns).
Like arrays, the indexing of the rectangular grid begins in the top-left corner and goes right and down.
Parameters
-----------
shape : (int, int)
The dimensions of the rectangular grid of pixels (y_pixels, x_pixel)
"""
if shape[0] <= 2 or shape[1] <= 2:
raise exc.PixelizationException(
"The rectangular pixelization must be at least dimensions 3x3")
self.shape = (int(shape[0]), int(shape[1]))
self.pixels = self.shape[0] * self.shape[1]
super(Rectangular, self).__init__()
