def __init__(self, dataset, xy_mask, uv_mask=None):
self.dataset = dataset
self.xy_mask = xy_mask
self.grid_3d = Grid3D(grid_2d=al.Grid.uniform(
shape_2d=xy_mask.shape_2d,
pixel_scales=xy_mask.pixel_scales,
sub_size=xy_mask.sub_size),
n_channels=xy_mask.n_channels)
self.uv_wavelengths = dataset.uv_wavelengths
self.visibilities = dataset.visibilities
if uv_mask is None:
self.uv_mask = np.full(shape=self.visibilities.shape,
fill_value=False)
else:
self.uv_mask = uv_mask
self.uv_mask_real_and_imag_averaged = np.full(
shape=self.uv_mask.shape[:-1], fill_value=False)
self.noise_map = dataset.noise_map
self.noise_map_real_and_imag_averaged = np.average(a=self.noise_map,
axis=-1)
self.z_step_kms = dataset.z_step_kms
def __init__(self, dataset, xy_mask, uv_mask=None):
self.dataset = dataset
self.xy_mask = xy_mask # NOTE: rename to real_space_mask
# NOTE: This should change and instead be initialized from mask
if xy_mask.pixel_scales is not None:
self.grid_3d = Grid3D(
grid_2d=aa.structures.grids.MaskedGrid.from_mask(
mask=xy_mask.mask_2d
),
n_channels=xy_mask.n_channels
)
self.uv_wavelengths = dataset.uv_wavelengths
self.visibilities = dataset.visibilities
self.noise_map = dataset.noise_map
if uv_mask is None:
self.uv_mask = np.full(
shape=self.visibilities.shape,
fill_value=False
)
else:
self.uv_mask = uv_mask
# TODO: Turn this into a function
self.uv_mask_real_and_imag_averaged = np.full(
shape=self.uv_mask.shape[:-1],
fill_value=False
)
for i in range(self.uv_mask.shape[0]):
for j in range(self.uv_mask.shape[1]):
if self.uv_mask[i, j, 0] == self.uv_mask[i, j, 1] == True:
self.uv_mask_real_and_imag_averaged[i, j] = True
self.noise_map_real_and_imag_averaged = np.average(
a=self.noise_map, axis=-1
)
self.z_step_kms = dataset.z_step_kms
def __init__(self, dataset, mask):
self.dataset = dataset
# TODO: Check that dataset has attribute data.
# TODO: Check that mask and dataset.data have the same shape.
self.mask = mask
#if mask.pixel_scales is not None:
self.grid_3d = Grid3D(grid_2d=al.Grid.uniform(
shape_2d=mask.shape_2d,
pixel_scales=mask.pixel_scales,
sub_size=mask.sub_size),
n_channels=mask.n_channels)
self.data = dataset.data
self.noise_map = dataset.noise_map
self.z_step_kms = dataset.z_step_kms
def __init__(self, dataset, xy_mask, uv_mask=None, region=None):
self.dataset = dataset
self.xy_mask = xy_mask
# NOTE: This should change and instead be initialized from mask
if xy_mask.pixel_scales is not None:
self.grid_3d = Grid3D(
grid_2d=aa.structures.grids.MaskedGrid.from_mask(
mask=xy_mask.mask_2d
),
n_channels=xy_mask.n_channels
)
self.uv_wavelengths = dataset.uv_wavelengths
self.visibilities = dataset.visibilities
if uv_mask is None:
self.uv_mask = np.full(
shape=self.visibilities.shape,
fill_value=False
)
else:
self.uv_mask = uv_mask
# NOTE: We cant have real and imag being different, either both are True of both are False.
# TODO: If both real and imag are True then this is True, but if one is False use that for the averaging.
self.uv_mask_real_and_imag_averaged = np.full(
shape=self.uv_mask.shape[:-1],
fill_value=False
)
self.noise_map = dataset.noise_map
self.noise_map_real_and_imag_averaged = np.average(
a=self.noise_map, axis=-1
)
self.z_step_kms = dataset.z_step_kms
def __init__(self, dataset, xy_mask, uv_mask=None):
self.dataset = dataset
self.xy_mask = xy_mask
self.grid_3d = Grid3D(grid_2d=al.Grid.uniform(
shape_2d=xy_mask.shape_2d,
pixel_scales=xy_mask.pixel_scales,
sub_size=xy_mask.sub_size),
n_channels=xy_mask.n_channels)
self.uv_wavelengths = dataset.uv_wavelengths
self.visibilities = dataset.visibilities
self.noise_map = dataset.noise_map
if uv_mask is None:
self.uv_mask = np.full(shape=self.visibilities.shape,
fill_value=False)
else:
if uv_mask.shape == self.visibilities.shape:
self.uv_mask = uv_mask
else:
raise ValueError(
"The shape of the uv_mask does not match the shape of the visibilities"
)
self.uv_mask_real_and_imag_averaged = np.full(
shape=self.uv_mask.shape[:-1], fill_value=False)
for i in range(self.uv_mask.shape[0]):
for j in range(self.uv_mask.shape[1]):
if self.uv_mask[i, j, 0] == self.uv_mask[i, j, 1] == True:
self.uv_mask_real_and_imag_averaged[i, j] = True
self.noise_map_real_and_imag_averaged = np.average(a=self.noise_map,
axis=-1)
self.z_step_kms = dataset.z_step_kms
raise ValueError("...")
return mask.astype(bool)
if __name__ == "__main__":
transformer_class = al.TransformerFINUFFT
grid_3d = Grid3D(
grid_2d=al.Grid.uniform(
shape_2d=(
n_pixels,
n_pixels
),
pixel_scales=(
pixel_scale,
pixel_scale
),
sub_size=1
),
n_channels=n_channels
)
# xy_mask = Mask3D.unmasked(
# shape_3d=grid_3d.shape_3d,
# pixel_scales=grid_3d.pixel_scales,
# sub_size=grid_3d.sub_size,
# )
xy_mask = Mask3D.manual(
mask_2d=al.Mask.circular(
shape_2d=grid_3d.shape_2d,
