def deflections_2d_from_grid(self, grid):
if self.preload_grid is not None and self.preload_deflections is not None:
if grid.sub_shape_slim == self.preload_grid.sub_shape_slim:
if np.allclose(grid, self.preload_grid, 1e-8):
return self.normalization_scale * self.preload_deflections
if (self.preload_blurring_grid is not None
and self.preload_blurring_deflections is not None):
if grid.sub_shape_slim == self.preload_blurring_grid.sub_shape_slim:
if np.allclose(grid, self.preload_blurring_grid, 1e-8):
return self.normalization_scale * self.preload_blurring_deflections
deflections_y = self.normalization_scale * griddata(
points=self.image_plane_grid, values=self.deflections_y, xi=grid)
deflections_x = self.normalization_scale * griddata(
points=self.image_plane_grid, values=self.deflections_x, xi=grid)
if np.isnan(deflections_y).any() or np.isnan(deflections_x).any():
raise exc.ProfileException(
"The grid input into the DefectionsInput.deflections_2d_from_grid() method has (y,x)"
"coodinates extending beyond the input image_plane_grid."
""
"Update the image_plane_grid to include deflection angles reaching to larger"
"radii or reduce the input grid. ")
return np.stack((deflections_y, deflections_x), axis=-1)
def normalization_via_mass_angular_from(
self,
mass_angular,
radius,
normalization_min=1e-15,
normalization_max=1e15,
bins=200,
):
normalization_list = np.logspace(np.log10(normalization_min),
np.log10(normalization_max), bins)
mass_angulars = [
self.mass_angular_via_normalization_from(
normalization=normalization, radius=radius)
for normalization in normalization_list
]
normalization_list = [
normalization
for normalization, mass in zip(normalization_list, mass_angulars)
if mass is not None
]
mass_angulars = list(filter(None, mass_angulars))
if ((len(mass_angulars) < 2) or (mass_angulars[0] > mass_angular)
or (mass_angulars[-1] < mass_angular)):
raise exc.ProfileException(
"The normalization could not be computed from the Einstein Radius via the average of the convergence. "
""
"The input einstein_radius may be too small or large to feasibly be computed by integrating the "
"convergence. Alternative the normalization range or number of bins may need to be changed to "
"capture the true einstein_radius value.")
def func(normalization, mass_angular_root, radius):
mass_angular = self.mass_angular_via_normalization_from(
normalization=normalization, radius=radius)
return mass_angular - mass_angular_root
return root_scalar(
func,
bracket=[normalization_list[0], normalization_list[-1]],
args=(mass_angular, radius),
).root
def normalization_via_einstein_radius_from(self,
einstein_radius,
normalization_min=1e-9,
normalization_max=1e9,
bins=100):
normalization_list = np.logspace(np.log10(normalization_min),
np.log10(normalization_max), bins)
einstein_radii = [
self.einstein_radius_via_normalization_from(
normalization=normalization)
for normalization in normalization_list
]
normalization_list = [
normalization
for normalization, radii in zip(normalization_list, einstein_radii)
if radii is not None
]
einstein_radii = list(filter(None, einstein_radii))
if ((len(einstein_radii) < 2) or (einstein_radii[0] > einstein_radius)
or (einstein_radii[-1] < einstein_radius)):
raise exc.ProfileException(
"The normalization could not be computed from the Einstein Radius via the average of the convergence. "
""
"The input einstein_radius may be too small or large to feasibly be computed by integrating the "
"convergence. Alternative the normalization range or number of bins may need to be changed to "
"capture the true einstein_radius value.")
def func(normalization, einstein_radius_root):
einstein_radius = self.einstein_radius_via_normalization_from(
normalization=normalization)
return einstein_radius - einstein_radius_root
return root_scalar(
func,
bracket=[normalization_list[0], normalization_list[-1]],
args=(einstein_radius, ),
).root