import numpy as np
from typing import Tuple
import autoarray as aa
from autogalaxy.profiles.mass.abstract.abstract import MassProfile
[docs]
class PowerLawCore(MassProfile):
def __init__(
self,
centre: Tuple[float, float] = (0.0, 0.0),
ell_comps: Tuple[float, float] = (0.0, 0.0),
einstein_radius: float = 1.0,
slope: float = 2.0,
core_radius: float = 0.01,
):
"""
Represents a cored elliptical power-law density distribution
Parameters
----------
centre
The (y,x) arc-second coordinates of the profile centre.
ell_comps
The first and second ellipticity components of the elliptical coordinate system.
einstein_radius
The arc-second Einstein radius.
slope
The density slope of the power-law (lower value -> shallower profile, higher value -> steeper profile).
core_radius
The arc-second radius of the inner core.
"""
super().__init__(centre=centre, ell_comps=ell_comps)
self.einstein_radius = einstein_radius
self.slope = slope
self.core_radius = core_radius
[docs]
def einstein_radius_rescaled(self, xp=np):
"""
Rescale the einstein radius by slope and axis_ratio, to reduce its degeneracy with other mass-profiles
parameters.
"""
return (
(3 - self.slope) / (1 + self.axis_ratio(xp))
) * self.einstein_radius ** (self.slope - 1)
[docs]
@aa.over_sample
@aa.decorators.to_array
@aa.decorators.transform
def convergence_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
"""
Returns the two dimensional projected convergence on a grid of (y,x) arc-second coordinates.
The `grid_2d_to_structure` decorator reshapes the ndarrays the convergence is outputted on. See
*aa.grid_2d_to_structure* for a description of the output.
Parameters
----------
grid
The grid of (y,x) arc-second coordinates the convergence is computed on.
"""
grid_eta = self.elliptical_radii_grid_from(grid=grid, xp=xp, **kwargs)
return self.convergence_func(grid_radius=grid_eta)
[docs]
@aa.over_sample
@aa.decorators.to_array
@aa.decorators.transform
def potential_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
"""
Calculate the potential on a grid of (y,x) arc-second coordinates.
Parameters
----------
grid
The grid of (y,x) arc-second coordinates the deflection angles are computed on.
"""
from scipy.integrate import quad
potential_grid = np.zeros(grid.shape[0])
for i in range(grid.shape[0]):
potential_grid[i] = quad(
self.potential_func,
a=0.0,
b=1.0,
args=(
grid.array[i, 0],
grid.array[i, 1],
self.axis_ratio(xp),
self.slope,
self.core_radius,
),
)[0]
return self.einstein_radius_rescaled(xp) * self.axis_ratio(xp) * potential_grid
[docs]
@aa.decorators.to_vector_yx
@aa.decorators.transform(rotate_back=True)
def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
"""
Calculate the deflection angles on a grid of (y,x) arc-second coordinates.
Parameters
----------
grid
The grid of (y,x) arc-second coordinates the deflection angles are computed on.
"""
from scipy.integrate import quad
def calculate_deflection_component(npow, index):
einstein_radius_rescaled = self.einstein_radius_rescaled(xp)
deflection_grid = np.array(self.axis_ratio(xp) * grid.array[:, index])
for i in range(grid.shape[0]):
deflection_grid[i] *= (
einstein_radius_rescaled
* quad(
self.deflection_func,
a=0.0,
b=1.0,
args=(
grid.array[i, 0],
grid.array[i, 1],
npow,
self.axis_ratio(xp),
self.slope,
self.core_radius,
),
)[0]
)
return deflection_grid
deflection_y = calculate_deflection_component(1.0, 0)
deflection_x = calculate_deflection_component(0.0, 1)
return np.vstack((deflection_y, deflection_x)).T
[docs]
def convergence_func(self, grid_radius: float, xp=np) -> float:
return self.einstein_radius_rescaled(xp) * (
self.core_radius**2 + grid_radius**2
) ** (-(self.slope - 1) / 2.0)
[docs]
@staticmethod
def potential_func(u, y, x, axis_ratio, slope, core_radius):
eta = np.sqrt((u * ((x**2) + (y**2 / (1 - (1 - axis_ratio**2) * u)))))
return (
(eta / u)
* ((3.0 - slope) * eta) ** -1.0
* (
(core_radius**2.0 + eta**2.0) ** ((3.0 - slope) / 2.0)
- core_radius ** (3 - slope)
)
/ ((1 - (1 - axis_ratio**2) * u) ** 0.5)
)
[docs]
@staticmethod
def deflection_func(u, y, x, npow, axis_ratio, slope, core_radius):
_eta_u = np.sqrt((u * ((x**2) + (y**2 / (1 - (1 - axis_ratio**2) * u)))))
return (core_radius**2 + _eta_u**2) ** (-(slope - 1) / 2.0) / (
(1 - (1 - axis_ratio**2) * u) ** (npow + 0.5)
)
[docs]
def ellipticity_rescale(self, xp=np):
return (1.0 + self.axis_ratio(xp=xp)) / 2.0
@property
def unit_mass(self):
return "angular"
[docs]
class PowerLawCoreSph(PowerLawCore):
def __init__(
self,
centre: Tuple[float, float] = (0.0, 0.0),
einstein_radius: float = 1.0,
slope: float = 2.0,
core_radius: float = 0.01,
):
"""
Represents a cored spherical power-law density distribution
Parameters
----------
centre
The (y,x) arc-second coordinates of the profile centre.
einstein_radius
The arc-second Einstein radius.
slope
The density slope of the power-law (lower value -> shallower profile, higher value -> steeper profile).
core_radius
The arc-second radius of the inner core.
"""
super().__init__(
centre=centre,
ell_comps=(0.0, 0.0),
einstein_radius=einstein_radius,
slope=slope,
core_radius=core_radius,
)
[docs]
@aa.decorators.to_vector_yx
@aa.decorators.transform
def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
"""
Calculate the deflection angles on a grid of (y,x) arc-second coordinates.
Parameters
----------
grid
The grid of (y,x) arc-second coordinates the deflection angles are computed on.
"""
eta = self.radial_grid_from(grid=grid, xp=xp, **kwargs)
deflection = xp.multiply(
2.0 * self.einstein_radius_rescaled(xp),
xp.divide(
xp.add(
xp.power(
xp.add(self.core_radius**2, xp.square(eta.array)),
(3.0 - self.slope) / 2.0,
),
-self.core_radius ** (3 - self.slope),
),
xp.multiply((3.0 - self.slope), eta.array),
),
)
return self._cartesian_grid_via_radial_from(grid=grid, radius=deflection, xp=xp)