def test__image_2d_from(self, sub_grid_2d_7x7, lp_0, gal_x1_lp, lp_1, gal_x2_lp):
galaxy = ag.Galaxy(redshift=0.5)
image = galaxy.image_2d_from(grid=sub_grid_2d_7x7)
assert (image == np.zeros(shape=sub_grid_2d_7x7.sub_shape_slim)).all()
lp_image = lp_0.image_2d_from(grid=np.array([[1.05, -0.55]]))
gal_image = gal_x1_lp.image_2d_from(grid=np.array([[1.05, -0.55]]))
assert lp_image == gal_image
lp_image = lp_0.image_2d_from(grid=np.array([[1.05, -0.55]]))
lp_image += lp_1.image_2d_from(grid=np.array([[1.05, -0.55]]))
gal_image = gal_x2_lp.image_2d_from(grid=np.array([[1.05, -0.55]]))
assert lp_image == gal_image
lp_image = lp_0.image_2d_from(grid=ag.Grid2DIrregular([(1.05, -0.55)]))
gal_image = gal_x1_lp.image_2d_from(grid=ag.Grid2DIrregular([(1.05, -0.55)]))
assert lp_image.in_list[0] == gal_image.in_list[0]
def test__grid_2d_irrergular__moves_radial_coordinates__does_not_double_transform():
grid_2d_irregular = ag.Grid2DIrregular(grid=[[0.0, 0.0]])
grid_2d_irregular_offset = ag.Grid2DIrregular(grid=[[0.0001, 0.0001]])
isothermal = ag.mp.EllIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
convergence_1 = isothermal.convergence_2d_from(grid=grid_2d_irregular)
convergence_0 = isothermal.convergence_2d_from(grid=grid_2d_irregular_offset)
assert convergence_0 == pytest.approx(convergence_1, 1.0e-8)
grid_2d_irregular = ag.Grid2DIrregular(grid=[[0.5, 0.5]])
grid_2d_irregular_offset = ag.Grid2DIrregular(grid=[[0.5001, 0.5001]])
isothermal = ag.mp.EllIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
convergence_1 = isothermal.convergence_2d_from(grid=grid_2d_irregular)
convergence_0 = isothermal.convergence_2d_from(grid=grid_2d_irregular_offset)
assert convergence_0 != pytest.approx(convergence_1, 1.0e-8)
isothermal = ag.mp.EllIsothermal(centre=(0.5, 0.5), einstein_radius=1.0)
convergence_1 = isothermal.convergence_2d_from(grid=grid_2d_irregular)
convergence_0 = isothermal.convergence_2d_from(grid=grid_2d_irregular_offset)
assert convergence_0 == pytest.approx(convergence_1, 1.0e-8)
def test__hessian_from_grid(self):
sie = MockEllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.0, -0.111111),
einstein_radius=2.0)
grid = ag.Grid2DIrregular(grid=[(0.5, 0.5), (1.0, 1.0)])
hessian_yy, hessian_xy, hessian_yx, hessian_xx = sie.hessian_from_grid(
grid=grid)
print(hessian_yy, hessian_xy, hessian_yx, hessian_xx)
assert hessian_yy == pytest.approx(np.array([1.3883822, 0.694127]),
1.0e-4)
assert hessian_xy == pytest.approx(np.array([-1.388124, -0.694094]),
1.0e-4)
assert hessian_yx == pytest.approx(np.array([-1.388165, -0.694099]),
1.0e-4)
assert hessian_xx == pytest.approx(np.array([1.3883824, 0.694127]),
1.0e-4)
grid = ag.Grid2DIrregular(grid=[(1.0, 0.0), (0.0, 1.0)])
hessian_yy, hessian_xy, hessian_yx, hessian_xx = sie.hessian_from_grid(
grid=grid)
assert hessian_yy == pytest.approx(np.array([0.0, 1.777699]), 1.0e-4)
assert hessian_xy == pytest.approx(np.array([0.0, 0.0]), 1.0e-4)
assert hessian_yx == pytest.approx(np.array([0.0, 0.0]), 1.0e-4)
assert hessian_xx == pytest.approx(np.array([2.22209, 0.0]), 1.0e-4)
def test__compare_sis_deflection_magnitudes_to_known_values(self):
sis = MockSphIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
grid = ag.Grid2DIrregular([(1.0, 0.0), (0.0, 1.0)])
deflection_magnitudes = sis.deflection_magnitudes_from_grid(grid=grid)
assert deflection_magnitudes == pytest.approx(np.array([1.0, 1.0]),
1.0e-4)
sis = MockSphIsothermal(centre=(0.0, 0.0), einstein_radius=2.0)
grid = ag.Grid2DIrregular([(2.0, 0.0), (0.0, 2.0)])
deflection_magnitudes = sis.deflection_magnitudes_from_grid(grid=grid)
assert deflection_magnitudes == pytest.approx(np.array([2.0, 2.0]),
1.0e-4)
grid = ag.Grid2D.uniform(shape_native=(5, 5),
pixel_scales=0.1,
sub_size=1)
deflections = sis.deflections_2d_from_grid(grid=grid)
magitudes_manual = np.sqrt(
np.square(deflections[:, 0]) + np.square(deflections[:, 1]))
deflection_magnitudes = sis.deflection_magnitudes_from_grid(grid=grid)
assert deflection_magnitudes == pytest.approx(magitudes_manual, 1.0e-4)
def test__potential_2d_from__no_mass_profile_list__potential_returned_as_0s_of_shape_grid(
self, sub_grid_2d_7x7, mp_0, gal_x1_mp, mp_1, gal_x2_mp
):
grid = ag.Grid2D.manual_native(grid=[[[1.05, -0.55]]], pixel_scales=1.0)
galaxy = ag.Galaxy(redshift=0.5)
potential = galaxy.potential_2d_from(grid=sub_grid_2d_7x7)
assert (potential.slim == np.zeros(shape=sub_grid_2d_7x7.sub_shape_slim)).all()
mp_potential = mp_0.potential_2d_from(grid=grid)
gal_potential = gal_x1_mp.potential_2d_from(grid=np.array([[1.05, -0.55]]))
assert mp_potential == gal_potential
mp_potential = mp_0.potential_2d_from(grid=grid)
mp_potential += mp_1.potential_2d_from(grid=grid)
gal_potential = gal_x2_mp.potential_2d_from(grid=grid)
assert mp_potential == gal_potential
grid = ag.Grid2DIrregular([(1.05, -0.55)])
mp_potential = mp_0.potential_2d_from(grid=grid)
gal_potential = gal_x1_mp.potential_2d_from(grid=grid)
assert mp_potential == gal_potential
assert mp_potential.in_list[0] == gal_potential.in_list[0]
def test__shear_via_hessian_from_grid(self):
buffer = 0.00001
grid = ag.Grid2DIrregular(
grid=[(1.075, -0.125), (-0.875, -0.075), (-0.925,
-0.075), (0.075, 0.925)])
sis = ag.mp.EllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.001, 0.001),
einstein_radius=1.0)
shear = sis.shear_via_hessian_from_grid(grid=grid, buffer=buffer)
assert shear.in_list[0] == pytest.approx(0.461447, 1.0e-4)
assert shear.in_list[1] == pytest.approx(0.568875, 1.0e-4)
assert shear.in_list[2] == pytest.approx(0.538326, 1.0e-4)
assert shear.in_list[3] == pytest.approx(0.539390, 1.0e-4)
sis = ag.mp.EllIsothermal(centre=(0.2, 0.1),
elliptical_comps=(0.3, 0.4),
einstein_radius=1.5)
shear = sis.shear_2d_from_grid(grid=grid)
print((shear[:, 0]**2 + shear[:, 1]**2)**0.5)
shear = sis.shear_via_hessian_from_grid(grid=grid, buffer=buffer)
assert shear.in_list[0] == pytest.approx(0.41597, 1.0e-4)
assert shear.in_list[1] == pytest.approx(0.38299, 1.0e-4)
assert shear.in_list[2] == pytest.approx(0.36522, 1.0e-4)
assert shear.in_list[3] == pytest.approx(0.82750, 1.0e-4)
def test__convergence_via_hessian_from_grid(self):
buffer = 0.0001
grid = ag.Grid2DIrregular(
grid=[(1.075, -0.125), (-0.875, -0.075), (-0.925,
-0.075), (0.075, 0.925)])
sis = MockEllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.001, 0.001),
einstein_radius=1.0)
convergence = sis.convergence_via_hessian_from_grid(grid=grid,
buffer=buffer)
assert convergence.in_list[0] == pytest.approx(0.461447, 1.0e-4)
assert convergence.in_list[1] == pytest.approx(0.568875, 1.0e-4)
assert convergence.in_list[2] == pytest.approx(0.538326, 1.0e-4)
assert convergence.in_list[3] == pytest.approx(0.539390, 1.0e-4)
sis = ag.mp.EllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.3, 0.4),
einstein_radius=1.5)
convergence = sis.convergence_via_hessian_from_grid(grid=grid,
buffer=buffer)
assert convergence.in_list[0] == pytest.approx(0.35313, 1.0e-4)
assert convergence.in_list[1] == pytest.approx(0.46030, 1.0e-4)
assert convergence.in_list[2] == pytest.approx(0.43484, 1.0e-4)
assert convergence.in_list[3] == pytest.approx(1.00492, 1.0e-4)
def test__convergence_1d_from__grid_2d_irregular_in__returns_1d_quantities(
self):
grid_2d = ag.Grid2DIrregular(grid=[[1.0, 1.0], [2.0, 2.0], [4.0, 4.0]])
sie = ag.mp.EllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.0, 0.0),
einstein_radius=1.0)
convergence_1d = sie.convergence_1d_from_grid(grid=grid_2d)
convergence_2d = sie.convergence_2d_from_grid(grid=grid_2d)
print(convergence_1d)
assert convergence_1d[0] == pytest.approx(convergence_2d[0], 1.0e-4)
assert convergence_1d[1] == pytest.approx(convergence_2d[1], 1.0e-4)
assert convergence_1d[2] == pytest.approx(convergence_2d[2], 1.0e-4)
sie = ag.mp.EllIsothermal(centre=(0.2, 0.2),
elliptical_comps=(0.3, 0.3),
einstein_radius=1.0)
convergence_1d = sie.convergence_1d_from_grid(grid=grid_2d)
convergence_2d = sie.convergence_2d_from_grid(grid=grid_2d)
assert convergence_1d[0] == pytest.approx(convergence_2d[0], 1.0e-4)
assert convergence_1d[1] == pytest.approx(convergence_2d[1], 1.0e-4)
assert convergence_1d[2] == pytest.approx(convergence_2d[2], 1.0e-4)
def test__convergence_1d_from(
self, sub_grid_1d_7, mp_0, gal_x1_mp, mp_1, gal_x2_mp
):
grid = ag.Grid2D.manual_native(
[[[1.05, -0.55], [2.05, -0.55]]], pixel_scales=1.0
)
galaxy = ag.Galaxy(redshift=0.5)
convergence = galaxy.convergence_1d_from(grid=sub_grid_1d_7)
assert (convergence.slim == np.zeros(shape=sub_grid_1d_7.sub_shape_slim)).all()
mp_convergence = mp_0.convergence_1d_from(grid=grid)
gal_convergence = gal_x1_mp.convergence_1d_from(grid=grid)
assert mp_convergence == gal_convergence
mp_convergence = mp_0.convergence_1d_from(grid=grid)
mp_convergence += mp_1.convergence_1d_from(grid=grid)
gal_convergence = gal_x2_mp.convergence_1d_from(grid=grid)
assert mp_convergence == gal_convergence
grid = ag.Grid2DIrregular([(1.05, -0.55), (2.05, -0.55)])
mp_convergence = mp_0.convergence_1d_from(grid=grid)
gal_convergence = gal_x1_mp.convergence_1d_from(grid=grid)
assert (mp_convergence == gal_convergence).all()
assert mp_convergence.in_list[0] == gal_convergence.in_list[0]
# Test explicitly for a profile with an offset centre and ellipticity, given the 1D to 2D projections are nasty.
grid = ag.Grid2D.manual_native(
[[(1.05, -0.55), (2.05, -0.55)]], pixel_scales=1.0
)
elliptical_mp = ag.mp.EllIsothermal(
centre=(0.5, 1.0), elliptical_comps=(0.2, 0.3), einstein_radius=1.0
)
galaxy = ag.Galaxy(redshift=0.5, mass=elliptical_mp)
mp_convergence = elliptical_mp.convergence_1d_from(grid=grid)
gal_convergence = galaxy.convergence_1d_from(grid=grid)
assert (mp_convergence == gal_convergence).all()
def test__magnification_via_hessian_from_grid(self):
sie = MockEllIsothermal(centre=(0.0, 0.0),
elliptical_comps=(0.0, -0.111111),
einstein_radius=2.0)
grid = ag.Grid2DIrregular(grid=[(0.5, 0.5), (1.0, 1.0)])
magnification = sie.magnification_via_hessian_from_grid(grid=grid)
assert magnification.in_list[0] == pytest.approx(-0.56303, 1.0e-4)
assert magnification.in_list[1] == pytest.approx(-2.57591, 1.0e-4)
def test__image_1d_from(self, sub_grid_1d_7, lp_0, gal_x1_lp, lp_1, gal_x2_lp):
grid = ag.Grid2D.manual_native([[[1.05, -0.55]]], pixel_scales=1.0)
galaxy = ag.Galaxy(redshift=0.5)
image_1d = galaxy.image_1d_from(grid=sub_grid_1d_7)
assert (image_1d == np.zeros(shape=sub_grid_1d_7.sub_shape_slim)).all()
lp_image = lp_0.image_1d_from(grid=grid)
gal_image = gal_x1_lp.image_1d_from(grid=grid)
assert lp_image == gal_image
lp_image = lp_0.image_1d_from(grid=grid)
lp_image += lp_1.image_1d_from(grid=grid)
gal_image = gal_x2_lp.image_1d_from(grid=grid)
assert lp_image == gal_image
grid = ag.Grid2DIrregular([(1.05, -0.55)])
lp_image = lp_0.image_1d_from(grid=grid)
gal_image = gal_x1_lp.image_1d_from(grid=grid)
assert lp_image.in_list[0] == gal_image.in_list[0]
# Test explicitly for a profile with an offset centre and ellipticity, given the 1D to 2D projections are nasty.
grid = ag.Grid2D.manual_native(
[[(1.05, -0.55), (2.05, -0.55)]], pixel_scales=1.0
)
elliptical_lp = ag.lp.EllSersic(
centre=(0.5, 1.0), elliptical_comps=(0.2, 0.3), intensity=1.0
)
galaxy = ag.Galaxy(redshift=0.5, mass=elliptical_lp)
lp_image = elliptical_lp.image_1d_from(grid=grid)
gal_lp_image = galaxy.image_1d_from(grid=grid)
assert (lp_image == gal_lp_image).all()
def test__deflections_yx_2d_from__no_mass_profile_list__deflections_returned_as_0s_of_shape_grid(
self, sub_grid_2d_7x7, mp_0, gal_x1_mp, mp_1, gal_x2_mp
):
grid = ag.Grid2D.manual_native([[[1.05, -0.55]]], pixel_scales=1.0)
galaxy = ag.Galaxy(redshift=0.5)
deflections = galaxy.deflections_yx_2d_from(grid=sub_grid_2d_7x7)
assert (
deflections.slim == np.zeros(shape=(sub_grid_2d_7x7.sub_shape_slim, 2))
).all()
galaxy = ag.Galaxy(redshift=0.5)
deflections = galaxy.deflections_yx_2d_from(grid=sub_grid_2d_7x7)
assert (deflections.binned.native == np.zeros(shape=(7, 7, 2))).all()
mp_deflections = mp_0.deflections_yx_2d_from(grid=grid)
gal_deflections = gal_x1_mp.deflections_yx_2d_from(
grid=np.array([[1.05, -0.55]])
)
assert (mp_deflections == gal_deflections).all()
mp_deflections = mp_0.deflections_yx_2d_from(grid=grid)
mp_deflections += mp_1.deflections_yx_2d_from(grid=grid)
gal_deflections = gal_x2_mp.deflections_yx_2d_from(
grid=np.array([[1.05, -0.55]])
)
assert (mp_deflections == gal_deflections).all()
grid = ag.Grid2DIrregular([(1.05, -0.55)])
mp_deflections = mp_0.deflections_yx_2d_from(grid=grid)
gal_deflections = gal_x1_mp.deflections_yx_2d_from(grid=grid)
assert type(gal_deflections) == ag.VectorYX2DIrregular
assert mp_deflections.in_list[0][0] == gal_deflections.in_list[0][0]
assert mp_deflections.in_list[0][1] == gal_deflections.in_list[0][1]
def test__convergence_2d_from(
self, sub_grid_2d_7x7, mp_0, gal_x1_mp, mp_1, gal_x2_mp
):
grid = ag.Grid2D.manual_native([[[1.05, -0.55]]], pixel_scales=1.0)
galaxy = ag.Galaxy(redshift=0.5)
convergence = galaxy.convergence_2d_from(grid=sub_grid_2d_7x7)
assert (
convergence.slim == np.zeros(shape=sub_grid_2d_7x7.sub_shape_slim)
).all()
mp_convergence = mp_0.convergence_2d_from(grid=grid)
gal_convergence = gal_x1_mp.convergence_2d_from(grid=grid)
assert mp_convergence == gal_convergence
mp_convergence = mp_0.convergence_2d_from(grid=grid)
mp_convergence += mp_1.convergence_2d_from(grid=grid)
gal_convergence = gal_x2_mp.convergence_2d_from(grid=grid)
assert mp_convergence == gal_convergence
grid = ag.Grid2DIrregular([(1.05, -0.55)])
mp_convergence = mp_0.convergence_2d_from(grid=grid)
gal_convergence = gal_x1_mp.convergence_2d_from(grid=grid)
assert mp_convergence == gal_convergence
assert mp_convergence.in_list[0] == gal_convergence.in_list[0]
