class TestTNFW(object):
def setup(self):
self.nfw = NFW()
self.tnfw = TNFW()
def test_deflection(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.0 * Rs, 5 * Rs, 1000)
y = np.linspace(0., 1, 1000)
xdef_t, ydef_t = self.tnfw.derivatives(x, y, Rs, alpha_Rs, r_trunc)
xdef, ydef = self.nfw.derivatives(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(xdef_t, xdef, 5)
np.testing.assert_almost_equal(ydef_t, ydef, 5)
def test_potential(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 1000)
y = np.linspace(0.2, 1, 1000)
pot_t = self.tnfw.function(x, y, Rs, alpha_Rs, r_trunc)
pot = self.nfw.function(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(pot, pot_t, 4)
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1, 0.7, 100)
pot1 = self.tnfw.function(x, 0, Rs, alpha_Rs, r_trunc)
pot_nfw1 = self.nfw.function(x, 0, Rs, alpha_Rs)
npt.assert_almost_equal(pot1, pot_nfw1, 5)
def test_gamma(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 1000)
y = np.linspace(0.2, 1, 1000)
g1t, g2t = self.tnfw.nfwGamma((x**2 + y**2)**.5, Rs, alpha_Rs, r_trunc,
x, y)
g1, g2 = self.nfw.nfwGamma((x**2 + y**2)**.5, Rs, alpha_Rs, x, y)
np.testing.assert_almost_equal(g1t, g1, 5)
np.testing.assert_almost_equal(g2t, g2, 5)
def test_hessian(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 100)
y = np.linspace(0.2, 1, 100)
xxt, yyt, xyt = self.tnfw.hessian(x, y, Rs, alpha_Rs, r_trunc)
xx, yy, xy = self.nfw.hessian(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(xy, xyt, 4)
np.testing.assert_almost_equal(yy, yyt, 4)
np.testing.assert_almost_equal(xy, xyt, 4)
Rs = 0.2
r_trunc = 5
xxt, yyt, xyt = self.tnfw.hessian(Rs, 0, Rs, alpha_Rs, r_trunc)
xxt_delta, yyt_delta, xyt_delta = self.tnfw.hessian(
Rs + 0.000001, 0, Rs, alpha_Rs, r_trunc)
npt.assert_almost_equal(xxt, xxt_delta, decimal=6)
def test_density_2d(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 3 * Rs, 1000)
y = np.linspace(0.2, 0.5, 1000)
kappa_t = self.tnfw.density_2d(x, y, Rs, alpha_Rs, r_trunc)
kappa = self.nfw.density_2d(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(kappa, kappa_t, 5)
def test_transform(self):
rho0, Rs = 1, 2
trs = self.tnfw._rho02alpha(rho0, Rs)
rho_out = self.tnfw._alpha2rho0(trs, Rs)
npt.assert_almost_equal(rho0, rho_out)
def test_numerical_derivatives(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1.5 * Rs
diff = 1e-9
x0, y0 = 0.1, 0.1
x_def_t, y_def_t = self.tnfw.derivatives(x0, y0, Rs, alpha_Rs, r_trunc)
x_def_t_deltax, _ = self.tnfw.derivatives(x0 + diff, y0, Rs, alpha_Rs,
r_trunc)
x_def_t_deltay, y_def_t_deltay = self.tnfw.derivatives(
x0, y0 + diff, Rs, alpha_Rs, r_trunc)
actual = self.tnfw.hessian(x0, y0, Rs, alpha_Rs, r_trunc)
f_xx_approx = (x_def_t_deltax - x_def_t) * diff**-1
f_yy_approx = (y_def_t_deltay - y_def_t) * diff**-1
f_xy_approx = (x_def_t_deltay - y_def_t) * diff**-1
numerical = [f_xx_approx, f_yy_approx, f_xy_approx]
for (approx, true) in zip(numerical, actual):
npt.assert_almost_equal(approx, true)
class TestTNFW(object):
def setup(self):
self.nfw = NFW()
self.tnfw = TNFW()
def test_deflection(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.0 * Rs, 5 * Rs, 1000)
y = np.linspace(0., 1, 1000)
xdef_t, ydef_t = self.tnfw.derivatives(x, y, Rs, alpha_Rs, r_trunc)
xdef, ydef = self.nfw.derivatives(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(xdef_t, xdef, 5)
np.testing.assert_almost_equal(ydef_t, ydef, 5)
def test_potential_limit(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 1000)
y = np.linspace(0.2, 1, 1000)
pot_t = self.tnfw.function(x, y, Rs, alpha_Rs, r_trunc)
pot = self.nfw.function(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(pot, pot_t, 4)
def test_potential_derivative(self):
Rs = 1.2
alpha_Rs = 1
r_trunc = 3 * Rs
R = np.linspace(0.5 * Rs, 2.2 * Rs, 5000)
dx = R[1] - R[0]
alpha_tnfw = self.tnfw.nfwAlpha(R, Rs, 1, r_trunc, R, 0)[0]
potential_array = self.tnfw.nfwPot(R, Rs, 1, r_trunc)
alpha_tnfw_num_array = np.gradient(potential_array, dx)
potential_from_float = [
self.tnfw.nfwPot(R_i, Rs, 1, r_trunc) for R_i in R
]
alpha_tnfw_num_from_float = np.gradient(potential_from_float, dx)
npt.assert_almost_equal(alpha_tnfw_num_array, alpha_tnfw, 4)
npt.assert_almost_equal(alpha_tnfw_num_from_float, alpha_tnfw, 4)
def test_gamma(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 1000)
y = np.linspace(0.2, 1, 1000)
g1t, g2t = self.tnfw.nfwGamma((x**2 + y**2)**.5, Rs, alpha_Rs, r_trunc,
x, y)
g1, g2 = self.nfw.nfwGamma((x**2 + y**2)**.5, Rs, alpha_Rs, x, y)
np.testing.assert_almost_equal(g1t, g1, 5)
np.testing.assert_almost_equal(g2t, g2, 5)
def test_hessian(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 5 * Rs, 100)
y = np.linspace(0.2, 1, 100)
xxt, yyt, xyt = self.tnfw.hessian(x, y, Rs, alpha_Rs, r_trunc)
xx, yy, xy = self.nfw.hessian(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(xy, xyt, 4)
np.testing.assert_almost_equal(yy, yyt, 4)
np.testing.assert_almost_equal(xy, xyt, 4)
Rs = 0.2
r_trunc = 5
xxt, yyt, xyt = self.tnfw.hessian(Rs, 0, Rs, alpha_Rs, r_trunc)
xxt_delta, yyt_delta, xyt_delta = self.tnfw.hessian(
Rs + 0.000001, 0, Rs, alpha_Rs, r_trunc)
npt.assert_almost_equal(xxt, xxt_delta, decimal=6)
def test_density_2d(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1000000000000 * Rs
x = np.linspace(0.1 * Rs, 3 * Rs, 1000)
y = np.linspace(0.2, 0.5, 1000)
kappa_t = self.tnfw.density_2d(x, y, Rs, alpha_Rs, r_trunc)
kappa = self.nfw.density_2d(x, y, Rs, alpha_Rs)
np.testing.assert_almost_equal(kappa, kappa_t, 5)
def test_transform(self):
rho0, Rs = 1, 2
trs = self.tnfw._rho02alpha(rho0, Rs)
rho_out = self.tnfw._alpha2rho0(trs, Rs)
npt.assert_almost_equal(rho0, rho_out)
def test_numerical_derivatives(self):
Rs = 0.2
alpha_Rs = 0.1
r_trunc = 1.5 * Rs
diff = 1e-9
x0, y0 = 0.1, 0.1
x_def_t, y_def_t = self.tnfw.derivatives(x0, y0, Rs, alpha_Rs, r_trunc)
x_def_t_deltax, _ = self.tnfw.derivatives(x0 + diff, y0, Rs, alpha_Rs,
r_trunc)
x_def_t_deltay, y_def_t_deltay = self.tnfw.derivatives(
x0, y0 + diff, Rs, alpha_Rs, r_trunc)
actual = self.tnfw.hessian(x0, y0, Rs, alpha_Rs, r_trunc)
f_xx_approx = (x_def_t_deltax - x_def_t) * diff**-1
f_yy_approx = (y_def_t_deltay - y_def_t) * diff**-1
f_xy_approx = (x_def_t_deltay - y_def_t) * diff**-1
numerical = [f_xx_approx, f_yy_approx, f_xy_approx]
for (approx, true) in zip(numerical, actual):
npt.assert_almost_equal(approx, true)
def test_F_function_at_one(self):
f_tnfw = self.tnfw.F(1.)
npt.assert_(f_tnfw == 1)
f_tnfw = self.tnfw.F(np.ones((2, 2)))
f_tnfw = f_tnfw.ravel()
for value in f_tnfw:
npt.assert_(value == 1)