def test_errors():
with pytest.raises(Exception):
ds.Sigma_at_R(min(Rxi) * 0.9, Rxi, xihm, M, c, Om)
ds.Sigma_at_R(max(Rxi) * 1.1, Rxi, xihm, M, c, Om)
Sigma = ds.Sigma_at_R(R, Rxi, xihm, M, c, Om)
with pytest.raises(Exception):
ds.DeltaSigma_at_R(min(R) * 0.9, R, Sigma, M, c, Om)
ds.DeltaSigma_at_R(max(R) * 1.1, R, Sigma, M, c, Om)
def test_Sigma_and_xi():
#Test that changes to xi_hm below a cut only affect Sigma below that cut
arrout = ds.Sigma_at_R(R, Rxi, xihm, M, c, Om)
norm = arrout.copy()
xi2 = xihm.copy()
cut = 0.2
inds = np.where(Rxi < cut)[0]
xi2[inds] *= 2
arrout2 = ds.Sigma_at_R(R, Rxi, xi2, M, c, Om)
arrout /= norm
arrout2 /= norm
inds = np.where(R < cut)[0]
npt.assert_array_less(arrout[inds], arrout2[inds])
inds = np.where(R >= cut)[0]
#Splines mean that we won't get perfectly equal
npt.assert_array_almost_equal(arrout[inds], arrout2[inds], decimal=2)
def test_analytic_Sigma():
r = np.logspace(-2, 3, num=1000)
R = np.logspace(-2, 2, num=1000)
xiin = r**-3
rhom = 2.77533742639e+11 * Om
true = 2 * rhom * R**-2 * 1e-12 #Mpc to pc
Sigma = ds.Sigma_at_R(R, r, xiin, M, c, Om)
ratio = Sigma / true
npt.assert_array_almost_equal(np.ones_like(R), ratio, decimal=2)
def test_DeltaSigma():
#Try with nfw
snfw = ds.Sigma_nfw_at_R(R, M, c, Om)
arrout = ds.DeltaSigma_at_R(R, R, snfw, M, c, Om)
assert len(arrout) == len(R)
for i in range(len(R)):
npt.assert_equal(arrout[i], ds.DeltaSigma_at_R(R[i], R, snfw, M, c,
Om))
#Now try with general Sigma(R)
sig = ds.Sigma_at_R(R, Rxi, xihm, M, c, Om)
arrout = ds.DeltaSigma_at_R(R, R, sig, M, c, Om)
assert len(arrout) == len(R)
for i in range(len(R)):
npt.assert_equal(arrout[i], ds.DeltaSigma_at_R(R[i], R, sig, M, c, Om))
def test_Sigma():
arrout = ds.Sigma_at_R(R, Rxi, xihm, M, c, Om)
assert len(arrout) == len(R)
for i in range(len(R)):
npt.assert_equal(arrout[i], ds.Sigma_at_R(R[i], Rxi, xihm, M, c, Om))