def test_cl2cov_log_shift_err():
"""Test that cl2cov raises an exception when log covariance is requested but no shift is specified."""
nside = 16
cl = [0]
with np.testing.assert_raises_regex(Exception, 'Shift'):
cov.cl2cov_mat(cl, nside, log=True)
def test_cl2cov_cl_dim():
"""Test that cl2cov raises an exception when cl is a not a 1D array."""
nside = 16
cl = np.zeros((10, 10))
with np.testing.assert_raises_regex(Exception, '1D'):
cov.cl2cov_mat(cl, nside)
def test_cl2cov_indices_unique():
"""Test that cl2cov raises an exception when indices is not unique."""
nside = 16
cl = np.zeros(10)
inds = [0, 0]
with np.testing.assert_raises_regex(Exception, 'unique'):
cov.cl2cov_mat(cl, nside, indices=inds)
def test_cl2cov_indices_dim():
"""Test that cl2cov raises an exception when indices is a not a 1D array."""
nside = 16
cl = np.zeros(10)
inds = np.arange(100).reshape([10, -1])
with np.testing.assert_raises_regex(Exception, '1D'):
cov.cl2cov_mat(cl, nside, indices=inds)
def test_cl2cov_truncate():
"""Test that cl2cov will truncate l modes greater than lmax=0 and will return monopole results."""
nside = 4
cl = np.ones(1000)
cov_mat = cov.cl2cov_mat(cl, nside, lmax=0)
np.testing.assert_almost_equal(cov_mat, cov_mat[0, 0])
def test_cl2cov_zero():
"""Test that covariance matrix is all zeros when power spectrum is zero."""
nside = 4
cl = np.zeros(1000)
cov_mat = cov.cl2cov_mat(cl, nside)
np.testing.assert_almost_equal(cov_mat, 0)
def test_cl2cov_monopole():
"""Test that covariance matrix is constant with monopole power spectrum."""
nside = 4
cl = np.zeros(1000)
cl[0] = 1
cov_mat = cov.cl2cov_mat(cl, nside)
np.testing.assert_almost_equal(cov_mat, cov_mat[0, 0])
def test_cl2cov_log():
"""Test that cl2cov raises an exception when log covariance is requested but no shift is specified."""
nside = 16
cl = [1]
xi_ii = cov.cl2xi_theta(cl, 0)
cov_mat = cov.cl2cov_mat(cl, nside, log=True, shift=1)
np.testing.assert_almost_equal(cov_mat, np.log(xi_ii + 1))
def test_cl2cov_dipole():
"""Test that covariance matrix has opposite signs at poles with dipole power spectrum."""
nside = 4
cl = np.zeros(1000)
cl[1] = 1
cov_mat = cov.cl2cov_mat(cl, nside)
# Using the second to last pixel for antipodal point due to geometry of Healpix.
np.testing.assert_almost_equal(cov_mat[0, 0], -cov_mat[0, -2])