def test_nfw_centered(): c = ClusterEnsemble(toy_data_z) def _check_sigma(i, j): assert_allclose(c.sigma_nfw[j].value, toy_data_sigma[i, j], rtol=1e-4) def _check_deltasigma(i, j): assert_allclose(c.deltasigma_nfw[j].value, toy_data_deltasigma[i, j], rtol=1e-4) for i, n200 in enumerate(toy_data_n200): c.n200 = n200 c.calc_nfw(toy_data_rbins) for j in range(c.z.shape[0]): yield _check_sigma, i, j yield _check_deltasigma, i, j
def test_nfw_centered(): c = ClusterEnsemble(toy_data_z) def _check_sigma(i, j): assert_allclose(c.sigma_nfw[j].value, toy_data_sigma[i, j], rtol=1e-4) def _check_deltasigma(i, j): assert_allclose(c.deltasigma_nfw[j].value, toy_data_deltasigma[i, j], rtol=1e-4) for i, n200 in enumerate(toy_data_n200): c.n200 = n200 c.calc_nfw(toy_data_rbins) for j in range(c.z.shape[0]): yield _check_sigma, i, j yield _check_deltasigma, i, j
def test_nfw_offset(): c = ClusterEnsemble(toy_data_z) def _check_sigma(i, j): assert_allclose(c.sigma_nfw[j].value, toy_data_sigma_off[i, j], rtol=10**-4) def _check_deltasigma(i, j): assert_allclose(c.deltasigma_nfw[j].value, toy_data_deltasigma_off[i, j], rtol=10**-4) for i, n200 in enumerate(toy_data_n200[:-1]): c.n200 = n200 c.calc_nfw(toy_data_rbins, offsets=toy_data_offset) for j in range(c.z.shape[0]): yield _check_sigma, i, j yield _check_deltasigma, i, j
def test_nfw_offset(): c = ClusterEnsemble(toy_data_z) def _check_sigma(i, j): assert_allclose(c.sigma_nfw[j].value, toy_data_sigma_off[i, j], rtol=10**-4) def _check_deltasigma(i, j): assert_allclose(c.deltasigma_nfw[j].value, toy_data_deltasigma_off[i, j], rtol=10**-4) for i, n200 in enumerate(toy_data_n200[:-1]): c.n200 = n200 c.calc_nfw(toy_data_rbins, offsets=toy_data_offset) for j in range(c.z.shape[0]): yield _check_sigma, i, j yield _check_deltasigma, i, j
def test_for_infs_in_miscentered_c_calc(): c = ClusterEnsemble(toy_data_z) def _check_sigma_off(arr): if np.isnan(arr.sum()): raise ValueError('sigma_off result contains NaN', arr) if np.isinf(arr.sum()): raise ValueError('sigma_off result contains Inf', arr) def _check_deltasigma_off(arr): if np.isnan(arr.sum()): raise ValueError('sigma_off result contains NaN', arr) if np.isinf(arr.sum()): raise ValueError('sigma_off result contains Inf', arr) # last element in toy_data is n200=0 -> NaN (skip for this check) for n200 in toy_data_n200[:-1]: c.n200 = n200 c.calc_nfw(toy_data_rbins, offsets=toy_data_offset) for i in range(c.z.shape[0]): yield _check_sigma_off, c.sigma_nfw[i].value yield _check_deltasigma_off, c.deltasigma_nfw[i].value
def test_for_infs_in_miscentered_c_calc(): c = ClusterEnsemble(toy_data_z) def _check_sigma_off(arr): if np.isnan(arr.sum()): raise ValueError('sigma_off result contains NaN', arr) if np.isinf(arr.sum()): raise ValueError('sigma_off result contains Inf', arr) def _check_deltasigma_off(arr): if np.isnan(arr.sum()): raise ValueError('sigma_off result contains NaN', arr) if np.isinf(arr.sum()): raise ValueError('sigma_off result contains Inf', arr) # last element in toy_data is n200=0 -> NaN (skip for this check) for n200 in toy_data_n200[:-1]: c.n200 = n200 c.calc_nfw(toy_data_rbins, offsets=toy_data_offset) for i in range(c.z.shape[0]): yield _check_sigma_off, c.sigma_nfw[i].value yield _check_deltasigma_off, c.deltasigma_nfw[i].value