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
