def test_outputs_bias_at_M():
#List vs. numpy.array
npt.assert_array_equal(bias.bias_at_M(Ma, klin, plin, Omega_m), bias.bias_at_M(Ma.tolist(), klin, plin, Omega_m))
#Single value vs numpy.array
arrout = bias.bias_at_M(Ma, klin, plin, Omega_m)
for i in range(len(Ma)):
npt.assert_equal(bias.bias_at_M(Ma[i], klin, plin, Omega_m), arrout[i])
def test_R_vs_M():
R = 1.0 #Mpc/h
M = 4.*np.pi/3. * Omega_m * rhomconst * R**3
npt.assert_almost_equal(bias.bias_at_R(R, klin, plin), bias.bias_at_M(M, klin, plin, Omega_m))
R = np.array([1.2, 1.4, 1.5])
M = 4.*np.pi/3. * Omega_m * rhomconst * R**3
npt.assert_array_almost_equal(bias.bias_at_R(R, klin, plin), bias.bias_at_M(M, klin, plin, Omega_m))
def test_single_vs_array():
#First sigma2
a1 = peaks.sigma2_at_M(Ma, klin, plin, Omega_m)
a2 = np.array([peaks.sigma2_at_M(Mi, klin, plin, Omega_m) for Mi in Ma])
npt.assert_array_equal(a1, a2)
a1 = peaks.sigma2_at_R(Ra, klin, plin)
a2 = np.array([peaks.sigma2_at_R(Ri, klin, plin) for Ri in Ra])
npt.assert_array_equal(a1, a2)
#Now the bias
a1 = bias.bias_at_M(Ma, klin, plin, Omega_m)
a2 = np.array([bias.bias_at_M(Mi, klin, plin, Omega_m) for Mi in Ma])
npt.assert_array_equal(a1, a2)
a1 = bias.bias_at_R(Ra, klin, plin)
a2 = np.array([bias.bias_at_R(Ri, klin, plin) for Ri in Ra])
npt.assert_array_equal(a1, a2)
def test_Cordering():
Me,be = np.loadtxt(join(dirname(__file__),datapath+"bias_camb_z0.0.dat")).T
ke,pe = np.loadtxt(join(dirname(__file__),datapath+"ps_camb_z0.0.dat")).T
pe = 2*np.pi**2*pe/ke**3
#Me = np.ascontiguousarray(Me) #test passes if this is not required
#ke = np.ascontiguousarray(ke) #test passes if this is not required
#pe = np.ascontiguousarray(pe) #test passes if this is not required
bt = bias.bias_at_M(Me, ke, pe, Omega_m)
r = be/bt
npt.assert_array_almost_equal(r, np.ones_like(r), decimal=3)
def test_derivatives():
M = np.logspace(13, 14, 1000)
dbdM = bias.dbiasdM_at_M(M, klin, plin, Omega_m)
npt.assert_equal(sorted(dbdM), dbdM[::-1])
b = bias.bias_at_M(M, klin, plin, Omega_m)
dM = M[1:] - M[:-1]
db = b[1:] - b[:-1]
deriv = db/dM
pd = dbdM[:-1] / deriv
npt.assert_array_almost_equal(pd, np.ones_like(pd), 1e-2)
def test_exceptions_bias_at_M():
with pytest.raises(TypeError):
bias.bias_at_M()
with pytest.raises(TypeError):
bias.bias_at_M(Mass, klin, plin)
with pytest.raises(TypeError):
bias.bias_at_M(Mass, klin, plin, Omega_m, Omega_m)
def test_mass_dependence():
masses = np.logspace(13, 15, num=100)
arrout = bias.bias_at_M(masses, klin, plin, Omega_m)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
Rs = (masses/(4./3.*np.pi*Omega_m*rhomconst))**(1./3.)
arrout = bias.bias_at_R(Rs, klin, plin)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
nus = peaks.nu_at_M(masses, klin, plin, Omega_m)
arrout = bias.bias_at_nu(nus)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
def test_combination():
xinfw = xi.xi_nfw_at_r(ra, Mass, conc, Omega_m)
ximm = xi.xi_mm_at_r(ra, knl, pnl)
from cluster_toolkit import bias
b = bias.bias_at_M(Mass, klin, plin, Omega_m)
xi2h = xi.xi_2halo(b, ximm)
xihm = xi.xi_hm(xinfw, xi2h)
xihm2 = xi.xi_hm(xinfw, xi2h, combination='max')
npt.assert_array_equal(xihm, xihm2)
xihm2 = xi.xi_hm(xinfw, xi2h, combination='sum')
npt.assert_raises(AssertionError, npt.assert_array_equal, xihm, xihm2)
with pytest.raises(Exception):
xi.xi_hm(xinfw, xi2h, combination='blah')
def test_s2_and_nu_functions():
#Test the mass calls
s2 = peaks.sigma2_at_M(Mass, klin, plin, Omega_m)
nu = peaks.nu_at_M(Mass, klin, plin, Omega_m)
npt.assert_equal(1.686/np.sqrt(s2), nu)
s2 = peaks.sigma2_at_M(Ma, klin, plin, Omega_m)
nu = peaks.nu_at_M(Ma, klin, plin, Omega_m)
npt.assert_array_equal(1.686/np.sqrt(s2), nu)
out = bias.bias_at_M(Ma, klin, plin, Omega_m)
out2 = bias.bias_at_nu(nu)
npt.assert_array_equal(out, out2)
#Now test the R calls
R = 1.0 #Mpc/h; arbitrary
s2 = peaks.sigma2_at_R(R, klin, plin)
nu = peaks.nu_at_R(R, klin, plin)
npt.assert_equal(1.686/np.sqrt(s2), nu)
out = bias.bias_at_R(R, klin, plin)
out2 = bias.bias_at_nu(nu)
npt.assert_array_equal(out, out2)
