def check_corr_3dRSD(cosmo):
# Scale factor
a = 0.8
# Cosine of the angle
mu = 0.7
# Growth rate divided by galaxy bias
beta = 0.5
# Distances (in Mpc)
s_int = 50
s = 50.
s_lst = np.linspace(50, 100, 10)
# Make sure 3d correlation functions work for valid inputs
corr1 = ccl.correlation_3dRsd(cosmo, a, s_int, mu, beta)
corr2 = ccl.correlation_3dRsd(cosmo, a, s, mu, beta)
corr3 = ccl.correlation_3dRsd(cosmo, a, s_lst, mu, beta)
assert_(all_finite(corr1))
assert_(all_finite(corr2))
assert_(all_finite(corr3))
corr4 = ccl.correlation_3dRsd_avgmu(cosmo, a, s_int, beta)
corr5 = ccl.correlation_3dRsd_avgmu(cosmo, a, s, beta)
corr6 = ccl.correlation_3dRsd_avgmu(cosmo, a, s_lst, beta)
assert_(all_finite(corr4))
assert_(all_finite(corr5))
assert_(all_finite(corr6))
corr7 = ccl.correlation_multipole(cosmo, a, beta, 0, s_lst)
corr8 = ccl.correlation_multipole(cosmo, a, beta, 2, s_lst)
corr9 = ccl.correlation_multipole(cosmo, a, beta, 4, s_lst)
assert_(all_finite(corr7))
assert_(all_finite(corr8))
assert_(all_finite(corr9))
# Distances (in Mpc)
pie = 50.
sig_int = 50
sig = 50.
sig_lst = np.linspace(50, 100, 10)
corr10 = ccl.correlation_pi_sigma(cosmo, a, beta, pie, sig_int)
corr11 = ccl.correlation_pi_sigma(cosmo, a, beta, pie, sig)
corr12 = ccl.correlation_pi_sigma(cosmo, a, beta, pie, sig_lst)
assert_(all_finite(corr10))
assert_(all_finite(corr11))
assert_(all_finite(corr12))
#free spline
ccl.correlation_spline_free()
def test_correlation_3dRSD_multipole_smoke(sval, l):
a = 0.8
beta = 0.5
corr = ccl.correlation_multipole(COSMO, a, beta, l, sval)
assert np.all(np.isfinite(corr))
assert np.shape(corr) == np.shape(sval)
rmin=10.0,
rmax=200.,
poles=[])
corr2, _ = corrfn2.run()
# Output correlation function
print(corr)
r = corr['r']
rr = np.linspace(2., 200., 300)
h = box.cosmo['h']
plt.subplot(211)
plt.plot(
rr,
rr**2. * ccl.correlation_multipole(box.cosmo, a=1., l=0, s=rr, beta=0.),
'k-')
plt.plot((r), (r)**2. * corr['corr'], 'r.')
plt.plot((r), (r)**2. * corr2['corr'], 'bx')
plt.xlabel("r", fontsize=16)
plt.ylabel(r"$r^2 \xi(r)$", fontsize=16)
plt.subplot(212)
k = np.logspace(-3., 0., 200)
plt.plot(k, ccl.linear_matter_power(box.cosmo, a=1., k=k), 'k-')
plt.plot(k, ccl.nonlin_matter_power(box.cosmo, a=1., k=k), 'r--')
plt.xlabel("k", fontsize=16)
plt.ylabel("P(k)", fontsize=16)
plt.xscale('log')
plt.yscale('log')
plt.xscale('log')
plt.yscale('log')
#plt.show()
#sys.exit(0)
# Plot correlation functions and vanilla theoretical prediction
plt.figure()
plt.subplot(111)
r = corr_true['r']
h = box.cosmo['h']
rr = np.linspace(2., 200., 300)
xi = ccl.correlation_multipole(box.cosmo,
a=box.scale_factor,
l=0,
s=rr,
beta=0.)
plt.subplot(111)
plt.plot(rr, rr**2. * xi * tracer.signal_amplitude()**2., 'k-')
plt.plot(r, r**2. * corr_true['corr'], 'r.', label="True field")
plt.plot(r, r**2. * corr_proc4['corr'], 'bx', label="4 modes")
plt.plot(r, r**2. * corr_proc4_hp['corr'], 'ys', label="4 modes (high-pass)")
plt.plot(r, r**2. * corr_proc8['corr'], 'g+', label="4 modes")
plt.xlabel("r", fontsize=16)
plt.ylabel(r"$r^2 \xi(r)$", fontsize=16)
plt.show()
