def test_box_power_spectrum():
"""Check that the theoretical and box power spectra can be calculated."""
# Realise Gaussian box
np.random.seed(14)
box = CosmoBox(cosmo=default_cosmo,
box_scale=(1e3, 1e3, 1e3),
nsamp=64,
realise_now=False)
box.realise_density()
# Calculate binned power spectrum and theoretical power spectrum
re_k, re_pk, re_stddev = box.binned_power_spectrum()
th_k, th_pk = box.theoretical_power_spectrum()
# Check that sigma(R) and sigma_8 can be calculated
sigR = box.sigmaR(R=8.) # R in units of Mpc/h
sig8 = box.sigma8()
assert np.isclose(sigR, sig8)
# Run built-in test to print a report on sampling accuracy
box.test_sampling_error()
# Check that sigma_8 calculated from box is close to input cosmo sigma_8
# (this depends on box size/resolution)
assert np.abs(sig8 - box.cosmo['sigma8']) < 0.09 # 0.09 is empirical
sys.exit(0)
# Log-normal box
delta_ln = box.lognormal(box.delta_x)
lnre_k, lnre_pk, lnre_stddev \
= box.binned_power_spectrum(delta_k=fft.fftn(delta_ln))
plt.matshow(delta_ln[0], vmin=-1., vmax=2., cmap='cividis')
plt.title("Log-normal density")
plt.colorbar()
# Tests
box.test_sampling_error()
box.test_parseval()
# Plot some stuff
fig = plt.figure()
plt.plot(th_k, th_pk, 'b-', label="Theoretical P(k)")
#plt.errorbar(re_k, re_pk, yerr=re_stddev, fmt=".", color='r')
plt.plot(re_k, re_pk, 'r.', label="P(k) from density field")
plt.plot(lnre_k, lnre_pk, 'gx', label="P(k) from log-normal")
plt.xscale('log')
plt.yscale('log')
plt.legend(loc='lower left', frameon=False)
"""
plt.subplot(212)