def gal_power_discrete(model):
# set up fake 1D k, mu bins
k_1d = numpy.arange(0.01, 0.4, 0.005)
mu_1d = numpy.linspace(0, 1.0, 100)
# convert to a 2D grid
# shape is (78, 100)
k, mu = numpy.meshgrid(k_1d, mu_1d, indexing='ij')
# assign random weights for each bin for illustration purposes
modes = numpy.random.random(size=k.shape)
# simulate missing data
missing = numpy.random.randint(0, numpy.prod(modes.shape), size=10)
modes.flat[missing] = numpy.nan
# initialize the grid
grid = PkmuGrid([k_1d, mu_1d], k, mu, modes)
# edges of the mu bins
mu_bounds = [(0., 0.2), (0.2, 0.4), (0.4, 0.6), (0.6, 0.8), (0.8, 1.0)]
# the transfer function, with specified valid k range
transfer = PkmuTransfer(grid, mu_bounds, kmin=0.01, kmax=0.4)
# evaluate the model with this transfer function
Pkmu_binned = model.from_transfer(transfer)
# get the coordinate arrays from the grid
k, mu = transfer.coords # this has shape of (Nk, Nmu)
for i in range(mu.shape[1]):
plt.loglog(k[:, i],
Pkmu_binned[:, i],
label=r"$\mu = %.1f$" % mu[:, i].mean())
plt.legend(loc=0)
plt.xlabel(r"$k$ $[h \mathrm{Mpc}^{-1}]$", fontsize=10)
plt.ylabel(r"$P$ $[h^{-3} \mathrm{Mpc}^3]$", fontsize=10)
savefig("pkmu_binned_plot.png")
# the multipoles to compute
ells = [0, 2, 4]
# the transfer function, with specified valid k range
transfer = PolesTransfer(grid, ells, kmin=0.01, kmax=0.4)
# evaluate the model with this transfer function
poles_binned = model.from_transfer(transfer) # shape is (78, 3)
# get the coordinate arrays from the grid
k, mu = transfer.coords # this has shape of (Nk, Nmu)
for i, iell in enumerate(ells):
plt.loglog(k[:, i], poles_binned[:, i], label=r"$\ell = %d$" % iell)
plt.legend(loc=0)
plt.xlabel(r"$k$ $[h \mathrm{Mpc}^{-1}]$", fontsize=10)
plt.ylabel(r"$P_\ell$ $[h^{-3} \mathrm{Mpc}^3]$", fontsize=10)
savefig("poles_binned_plot.png")
