def _return_covariance(kind, grid, **kwargs):
"""
Internal helper function to return gaussian covariance
"""
kmin = kwargs['kmin']; kmax = kwargs['kmax']
power = kwargs['power']
if kind == 'pole':
transfer = PolesTransfer(grid, kwargs['ells'], kmin=kmin, kmax=kmax, power=power)
elif kind == 'pkmu':
transfer = PkmuTransfer(grid, kwargs['mu_bounds'], kmin=kmin, kmax=kmax, power=power)
else:
raise ValueError("`kind` must be `pole` or `pkmu`")
coords = transfer.coords_flat
return transfer.to_covariance(components=kwargs['components']), coords
def cutsky_pole_gausscov(pkmu, ells, cosmo, zbins, nbar_spline, P0, fsky, kmin=-np.inf, kmax=np.inf):
"""
Compute the gaussian covariance for a set of cutsky multipole measurements,
from the measured P(k,mu) simulation boxes
Parameters
----------
pkmu : nbodykit.PkmuResult
the mean P(k,mu) periodic measurement, on the finely-binned grid
ells : array_like, (Nell,)
the desired multipole numbers
cosmo : pygcl.Cosmology
the cosmology instance used in the volume calculation
zbins : array_like
bins in redshift, used in the volume calculation
nbar_spline : callable
a function returning n(z)
P0 : float
the value of P0 used in the FKP weight
fsky : float
the fraction of the sky area covered
kmin : {float, array_like}, optional
minimum wavenumber to trim by (inclusively), in h/Mpc
kmax : {float, array_like}, optional
maximum wavenumber to trim by (inclusively), in h/Mpc
Returns
-------
cov : (N, N)
the covariance matrix for the multipole measurements
coords : list
list of the flat coordinates corresponding to the the cov
"""
# initialize the grid transfer
data = pkmu.data.copy()
grid = PkmuGrid.from_structured([pkmu.coords['k_cen'], pkmu.coords['mu_cen']], data)
# return the Gaussian covariance components
power = data['power'] - tools.get_Pshot(pkmu)
transfer = PolesTransfer(grid, ells, kmin=kmin, kmax=kmax, power=power)
coords = transfer.coords_flat
C = transfer.to_cutsky_covariance(cosmo, zbins, nbar_spline, P0, fsky)
return C, coords
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")
