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 data_pole_gausscov(pkmu, ells, kmin=-np.inf, kmax=np.inf, components=False):
"""
Compute the gaussian covariance for a set of multipole measurements,
from the measured P(k,mu) observation
Parameters
----------
pkmu : nbodykit.PkmuResult
the mean P(k,mu) measurement, on the finely-binned grid
ells : array_like, (Nell,)
the desired multipole numbers
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
components : bool, optional
If `True`, return only the ``mean_power`` and ``modes``
Returns
-------
if components == False
cov : (N, N)
the covariance matrix for the multipole measurements
else
mean_power : (N, N), optional
the mean power, cov = 2 * mean_power**2 / modes
modes : (N, N), optional
the number of modes
"""
# 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
kw = {'ells':ells, 'kmin':kmin, 'kmax':kmax, 'power':data['power'], 'components':components}
return _return_covariance('pole', grid, **kw)
def data_pkmu_gausscov(pkmu, mu_bounds, kmin=-np.inf, kmax=np.inf, components=False):
"""
Compute the gaussian covariance for a set of P(k,mu) measurements
Parameters
----------
pkmu : nbodykit.PkmuResult
the mean P(k,mu) measurement, on the finely-binned grid
mu_bounds : array_like
a list of tuples specifying (lower, upper) for each desired
mu bin, i.e., [(0., 0.2), (0.2, 0.4), (0.4, 0.6), (0.6, 0.8), (0.8, 1.0)]
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
components : bool, optional
If `True`, return only the ``mean_power`` and ``modes``
Returns
-------
if components == False
cov : (N, N)
the covariance matrix for the multipole measurements
else
mean_power : (N, N), optional
the mean power, cov = 2 * mean_power**2 / modes
modes : (N, N), optional
the number of modes
"""
# 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
kw = {'mu_bounds':mu_bounds, 'kmin':kmin, 'kmax':kmax, 'power':data['power'], 'components':components}
return _return_covariance('pkmu', grid, **kw)
