def power(self, k, mu, flatten=False):
"""
The redshift space power spectrum as a function of ``k`` and ``mu``
This includes terms up to ``mu**self.max_mu``.
Parameters
----------
k : float or array_like
The wavenumbers in `h/Mpc` to evaluate the model at
mu : float, array_like
The mu values to evaluate the power at.
Returns
-------
pkmu : float, array_like
The power model P(k, mu). If `mu` is a scalar, return dimensions
are `(len(self.k), )`. If `mu` has dimensions (N, ), the return
dimensions are `(len(k), N)`, i.e., each column corresponds is the
model evaluated at different `mu` values. If `flatten = True`, then
the returned array is raveled, with dimensions of `(N*len(self.k), )`
"""
# the return array
pkmu = self._power(k, mu)
if flatten:
pkmu = np.ravel(pkmu, order='F')
return pkmu
def power(self, k, mu, flatten=False):
"""
Return the redshift space power spectrum at the specified value of mu,
including terms up to ``mu**self.max_mu``.
Parameters
----------
k : float or array_like
The wavenumbers in `h/Mpc` to evaluate the model at
mu : float, array_like
The mu values to evaluate the power at.
Returns
-------
pkmu : float, array_like
The power model P(k, mu). If `mu` is a scalar, return dimensions
are `(len(self.k), )`. If `mu` has dimensions (N, ), the return
dimensions are `(len(k), N)`, i.e., each column corresponds is the
model evaluated at different `mu` values. If `flatten = True`, then
the returned array is raveled, with dimensions of `(N*len(self.k), )`
"""
# the linear kaiser P(k,mu)
pkmu = super(QuasarSpectrum, self).power(k, mu)
# add FOG damping
G = self.FOG(k, mu, self.sigma_fog)
pkmu *= G**2
# add shot noise offset
pkmu += self.N
if flatten:
pkmu = np.ravel(pkmu, order='F')
return pkmu
def power(self, k, mu, flatten=False):
"""
The total redshift-space galaxy power spectrum at ``k`` and ``mu``
Parameters
----------
k : float, array_like
The wavenumbers to evaluate the power spectrum at, in `h/Mpc`
mu : float, array_like
The cosine of the angle from the line of sight. If a float is provided,
the value is used for all input `k` values. If array-like and `mu` has
the same shape as `k`, the power at each (k,mu) pair is returned. If
`mu` has a shape different than `k`, the returned power has shape
``(len(k), len(mu))``.
flatten : bool, optional
If `True`, flatten the return array, which will have a length of
`len(k) * len(mu)`
"""
toret = self._Pgal(k, mu)
return toret if not flatten else np.ravel(toret, order='F')
def Pgal_ss(self, k, mu, flatten=False):
"""
The auto power spectrum of all satellites
"""
toret = self._Pgal['Pss'](k, mu)
return toret if not flatten else np.ravel(toret, order='F')
def Pgal_sAsB(self, k, mu, flatten=False):
"""
The cross power spectrum of type A and type B satellites
"""
toret = self._Pgal['Pss']['PsAsB'](k, mu)
return toret if not flatten else np.ravel(toret, order='F')
def Pgal_cs(self, k, mu, flatten=False):
"""
The cross power spectrum of centrals and satellites
"""
toret = self._Pgal['Pcs'](k, mu)
return toret if not flatten else np.ravel(toret, order='F')
def Pgal_cBsB(self, k, mu, flatten=False):
"""
The cross power spectrum of type B centrals and type B sats
"""
toret = self._Pgal['Pcs']['PcBsB'](k, mu)
return toret if not flatten else np.ravel(toret, order='F')
def Pgal_cc(self, k, mu, flatten=False):
"""
The auto power spectrum of all centrals
"""
toret = self._Pgal['Pcc'](k, mu)
return toret if not flatten else np.ravel(toret, order='F')
