def n_chi(chi):
"""Vectorized."""
# Invert the distance redshift relation by interpolating.
c = Cosmology()
redshifts_s = np.linspace(0.01, 3, 50)
chi_s = c.comoving_distance(redshifts_s)
redshift_interp = interpolate.interp1d(chi_s, redshifts_s, kind='cubic')
redshifts = redshift_interp(chi)
# Calculate the source density at both redshifts.
dL = c.luminosity_distance(redshifts)
dL_star = c.luminosity_distance(Z_STAR)
# Minimum detectable L/L*
x_min = (dL / dL_star)**2
# Get source density by integrating the luminocity function.
n = []
dndL_fun = lambda x : x**ALPHA * np.exp(-x)
for x_m in x_min:
this_n, err = integrate.quad(dndL_fun, x_m, np.inf)
n.append(this_n)
n = np.array(n)
return n
def interpolator():
c = Cosmology()
chi = c.comoving_distance(REDSHIFTS)
interpolator = interpolate.interp2d(K, chi, DATA, kind='cubic')
return interpolator
class RedshiftCorrelation(object):
r"""A class for calculating redshift-space correlations.
The mapping from real to redshift space produces anisotropic
correlations, this class calculates them within the linear
regime. As a minimum the velocity power spectrum `ps_vv` must be
specified, the statistics of the observable can be specified
explicitly (in `ps_dd` and `ps_dv`), or as a `bias` relative to
the velocity spectrum.
As the integrals to calculate the correlations can be slow, this
class can construct a table for interpolating them. This table can
be saved to a file, and reloaded as need.
Parameters
----------
ps_vv : function, optional
A function which gives the velocity power spectrum at a
wavenumber k (in units of h Mpc^{-1}).
ps_dd : function, optional
A function which gives the power spectrum of the observable.
ps_dv : function, optional
A function which gives the cross power spectrum of the
observable and the velocity.
redshift : scalar, optional
The redshift at which the power spectra are
calculated. Defaults to a redshift, z = 0.
bias : scalar, optional
The bias between the observable and the velocities (if the
statistics are not specified directly). Defaults to a bias of
1.0.
Attributes
----------
ps_vv, ps_dd, ps_dv : function
The statistics of the obserables and velocities (see Parameters).
ps_redshift : scalar
Redshift of the power spectra (see Parameters).
bias : scalar
Bias of the observable (see Parameters).
cosmology : instance of Cosmology()
An instance of the Cosmology class to allow mapping of
redshifts to Cosmological distances.
Notes
-----
To allow more sophisticated behaviour the four methods
`growth_factor`, `growth_rate`, `bias_z` and `prefactor` may be
replaced. These return their respective quantities as functions of
redshift. See their method documentation for details. This only
really makes sense when using `_vv_only`, though the functions can
be still be used to allow some redshift scaling of individual
terms.
"""
ps_vv = None
ps_dd = None
ps_dv = None
ps_2d = False
ps_redshift = 0.0
bias = 1.0
_vv_only = False
_cached = False
_vv0i = None
_vv2i = None
_vv4i = None
_dd0i = None
_dv0i = None
_dv2i = None
cosmology = Cosmology()
def __init__(self, ps_vv=None, ps_dd=None, ps_dv=None, redshift=0.0, bias=1.0):
self.ps_vv = ps_vv
self.ps_dd = ps_dd
self.ps_dv = ps_dv
self.ps_redshift = redshift
self.bias = bias
self._vv_only = False if ps_dd and ps_dv else True
@classmethod
def from_file_matterps(cls, fname, redshift=0.0, bias=1.0):
r"""Initialise from a cached, single power spectrum, file.
Parameters
----------
fname : string
Name of the cache file.
redshift : scalar, optional
Redshift that the power spectrum is defined at (default z = 0).
bias : scalar, optional
The bias of the observable relative to the velocity field.
"""
rc = cls(redshift=redshift, bias=bias)
rc._vv_only = True
rc._load_cache(fname)
return rc
@classmethod
def from_file_fullps(cls, fname, redshift=0.0):
r"""Initialise from a cached, multi power spectrum, file.
Parameters
----------
fname : string
Name of the cache file.
redshift : scalar, optional
Redshift that the power spectra are defined at (default z = 0).
"""
rc = cls(redshift=redshift)
rc._vv_only = False
rc._load_cache(fname)
return rc
def powerspectrum(self, kpar, kperp, z1=None, z2=None):
r"""A vectorized routine for calculating the redshift space powerspectrum.
Parameters
----------
kpar : array_like
The parallel component of the k-vector.
kperp : array_like
The perpendicular component of the k-vector.
z1, z2 : array_like, optional
The redshifts of the wavevectors to correlate. If either
is None, use the default redshift `ps_redshift`.
Returns
-------
ps : array_like
The redshift space power spectrum at the given k-vector and redshift.
"""
if z1 == None:
z1 = self.ps_redshift
if z2 == None:
z2 = self.ps_redshift
b1 = self.bias_z(z1)
b2 = self.bias_z(z2)
f1 = self.growth_rate(z1)
f2 = self.growth_rate(z2)
D1 = self.growth_factor(z1) / self.growth_factor(self.ps_redshift)
D2 = self.growth_factor(z2) / self.growth_factor(self.ps_redshift)
pf1 = self.prefactor(z1)
pf2 = self.prefactor(z2)
k2 = kpar ** 2 + kperp ** 2
k = k2 ** 0.5
mu = kpar / k
mu2 = kpar ** 2 / k2
if self._vv_only:
if self.ps_2d:
ps = self.ps_vv(k, mu) * (b1 + mu2 * f1) * (b2 + mu2 * f2)
else:
ps = self.ps_vv(k) * (b1 + mu2 * f1) * (b2 + mu2 * f2)
else:
ps = (
b1 * b2 * self.ps_dd(k)
+ mu2 * self.ps_dv(k) * (f1 * b2 + f2 * b1)
+ mu2 ** 2 * f1 * f2 * self.ps_vv(k)
)
return D1 * D2 * pf1 * pf2 * ps
def powerspectrum_1D(self, k_vec, z1, z2, numz):
r"""A vectorized routine for calculating the real space powerspectrum.
Parameters
----------
k_vec: array_like
The magnitude of the k-vector
redshift: scalar
Redshift at which to evaluate other parameters
Returns
-------
ps: array_like
The redshift space power spectrum at the given k-vector and redshift.
Note that this uses the same ps_vv as the realisation generator until
the full dd, dv, vv calculation is ready.
TODO: evaluate this using the same weight function in z as the data.
"""
c1 = self.cosmology.comoving_distance(z1)
c2 = self.cosmology.comoving_distance(z2)
# Construct an array of the redshifts on each slice of the cube.
comoving_inv = inverse_approx(self.cosmology.comoving_distance, z1, z2)
da = np.linspace(c1, c2, numz + 1, endpoint=True)
za = comoving_inv(da)
# Calculate the bias and growth factors for each slice of the cube.
mz = self.mean(za)
bz = self.bias_z(za)
fz = self.growth_rate(za)
Dz = self.growth_factor(za) / self.growth_factor(self.ps_redshift)
pz = self.prefactor(za)
dfactor = np.mean(Dz * pz * bz)
vfactor = np.mean(Dz * pz * fz)
return self.ps_vv(k_vec) * dfactor * dfactor
def redshiftspace_correlation(self, pi, sigma, z1=None, z2=None):
"""The correlation function in the flat-sky approximation.
This is a vectorized function. The inputs `pi` and `sigma`
must have the same shape (if arrays), or be scalars. This
function will perform redshift dependent scaling for the
growth and biases.
Parameters
----------
pi : array_like
The separation in the radial direction (in units of h^{-1} Mpc).
sigma : array_like
The separation in the transverse direction (in units of h^{-1} Mpc).
z1 : array_like, optional
The redshift corresponding to the first point.
z2 : array_like, optional
The redshift corresponding to the first point.
Returns
-------
corr : array_like
The correlation function evaluated at the input values.
Notes
-----
If neither `z1` or `z2` are provided assume that both points
are at the same redshift as the power spectrum. If only `z1`
is provided assume the second point is at the same redshift as
the first.
"""
r = (pi ** 2 + sigma ** 2) ** 0.5
# Calculate mu with a small constant added to regularise cases
# of pi = sigma = 0
mu = pi / (r + 1e-100)
if z1 == None and z2 == None:
z1 = self.ps_redshift
z2 = self.ps_redshift
elif z2 == None:
z2 = z1
if self._cached:
xvv_0 = self._vv0i(r)
xvv_2 = self._vv2i(r)
xvv_4 = self._vv4i(r)
if self._vv_only:
xdd_0 = xvv_0
xdv_0 = xvv_0
xdv_2 = xvv_2
else:
xdd_0 = self._dd0i(r)
xdv_0 = self._dv0i(r)
xdv_2 = self._dv2i(r)
else:
xvv_0 = _integrate(r, 0, self.ps_vv)
xvv_2 = _integrate(r, 2, self.ps_vv)
xvv_4 = _integrate(r, 4, self.ps_vv)
if self._vv_only:
xdd_0 = xvv_0
xdv_0 = xvv_0
xdv_2 = xvv_2
else:
xdd_0 = _integrate(r, 0, self.ps_dd)
xdv_0 = _integrate(r, 0, self.ps_dv)
xdv_2 = _integrate(r, 2, self.ps_dv)
# if self._vv_only:
b1 = self.bias_z(z1)
b2 = self.bias_z(z2)
f1 = self.growth_rate(z1)
f2 = self.growth_rate(z2)
xdd_0 *= b1 * b2
xdv_0 *= 0.5 * (b1 * f2 + b2 * f1)
xdv_2 *= 0.5 * (b1 * f2 + b2 * f1)
xvv_0 *= f1 * f2
xvv_2 *= f1 * f2
xvv_4 *= f1 * f2
D1 = self.growth_factor(z1) / self.growth_factor(self.ps_redshift)
D2 = self.growth_factor(z2) / self.growth_factor(self.ps_redshift)
pf1 = self.prefactor(z1)
pf2 = self.prefactor(z2)
pl0 = 1.0
pl2 = _pl(2, mu)
pl4 = _pl(4, mu)
return (
(
(xdd_0 + 2.0 / 3.0 * xdv_0 + 1.0 / 5.0 * xvv_0) * pl0
- (4.0 / 3.0 * xdv_2 + 4.0 / 7.0 * xvv_2) * pl2
+ 8.0 / 35.0 * xvv_4 * pl4
)
* D1
* D2
* pf1
* pf2
)
def angular_correlation(self, theta, z1, z2):
r"""Angular correlation function (in a flat-sky approximation).
Parameters
----------
theta : array_like
The angle between the two points in radians.
z1, z2 : array_like
The redshift of the points.
Returns
-------
corr : array_like
The correlation function at the points.
"""
za = (z1 + z2) / 2.0
sigma = theta * self.cosmology.proper_distance(za)
pi = self.cosmology.comoving_distance(z2) - self.cosmology.comoving_distance(z1)
return self.redshiftspace_correlation(pi, sigma, z1, z2)
def _load_cache(self, fname):
if not exists(fname):
raise Exception("Cache file does not exist.")
a = np.loadtxt(fname)
ra = a[:, 0]
vv0 = a[:, 1]
vv2 = a[:, 2]
vv4 = a[:, 3]
if not self._vv_only:
if a.shape[1] != 7:
raise Exception("Cache file has wrong number of columns.")
dd0 = a[:, 4]
dv0 = a[:, 5]
dv2 = a[:, 6]
self._vv0i = cs.Interpolater(ra, vv0)
self._vv2i = cs.Interpolater(ra, vv2)
self._vv4i = cs.Interpolater(ra, vv4)
if not self._vv_only:
self._dd0i = cs.Interpolater(ra, dd0)
self._dv0i = cs.Interpolater(ra, dv0)
self._dv2i = cs.Interpolater(ra, dv2)
self._cached = True
def gen_cache(self, fname=None, rmin=1e-3, rmax=1e4, rnum=1000):
r"""Generate the cache.
Calculate a table of the integrals required for the
correlation functions, and save them to a named file (if
given).
Parameters
----------
fname : filename, optional
The file to save the cache into. If not set, the cache is
generated but not saved.
rmin : scalar
The minimum r-value at which to generate the integrals (in
h^{-1} Mpc)
rmax : scalar
The maximum r-value at which to generate the integrals (in
h^{-1} Mpc)
rnum : integer
The number of points to generate (using a log spacing).
"""
ra = np.logspace(np.log10(rmin), np.log10(rmax), rnum)
vv0 = _integrate(ra, 0, self.ps_vv)
vv2 = _integrate(ra, 2, self.ps_vv)
vv4 = _integrate(ra, 4, self.ps_vv)
if not self._vv_only:
dd0 = _integrate(ra, 0, self.ps_dd)
dv0 = _integrate(ra, 0, self.ps_dv)
dv2 = _integrate(ra, 2, self.ps_dv)
if fname and not exists(fname):
if self._vv_only:
np.savetxt(fname, np.dstack([ra, vv0, vv2, vv4])[0])
else:
np.savetxt(fname, np.dstack([ra, vv0, vv2, vv4, dd0, dv0, dv2])[0])
self._cached = True
self._vv0i = cs.Interpolater(ra, vv0)
self._vv2i = cs.Interpolater(ra, vv2)
self._vv4i = cs.Interpolater(ra, vv4)
if not self._vv_only:
self._dd0i = cs.Interpolater(ra, dd0)
self._dv0i = cs.Interpolater(ra, dv0)
self._dv2i = cs.Interpolater(ra, dv2)
def bias_z(self, z):
r"""The linear bias at redshift z.
The bias relative to the matter as a function of
redshift. In this simple version the bias is assumed
constant. Inherit, and override to use a more complicated
model.
Parameters
----------
z : array_like
The redshift to calculate at.
Returns
-------
bias : array_like
The bias at `z`.
"""
return self.bias * np.ones_like(z)
def growth_factor(self, z):
r"""The growth factor D_+ as a function of redshift.
The linear growth factor at a particular
redshift, defined as:
.. math:: \delta(k; z) = D_+(z; z_0) \delta(k; z_0)
Normalisation can be arbitrary. For the moment
assume that \Omega_m ~ 1, and thus the growth is linear in the
scale factor.
Parameters
----------
z : array_like
The redshift to calculate at.
Returns
-------
growth_factor : array_like
The growth factor at `z`.
"""
return 1.0 / (1.0 + z)
def growth_rate(self, z):
r"""The growth rate f as a function of redshift.
The linear growth rate at a particular redshift defined as:
.. math:: f = \frac{d\ln{D_+}}{d\ln{a}}
For the moment assume that \Omega_m ~ 1, and thus the growth rate is
unity.
Parameters
----------
z : array_like
The redshift to calculate at.
Returns
-------
growth_rate : array_like
The growth factor at `z`.
"""
return 1.0 * np.ones_like(z)
def prefactor(self, z):
r"""An arbitrary scaling multiplying on each perturbation.
This factor can be redshift dependent. It results in scaling
the entire correlation function by prefactor(z1) *
prefactor(z2).
Parameters
----------
z : array_like
The redshift to calculate at.
Returns
-------
prefactor : array_like
The prefactor at `z`.
"""
return 1.0 * np.ones_like(z)
def mean(self, z):
r"""Mean value of the field at a given redshift.
Parameters
----------
z : array_like
redshift to calculate at.
Returns
-------
mean : array_like
the mean value of the field at each redshift.
"""
return np.ones_like(z) * 0.0
_sigma_v = 0.0
def sigma_v(self, z):
"""Return the pairwise velocity dispersion at a given redshift.
This is stored internally as `self._sigma_v` in units of km/s
Note that e.g. WiggleZ reports sigma_v in h km/s
"""
print("using sigma_v (km/s): " + repr(self._sigma_v))
sigma_v_hinvMpc = self._sigma_v / 100.0
return np.ones_like(z) * sigma_v_hinvMpc
def velocity_damping(self, kpar):
"""The velocity damping term for the non-linear power spectrum."""
return (1.0 + (kpar * self.sigma_v(self.ps_redshift)) ** 2.0) ** -1.0
def _realisation_dv(self, d, n):
"""Generate the density and line of sight velocity fields in a
3d cube.
"""
if not self._vv_only:
raise Exception(
"Doesn't work for independent fields, I need to think a bit more first."
)
def psv(karray):
"""Assume k0 is line of sight"""
k = (karray ** 2).sum(axis=3) ** 0.5
return self.ps_vv(k) * self.velocity_damping(karray[..., 0])
# Generate an underlying random field realisation of the
# matter distribution.
print("Gen field.")
rfv = gaussianfield.RandomField(npix=n, wsize=d)
rfv.powerspectrum = psv
vf0 = rfv.getfield()
# Construct an array of \mu^2 for each Fourier mode.
print("Construct kvec")
spacing = rfv._w / rfv._n
kvec = fftutil.rfftfreqn(rfv._n, spacing / (2 * math.pi))
print("Construct mu2")
mu2arr = kvec[..., 0] ** 2 / (kvec ** 2).sum(axis=3)
mu2arr.flat[0] = 0.0
del kvec
df = vf0
print("FFT vel")
# Construct the line of sight velocity field.
# TODO: is the s=rfv._n the correct thing here?
vf = fftutil.irfftn(mu2arr * fftutil.rfftn(vf0))
# return (df, vf, rfv, kvec)
return (df, vf) # , rfv)
def realisation(
self,
z1,
z2,
thetax,
thetay,
numz,
numx,
numy,
zspace=True,
refinement=1,
report_physical=False,
density_only=False,
no_mean=False,
no_evolution=False,
pad=5,
):
r"""Simulate a redshift-space volume.
Generates a 3D (angle-angle-redshift) volume from the given
power spectrum. Currently only works with simply biased power
spectra (i.e. vv_only). This routine uses a flat sky
approximation, and so becomes inaccurate when a large volume
of the sky is simulated.
Parameters
----------
z1, z2 : scalar
Lower and upper redshifts of the box.
thetax, thetay : scalar
The angular size (in degrees) of the box.
numz : integer
The number of bins in redshift.
numx, numy : integer
The number of angular pixels along each side.
zspace : boolean, optional
If True (default) redshift bins are equally spaced in
redshift. Otherwise space equally in the scale factor
(useful for generating an equal range in frequency).
density_only: boolean
no velocity contribution
no_mean: boolean
do not add the mean temperature
no_evolution: boolean
do not let b(z), D(z) etc. evolve: take their mean
pad: integer
number of pixels over which to pad the physical region for
interpolation onto freq, ra, dec; match spline order?
Returns
-------
cube : np.ndarray
The volume cube.
"""
d1 = self.cosmology.proper_distance(z1)
d2 = self.cosmology.proper_distance(z2)
c1 = self.cosmology.comoving_distance(z1)
c2 = self.cosmology.comoving_distance(z2)
c_center = (c1 + c2) / 2.0
# Make cube pixelisation finer, such that angular cube will
# have sufficient resolution on the closest face.
d = np.array([c2 - c1, thetax * d2 * units.degree, thetay * d2 * units.degree])
# Note that the ratio of deltas in Ra, Dec in degrees may
# be different than the Ra, Dec in physical coordinates due to
# rounding onto this grid
n = np.array([numz, int(d2 / d1 * numx), int(d2 / d1 * numy)])
# Fix padding such the n + pad is even in the last element
if (n[-1] + pad) % 2 != 0:
pad += 1
# Enlarge cube size by pad in each dimension, so raytraced cube
# sits exactly within the gridded points.
d = d * (n + pad).astype(float) / n.astype(float)
c1 = c_center - (c_center - c1) * (n[0] + pad) / float(n[0])
c2 = c_center + (c2 - c_center) * (n[0] + pad) / float(n[0])
n = n + pad
# now multiply by scaling for a finer sub-grid.
n = refinement * n
print(
"Generating cube: (%f to %f) x %f x %f (%d, %d, %d) (h^-1 cMpc)^3"
% (c1, c2, d[1], d[2], n[0], n[1], n[2])
)
cube = self._realisation_dv(d, n)
# TODO: this is probably unnecessary now (realisation used to change
# shape through irfftn)
n = cube[0].shape
# Construct an array of the redshifts on each slice of the cube.
comoving_inv = inverse_approx(self.cosmology.comoving_distance, z1, z2)
da = np.linspace(c1, c2, n[0], endpoint=True)
za = comoving_inv(da)
# Calculate the bias and growth factors for each slice of the cube.
mz = self.mean(za)
bz = self.bias_z(za)
fz = self.growth_rate(za)
Dz = self.growth_factor(za) / self.growth_factor(self.ps_redshift)
pz = self.prefactor(za)
# Construct the observable and velocity fields.
if not no_evolution:
df = cube[0] * (Dz * pz * bz)[:, np.newaxis, np.newaxis]
vf = cube[1] * (Dz * pz * fz)[:, np.newaxis, np.newaxis]
else:
df = cube[0] * np.mean(Dz * pz * bz)
vf = cube[1] * np.mean(Dz * pz * fz)
# Construct the redshift space cube.
rsf = df
if not density_only:
rsf += vf
if not no_mean:
rsf += mz[:, np.newaxis, np.newaxis]
# Find the distances that correspond to a regular redshift
# spacing (or regular spacing in a).
if zspace:
za = np.linspace(z1, z2, numz, endpoint=False)
else:
za = (
1.0
/ np.linspace(1.0 / (1 + z2), 1.0 / (1 + z1), numz, endpoint=False)[
::-1
]
- 1.0
)
da = self.cosmology.proper_distance(za)
xa = self.cosmology.comoving_distance(za)
print("Constructing mapping..")
# Construct the angular offsets into cube
tx = np.linspace(-thetax / 2.0, thetax / 2.0, numx) * units.degree
ty = np.linspace(-thetay / 2.0, thetay / 2.0, numy) * units.degree
# tgridx, tgridy = np.meshgrid(tx, ty)
tgridy, tgridx = np.meshgrid(ty, tx)
tgrid2 = np.zeros((3, numx, numy))
acube = np.zeros((numz, numx, numy))
# Iterate over redshift slices, constructing the coordinates
# and interpolating into the 3d cube. Note that the multipliers scale
# from 0 to 1, or from i=0 to i=N-1
for i in range(numz):
# print "Slice:", i
tgrid2[0, :, :] = (xa[i] - c1) / (c2 - c1) * (n[0] - 1.0)
tgrid2[1, :, :] = (tgridx * da[i]) / d[1] * (n[1] - 1.0) + 0.5 * (
n[1] - 1.0
)
tgrid2[2, :, :] = (tgridy * da[i]) / d[2] * (n[2] - 1.0) + 0.5 * (
n[2] - 1.0
)
# if(zi > numz - 2):
# TODO: what order here?; do end-to-end P(k) study
# acube[i,:,:] = scipy.ndimage.map_coordinates(rsf, tgrid2, order=2)
acube[i, :, :] = scipy.ndimage.map_coordinates(rsf, tgrid2, order=1)
if report_physical:
return acube, rsf, (c1, c2, d[1], d[2])
else:
return acube
def angular_powerspectrum_full(self, la, za1, za2):
r"""The angular powerspectrum C_l(z1, z2). Calculate explicitly.
Parameters
----------
l : array_like
The multipole moments to return at.
z1, z2 : array_like
The redshift slices to correlate.
Returns
-------
arr : array_like
The values of C_l(z1, z2)
"""
from ..util import sphfunc
def _ps_single(l, z1, z2):
if not self._vv_only:
raise Exception("Only works for vv_only at the moment.")
b1, b2 = self.bias_z(z1), self.bias_z(z2)
f1, f2 = self.growth_rate(z1), self.growth_rate(z2)
pf1, pf2 = self.prefactor(z1), self.prefactor(z2)
D1 = self.growth_factor(z1) / self.growth_factor(self.ps_redshift)
D2 = self.growth_factor(z2) / self.growth_factor(self.ps_redshift)
x1 = self.cosmology.comoving_distance(z1)
x2 = self.cosmology.comoving_distance(z2)
d1 = math.pi / (x1 + x2)
def _int_lin(k):
return (
k ** 2
* self.ps_vv(k)
* (b1 * sphfunc.jl(l, k * x1) - f1 * sphfunc.jl_d2(l, k * x1))
* (b2 * sphfunc.jl(l, k * x2) - f2 * sphfunc.jl_d2(l, k * x2))
)
def _int_log(lk):
k = np.exp(lk)
return k * _int_lin(k)
def _int_offset(k):
return (
_int_lin(k)
+ 4 * _int_lin(k + d1)
+ 6 * _int_lin(k + 2 * d1)
+ 4 * _int_lin(k + 3 * d1)
+ _int_lin(k + 4 * d1)
) / 16.0
def _int_taper(k):
return (
15.0 * _int_lin(k)
+ 11.0 * _int_lin(k + d1)
+ 5.0 * _int_lin(k + 2 * d1)
+ _int_lin(k + 3 * d1)
) / 16.0
def _integrator(f, a, b):
return integrate.chebyshev(f, a, b, epsrel=1e-8, epsabs=1e-10)
mink = 1e-2 * l / (x1 + x2)
cutk = 2e0 * l / (x1 + x2)
cutk2 = 20.0 * l / (x1 + x2)
maxk = 1e2 * l / (x1 + x2)
i1 = _integrator(_int_log, np.log(mink), np.log(cutk))
i2 = _integrator(_int_taper, cutk, cutk + d1)
i3 = _integrator(_int_offset, cutk, cutk2)
i4 = _integrator(_int_offset, cutk2, maxk)
cl = (i1 + i2 + i3 + i4) * D1 * D2 * pf1 * pf2 * (2 / np.pi)
return cl
bobj = np.broadcast(la, za1, za2)
if not bobj.shape:
# Broadcast from scalars
return _ps_single(la, za1, za2)
else:
# Broadcast from arrays
cla = np.empty(bobj.shape)
cla.flat = [_ps_single(l, z1, z2) for (l, z1, z2) in bobj]
return cla
_aps_cache = False
def save_fft_cache(self, fname):
"""Save FFT angular powerspecturm cache."""
if not self._aps_cache:
self.angular_powerspectrum_fft(
np.array([100]), np.array([1.0]), np.array([1.0])
)
np.savez(fname, dd=self._aps_dd, dv=self._aps_dv, vv=self._aps_vv)
def load_fft_cache(self, fname):
"""Load FFT angular powerspectrum cache."""
a = np.load(fname)
self._aps_dd = a["dd"]
self._aps_dv = a["dv"]
self._aps_vv = a["vv"]
self._aps_cache = True
_freq_window = 0.0
def angular_powerspectrum_fft(self, la, za1, za2):
"""The angular powerspectrum C_l(z1, z2) in a flat-sky limit.
Uses FFT based method to generate a lookup table for fast computation.
Parameters
----------
l : array_like
The multipole moments to return at.
z1, z2 : array_like
The redshift slices to correlate.
Returns
-------
arr : array_like
The values of C_l(z1, z2)
"""
kperpmin = 1e-4
kperpmax = 40.0
nkperp = 500
kparmax = 20.0
nkpar = 32768
if not self._aps_cache:
kperp = np.logspace(np.log10(kperpmin), np.log10(kperpmax), nkperp)[
:, np.newaxis
]
kpar = np.linspace(0, kparmax, nkpar)[np.newaxis, :]
k = (kpar ** 2 + kperp ** 2) ** 0.5
mu = kpar / k
mu2 = kpar ** 2 / k ** 2
if self.ps_2d:
self._dd = (
self.ps_vv(k, mu)
* np.sinc(kpar * self._freq_window / (2 * np.pi)) ** 2
)
else:
self._dd = (
self.ps_vv(k) * np.sinc(kpar * self._freq_window / (2 * np.pi)) ** 2
)
self._dv = self._dd * mu2
self._vv = self._dd * mu2 ** 2
self._aps_dd = scipy.fftpack.dct(self._dd, type=1) * kparmax / (2 * nkpar)
self._aps_dv = scipy.fftpack.dct(self._dv, type=1) * kparmax / (2 * nkpar)
self._aps_vv = scipy.fftpack.dct(self._vv, type=1) * kparmax / (2 * nkpar)
self._aps_cache = True
xa1 = self.cosmology.comoving_distance(za1)
xa2 = self.cosmology.comoving_distance(za2)
b1, b2 = self.bias_z(za1), self.bias_z(za2)
f1, f2 = self.growth_rate(za1), self.growth_rate(za2)
pf1, pf2 = self.prefactor(za1), self.prefactor(za2)
D1 = self.growth_factor(za1) / self.growth_factor(self.ps_redshift)
D2 = self.growth_factor(za2) / self.growth_factor(self.ps_redshift)
xc = 0.5 * (xa1 + xa2)
rpar = np.abs(xa2 - xa1)
# Bump anything that is zero upwards to avoid a log zero warning.
la = np.where(la == 0.0, 1e-10, la)
x = (
(np.log10(la) - np.log10(xc * kperpmin))
/ np.log10(kperpmax / kperpmin)
* (nkperp - 1)
)
y = rpar / (math.pi / kparmax)
def _interp2d(arr, x, y):
x, y = np.broadcast_arrays(x, y)
sh = x.shape
x, y = x.flatten(), y.flatten()
v = np.zeros_like(x)
bilinearmap.interp(arr, x, y, v)
return v.reshape(sh)
psdd = _interp2d(self._aps_dd, x, y)
psdv = _interp2d(self._aps_dv, x, y)
psvv = _interp2d(self._aps_vv, x, y)
return (D1 * D2 * pf1 * pf2 / (xc ** 2 * np.pi)) * (
(b1 * b2) * psdd + (f1 * b2 + f2 * b1) * psdv + (f1 * f2) * psvv
)
## By default use the flat sky approximation.
# angular_powerspectrum = profile(angular_powerspectrum_fft)
angular_powerspectrum = angular_powerspectrum_fft
