def sigma2_at_M(M, k, P, Omega_m):
"""RMS variance in top hat sphere of lagrangian radius R [Mpc/h comoving] corresponding to a mass M [Msun/h] of linear power spectrum.
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Omega_matter, matter density fraction.
Returns:
float or array like: RMS variance of top hat sphere.
"""
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if isinstance(M, list) or isinstance(M, np.ndarray):
M = _ArrayWrapper(M, allow_multidim=True)
s2 = _ArrayWrapper.zeros_like(M)
rc = cluster_toolkit._lib.sigma2_at_M_arr(M.cast(), len(M), k.cast(),
P.cast(), len(k), Omega_m,
s2.cast())
_handle_gsl_error(rc, sigma2_at_M)
return s2.finish()
else:
return cluster_toolkit._lib.sigma2_at_M(M, k.cast(), P.cast(), len(k),
Omega_m)
def G_at_M(M, k, P, Omega_m, d=1.97, e=1.0, f=0.51, g=1.228):
"""Tinker et al. 2008 appendix C multiplicity funciton G(M) as
a function of mass. Default behavior is for :math:`M_{200m}` mass
definition.
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of the matter power spectrum in h/Mpc comoving.
P_lin (array like): Linear matter power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Matter density fraction.
d (float; optional): First Tinker parameter. Default is 1.97.
e (float; optional): Second Tinker parameter. Default is 1.
f (float; optional): Third Tinker parameter. Default is 0.51.
g (float; optional): Fourth Tinker parameter. Default is 1.228.
Returns:
float or array like: Halo multiplicity :math:`G(M)`.
"""
M = _ArrayWrapper(M, 'M')
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
G = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.G_at_M_arr(M.cast(), len(M), k.cast(), P.cast(),
len(k), Omega_m, d, e, f, g, G.cast())
return G.finish()
def xi_hm(xi_1halo, xi_2halo, combination="max"):
"""Halo-matter correlation function
Note: at the moment you can combine the 1-halo and 2-halo terms by either taking the max of the two or the sum of the two. The 'combination' field must be set to either 'max' (default) or 'sum'.
Args:
xi_1halo (float or array like): 1-halo term
xi_2halo (float or array like, same size as xi_1halo): 2-halo term
combination (string; optional): specifies how the 1-halo and 2-halo terms are combined, default is 'max' which takes the max of the two
Returns:
float or array like: Halo-matter correlation function
"""
if combination == "max":
switch = 0
elif combination == 'sum':
switch = 1
else:
raise Exception("Combinations other than maximum not implemented yet")
xi_1halo = _ArrayWrapper(xi_1halo, allow_multidim=True)
xi_2halo = _ArrayWrapper(xi_2halo, allow_multidim=True)
xi = _ArrayWrapper.zeros_like(xi_1halo)
cluster_toolkit._lib.calc_xi_hm(len(xi_1halo), xi_1halo.cast(),
xi_2halo.cast(), xi.cast(), switch)
return xi.finish()
def xi_mm_at_r(r, k, P, N=500, step=0.005, exact=False):
"""Matter-matter correlation function.
Args:
r (float or array like): 3d distances from halo center in Mpc/h comoving
k (array like): Wavenumbers of power spectrum in h/Mpc comoving
P (array like): Matter power spectrum in (Mpc/h)^3 comoving
N (int; optional): Quadrature step count, default is 500
step (float; optional): Quadrature step size, default is 5e-3
exact (boolean): Use the slow, exact calculation; default is False
Returns:
float or array like: Matter-matter correlation function
"""
r = _ArrayWrapper(r, 'r')
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
xi = _ArrayWrapper.zeros_like(r)
if not exact:
rc = cluster_toolkit._lib.calc_xi_mm(r.cast(), len(r), k.cast(),
P.cast(), len(k), xi.cast(),
N, step)
_handle_gsl_error(rc, xi_mm_at_r)
else:
if r.arr.max() > 1e3:
raise Exception("max(r) cannot be >1e3 for numerical stability.")
rc = cluster_toolkit._lib.calc_xi_mm_exact(r.cast(), len(r),
k.cast(), P.cast(),
len(k), xi.cast())
_handle_gsl_error(rc, xi_mm_at_r)
return xi.finish()
def xi_DK_appendix2(r, M, conc, be, se, k, P, om, bias, xi_mm, delta=200, rhos=-1., alpha=-1., beta=-1., gamma=-1.):
"""Diemer-Kravtsov 2014 profile, second form from the appendix, eq. A4.
Args:
r (float or array like): radii in Mpc/h comoving
M (float): mass in Msun/h
conc (float): Einasto concentration
be (float): DK transition parameter
se (float): DK transition parameter
k (array like): wavenumbers in h/Mpc
P (array like): matter power spectrum in [Mpc/h]^3
Omega_m (float): matter density fraction
bias (float): halo bias
xi_mm (float or array like): matter correlation function at r
delta (float): overdensity of matter. Optional, default is 200
rhos (float): Einasto density. Optional, default is compute from the mass
alpha (float): Einasto parameter. Optional, default is computed from peak height
beta (float): DK 2-halo parameter. Optional, default is 4
gamma (float): DK 2-halo parameter. Optional, default is 8
Returns:
float or array like: DK profile evaluated at the input radii
"""
r = _ArrayWrapper(r, 'r')
k = _ArrayWrapper(k)
P = _ArrayWrapper(P)
xi_mm = _ArrayWrapper(xi_mm)
xi = _ArrayWrapper.zeros_like(r)
cluster_toolkit._lib.calc_xi_DK_app2(r.cast(), len(r), M, rhos, conc, be,
se, alpha, beta, gamma, delta,
k.cast(), P.cast(), len(k), om, bias,
xi_mm.cast(), xi.cast())
return xi.finish()
def DeltaSigma_mis_at_R(R, Rsigma, Sigma_mis):
"""Miscentered excess surface mass density profile at R. Units are Msun h/pc^2 comoving.
Args:
R (float or array like): Projected radii to evaluate profile.
Rsigma (array like): Projected radii of miscentered Sigma profile.
Sigma_mis (array like): Miscentered Sigma profile.
Returns:
float array like: Miscentered excess surface mass density profile.
"""
R = _ArrayWrapper(R, 'R')
if np.min(R.arr) < np.min(Rsigma):
raise Exception("Minimum R must be >= min(R_Sigma)")
if np.max(R.arr) > np.max(Rsigma):
raise Exception("Maximum R must be <= max(R_Sigma)")
Rsigma = _ArrayWrapper(Rsigma, allow_multidim=True)
Sigma_mis = _ArrayWrapper(Sigma_mis, allow_multidim=True)
if Rsigma.shape != Sigma_mis.shape:
raise ValueError('Rsigma and Sigma must have the same shape')
DeltaSigma_mis = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.DeltaSigma_mis_at_R_arr(R.cast(),
len(R), Rsigma.cast(),
Sigma_mis.cast(), len(Rsigma),
DeltaSigma_mis.cast())
return DeltaSigma_mis.finish()
def dndM_at_M(M, k, P, Omega_m, d=1.97, e=1.0, f=0.51, g=1.228):
"""Tinker et al. 2008 appendix C mass function at a given mass.
Default behavior is for :math:`M_{200m}` mass definition.
NOTE: by default, this function is only valid at :math:`z=0`. For use
at higher redshifts either recompute the parameters yourself, or
wait for this behavior to be patched.
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of the matter power spectrum in h/Mpc comoving.
P_lin (array like): Linear matter power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Matter density fraction.
d (float; optional): First Tinker parameter. Default is 1.97.
e (float; optional): Second Tinker parameter. Default is 1.
f (float; optional): Third Tinker parameter. Default is 0.51.
g (float; optional): Fourth Tinker parameter. Default is 1.228.
Returns:
float or array like: Mass function :math:`dn/dM`.
"""
M = _ArrayWrapper(M, 'M')
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
dndM = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.dndM_at_M_arr(M.cast(), len(M), k.cast(), P.cast(),
len(k), Omega_m, d, e, f, g,
dndM.cast())
return dndM.finish()
def _calc_nu_at_R(R, k, P, nu):
"""Direct call to vectorized version of peak height of R.
"""
R = _ArrayWrapper(R, allow_multidim=True)
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
nu = _ArrayWrapper(nu, allow_multidim=True)
rc = cluster_toolkit._lib.nu_at_R_arr(R.cast(), len(R), k.cast(), P.cast(),
len(k), nu.cast())
_handle_gsl_error(rc, _calc_nu_at_R)
def _calc_sigma2_at_R(R, k, P, s2):
"""Direct call to vectorized version of RMS variance in top hat
sphere of radius R [Mpc/h comoving] of linear power spectrum.
"""
R = _ArrayWrapper(R, allow_multidim=True)
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
s2 = _ArrayWrapper(s2, allow_multidim=True)
cluster_toolkit._lib.sigma2_at_R_arr(R.cast(), len(R), k.cast(), P.cast(),
len(k), s2.cast())
def _calc_nu_at_M(M, k, P, Omega_m, nu):
"""Direct call to vectorized version of peak height of M.
"""
M = _ArrayWrapper(M, allow_multidim=True)
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
nu = _ArrayWrapper(nu, allow_multidim=True)
rc = cluster_toolkit._lib.nu_at_M_arr(M.cast(), len(M), k.cast(), P.cast(),
len(k), Omega_m, nu.cast())
_handle_gsl_error(rc, _calc_nu_at_M)
def Sigma_mis_at_R(R,
Rsigma,
Sigma,
M,
conc,
Omega_m,
Rmis,
delta=200,
kernel="rayleigh"):
"""Miscentered surface mass density [Msun h/pc^2 comoving]
convolved with a distribution for Rmis. Units are Msun h/pc^2 comoving.
Args:
R (float or array like): Projected radii Mpc/h comoving.
Rsigma (array like): Projected radii of the centered surface mass density profile.
Sigma (float or array like): Surface mass density Msun h/pc^2 comoving.
M (float): Halo mass Msun/h.
conc (float): concentration.
Omega_m (float): Matter density fraction.
Rmis (float): Miscentered distance in Mpc/h comoving.
delta (int; optional): Overdensity, default is 200.
kernel (string; optional): Kernal for convolution. Options: rayleigh or gamma.
Returns:
float or array like: Miscentered projected surface mass density.
"""
R = _ArrayWrapper(R, 'R')
# Exception checking
if np.min(R.arr) < np.min(Rsigma):
raise Exception("Minimum R must be >= min(R_Sigma)")
if np.max(R.arr) > np.max(Rsigma):
raise Exception("Maximum R must be <= max(R_Sigma)")
if kernel == "rayleigh":
integrand_switch = 0
elif kernel == "gamma":
integrand_switch = 1
else:
raise Exception("Miscentering kernel must be either " +
"'rayleigh' or 'gamma'")
Rsigma = _ArrayWrapper(Rsigma, allow_multidim=True)
Sigma = _ArrayWrapper(Sigma, allow_multidim=True)
if Rsigma.shape != Sigma.shape:
raise ValueError('Rsigma and Sigma must have the same shape')
Sigma_mis = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.Sigma_mis_at_R_arr(R.cast(), len(R), Rsigma.cast(),
Sigma.cast(), len(Rsigma), M, conc,
delta, Omega_m, Rmis,
integrand_switch, Sigma_mis.cast())
return Sigma_mis.finish()
def _calc_sigma2_at_M(M, k, P, Omega_m, s2):
"""Direct call to vectorized version of RMS variance in top hat sphere of lagrangian radius R [Mpc/h comoving] corresponding to a mass M [Msun/h] of linear power spectrum.
"""
M = _ArrayWrapper(M, allow_multidim=True)
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
s2 = _ArrayWrapper(s2, allow_multidim=True)
rc = cluster_toolkit._lib.sigma2_at_M_arr(M.cast(), len(M), k.cast(),
P.cast(), len(k), Omega_m,
s2.cast())
_handle_gsl_error(rc, _calc_sigma2_at_M)
def concentration_at_M(Mass,
k,
P,
n_s,
Omega_b,
Omega_m,
h,
T_CMB=2.7255,
delta=200,
Mass_type="crit"):
"""Concentration of the NFW profile at mass M [Msun/h].
Only implemented relation at the moment is Diemer & Kravtsov (2015).
Note: only single concentrations at a time are allowed at the moment.
Args:
Mass (float): Mass in Msun/h.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Linear matter power spectrum in (Mpc/h)^3 comoving.
n_s (float): Power spectrum tilt.
Omega_b (float): Baryonic matter density fraction.
Omega_m (float): Matter density fraction.
h (float): Reduced Hubble constant.
T_CMB (float): CMB temperature in Kelvin, default is 2.7.
delta (int; optional): Overdensity, default is 200.
Mass_type(string; optional); Defines either Mcrit or Mmean. Default is mean. Choose "crit" for Mcrit. Other values will raise an exception.
Returns:
float: NFW concentration.
"""
if delta != 200:
raise Exception(
"ConcentrationError: delta=%d. Currently only delta=200 supported"
% delta)
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if Mass_type is "mean":
return cluster_toolkit._lib.DK15_concentration_at_Mmean(
Mass, k.cast(), P.cast(), len(k), delta, n_s, Omega_b, Omega_m, h,
T_CMB)
elif Mass_type is "crit":
return cluster_toolkit._lib.DK15_concentration_at_Mcrit(
Mass, k.cast(), P.cast(), len(k), delta, n_s, Omega_b, Omega_m, h,
T_CMB)
else:
raise Exception(
"ConcentrationError: must choose either 'mean' or 'crit', %s is not supported"
% Mass_type)
def _dndM_sigma2_precomputed(M,
sigma2,
dsigma2dM,
Omega_m,
d=1.97,
e=1.0,
f=0.51,
g=1.228):
M = _ArrayWrapper(M, allow_multidim=True)
sigma2 = _ArrayWrapper(sigma2, allow_multidim=True)
dsigma2dM = _ArrayWrapper(dsigma2dM, allow_multidim=True)
dndM = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.dndM_sigma2_precomputed(M.cast(), sigma2.cast(),
dsigma2dM.cast(), len(M),
Omega_m, d, e, f, g,
dndM.cast())
return dndM.finish()
def _bias_at_nu_FREEPARAMS(nu, A, a, B, b, C, c, delta=200):
"""A special function used only for quickly computing best fit parameters
for the halo bias models.
"""
nu = _ArrayWrapper(nu, allow_multidim=True)
bias = _ArrayWrapper.zeros_like(nu)
cluster_toolkit._lib.bias_at_nu_arr_FREEPARAMS(nu.cast(), len(nu),
delta, A, a, B, b, C, c,
bias.cast())
return bias.finish()
def n_in_bins(edges, Marr, dndM):
"""Tinker et al. 2008 appendix C binned mass function.
Args:
edges (array like): Edges of the mass bins.
Marr (array like): Array of locations that dndM has been evaluated at.
dndM (array like): Array of dndM.
Returns:
numpy.ndarray: number density of halos in the mass bins. Length is :code:`len(edges)-1`.
"""
edges = _ArrayWrapper(edges, 'edges')
n = _ArrayWrapper.zeros(len(edges) - 1)
Marr = _ArrayWrapper(Marr, 'Marr')
dndM = _ArrayWrapper(dndM, 'dndM')
rc = cluster_toolkit._lib.n_in_bins(edges.cast(), len(edges), Marr.cast(),
dndM.cast(), len(Marr), n.cast())
_handle_gsl_error(rc, n_in_bins)
return n.finish()
def bias_at_R(R, k, P, delta=200):
"""Tinker 2010 bais at mass M [Msun/h] corresponding to radius R [Mpc/h comoving].
Args:
R (float or array like): Lagrangian radius in Mpc/h comoving.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: Halo bias.
"""
R = _ArrayWrapper(R, 'R')
k = _ArrayWrapper(k)
P = _ArrayWrapper(P)
bias = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.bias_at_R_arr(R.cast(), len(R), delta, k.cast(),
P.cast(), len(k), bias.cast())
return bias.finish()
def nu_at_R(R, k, P):
"""Peak height of top hat sphere of radius R [Mpc/h comoving] of linear power spectrum.
Args:
R (float or array like): Radius in Mpc/h comoving.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Returns:
float or array like: Peak height.
"""
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if isinstance(R, list) or isinstance(R, np.ndarray):
R = _ArrayWrapper(R)
nu = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.nu_at_R_arr(R.cast(), len(R), k.cast(), P.cast(),
len(k), nu.cast())
return nu.finish()
else:
return cluster_toolkit._lib.nu_at_R(R, k.cast(), P.cast(), len(k))
def Sigma_mis_single_at_R(R, Rsigma, Sigma, M, conc, Omega_m, Rmis, delta=200):
"""Miscentered surface mass density [Msun h/pc^2 comoving] of a profile miscentered by an
amount Rmis Mpc/h comoving. Units are Msun h/pc^2 comoving.
Args:
R (float or array like): Projected radii Mpc/h comoving.
Rsigma (array like): Projected radii of the centered surface mass density profile.
Sigma (float or array like): Surface mass density Msun h/pc^2 comoving.
M (float): Halo mass Msun/h.
conc (float): concentration.
Omega_m (float): Matter density fraction.
Rmis (float): Miscentered distance in Mpc/h comoving.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: Miscentered projected surface mass density.
"""
R = _ArrayWrapper(R, 'R')
if np.min(R.arr) < np.min(Rsigma):
raise Exception("Minimum R must be >= min(R_Sigma)")
if np.max(R.arr) > np.max(Rsigma):
raise Exception("Maximum R must be <= max(R_Sigma)")
Rsigma = _ArrayWrapper(Rsigma, allow_multidim=True)
Sigma = _ArrayWrapper(Sigma, allow_multidim=True)
if Rsigma.shape != Sigma.shape:
raise ValueError('Rsigma and Sigma must have the same shape')
Sigma_mis = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.Sigma_mis_single_at_R_arr(R.cast(), len(R),
Rsigma.cast(), Sigma.cast(),
len(Rsigma), M, conc, delta,
Omega_m, Rmis,
Sigma_mis.cast())
return Sigma_mis.finish()
def sigma2_at_R(R, k, P):
"""RMS variance in top hat sphere of radius R [Mpc/h comoving] of linear power spectrum.
Args:
R (float or array like): Radius in Mpc/h comoving.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Returns:
float or array like: RMS variance of a top hat sphere.
"""
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if isinstance(R, list) or isinstance(R, np.ndarray):
R = _ArrayWrapper(R)
s2 = _ArrayWrapper.zeros_like(R)
rc = cluster_toolkit._lib.sigma2_at_R_arr(R.cast(), len(R), k.cast(),
P.cast(), len(k), s2.cast())
_handle_gsl_error(rc, sigma2_at_R)
return s2.finish()
else:
return cluster_toolkit._lib.sigma2_at_R(R, k.cast(), P.cast(), len(k))
def dbiasdM_at_M(M, k, P, Omega_m, delta=200):
"""d/dM of Tinker et al. 2010 bais at mass M [Msun/h].
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Matter density fraction.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: Derivative of the halo bias.
"""
M = _ArrayWrapper(M, 'M')
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
deriv = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.dbiasdM_at_M_arr(M.cast(), len(M), delta, k.cast(),
P.cast(), len(k), Omega_m,
deriv.cast())
return deriv.finish()
def average_profile_in_bins(Redges, R, prof):
"""Average profile in bins.
Calculates the average of some projected profile in a
radial bins in Mpc/h comoving.
Args:
Redges (array like): Array of radial bin edges.
R (array like): Radii of the profile.
prof (array like): Projected profile.
Returns:
numpy.array: Average profile in bins between the edges provided.
"""
Redges = _ArrayWrapper(Redges)
R = _ArrayWrapper(R)
prof = _ArrayWrapper(prof)
if Redges.ndim == 0:
raise Exception("Must supply a left and right edge.")
if Redges.ndim > 1:
raise Exception("Redges cannot be a >1D array.")
if np.min(Redges.arr) < np.min(R.arr):
raise Exception("Minimum edge must be >= minimum R")
if np.max(Redges.arr) > np.max(R.arr):
raise Exception("Maximum edge must be <= maximum R")
ave_prof = _ArrayWrapper(np.zeros(len(Redges) - 1))
r = cluster_toolkit._lib.average_profile_in_bins(Redges.cast(),
len(Redges), R.cast(),
len(R), prof.cast(),
ave_prof.cast())
_handle_gsl_error(r, average_profile_in_bins)
return ave_prof.finish()
def Sigma_REC_from_DeltaSigma(R, DeltaSigma):
"""Reconstructed Sigma(R) profile, also known as 'Y'
in the same units as DeltaSigma.
Note: R and DeltaSigma must have the same shape,
and have more than 1 element. The returned array
has one fewer elements.
Note: R must have constant logarithmic spacing.
Args:
R (array like): Projected radii.
DeltaSigma (array like): Differential surface mass density.
Returns:
Reconstructed surface mass density.
"""
R = _ArrayWrapper(R, allow_multidim=True)
DeltaSigma = _ArrayWrapper(DeltaSigma, allow_multidim=True)
if R.shape != DeltaSigma.shape:
raise Exception("R and DeltaSigma must have the same shape.")
lnR = np.log(R)
for i in range(len(lnR) - 2):
dlnR0 = lnR[i] - lnR[i + 1]
dlnR1 = lnR[i + 1] - lnR[i + 2]
if not (np.fabs(dlnR0 - dlnR1) < 1e-6):
raise Exception("R must have constant logarithmic spacing.")
continue
#Note the order here, we integrate DOWNWARD
dlnR = lnR[0] - lnR[1]
Sigma = _ArrayWrapper.zeros(len(R) - 1)
cluster_toolkit._lib.Sigma_REC_from_DeltaSigma(dlnR, DeltaSigma.cast(),
len(R), Sigma.cast())
return Sigma.finish()
def bias_at_M(M, k, P, Omega_m, delta=200):
"""Tinker et al. 2010 bais at mass M [Msun/h].
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Matter density fraction.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: Halo bias.
"""
M = _ArrayWrapper(M, 'M')
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if k.shape != P.shape:
raise ValueError('k and P must have the same shape')
bias = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.bias_at_M_arr(M.cast(), len(M), delta, k.cast(),
P.cast(), len(k), Omega_m, bias.cast())
return bias.finish()
def nu_at_M(M, k, P, Omega_m):
"""Peak height of top hat sphere of lagrangian radius R [Mpc/h comoving] corresponding to a mass M [Msun/h] of linear power spectrum.
Args:
M (float or array like): Mass in Msun/h.
k (array like): Wavenumbers of power spectrum in h/Mpc comoving.
P (array like): Power spectrum in (Mpc/h)^3 comoving.
Omega_m (float): Omega_matter, matter density fraction.
Returns:
nu (float or array like): Peak height.
"""
k = _ArrayWrapper(k, allow_multidim=True)
P = _ArrayWrapper(P, allow_multidim=True)
if isinstance(M, list) or isinstance(M, np.ndarray):
M = _ArrayWrapper(M)
nu = _ArrayWrapper.zeros_like(M)
cluster_toolkit._lib.nu_at_M_arr(M.cast(), len(M), k.cast(), P.cast(),
len(k), Omega_m, nu.cast())
return nu.finish()
else:
return cluster_toolkit._lib.nu_at_M(M, k.cast(), P.cast(), len(k),
Omega_m)
def xi_2halo(bias, xi_mm):
"""2-halo term in halo-matter correlation function
Args:
bias (float): Halo bias
xi_mm (float or array like): Matter-matter correlation function
Returns:
float or array like: 2-halo term in halo-matter correlation function
"""
xi_mm = _ArrayWrapper(xi_mm, allow_multidim=True)
xi = _ArrayWrapper.zeros_like(xi_mm)
cluster_toolkit._lib.calc_xi_2halo(len(xi_mm), bias, xi_mm.cast(),
xi.cast())
return xi.finish()
def bias_at_nu(nu, delta=200):
"""Tinker 2010 bais at peak height nu.
Args:
nu (float or array like): Peak height.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: Halo bias.
"""
nu = _ArrayWrapper(nu, 'nu')
bias = _ArrayWrapper.zeros_like(nu)
cluster_toolkit._lib.bias_at_nu_arr(nu.cast(), len(nu), delta, bias.cast())
return bias.finish()
def boost_nfw_at_R(R, B0, R_scale):
"""NFW boost factor model.
Args:
R (float or array like): Distances on the sky in the same units as R_scale. Mpc/h comoving suggested for consistency with other modules.
B0 (float): NFW profile amplitude.
R_scale (float): NFW profile scale radius.
Returns:
float or array like: NFW boost factor profile; B = (1-fcl)^-1.
"""
R = _ArrayWrapper(R, 'R')
boost = _ArrayWrapper.zeros_like(R)
cluster_toolkit._lib.boost_nfw_at_R_arr(R.cast(), len(R), B0, R_scale,
boost.cast())
return boost.finish()
def xi_nfw_at_r(r, M, c, Omega_m, delta=200):
"""NFW halo profile correlation function.
Args:
r (float or array like): 3d distances from halo center in Mpc/h comoving
M (float): Mass in Msun/h
c (float): Concentration
Omega_m (float): Omega_matter, matter fraction of the density
delta (int; optional): Overdensity, default is 200
Returns:
float or array like: NFW halo profile.
"""
r = _ArrayWrapper(r, 'r')
xi = _ArrayWrapper.zeros_like(r)
cluster_toolkit._lib.calc_xi_nfw(r.cast(), len(r), M, c, delta,
Omega_m, xi.cast())
return xi.finish()
def rho_nfw_at_r(r, M, c, Omega_m, delta=200):
"""NFW halo density profile.
Args:
r (float or array like): 3d distances from halo center in Mpc/h comoving.
M (float): Mass in Msun/h.
c (float): Concentration.
Omega_m (float): Omega_matter, matter fraction of the density.
delta (int; optional): Overdensity, default is 200.
Returns:
float or array like: NFW halo density profile in Msun h^2/Mpc^3 comoving.
"""
r = _ArrayWrapper(r, 'r')
rho = _ArrayWrapper.zeros_like(r)
cluster_toolkit._lib.calc_rho_nfw(r.cast(), len(r), M, c, delta, Omega_m,
rho.cast())
return rho.finish()
