def dndlnM0 ( M ,
ps ,
z = 0. ,
khmin = 1.0e-5,
khmax = 2.0 ,
logkhint = 0.005 ,
bgtype = "cb",
powerspectrumfile = "LCDM_matterpower.dat" ,
cosmo = None,
deltac = 1.674 ,
fittingform = "Bhattacharya10",
**params ):
"""
returns the mass function dn/dln(M) in units of h^3 Mpc^{-3}
args:
M: mandatory, arraylike
mass bin in units of solar Mass
ps: provide power spectrum at z = 0
powerspectrumfile : optional, string, defaults to
LCDM_matterpower.dat
name of the power spectrum file from CAMB
cosmo: optional defaults to Planck13
cosmology model
returns:
numpy array containing mass function in units of Mpc^{-3}
CHANGES:
added argument deltac with default value 1.674
"""
#z = np.asarray(z, dtype = float)
h = cosmo.H0/100.0
#rhocr = critdensity( h = h ,
# unittype = "solarmassperMpc3")
sig = psu.sigmaM (M ,
ps ,
bgtype = bgtype,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
z = 0.,
cosmo = cosmo ,
**params)
sigm = cosmo.growth(z=z)[0]*sig
dlsinvdlM = -psu.dlnsigmadlnM (M ,
ps ,
z = 0. ,
bgtype = bgtype ,
cosmo = cosmo ,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
**params )
#used this to figure out what problems were
#print z, sigm, dlsinvdlM , deltac
if fittingform == "Bhattacharya10":
f_sigma = mf.__fsigmaBhattacharya (
sigma = sigm,
deltac = deltac ,
z = z ,
A0 = 0.333 ,
a0 = 0.788 ,
p0 = 0.807 ,
q0 = 1.795 ,
alpha1 = 0.11 ,
alpha2 = 0.01 ,
alpha3 = 0.0 ,
alpha4 = 0.0,
Mlow = 6e11 ,
Mhigh = 3e15)
elif fittingform == "MICE" :
f_sigma = mf.fsigmaMICE(sigma = sigm, z = z)
else:
raise ValueError("This fitting form is not implemented")
rhobg = psu.__rhobg( z =0. , bgtype = bgtype,
unittype = "solarmassperMpc3", cosmo = cosmo)
dndlnM = rhobg *f_sigma *dlsinvdlM /M
#dndlnM = dlsinvdlM *f_sigma/M * rhobg
#critdensity(h = cosmo.h, unittype = "solarmassperMpc3")*cosmo.Om0
return dndlnM
def ____dndlnM ( M ,
ps ,
z = 0. ,
khmin = 1.0e-5,
khmax = 2.0 ,
logkhint = 0.005 ,
bgtype = "cb",
powerspectrumfile = "LCDM_matterpower.dat" ,
cosmo = None,
deltac = 1.686 ,
fittingform = "Bhattacharya10",
**params ):
"""
returns the mass function dn/dln(M) in units of h^3 Mpc^{-3}
args:
M: mandatory, arraylike
mass bin in units of solar Mass
powerspectrumfile : optional, string, defaults to
LCDM_matterpower.dat
name of the power spectrum file from CAMB
cosmo: optional defaults to Planck13
cosmology model
returns:
numpy array containing mass function in units of Mpc^{-3}
CHANGES:
added argument deltac with default value 1.674
changed back to 1.686 R. Biswas
"""
h = cosmo.H0/100.0
sig = psu.sigmaM (M ,
ps ,
bgtype = bgtype,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
z = 0. ,
cosmo = cosmo ,
**params)
sigm = sig
dlsinvdlM = -psu.dlnsigmadlnM (M ,
ps ,
z = 0.,
bgtype = bgtype ,
cosmo = cosmo ,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
**params )
if fittingform == "Bhattacharya10":
f_sigma = mf.__fsigmaBhattacharya (
sigma = sigm,
deltac = deltac ,
z = z ,
A0 = 0.333 ,
a0 = 0.788 ,
p0 = 0.807 ,
q0 = 1.795 ,
alpha1 = 0.11 ,
alpha2 = 0.01 ,
alpha3 = 0.0 ,
alpha4 = 0.0,
Mlow = 6e11 ,
Mhigh = 3e15)
elif fittingform == "MICE" :
f_sigma = mf.fsigmaMICE(sigma = sigm, z = z)
else:
raise ValueError("This fitting form is not implemented")
rhobg = psu.__rhobg( z =0. , bgtype = bgtype,
unittype = "solarmassperMpc3", cosmo = cosmo)
#dndlnM = rhobg *sigm *dlsinvdlM /M
dndlnM = rhobg *f_sigma *dlsinvdlM /M
#dndlnM = dlsinvdlM *f_sigma/M * rhobg
#critdensity(h = cosmo.h, unittype = "solarmassperMpc3")*cosmo.Om0
#dndlnM = rhobg * np.ones(len(sigm))
#dndlnM = rhobg * f_sigma
return dndlnM
def dndlnM ( M ,
ps ,
z = 0. ,
khmin = 1.0e-5,
khmax = 2.0 ,
logkhint = 0.005 ,
bgtype = "cb",
#powerspectrumfile = "LCDM_matterpower.dat" ,
cosmo = None,
deltac = 1.674 ,
fittingform = "Bhattacharya10",
**params ):
"""
returns the mass function dn/dln(M) in units of h^3 Mpc^{-3}
args:
M: mandatory, arraylike
mass in units of solar Mass
ps: provide power spectrum tuple from CAMB at z = 0. The
required tuple is (koverh, ps)
cosmo: cosmology model of type FCPL
deltac : float
delta c value used in the fitting function
fittingform: string, optional defaults to "Bhattacharya10"
choices:
"Bhattacharya10": Bhattacharya 2010 fitting form with
values suggested in paper"
"MICE" : Crocce etal fitting form with
values suggested in paper
returns:
numpy array containing mass function in units of Mpc^{-3}
CHANGES:
added argument deltac with default value 1.674
"""
#z = np.asarray(z, dtype = float)
h = cosmo.H0/100.0
#rhocr = critdensity( h = h ,
# unittype = "solarmassperMpc3")
sig = psu.sigmaM (M ,
ps ,
bgtype = bgtype,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
z = 0.,
cosmo = cosmo ,
**params)
sigm = sig
#sigm = cosmo.growth(z=z)[0]*sig
#print sigm
dlsinvdlM = -psu.dlnsigmadlnM (M ,
ps ,
z = 0. ,
bgtype = bgtype ,
cosmo = cosmo ,
khmin = khmin ,
khmax = khmax ,
logkhint = logkhint ,
**params )
#used this to figure out what problems were
#print z, sigm, dlsinvdlM , deltac
if fittingform == "Bhattacharya10":
f_sigma = mf.__fsigmaBhattacharya (
sigma = sigm,
deltac = deltac ,
z = z ,
A0 = 0.333 ,
a0 = 0.788 ,
p0 = 0.807 ,
q0 = 1.795 ,
alpha1 = 0.11 ,
alpha2 = 0.01 ,
alpha3 = 0.0 ,
alpha4 = 0.0,
Mlow = 6e11 ,
Mhigh = 3e15)
elif fittingform == "MICE" :
f_sigma = mf.fsigmaMICE(sigma = sigm, z = z)
else:
raise ValueError("This fitting form is not implemented")
rhobg = psu.__rhobg( z =0. , bgtype = bgtype,
unittype = "solarmassperMpc3", cosmo = cosmo)
#dndlnM = rhobg *f_sigma *dlsinvdlM /M
#dndlnM = rhobg *sigm *dlsinvdlM /M
dndlnM = dlsinvdlM *f_sigma/M * rhobg
#critdensity(h = cosmo.h, unittype = "solarmassperMpc3")*cosmo.Om0
#return dndlnM
#dndlnM = rhobg*f_sigma
#dndlnM = rhobg*np.ones(len(sigm))
#dndlnM = mf.__fsigmaBhattacharya(sigma = sigm, deltac = 1.686, z = z)
return dndlnM
sigmaM_ax1.set_xlabel(r'$M (h^{-1}M_\odot) $')
#fsigma as a function of halo mass
dfsigfig , dfsig_ax0, dfsig_ax1 = pu.settwopanel()
dfsig_ax0.plot(MassesinhoverM , sumanfsigma , "g", label = "Suman TF")
dfsig_ax0.plot(MassesinhoverM , mf.__fsigmaBhattacharya(psu.sigmaM(M = Masses, ps = psfrompk, cosmo = M000)) , "rs", label = "PS")
dfsig_ax0.set_ylabel(r'$f_{\sigma}(M)$')
dfsig_ax0.legend(loc = "best", numpoints = 1)
dfsig_ax0.set_xscale('log')
dfsig_ax1.plot(MassesinhoverM , sumanfsigma/ mf.__fsigmaBhattacharya(psu.sigmaM(M = Masses, ps = psfrompk, cosmo = M000)) , "rs", label = "PS")
dfsig_ax1.set_xscale('log')
#dlnsigmainv dlnM as a function of halo mass
dlsidlMfig , dlsidlM_ax0, dlsidlM_ax1 = pu.settwopanel()
dlsidlM_ax0.loglog( MassesinhoverM , -sumandlsidlm, "go", label = "TF")
dlsidlM_ax0.loglog( MassesinhoverM , psu.dlnsigmadlnM(M= Masses, ps = psfrompk, cosmo = M000) , "r+" , label = "PS")
dlsidlM_ax0.set_ylabel(r'$dlnsigma/dlnM$')
dlsidlM_ax0.legend(loc = "best", numpoints = 1)
dlsidlM_ax1.plot( MassesinhoverM , sumandlsidlm / psu.dlnsigmadlnM (M = Masses, ps = psfrompk, cosmo = M000))
dlsidlM_ax1.grid(True)
dlsidlM_ax1.set_xscale("log")
#mass function as a function of halo mass
#dndlnmfig, dndlnm_ax0 , dndlnm_ax1 = pu.settwopanel(setdifflimits = None)
dndlnmfig, dndlnm_ax0 , dndlnm_ax1 = pu.settwopanel()
dndlnm_ax0.loglog(MassesinhoverM , -sumandndlnM , label = "Suman")
dndlnm_ax0.loglog(MassesinhoverM , psu.dndlnM(M = Masses, ps = psfrompk, cosmo = M000)/M000.h**3.0, label = "PS")
dndlnm_ax0.set_ylabel(r'$dn/d\ln{(M)} (h^3 Mpc^{-3} $')
dndlnm_ax0.set_xlabel(r'$M (h^{-1} M_\odot )$')
dndlnm_ax0.legend(loc = "best", numpoints = 1)
