def get_args(i):
Ms = np.array([])
bs = np.array([])
tbs = np.array([])
pd = np.array([])
pde = np.array([])
bes = np.array([])
icovs = np.array([])
boxes = np.array([])
snaps = np.array([])
cosmo, h, Omega_m = get_cosmo(i)
hmf = mf_obj(i)
k = np.logspace(-5, 1, num=1000) #Mpc^-1
kh = k/h
nus = [] #sigma^2
for j in range(0,10): #snap
z = zs[j]
M, Mlo, Mhigh, b, be = np.loadtxt("/Users/tmcclintock/Data/linear_bias_test/TestBox%03d-combined_Z%d_DS50_linearbias.txt"%(i,j)).T
M = np.ascontiguousarray(M)
Mlo = np.ascontiguousarray(Mlo)
Mhigh = np.ascontiguousarray(Mhigh)
inds = Mhigh > 1e99
Mhigh[inds] = 1e16
p = np.array([cosmo.pk_lin(ki, z) for ki in k])*h**3
nu = ph.nu_at_M(M, kh, p, Omega_m)
#Replace this part with the average bias
Mbins = np.array([Mlo, Mhigh]).T
n_bins = hmf.n_in_bins(Mbins, z) #Denominator
Marr = np.logspace(np.log10(M[0]*0.98), 16, 1000)
lMarr = np.log(Marr)
nuarr = ph.nu_at_M(Marr, kh, p, Omega_m)
dndlm = hmf.dndlM(Marr, z)
b_n = dndlm * ct.bias.bias_at_nu(nuarr)
b_n_spl = IUS(lMarr, b_n)
lMbins = np.log(Mbins)
tb = np.zeros_like(nu)
for ind in range(len(tb)):
tbi = quad(b_n_spl, lMbins[ind,0], lMbins[ind,1])
tbi2 = b_n_spl.integral(lMbins[ind,0], lMbins[ind,1])
#print tbi[0]/n_bins[ind], tbi2/n_bins[ind]
tb[ind] = tbi[0] / n_bins[ind]
#print b
#exit()
#print tb
#print ct.bias.bias_at_nu(nu) #instantaneous tinker bias
#tb = ct.bias.bias_at_nu(nu) #instantaneous tinker bias
tbs = np.concatenate((tbs, tb))
Ms=np.concatenate((Ms, M))
bs=np.concatenate((bs, b))
bes=np.concatenate((bes, be))
nus = np.concatenate((nus, nu))
pd = np.concatenate((pd, (b-tb)/tb))
pde = np.concatenate((pde, be/tb))
boxes = np.concatenate((boxes, np.ones_like(M)*i))
snaps = np.concatenate((snaps, np.ones_like(M)*j))
return nus, bs, bes, Ms, tbs, pd, pde, boxes, snaps
def test_s2_and_nu_functions():
#Test the mass calls
s2 = peaks.sigma2_at_M(Mass, klin, plin, Omega_m)
nu = peaks.nu_at_M(Mass, klin, plin, Omega_m)
npt.assert_equal(1.686 / np.sqrt(s2), nu)
s2 = peaks.sigma2_at_M(Ma, klin, plin, Omega_m)
nu = peaks.nu_at_M(Ma, klin, plin, Omega_m)
npt.assert_array_equal(1.686 / np.sqrt(s2), nu)
#Now test the R calls
R = 1.0 #Mpc/h; arbitrary
s2 = peaks.sigma2_at_R(R, klin, plin)
nu = peaks.nu_at_R(R, klin, plin)
npt.assert_equal(1.686 / np.sqrt(s2), nu)
def test_mass_dependence():
masses = np.logspace(13, 15, num=100)
arrout = bias.bias_at_M(masses, klin, plin, Omega_m)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
Rs = (masses/(4./3.*np.pi*Omega_m*rhomconst))**(1./3.)
arrout = bias.bias_at_R(Rs, klin, plin)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
nus = peaks.nu_at_M(masses, klin, plin, Omega_m)
arrout = bias.bias_at_nu(nus)
for i in range(len(masses)-1):
assert arrout[i] < arrout[i+1]
def calc_peak_height(cosmos, zs, k, p):
Nc = len(cosmos)
Nz = len(zs)
nus = np.zeros((Nc, Nz, len(M)))
for i in range(Nc):
obh2, och2, w, ns, ln10As, H0, Neff, s8 = cosmos[i]
h = H0 / 100.
Om = (obh2 + och2) / h**2
kh = k / h #now h/Mpc
for j in range(Nz):
ph3 = p[i, j] * h**3 #now (Mpc/h)^3
nus[i, j] = ctph.nu_at_M(M, kh, ph3, Om)
continue
print("Finished peak height box %d" % i)
continue
return nus