def phi_nie(sigmav,x1,x2,qe,xcore,z1,z2):
scr = mm.sigma_crit(z1,z2)
x = np.sqrt(xcore*xcore+x1*x1*(1.0-qe)+x2*x2*(1.0+qe))
#res2 = Dds/Ds*4.0*np.pi*sigmav**2.0/vc**2.0*mm.apr
ac = sigmav**2.0*mm.apr/(mm.G*mm.Da(z1)*scr)
res = ac*x
return res
def leq_nie_zeros(sigmav,x1,x2,qe,xcore,z1,z2,y10,y20):
scr = mm.sigma_crit(z1,z2+1e-7)
x = np.sqrt(xcore*xcore+x1*x1*(1.0-qe)+x2*x2*(1.0+qe))
ac = sigmav**2.0*mm.apr/(mm.G*mm.Da(z1)*scr)
res1 = ac*(1-qe)*x1/x
res2 = ac*(1+qe)*x2/x
return x1-res1-y10,x2-res2-y20
def alpha_nie(sigmav,x1,x2,qe,xcore,z1,z2):
scr = mm.sigma_crit(z1,z2+1e-7)
x = np.sqrt(xcore*xcore+x1*x1*(1.0-qe)+x2*x2*(1.0+qe))
ac = sigmav**2.0*mm.apr/(mm.G*mm.Da(z1)*scr)
res1 = ac*(1-qe)*x1/x
res2 = ac*(1+qe)*x2/x
return res1,res2
def kappa_nie(sigmav,x1,x2,qe,xcore,z1,z2):
scr = mm.sigma_crit(z1,z2)
x = np.sqrt(xcore*xcore+x1*x1*(1.0-qe)+x2*x2*(1.0+qe))
ac = sigmav**2.0*mm.apr/(mm.G*mm.Da(z1)*scr)
res = ac/(2.0*x)
return res
def get_halo_cc(om10_id):
db = om10.DB(catalog="./qso_mock.fits")
lens = db.get_lens(om10_id)
zl = lens.ZLENS[0]
zs = lens.ZSRC[0]
qe = 1.0 / (1.0 - lens.ELLIP[0])
phi = lens.PHIE[0]
vd0 = lens.VELDISP[0] # needed from OM10
mtotal0 = aa.m200_sv(vd0, zl) # needed from OM10
#----------------------------------------------------------------------
# index, snapid,haloid,subhid,ag_stellar,ag_total,eq_stellar,eq_total,ms_stellar,ms_total,vd_stellar
#
snapid, haloid, subhid, ag_total, eq_stellar, ms_total, vd_stellar = np.loadtxt(
"../produce_e_catalogs/results.dat",
usecols=(1, 2, 3, 5, 6, 9, 10),
unpack=True)
print "Input: ", qe, phi, vd0, mtotal0
nhalos = len(subhid)
ksi_om10 = (ag_total/phi-1.0)**2.0/nhalos*0.0+(eq_stellar/qe-1.0)**2.0/nhalos*0.4 \
+ (ms_total/mtotal0-1.0)**2.0/nhalos*0.01+(vd_stellar*(1.0+zl)/vd0-1.0)**2.0/nhalos*100.4
idx = ksi_om10 == np.min(ksi_om10)
print "Matched: ", eq_stellar[idx], ag_total[idx], vd_stellar[
idx], ms_total[idx]
snapid_n = int(snapid[idx])
#haloid_n = int(haloid[idx])
subhid_n = int(subhid[idx])
bsz = 30.0 # ckpc
nnn = 512
stars = il.snapshot.loadSubhalo(basePath, snapid_n, subhid_n, 'stars')
x1 = stars['Coordinates'][:, 0] #/1e3/(1.0+zl) # Mpc/h, comoving
x2 = stars['Coordinates'][:, 1] #/1e3/(1.0+zl) # Mpc/h, comoving
x3 = stars['Coordinates'][:, 2] #/1e3/(1.0+zl) # Mpc/h, comoving
ptnl_p = stars['Potential']
idx_ptnl_p_min = ptnl_p == ptnl_p.min()
xcp1 = x1[idx_ptnl_p_min]
xcp2 = x2[idx_ptnl_p_min]
xcp3 = x3[idx_ptnl_p_min]
(x1new, x2new) = trf.xy_rotate(x1, x2, xcp1, xcp2, -14.0)
sdens = read_sdens_from_illustris(stars, x1new, x2new, x3 - xcp3,
bsz / 512 * 10.0, nnn) * (1.0 + zl)**2.0
kappa = sdens / mm.sigma_crit(zl, zs)
bsz = bsz / 1e3 / mm.Dc(zl) * mm.apr #arcsec
dsx = bsz / nnn #arcsec
xi1, xi2 = make_r_coor(nnn, dsx)
ai1, ai2 = pcs.call_cal_alphas0(sdens, nnn, bsz, zl, zs)
mua = pcs.call_alphas_to_mu(ai1, ai2, nnn, dsx)
return xi1, xi2, ai1, ai2, mua, kappa
