def bulge_M2d(R):
return sersic.M2d(R, nser, reff * arcsec2kpc)
arcsec2kpc = np.deg2rad(1. / 3600.) * dd * 1000.
rhoc.append(wl_cosmology.rhoc(zd))
rein_phys_here = tein_here * arcsec2kpc
rein_phys.append(rein_phys_here)
s_cr = c**2 / (4. * np.pi * G) * ds / dds / dd / Mpc / M_Sun * kpc**2
mein_here = np.pi * rein_phys_here**2 * s_cr
reff1 = float(line[4])
nser1 = float(line[5])
# defines the stellar profile
ein_frac.append(sersic.M2d(tein_here, nser1, reff1))
# draws a sample of values of lmchab, to be used for importance sampling and MC integration
lmchab_samp_here = skewnorm.rvs(10.**mchab_logskew, mchab_mu, mchab_sig,
nint)
lmchab_samp.append(lmchab_samp_here)
lm200_samp.append(np.random.normal(0., 1., nint))
lc200_samp.append(np.random.normal(0., 1., nint))
laimf_samp.append(np.random.normal(0., 1., nint))
grid_file = h5py.File('grids/%s_tein_crosssect_grid.hdf5' % name, 'r')
lmstar_grid = grid_file['lmstar_grid'].value.copy()
nmstar = len(lmstar_grid)
lm200_grid = grid_file['lm200_grid'].value.copy()
def get_source_gammat(self):
self.sources['gammat'] = (self.S_h/np.pi/(self.sources['r']/self.rs_ang)**2*adcontr.M2d(self.sources['r'], self.fbar, self.reff_ang, self.rs_ang, self.c200, self.nu) + \
-self.S_h*adcontr.Sigma(self.sources['r'], self.fbar, self.reff_ang, self.rs_ang, self.c200, self.nu) + \
self.S_bulge*(sersic.M2d(self.sources['r'], self.nser, self.reff_ang)/(self.sources['r']/self.reff_ang)**2/pi - \
sersic.Sigma(self.sources['r']/self.reff_ang, self.nser, 1.))) / self.sources['s_cr']
f.close()
griddir = 'grids/'
line = lines[lensno + 1].split()
name = line[0]
zd = float(line[1])
zs = float(line[2])
rein = float(line[3]) # Einstein radius in arcseconds
reff = float(line[4])
nser = float(line[5])
# defines the stellar profile
ein_frac = sersic.M2d(rein, nser, reff)
def bulge_M2d(R):
return sersic.M2d(R, nser, reff * arcsec2kpc)
def bulge_Sigma(R):
return sersic.Sigma(R, nser, reff * arcsec2kpc)
lmchab_obs = float(line[6])
lmchab_err = float(line[7])
dd = wl_cosmology.Dang(zd)
ds = wl_cosmology.Dang(zs)
def Sigmabar(self, theta):
return (4.*self.S_s*nfw.gfunc(theta/self.rs_ang)/(theta/self.rs_ang)**2 + \
self.S_bulge*(sersic.M2d(theta, self.nser, self.reff_ang)/(theta/self.reff_ang)**2/pi)) /1e12
def alpha(self, theta, z):
return (4.*self.S_s*theta/(theta/self.rs_ang)**2*nfw.gfunc(theta/self.rs_ang) + \
self.S_bulge/pi*theta/(theta/self.reff_ang)**2*sersic.M2d(theta, self.nser, self.reff_ang)) / self.S_cr(z)
def gammat(self, theta, z):
return (2.*self.S_s*(2.*nfw.gfunc(theta/self.rs_ang)/(theta/self.rs_ang)**2 - nfw.Ffunc(theta/self.rs_ang)) + \
self.S_bulge*(sersic.M2d(theta, self.nser, self.reff_ang)/(theta/self.reff_ang)**2/pi - \
sersic.Sigma(theta/self.reff_ang, self.nser, 1.))) / self.S_cr(z)
def get_source_gammat(self):
self.sources['gammat'] = (2.*self.S_s*(2.*nfw.gfunc(self.sources['r']/self.rs_ang)/(self.sources['r']/self.rs_ang)**2 + \
- nfw.Ffunc(self.sources['r']/self.rs_ang)) + \
self.S_bulge*(sersic.M2d(self.sources['r'], self.nser, self.reff_ang)/(self.sources['r']/self.reff_ang)**2/pi - \
sersic.Sigma(self.sources['r']/self.reff_ang, self.nser, 1.))) / self.sources['s_cr']
def alpha(x):
# deflection angle (in kpc)
return (10.**lm200_samp[i] / nfw.M3d(r200, rs) * nfw.M2d(x, rs) +
10.**lmstar * sersic.M2d(x, nser, reff)) / np.pi / x / s_cr
