def demo_uk_interp():
co = CosmoParameters()
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey()
pshm = PowerSpectrumHaloMatter(co=co, nu=nu, su=su, zh=0.5)
pshm.calc_uk_M_interp(1e+14, 2e+14)
def demo_cov(plotting=False):
co = CosmoParameters(OmegaM=0.286, sigma8=0.82, h=0.7, OmegaDE=0.714)
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey(zs_min=0.56,
zs_max=0.65,
top_hat=True,
n_src_arcmin=10,
sigma_gamma=0.3)
cds = CovDeltaSigma(co=co, nu=nu, su=su)
rp_min = 1
rp_max = 10
n_rp = 2 #5
cds.calc_cov(rp_min=rp_min,
rp_max=rp_max,
n_rp=n_rp,
zh_min=0.1,
zh_max=0.3,
Mmin=1e14,
Mmax=1e16,
diag_only=False)
print(cds.cov_cosmic_shear)
print(cds.cov_shape_noise)
### compare with the old code
from clens.lensing.cov_DeltaSigma_old import CovDeltaSigmaOld
cdso = CovDeltaSigmaOld(co=co, nu=nu, su=su)
## cov
output = cdso.calc_cov_thin_slice(rp_min=rp_min,
rp_max=rp_max,
n_rp=n_rp,
zh_min=0.1,
zh_max=0.3,
Mmin=1e14,
Mmax=1e16)
#rp_mid, cov_cosmic_shear, cov_shape_noise, x = output
print(cdso.cov_cosmic_shear)
print(cdso.cov_shape_noise)
if plotting == True:
plt.plot(cds.rp_mid_list,
cds.cov_cosmic_shear.diagonal(),
label='cosmic shear')
plt.plot(cds.rp_mid_list,
cds.cov_shape_noise.diagonal(),
label='shape noise')
#plt.plot(cds.rp_mid_list, cds.cov_halo_intrinsic.diagonal(), label='halo intrinsic')
#plt.plot(cds.thmid_list*cn.radian_to_arcmin, cds.cov_sum.diagonal(), label='sum')
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r'$\rm r_p\ [Mpc]$')
plt.ylabel(r'$\rm Var[\Delta\Sigma]$')
plt.show()
def demo_cov(save_files=False):
co = CosmoParameters(OmegaM=0.286, sigma8=0.82, h=0.7, OmegaDE=0.714)
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1, lambda0=1)#1-1,no scatter
su = Survey()
cj = Covgammat(co=co, nu=nu, su=su)
thmin = 5e-4
thmax = 5e-2
nth = 2#10#4
zh_min = 0.1
zh_max = 0.3
Mmin = 1e14
Mmax = 1e16
cj.calc_cov_gammat_integration(thmin=thmin, thmax=thmax, nth=nth, zh_min=zh_min, zh_max=zh_max, Mmin=Mmin, Mmax=Mmax)
plt.figure(figsize=(14,7))
plt.subplot(121)
plt.plot(cj.thmid_list*cn.radian_to_arcmin, cj.cov_cosmic_shear.diagonal(), label='cosmic shear')
plt.plot(cj.thmid_list*cn.radian_to_arcmin, cj.cov_shape_noise.diagonal(), label='shape noise')
plt.plot(cj.thmid_list*cn.radian_to_arcmin, cj.cov_halo_intrinsic.diagonal(), label='halo intrinsic')
plt.plot(cj.thmid_list*cn.radian_to_arcmin, cj.cov_sum.diagonal(), label='sum')
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r'$\rm\theta\ [arcmin]$')
plt.ylabel(r'$\rm Var[\gamma_t]$')
plt.subplot(122)
plt.imshow(np.log10(cj.cov_sum))
plt.colorbar()
if save_files==True:
plot_loc = '../../plots/lensing/'
if os.path.isdir(plot_loc)==False: os.makedirs(plot_loc)
output_loc = '../../output/lensing/'
if os.path.isdir(output_loc)==False: os.makedirs(output_loc)
plt.savefig(plot_loc+'cov_cosmic_shear_nbins_%i.pdf'%(nth))
## save var_cosmic_shear
outfile = open(output_loc+'var_cosmic_shear_fullsky_z_%g_%g_M_%.0e_%.0e.dat'%(zh_min,zh_max,Mmin,Mmax),'w')
outfile.write('# theta[radian], var gammat \n')
for it in range(len(cj.thmid_list)):
outfile.write('%g %g\n'%(cj.thmid_list[it], cj.cov_cosmic_shear.diagonal()[it]))
outfile.close()
## save var_shape_noise
outfile = open(output_loc+'var_shape_noise_fullsky_z_%g_%g_M_%.0e_%.0e.dat'%(zh_min,zh_max,Mmin,Mmax),'w')
outfile.write('# theta[radian], var gammat \n')
for it in range(len(cj.thmid_list)):
outfile.write('%g %g\n'%(cj.thmid_list[it], cj.cov_shape_noise.diagonal()[it]))
outfile.close()
## save the entire cov
outfile = open(output_loc+'cov_sum_fullsky_z_%g_%g_M_%.0e_%.0e.dat'%(zh_min,zh_max,Mmin,Mmax),'w')
np.savetxt(outfile, cj.cov_sum)
outfile.close()
def demo_uk():
co = CosmoParameters()
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey()
pshm = PowerSpectrumHaloMatter(co=co, nu=nu, su=su, zh=0.5)
pshm._check_convergence_Ci()
pshm._check_convergence_Si()
plt.figure()
pshm.calc_uk(M200m=1e14, c=4, plotting=True)
pshm.calc_uk(M200m=1e16, c=4, plotting=True)
def demo_pk():
co = CosmoParameters()
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey()
pshm = PowerSpectrumHaloMatter(co=co, nu=nu, su=su, zh=0.5)
pshm.calc_Pk_hm_full(Mmin=1e14, Mmax=1e16)
plt.figure()
plt.loglog(pshm.k_list, pshm.P1h_list)
plt.loglog(pshm.k_list, pshm.P2h_list)
plt.loglog(pshm.k_list, np.exp(pshm.lnPk_lnk_interp(pshm.lnk_list)))
def demo_var_slicing():
co = CosmoParameters(OmegaM=0.286, sigma8=0.82, h=0.7, OmegaDE=0.714)
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1, lambda0=1)#1-1,no scatter
su = Survey()
cj = Covgammat(co=co, nu=nu, su=su, slicing=True, dz_slicing=0.03)
thmin = 5e-4
thmax = 5e-2
nth = 2#10#4
cj.calc_cov_gammat_integration(thmin=thmin, thmax=thmax, nth=nth, zh_min=0.1, zh_max=0.3, Mmin=1e14, Mmax=1e16, diag_only=True)
plt.plot(cj.thmid_list*cn.radian_to_arcmin, cj.var_cosmic_shear, label='cosmic shear, slicing',ls='--')
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r'$\rm\theta\ [arcmin]$')
plt.ylabel(r'$\rm Var[\gamma_t]$')
def demo_var_slicing():
co = CosmoParameters(OmegaM=0.286, sigma8=0.82, h=0.7, OmegaDE=0.714)
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey(zs_min=0.56,
zs_max=0.65,
top_hat=True,
n_src_arcmin=10,
sigma_gamma=0.3)
rp_min = 1
rp_max = 10
n_rp = 2 #5
cds = CovDeltaSigma(co=co, nu=nu, su=su, slicing=True, dz_slicing=0.033)
cds.calc_cov(rp_min=rp_min,
rp_max=rp_max,
n_rp=n_rp,
zh_min=0.1,
zh_max=0.3,
Mmin=1e14,
Mmax=1e16,
diag_only=True)
print(cds.cov_cosmic_shear)
Mmax_hiMsun = float(sys.argv[4])
n_rp = int(sys.argv[5])
scatter = float(sys.argv[6])
if scatter==0:
Mmin_obs = Mmin_hiMsun
else:
## NEW Mobs threshold with scatter
boxsize = 720
den, M_lim_noscat, Mobs_lim_scat = np.loadtxt('../data/abacus_scatter%g/%i_planck_10percent/z%g/Mobs_threshold.dat'%(scatter,boxsize,zh_mid), unpack=True)
select = (M_lim_noscat == Mmin_hiMsun)
Mmin_obs = Mobs_lim_scat[select]
co = CosmoParameters(h=0.673, OmegaDE=0.686, OmegaM=0.314, sigma8=0.83) # Abacus cosmology
if scatter==0:
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1, lambda0=1) # 1-1,no scatter
else:
nu = NuisanceParameters(sigma_lambda=scatter, lgM0=0, alpha_M=1, lambda0=1)
su = Survey(top_hat=True, zs_min=zs_mid-0.05, zs_max=zs_mid+0.05, n_src_arcmin=10)
cp = DemoAnalytic(co=co, nu=nu, su=su, zh_mid=zh_mid, Mmin_hiMsun=Mmin_obs, Mmax_hiMsun=Mmax_hiMsun, rp_min_hiMpc=0.1, rp_max_hiMpc=100., n_rp=n_rp)
cp.calc_cov_full() # takes some time
#cp.calc_cov_slice()# takes some time
#cp.calc_cov_full_no_shot_noise()# takes some time
#cp.plot_analytic()
kernel_list = self.kernel_z_interp(zl_list)
plt.plot(zl_list, kernel_list)
plt.xlabel(r'$\rm z_{lens}$')
plt.ylabel(
r'$\rm Kernel(z_l) = \displaystyle\int dz_s n(z_s)/\Sigma_{crit}(z_s, z_l)$'
)
plt.minorticks_on()
plt.xlim(0, None)
plt.subplot(224)
plt.title('lensing kernel vs. ' + r'$\rm\chi$')
chi_l_list = self.chi(z=zl_list).value
plt.plot(chi_l_list, kernel_list)
plt.minorticks_on()
plt.xlabel(r'$\rm \chi_{lens}\ [Mpc]$')
plt.ylabel(r'$\rm Kernel(\chi_l)$')
plt.minorticks_on()
plt.xlim(0, None)
plt.savefig('../../plots/lensing/kernel_sanity.pdf')
if __name__ == "__main__":
co = CosmoParameters()
su = Survey()
lk = LensingKernel(co=co, su=su)
lk.distance_sanity()
lk.kernel_sanity()
plt.show()
return mean_inv_Sigma_crit
def calc_gammmat(self):
rp, DeltaSigma = self.calc_DeltaSigma()
zh = 0.5 * (self.zh_min + self.zh_max)
mean_inv_Sigma_crit = self.mean_inv_Sigma_crit(zh)
gammat_avg = DeltaSigma * mean_inv_Sigma_crit #* (1e-12)
chi_l = self.chi(zh).value
theta = rp / chi_l
return theta, gammat_avg
if __name__ == "__main__":
co = CosmoParameters(OmegaM=0.286, sigma8=0.82, h=0.7, OmegaDE=0.714)
nu = NuisanceParameters(sigma_lambda=1e-5, lgM0=0, alpha_M=1,
lambda0=1) #1-1,no scatter
su = Survey(zs_min=0.508, zs_max=0.574)
lp = LensingProfiles(co=co,
nu=nu,
su=su,
zh_min=0.155,
zh_max=0.323,
Mmin=1e+14,
Mmax=1e+16)
theta, gammat = lp.calc_gammmat()
plt.plot(theta, gammat)
plt.xscale('log')
plt.yscale('log')
plt.show()