def test_cl():
bins_l = np.int64(np.linspace(10.,3000,100))
bins_u = bins_l[1:] -1
bins_l = bins_l[0:len(bins_l)-1]
binner = jc_utils.binner(bins_l,bins_u)
del bins_l,bins_u
import pylab as pl
pl.ioff()
from matplotlib.backends.backend_pdf import PdfPages
stats_len = jc_utils.stats(binner.Nbins())
for i,idx in enumerate_progress(xrange(nsims),label = 'test_cl::collecting cls'):
sim_cl_len = lib_cmb_unl.map2cl(lib_cmb_len.get_sim(idx))
stats_len.add(binner.bin_that(np.arange(len(sim_cl_len)),sim_cl_len))
camb_binned = binner.bin_that(np.arange(len(cl_len)),cl_len)
camb_unl_binned = binner.bin_that(np.arange(len(cl_unl)),cl_unl)
pp = PdfPages(path_to_figs+'/lenclvscamb.pdf')
pl.figure()
pl.title('len Cl vs CAMB, ' +str(nsims) + ' sims.')
pl.plot(binner.bin_centers(),stats_len.mean()/camb_binned -1.,label = 'sim/camb -1.,100 bins, res ' + str(HD_res))
pl.xlabel('$\ell$')
pl.ylim(-0.05,0.05)
pl.hlines([-0.001,0.001],np.min(binner.bins_l),np.max(binner.bins_r),linestyles='--',color = 'grey')
pl.legend(frameon = False)
pp.savefig()
pl.figure()
pl.title('cl_len / cl_unlCAMB, ' +str(nsims) + ' sims.')
pl.plot(binner.bin_centers(),stats_len.mean()/camb_unl_binned -1.,label = 'sim/camb_unl -1.,binned, res ' + str(HD_res))
pl.plot(binner.bin_centers(),camb_binned/camb_unl_binned -1.,label = 'camb_len/camb_unl -1.,binned.')
pl.xlabel('$\ell$')
pl.legend(frameon = False)
pp.savefig()
pp.close()
pl.close()
HD_shape = (2 ** HD_res, 2 ** HD_res) # resolution for CMB and lensing operation done at LD_shape = (2 ** LD_res, 2 ** LD_res) # resolution of the dat map lside = np.sqrt(4.*np.pi)*np.ones(2) # pixel res always the same always at 1.7 amin. lmax_cl = 10000 base_path = './inputs/planck_lensing_wp_highL_bestFit_20130627_' #base_path = '/Users/jcarron/SpyderProjects/jpipe/inputs/cls/base_plikHM_TT_lowTEB_lensing_' cl_unl = camb.spectra_fromcambfile(base_path+'lenspotentialCls.dat',lmax = lmax_cl)['tt'][:] cl_len = camb.spectra_fromcambfile(base_path+'lensedCls.dat',lmax = lmax_cl)['tt'][:] cl_pp = camb.spectra_fromcambfile(base_path+'lenspotentialCls.dat',lmax = lmax_cl)['pp'][:] cl_noise = (sN_uKamin * np.pi / 180. / 60.) ** 2 * np.ones(30000) # simple flat noise Cls bins_l = np.int64(np.linspace(10.,3000,100)) bins_u = bins_l[1:] -1 bins_l = bins_l[0:len(bins_l)-1] binner = jc_utils.binner(bins_l,bins_u) del bins_l,bins_u path_to_libs = '/Users/jcarron/data/flatsky_lens_simlibs/test_wPL2015' if path_to_libs is None else path_to_libs path_to_figs = path_to_libs + '/figs' if not os.path.exists(path_to_libs) :os.mkdir(path_to_libs) if not os.path.exists(path_to_figs) :os.mkdir(path_to_figs) lib_noise = sims.ffs_Gauss_simlib(path_to_libs + '/noise', cl_noise, HD_res, nsims_max = nsims) lib_cmb_unl = sims.ffs_Gauss_simlib(path_to_libs + '/unl_cmb', cl_unl, HD_res, nsims_max = nsims) lib_pp = sims.ffs_Gauss_simlib(path_to_libs + '/pp', cl_pp, HD_res, nsims_max = nsims) lib_OO = None lib_displ = sims.ffs_displ_2dsim(lib_pp, lib_OO, lib_dir=path_to_libs + '/displ', cache_sims = True) lib_cmb_len = sims.lencmb_sim_lib(lib_cmb_unl, lib_displ, lib_dir =path_to_libs + '/cmb_len', cache_sims=True) # Library for lensed CMB
