def get_fiducial_sim_lib(path_to_libs = None): from fslens.sims import sims import sims_generic import covmatrices import likelihoods import displacements as dp from fslens.misc import jc_camb as camb # Needs about 748 sim to 4pi surface with this params. # Let's do twice as much. """ There is a trade-off between high resolution and number of iteration in the conjugate gradient. If the resolution is lower each step is much faster but the inacuracy in the inverse maping drives the erros and make it need a higher number of iterates. E.g. for PL2015 params, with res 8 I need 50 iterate , done in 5 sec total. with res 9 I ned 20 iterates, done in 10 sec total. """ LD_res = 8 HD_res = 8 beam_FWHM_amin = 5. sN_uKamin = 35. nsims = 100 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 lcell_side_amin = 1.7 # length of lcell of the data resolution lside = lcell_side_amin / 60./180.*np.pi * LD_shape[0]*np.ones(2) # pixel res always the same always at 1.7 amin. cl_unl = camb.spectra_fromcambfile('/Users/jcarron/SpyderProjects/jpipe/inputs' '/cls/base_plikHM_TT_lowTEB_lensing_lenspotentialCls.dat')['tt'][:] cl_len = camb.spectra_fromcambfile('/Users/jcarron/SpyderProjects/jpipe/inputs' '/cls/base_plikHM_TT_lowTEB_lensing_lensedCls.dat')['tt'][:] cl_pp = camb.spectra_fromcambfile('/Users/jcarron/SpyderProjects/jpipe/inputs' '/cls/base_plikHM_TT_lowTEB_lensing_lenspotentialCls.dat')['pp'][:] cl_noise = (sN_uKamin * np.pi / 180. / 60.) ** 2*np.ones(20000) # simple flat noise Cls cl_pp[0:2] = cl_pp[2] path_to_libs = '/Users/jcarron/data/flatsky_lens_simlibs/test_wPL2015' if path_to_libs is None else path_to_libs lib_cmb_unl = sims_generic.Gauss_sim_generic(path_to_libs + '/unl_cmb', cl_unl, HD_shape, lside, nsims_max=nsims) lib_noise = sims_generic.Gauss_sim_generic(path_to_libs + '/noise', cl_noise, LD_shape, lside, nsims_max=nsims) lib_pp = sims_generic.Gauss_sim_generic(path_to_libs + '/pp', cl_pp, LD_shape, lside, nsims_max=nsims) lib_OO = None lib_displ = sims.displ_2dsim(lib_pp, lib_OO) lib_displ_sim0 = sims_generic.sim_lib_shuffle(lib_displ, shuffle=lambda idx : 0) lib_cmb_len = sims.lencmb_sim_lib(lib_cmb_unl, lib_displ) # Library for lensed CMB lib_cmb_len_displ0 = sims.lencmb_sim_lib(lib_cmb_unl, lib_displ_sim0) # Library for lensed CMB's by identical potential sim_lib = sims.flatsky_sim_lib(lib_cmb_len, lib_noise, lib_dir=path_to_libs + '/full_sims', beam_FWHM_amin=beam_FWHM_amin, cache_sims=True, mask_map = None) fixedphi_sim_lib = sims.flatsky_sim_lib(lib_cmb_len_displ0, lib_noise, beam_FWHM_amin=beam_FWHM_amin, lib_dir=path_to_libs+'/fxp_sims', cache_sims=True, mask_map = None) unl_cov = covmatrices.flatsky_unlcov(cl_unl, (HD_res, HD_res), lside, sN_uKamin=sN_uKamin, Beam_FWHM_amin=beam_FWHM_amin, verbose=False) len_cov = covmatrices.flatsky_lencov(cl_unl, cl_len, LD_res, HD_res, lside, f = dp.identity_displacement(HD_shape), f_inv = dp.identity_displacement(HD_shape), verbose=False, sN_uKamin=sN_uKamin, Beam_FWHM_amin =beam_FWHM_amin) Fisher_lib = likelihoods.flatsky_Fisher_lib(cl_unl, (HD_res, HD_res), lside, sN_uKamin, beam_FWHM_amin, verbose=False) return [sim_lib,fixedphi_sim_lib,LD_res,HD_res,lside,sN_uKamin,beam_FWHM_amin,cl_unl,cl_len,cl_pp,cl_noise,unl_cov,len_cov,Fisher_lib]
def plot_curvature(): """ Plot the total curvature (F. info) for phi and Omega for different volumes. """ import pylab as pl pl.ioff() pl.figure() d_ress = [0, 2] print "+++++++++++++++++++++++++++++++++++++++++++++++++++" for d_res in d_ress: if d_res == d_ress[0]: print "+plot_curvature:: doing res", HD_res + d_res else: print "+ doing res", HD_res + d_res res = HD_res + d_res lsides = lside * 2 ** d_res Fisher = likelihoods.flatsky_Fisher_lib(cl_unl, (res, res), lsides, sN_uKamin, beam_FWHM_amin, verbose=False) FT_p_lik = likelihoods.circulant_Gauss_pdf_fourierspace(cl_pp, (res, res), lsides) idcs = np.arange(1, 2 ** (res - 1)) Fpp, FOO, FpO = Fisher.get_curvature_matrix_PhiOmega_ell() Fpp = Fpp[0, idcs] FOO = FOO[0, idcs] Fpp += FT_p_lik.eval_curv().real[0, idcs] kx = Fisher.kx_from_index(idcs) Nlev_pp = 1.0 / Fpp * 2 # On complex modes Nlev_OO = 1.0 / FOO * 2 # On complex modes pl.loglog(kx, Nlev_pp * kx ** 2 * (kx + 1) ** 2 / 2.0 / np.pi * 1e7, label="$\phi$, res =" + str(res)) pl.loglog(kx, Nlev_OO * kx ** 2 * (kx + 1) ** 2 / 2.0 / np.pi * 1e7, label="$\Omega$, res =" + str(res)) pl.plot( np.arange(len(cl_pp)), cl_pp * np.arange(len(cl_pp)) ** 2 * (np.arange(len(cl_pp)) + 1) ** 2 / 2.0 / np.pi * 1e7, label="$C^{\phi\phi}$", ) pl.legend(frameon=False) pl.title("Total inverse curvature") pl.xlabel("$\ell$") pl.ylabel("$C_\ell \ell^2 (\ell + 1)^2 /2\pi $") print "+plot_curvature:: saving fig curvatures.pdf" pl.savefig(path_to_figs + "/curvatures.pdf") pl.close() print "+++++++++++++++++++++++++++++++++++++++++++++++++++"
def plot_noiselevels(): """ Tests the noise levels for phi and Omega for different volumes. """ import pylab as pl pl.ioff() pl.figure() d_ress = [0, 3] print "+++++++++++++++++++++++++++++++++++++++++++++++++++" for d_res in d_ress: if d_res == d_ress[0]: print "+test_noiselevels:: doing res", HD_res + d_res else: print "+ doing res", HD_res + d_res res = HD_res + d_res lsides = lside * 2 ** d_res Fisher = likelihoods.flatsky_Fisher_lib(cl_unl, (res, res), lsides, sN_uKamin, beam_FWHM_amin, verbose=False) Fpp, FOO, FpO = Fisher.get_curvature_matrix_PhiOmega_ell() Fpp *= 0.5 # alm FOO *= 0.5 # alm Nlev_pp = 1.0 / (Fpp[0, np.arange(1, 2 ** (res - 1))]) Nlev_OO = 1.0 / (FOO[0, np.arange(1, 2 ** (res - 1))]) kx = Fisher.kx_from_index(np.arange(1, 2 ** (res - 1))) pl.loglog(kx, Nlev_pp * kx ** 2 * (kx + 1) ** 2 / 2.0 / np.pi * 1e7, label="$\phi$, res =" + str(res)) pl.loglog(kx, Nlev_OO * kx ** 2 * (kx + 1) ** 2 / 2.0 / np.pi * 1e7, label="$\Omega$, res =" + str(res)) pl.legend(frameon=False) pl.title("Noise levels") pl.xlabel("$\ell$") pl.ylabel("$C_\ell \ell^2 (\ell + 1)^2 /2\pi $") print "+test_noiselevels::saving fig noise_levels.pdf" pl.savefig(path_to_figs + "/noise_levels.pdf") pl.close() print "+++++++++++++++++++++++++++++++++++++++++++++++++++"
cl_len, (LD_res, LD_res), lside, sN_uKamin=sN_uKamin, Beam_FWHM_amin=beam_FWHM_amin, verbose=False ) len_cov = covmatrices.flatsky_lencov( cl_unl, cl_len, LD_res, HD_res, lside, f=dp.identity_displacement(HD_shape), f_inv=dp.identity_displacement(HD_shape), verbose=False, sN_uKamin=sN_uKamin, Beam_FWHM_amin=beam_FWHM_amin, ) Fisher_lib = likelihoods.flatsky_Fisher_lib(cl_unl, (HD_res, HD_res), lside, sN_uKamin, beam_FWHM_amin, verbose=False) # Dumping params for convenience f = open(path_to_libs + "/params.dat", "w") f.write("LD res : " + str(LD_res) + "\n") f.write("HD res : " + str(LD_res) + "\n") f.write("sN_uKamin : " + str(np.round(sN_uKamin, 2)) + "\n") f.write("beam_FWHM_amin : " + str(np.round(beam_FWHM_amin, 2)) + "\n") f.write("nsims : " + str(nsims) + "\n") f.close() def generate_sims(sim_lib, label=""): for i, idx in enumerate_progress(xrange(nsims), "test_suite::regenerating sims: " + label): sim = sim_lib.get_sim(idx)