sN_uKamin = 35.0
nsims = 2
lcell_side_amin = np.sqrt(4.0 * np.pi) / (2 ** 13) / np.pi * 180 * 60.0 # length of lcell of the data resolution
path_to_libs = "/Users/jcarron/data/flatsky_lens_simlibs/test_wPL201512"
# """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# FIXME : problem when HD_res is not LD_res. I believe this is due to an inconsistent
# FIXME : pixwindow fct in covmatrices.
# FIXME : some aspects like noise-levels, cg. inversion etc are very dependent on the low-ell
# FIXME : interpolation scheme. Understand the best way to handle that.
# """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
bins_l = np.int64(np.logspace(1.5, 4.0, 50))
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
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 = (
lcell_side_amin / 60.0 / 180.0 * np.pi * LD_shape[0] * np.ones(2)
) # pixel res always the same always at 1.7 amin.
lmax_cl = 10000
cl_unl = camb.spectra_fromcambfile(
"/Users/jcarron/SpyderProjects/lenscov/additional_files/planck_lensing_wp_highL_bestFit_20130627_lenspotentialCls.dat",
lmax=lmax_cl,
)["tt"][:]
cl_len = camb.spectra_fromcambfile(
"/Users/jcarron/SpyderProjects/lenscov/additional_files/planck_lensing_wp_highL_bestFit_20130627_lensedCls.dat",
def plot_cls(parfile,type = 'len',mc_sims = None,Nbins = 100):
"""
Plots the sims cls in comparison to input cl_len.
"""
assert type in ['len','unl','pp','OO']
par = imp.load_source('par',parfile)
lib = {'len':par.lib_cmb_len,'unl':par.lib_cmb_unl,'pp':par.lib_pp,'OO':par.lib_OO}[type]
if lib is None :
print 'no such lib, returning'
return
input_cl = {'len':par.cl_len,'unl':par.cl_unl,'pp':par.cl_pp,'OO':par.lib_OO}[type]
if not os.path.exists(par.lib_dir + '/figs') : os.mkdir(par.lib_dir + '/figs')
bins_l = np.int64(np.linspace(10.,3000,Nbins))
bins_u = bins_l[1:] -1
bins_l = bins_l[0:len(bins_l)-1]
binner = utils.binner(bins_l,bins_u)
stats_len = utils.stats(binner.Nbins())
if mc_sims is None : mc_sims = np.arange(par.nsims)
for i,idx in utils.enumerate_progress(mc_sims,label = 'test_cl::collecting cls'):
sim_cl = par.lib_cmb_unl.map2cl(lib.get_sim(idx))
ell = np.arange(len(sim_cl))
stats_len.add(binner.bin_that(ell,sim_cl))
if i == 0 :
cl_unb = sim_cl.copy()
sig_unb = sim_cl.copy() ** 2
else :
cl_unb += sim_cl
sig_unb += sim_cl ** 2
cl_unb /= (i + 1.)
sig_unb /= (i + 1.)
sig_unb = np.sqrt(sig_unb - cl_unb ** 2) # Unbinned sigmas.
cl_input_binned = binner.bin_that(np.arange(len(par.cl_len)),input_cl)
prefix = '' if not hasattr(par,'prefix') else par.prefix
if not os.path.exists('./figs/' + prefix) :
os.mkdir('./figs/' + prefix)
pp = PdfPages('./figs/' + prefix+'/cl'+type +'_vs_input. Nb'+str(Nbins)+'.pdf')
pl.ioff()
pl.figure('Cl ratios')
pl.title('Binned cl vs input, ' +str(len(mc_sims)) + ' sims.')
pl.plot(binner.bin_centers(),stats_len.mean()/cl_input_binned -1.,label = 'sim/input -1.,binned, res ' + str(par.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.close()
pl.figure('Unbinned Cl mean')
pl.title('Unbinned Cl mean')
counts = par.lib_cmb_unl.get_ellcounts()
ell = np.arange(len(counts))
nz = np.where(counts[0:len(input_cl)-1] > 0)
pl.plot(ell[nz],cl_unb[nz]/input_cl[nz] -1.,label = 'cl sim / input -1 ')
pl.plot(ell[nz], np.sqrt(1./counts[nz]/len(mc_sims)),linestyle = '--',color = 'black',label = '$\sigma $ Gauss.')
pl.plot(ell[nz], -np.sqrt(1./counts[nz]/len(mc_sims)),linestyle = '--',color = 'black')
pl.plot(ell[nz],- np.sqrt(2./(2*ell[nz] + 1)/len(mc_sims)),linestyle = '--',color = 'black' )
pl.plot(ell[nz], np.sqrt(2./(2*ell[nz] + 1)/len(mc_sims)),linestyle = '--',color = 'black' )
pl.hlines([0.],0.,max(ell[nz]),color = 'grey',linestyle = '--')
pl.legend(frameon= False)
pp.savefig()
pl.close()
pl.figure('Unbinned Cl variance')
pl.title('Unbinned Cl variance')
counts = par.lib_cmb_unl.get_ellcounts()
ell = np.arange(len(counts))
nz = np.where(counts[0:len(input_cl) - 1] > 0)
pl.plot(ell[nz],sig_unb[nz]/np.sqrt(2 * input_cl[nz] ** 2 / (2 * ell[nz] + 1.)) -1.,label = 'sim sig./ Gauss sig -1. ')
pl.plot(ell[nz],sig_unb[nz]/np.sqrt(input_cl[nz] ** 2 / counts[nz]) -1.,label = 'incl. discrtness')
pl.ylim(-1,1.)
pl.hlines([0.],0.,max(ell[nz]),color = 'grey',linestyle = '--')
pl.legend(frameon = False)
pp.savefig()
pl.close()
pp.close()