def get_sim_stats_qcl(self,
k1,
mc_sims,
k2=None,
recache=False,
lmax=None):
"""Returns the sim-average of the input *plancklens.qecl* list QE power spectra average
Args:
k1: QE anisotropy key 1
mc_sims: the simulation indices to average the spectra over
k2: QE anisotropy key 2 (defaults to k1)
Returns:
*plancklens.utils.stats* instance
"""
if k2 is None: k2 = k1
if lmax is None: lmax = self.get_lmaxqcl(k1, k2)
tfname = os.path.join(
self.lib_dir, 'sim_qcl_stats_%s_%s_%s_%s.pk' %
(k1, k2, lmax, utils.mchash(mc_sims)))
if not os.path.exists(tfname) or recache:
stats_qcl = utils.stats(lmax + 1, docov=False)
for i, idx in utils.enumerate_progress(
mc_sims,
label='building sim_stats qcl (k1,k2)=' + str((k1, k2))):
stats_qcl.add(self.get_sim_qcl(k1, idx, k2=k2, lmax=lmax))
pk.dump(stats_qcl, open(tfname, 'wb'), protocol=2)
return pk.load(open(tfname, 'rb'))
def get_bamc(self):
"""Binned additive MC correction, with crude error bars.
This compares the reconstruction on the simulations to the FFP10 input lensing spectrum.
Note:
the approximate error corrections to the additive MC correction variance follows Appendix C of
https://arxiv.org/abs/1807.06210, check this for more details on its validity.
"""
assert self.k1[0] == 'p' and self.k2[
0] == 'p' and self.ksource == 'p', (self.k1, self.k2, self.ksource)
ss2 = 2 * self.parfile.qcls_ss.get_sim_stats_qcl(
self.k1, self.parfile.mc_sims_var, k2=self.k2).mean()
cl_pred = utils.camb_clfile(
os.path.join(
self.cls_path,
'FFP10_wdipole_lenspotentialCls.dat'))['pp'][:len(ss2)]
qc_norm = utils.cli(
self.parfile.qresp_dd.get_response(self.k1, self.ksource) *
self.parfile.qresp_dd.get_response(self.k2, self.ksource))
bp_stats = utils.stats(self.nbins)
bp_n1 = self.get_n1()
for i, idx in utils.enumerate_progress(self.parfile.mc_sims_var,
label='collecting BP stats'):
dd = self.parfile.qcls_dd.get_sim_qcl(self.k1, idx, k2=self.k2)
bp_stats.add(
self._get_binnedcl(qc_norm * (dd - ss2) - cl_pred) - bp_n1)
NMF = len(self.parfile.qcls_dd.mc_sims_mf)
NB = len(self.parfile.mc_sims_var)
return bp_stats.mean(), bp_stats.sigmas_on_mean() * np.sqrt(
(1. + 1. + 2. / NMF + 2 * NB / (float(NMF * NMF))))
def get_mcn0_cov(self, mc_sims_dd=None):
"""Covariance matrix obtained from the realization-independent debiaser.
"""
if mc_sims_dd is None: mc_sims_dd = self.parfile.mc_sims_var
mcn0_cov = utils.stats(self.nbins)
qc_norm = utils.cli(
self.parfile.qresp_dd.get_response(self.k1, self.ksource) *
self.parfile.qresp_dd.get_response(self.k2, self.ksource))
for i, idx in utils.enumerate_progress(mc_sims_dd):
dd = self.parfile.qcls_dd.get_sim_qcl(self.k1, idx, k2=self.k2)
mcn0_cov.add(self._get_binnedcl(qc_norm * dd))
return mcn0_cov.cov()
def get_nhl_cov(self, mc_sims_dd=None):
"""Covariance matrix obtained from the semi-analytical N0 debiaser.
"""
if mc_sims_dd is None: mc_sims_dd = self.parfile.mc_sims_var
nhl_cov = utils.stats(self.nbins)
qc_norm = utils.cli(
self.parfile.qresp_dd.get_response(self.k1, self.ksource) *
self.parfile.qresp_dd.get_response(self.k2, self.ksource))
for i, idx in utils.enumerate_progress(mc_sims_dd):
dd = self.parfile.qcls_dd.get_sim_qcl(self.k1, idx, k2=self.k2)
nhl_cov.add(
self._get_binnedcl(qc_norm *
(dd - self.parfile.nhl_dd.get_sim_nhl(
int(idx), self.k1, self.k2))))
return nhl_cov.cov()
def get_ampl_x_input(self, mc_sims=None):
"""Returns cross-correlation of phi-maps to input lensing maps.
Uses qlms_x_i library of parfile
"""
qlmi = self.parfile.qlms_x_in
if mc_sims is None:
mc_sims = np.unique(
np.concatenate(
[self.parfile.mc_sims_var, self.parfile.mc_sims_bias]))
xin = utils.stats(self.nbins)
qnorm = utils.cli(
self.parfile.qresp_dd.get_response(self.k1, self.ksource))
for i, idx in utils.enumerate_progress(mc_sims):
qi = qlmi.get_sim_qcl(self.k1, idx)
xin.add(self._get_binnedcl(qnorm * qi) / self.fid_bandpowers)
return xin