def mapmake_full(self, nside, mapname):
def _make_alm(mi):
print("Making %i" % mi)
mmode = self.mmode(mi)
sphmode = self.beamtransfer.project_vector_telescope_to_sky(mi, mmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, list(range(self.telescope.mmax + 1)))
if mpiutil.rank0:
alm = np.zeros(
(
self.telescope.nfreq,
self.telescope.num_pol_sky,
self.telescope.lmax + 1,
self.telescope.lmax + 1,
),
dtype=np.complex128,
)
for mi in range(self.telescope.mmax + 1):
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + "/" + mapname, "w") as f:
f.create_dataset("/map", data=skymap)
mpiutil.barrier()
def mapmake_svd(self, nside, mapname):
self.generate_mmodes_svd()
def _make_alm(mi):
svdmode = self.mmode_svd(mi)
sphmode = self.beamtransfer.project_vector_svd_to_sky(mi, svdmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm,
range(self.telescope.mmax + 1))
if mpiutil.rank0:
alm = np.zeros((self.telescope.nfreq, self.telescope.num_pol_sky,
self.telescope.lmax + 1, self.telescope.lmax + 1),
dtype=np.complex128)
for mi in range(self.telescope.mmax + 1):
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = self.beamtransfer.telescope.frequencies
f.attrs['polarization'] = np.array(
['I', 'Q', 'U',
'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def mapmake_kl(self, nside, mapname, wiener=False):
mapfile = self.output_directory + "/" + mapname
if os.path.exists(mapfile):
if mpiutil.rank0:
print("File %s exists. Skipping...")
return
kl = self.manager.kltransforms[self.klname]
if not kl.inverse:
raise Exception("Need the inverse to make a meaningful map.")
def _make_alm(mi):
print("Making %i" % mi)
klmode = self.mmode_kl(mi)
if wiener:
evals = kl.evals_m(mi, self.klthreshold)
if evals is not None:
klmode *= evals / (1.0 + evals)
isvdmode = kl.project_vector_kl_to_svd(
mi, klmode, threshold=self.klthreshold
)
sphmode = self.beamtransfer.project_vector_svd_to_sky(mi, isvdmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, list(range(self.telescope.mmax + 1)))
if mpiutil.rank0:
alm = np.zeros(
(
self.telescope.nfreq,
self.telescope.num_pol_sky,
self.telescope.lmax + 1,
self.telescope.lmax + 1,
),
dtype=np.complex128,
)
# Determine whether to use m=0 or not
mlist = list(range(1 if self.no_m_zero else 0, self.telescope.mmax + 1))
for mi in mlist:
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(mapfile, "w") as f:
f.create_dataset("/map", data=skymap)
mpiutil.barrier()
def mapmake_svd(self, nside, mapname):
self.generate_mmodes_svd()
def _make_alm(mi):
svdmode = self.mmode_svd(mi)
sphmode = self.beamtransfer.project_vector_svd_to_sky(mi, svdmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1))
if mpiutil.rank0:
alm = np.zeros((self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax + 1,
self.telescope.lmax + 1), dtype=np.complex128)
for mi in range(self.telescope.mmax + 1):
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
f.create_dataset('/map', data=skymap)
mpiutil.barrier()
def powerspectrum(self):
import scipy.linalg as la
if os.path.exists(self._psfile):
print "File %s exists. Skipping..." % self._psfile
return
ps = self.manager.psestimators[self.psname]
ps.genbands()
def _q_estimate(mi):
return ps.q_estimator(mi, self.mmode_kl(mi))
# Determine whether to use m=0 or not
mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)
qvals = mpiutil.parallel_map(_q_estimate, mlist)
qtotal = np.array(qvals).sum(axis=0)
fisher, bias = ps.fisher_bias()
powerspectrum = np.dot(la.inv(fisher), qtotal - bias)
if mpiutil.rank0:
with h5py.File(self._psfile, 'w') as f:
cv = la.inv(fisher)
err = cv.diagonal()**0.5
cr = cv / np.outer(err, err)
f.create_dataset('fisher/', data=fisher)
# f.create_dataset('bias/', data=self.bias)
f.create_dataset('covariance/', data=cv)
f.create_dataset('error/', data=err)
f.create_dataset('correlation/', data=cr)
f.create_dataset('bandpower/', data=ps.band_power)
#f.create_dataset('k_start/', data=ps.k_start)
#f.create_dataset('k_end/', data=ps.k_end)
#f.create_dataset('k_center/', data=ps.k_center)
#f.create_dataset('psvalues/', data=ps.psvalues)
f.create_dataset('powerspectrum', data=powerspectrum)
# Delete cache of bands for memory reasons
del ps.clarray
ps.clarray = None
mpiutil.barrier()
return powerspectrum
def powerspectrum(self):
import scipy.linalg as la
if os.path.exists(self._psfile):
print "File %s exists. Skipping..." % self._psfile
return
ps = self.manager.psestimators[self.psname]
ps.genbands()
def _q_estimate(mi):
return ps.q_estimator(mi, self.mmode_kl(mi))
# Determine whether to use m=0 or not
mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)
qvals = mpiutil.parallel_map(_q_estimate, mlist)
qtotal = np.array(qvals).sum(axis=0)
fisher, bias = ps.fisher_bias()
powerspectrum = np.dot(la.inv(fisher), qtotal - bias)
if mpiutil.rank0:
with h5py.File(self._psfile, 'w') as f:
cv = la.inv(fisher)
err = cv.diagonal()**0.5
cr = cv / np.outer(err, err)
f.create_dataset('fisher/', data=fisher)
# f.create_dataset('bias/', data=self.bias)
f.create_dataset('covariance/', data=cv)
f.create_dataset('error/', data=err)
f.create_dataset('correlation/', data=cr)
f.create_dataset('bandpower/', data=ps.band_power)
#f.create_dataset('k_start/', data=ps.k_start)
#f.create_dataset('k_end/', data=ps.k_end)
#f.create_dataset('k_center/', data=ps.k_center)
#f.create_dataset('psvalues/', data=ps.psvalues)
f.create_dataset('powerspectrum', data=powerspectrum)
# Delete cache of bands for memory reasons
del ps.clarray
ps.clarray = None
mpiutil.barrier()
return powerspectrum
def mapmake_kl(self, nside, mapname, wiener=False):
mapfile = self.output_directory + '/' + mapname
if os.path.exists(mapfile):
if mpiutil.rank0:
print "File %s exists. Skipping..."
return
kl = self.manager.kltransforms[self.klname]
if not kl.inverse:
raise Exception("Need the inverse to make a meaningful map.")
def _make_alm(mi):
print "Making %i" % mi
klmode = self.mmode_kl(mi)
if wiener:
evals = kl.evals_m(mi, self.klthreshold)
if evals is not None:
klmode *= (evals / (1.0 + evals))
isvdmode = kl.project_vector_kl_to_svd(mi, klmode, threshold=self.klthreshold)
sphmode = self.beamtransfer.project_vector_svd_to_sky(mi, isvdmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1), root=0, method='rand')
if mpiutil.rank0:
alm = np.zeros((self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax + 1,
self.telescope.lmax + 1), dtype=np.complex128)
# Determine whether to use m=0 or not
mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)
for mi in mlist:
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(mapfile, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = self.beamtransfer.telescope.frequencies
f.attrs['polarization'] = np.array(['I', 'Q', 'U', 'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def mapmake_kl(self, nside, mapname, wiener=False):
mapfile = self.output_directory + '/' + mapname
if os.path.exists(mapfile):
if mpiutil.rank0:
print "File %s exists. Skipping..."
return
kl = self.manager.kltransforms[self.klname]
if not kl.inverse:
raise Exception("Need the inverse to make a meaningful map.")
def _make_alm(mi):
print "Making %i" % mi
klmode = self.mmode_kl(mi)
if wiener:
evals = kl.evals_m(mi, self.klthreshold)
if evals is not None:
klmode *= (evals / (1.0 + evals))
isvdmode = kl.project_vector_kl_to_svd(mi, klmode, threshold=self.klthreshold)
sphmode = self.beamtransfer.project_vector_svd_to_sky(mi, isvdmode)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1))
if mpiutil.rank0:
alm = np.zeros((self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax + 1,
self.telescope.lmax + 1), dtype=np.complex128)
# Determine whether to use m=0 or not
mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)
for mi in mlist:
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(mapfile, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = self.beamtransfer.telescope.frequencies
f.attrs['polarization'] = np.array(['I', 'Q', 'U', 'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def mapmake_full(self, nside, mapname, nbin=None, dirty=False, method='svd', normalize=True, threshold=1.0e3):
nfreq = self.telescope.nfreq
if nbin is not None and (nbin <= 0 or nbin >= nfreq): # invalid nbin
nbin = None
def _make_alm(mi):
print "Making %i" % mi
mmode = self.mmode(mi)
if dirty:
sphmode = self.beamtransfer.project_vector_backward_dirty(mi, mmode, nbin, normalize, threshold)
else:
if method == 'svd':
sphmode = self.beamtransfer.project_vector_telescope_to_sky(mi, mmode, nbin)
elif method == 'tk':
sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(mi, mmode, nbin)
else:
raise ValueError('Unknown map-making method %s' % method)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1))
if mpiutil.rank0:
# get center freq of each bin
if nbin is not None:
n, s, e = mpiutil.split_m(nfreq, nbin)
cfreqs = np.array([ self.beamtransfer.telescope.frequencies[(s[i]+e[i])/2] for i in range(nbin) ])
else:
nbin = nfreq
cfreqs = self.beamtransfer.telescope.frequencies
alm = np.zeros((nbin, self.telescope.num_pol_sky, self.telescope.lmax + 1,
self.telescope.lmax + 1), dtype=np.complex128)
for mi in range(self.telescope.mmax + 1):
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = cfreqs
f.attrs['polarization'] = np.array(['I', 'Q', 'U', 'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def cross_powerspectrum(timestreams, psname, psfile):
import scipy.linalg as la
if os.path.exists(psfile):
print "File %s exists. Skipping..." % psfile
return
products = timestreams[0].manager
ps = products.psestimators[psname]
ps.genbands()
nstream = len(timestreams)
def _q_estimate(mi):
qp = np.zeros((nstream, nstream, ps.nbands), dtype=np.float64)
for ti in range(nstream):
for tj in range(ti + 1, nstream):
print "Making m=%i (%i, %i)" % (mi, ti, tj)
si = timestreams[ti]
sj = timestreams[tj]
qp[ti, tj] = ps.q_estimator(mi, si.mmode_kl(mi),
sj.mmode_kl(mi))
qp[tj, ti] = qp[ti, tj]
return qp
# Determine whether to use m=0 or not
mlist = range(1 if timestreams[0].no_m_zero else 0,
products.telescope.mmax + 1)
qvals = mpiutil.parallel_map(_q_estimate, mlist)
qtotal = np.array(qvals).sum(axis=0)
fisher, bias = ps.fisher_bias()
# Subtract bias and reshape into new array
qtotal = (qtotal - bias).reshape(nstream**2, ps.nbands).T
powerspectrum = np.dot(la.inv(fisher), qtotal)
powerspectrum = powerspectrum.T.reshape(nstream, nstream, ps.nbands)
if mpiutil.rank0:
with h5py.File(psfile, 'w') as f:
cv = la.inv(fisher)
err = cv.diagonal()**0.5
cr = cv / np.outer(err, err)
f.create_dataset('fisher/', data=fisher)
# f.create_dataset('bias/', data=self.bias)
f.create_dataset('covariance/', data=cv)
f.create_dataset('error/', data=err)
f.create_dataset('correlation/', data=cr)
f.create_dataset('bandpower/', data=ps.band_power)
#f.create_dataset('k_start/', data=ps.k_start)
#f.create_dataset('k_end/', data=ps.k_end)
#f.create_dataset('k_center/', data=ps.k_center)
#f.create_dataset('psvalues/', data=ps.psvalues)
f.create_dataset('powerspectrum', data=powerspectrum)
# Delete cache of bands for memory reasons
del ps.clarray
ps.clarray = None
mpiutil.barrier()
return powerspectrum
def mapmake_full(self,
nside,
mapname,
nbin=None,
dirty=False,
method='svd',
normalize=True,
threshold=1.0e3):
nfreq = self.telescope.nfreq
if nbin is not None and (nbin <= 0 or nbin >= nfreq): # invalid nbin
nbin = None
def _make_alm(mi):
print "Making %i" % mi
mmode = self.mmode(mi)
if dirty:
sphmode = self.beamtransfer.project_vector_backward_dirty(
mi, mmode, nbin, normalize, threshold)
else:
if method == 'svd':
sphmode = self.beamtransfer.project_vector_telescope_to_sky(
mi, mmode, nbin)
elif method == 'tk':
sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(
mi, mmode, nbin)
else:
raise ValueError('Unknown map-making method %s' % method)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm,
range(self.telescope.mmax + 1))
if mpiutil.rank0:
# get center freq of each bin
if nbin is not None:
n, s, e = mpiutil.split_m(nfreq, nbin)
cfreqs = np.array([
self.beamtransfer.telescope.frequencies[(s[i] + e[i]) / 2]
for i in range(nbin)
])
else:
nbin = nfreq
cfreqs = self.beamtransfer.telescope.frequencies
alm = np.zeros((nbin, self.telescope.num_pol_sky,
self.telescope.lmax + 1, self.telescope.lmax + 1),
dtype=np.complex128)
for mi in range(self.telescope.mmax + 1):
alm[..., mi] = alm_list[mi]
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = cfreqs
f.attrs['polarization'] = np.array(
['I', 'Q', 'U',
'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def generate(self, regen=False):
"""Calculate the total Fisher matrix and bias and save to a file.
Parameters
----------
regen : boolean, optional
Force regeneration if products already exist (default `False`).
"""
if mpiutil.rank0:
st = time.time()
print "======== Starting PS calculation ========"
ffile = self.psdir +'/fisher.hdf5'
if os.path.exists(ffile) and not regen:
print ("Fisher matrix file: %s exists. Skipping..." % ffile)
return
mpiutil.barrier()
# Pre-compute all the angular power spectra for the bands
self.genbands()
# Use parallel map to distribute Fisher calculation
fisher_bias = mpiutil.parallel_map(self.fisher_bias_m, range(self.telescope.mmax + 1))
# Unpack into separate lists of the Fisher matrix and bias
fisher, bias = zip(*fisher_bias)
# Sum over all m-modes to get the over all Fisher and bias
self.fisher = np.sum(np.array(fisher), axis=0).real # Be careful of the .real here
self.bias = np.sum(np.array(bias), axis=0).real # Be careful of the .real here
# Write out all the PS estimation products
if mpiutil.rank0:
et = time.time()
print "======== Ending PS calculation (time=%f) ========" % (et - st)
# Check to see ensure that Fisher matrix isn't all zeros.
if not (self.fisher == 0).all():
# Generate derived quantities (covariance, errors..)
cv = la.inv(self.fisher)
err = cv.diagonal()**0.5
cr = cv / np.outer(err, err)
else:
cv = np.zeros_like(self.fisher)
err = cv.diagonal()
cr = np.zeros_like(self.fisher)
f = h5py.File(self.psdir + '/fisher.hdf5', 'w')
f.attrs['bandtype'] = self.bandtype
f.create_dataset('fisher/', data=self.fisher)
f.create_dataset('bias/', data=self.bias)
f.create_dataset('covariance/', data=cv)
f.create_dataset('errors/', data=err)
f.create_dataset('correlation/', data=cr)
f.create_dataset('band_power/', data=self.band_power)
if self.bandtype == 'polar':
f.create_dataset('k_start/', data=self.k_start)
f.create_dataset('k_end/', data=self.k_end)
f.create_dataset('k_center/', data=self.k_center)
f.create_dataset('theta_start/', data=self.theta_start)
f.create_dataset('theta_end/', data=self.theta_end)
f.create_dataset('theta_center/', data=self.theta_center)
f.create_dataset('k_bands', data=self.k_bands)
f.create_dataset('theta_bands', data=self.theta_bands)
elif self.bandtype == 'cartesian':
f.create_dataset('kpar_start/', data=self.kpar_start)
f.create_dataset('kpar_end/', data=self.kpar_end)
f.create_dataset('kpar_center/', data=self.kpar_center)
f.create_dataset('kperp_start/', data=self.kperp_start)
f.create_dataset('kperp_end/', data=self.kperp_end)
f.create_dataset('kperp_center/', data=self.kperp_center)
f.create_dataset('kpar_bands', data=self.kpar_bands)
f.create_dataset('kperp_bands', data=self.kperp_bands)
f.close()
def mapmake_full(self, nside, mapname, nbin=None, dirty=False, method='svd', normalize=True, threshold=1.0e3, eps=0.01, correct_order=0, prior_map_file=None):
nfreq = self.telescope.nfreq
if nbin is None:
nbin = nfreq
else:
if (nbin < 1 or nbin > nfreq): # invalid nbin
nbin = nfreq
else:
nbin = int(nbin)
if prior_map_file is not None:
# read in the prior sky map
with h5py.File(prior_map_file, 'r') as f:
prior_map = f['map'][:] # shape (nbin, npol, npix)
# alm of the prior map
alm0 = hputil.sphtrans_sky(prior_map, lmax=self.telescope.lmax).reshape(nbin, self.telescope.num_pol_sky, self.telescope.lmax+1, self.telescope.lmax+1) # shape (nbin, npol, lmax+1, lmax+1)
else:
alm0 = None
def _make_alm(mi):
print "Making %i" % mi
mmode = self.mmode(mi)
if dirty:
sphmode = self.beamtransfer.project_vector_backward_dirty(mi, mmode, nbin, normalize, threshold)
else:
if method == 'svd':
sphmode = self.beamtransfer.project_vector_telescope_to_sky(mi, mmode, nbin)
elif method == 'tk':
# sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(mi, mmode, nbin, eps=eps)
mmode0 = alm0[:, :, :, mi] if alm0 is not None else None
sphmode = self.beamtransfer.project_vector_telescope_to_sky_tk(mi, mmode, nbin, eps=eps, correct_order=correct_order, mmode0=mmode0)
else:
raise ValueError('Unknown map-making method %s' % method)
return sphmode
alm_list = mpiutil.parallel_map(_make_alm, range(self.telescope.mmax + 1), root=0, method='rand')
if mpiutil.rank0:
# get center freq of each bin
n, s, e = mpiutil.split_m(nfreq, nbin)
cfreqs = np.array([ self.beamtransfer.telescope.frequencies[(s[i]+e[i])/2] for i in range(nbin) ])
alm = np.zeros((nbin, self.telescope.num_pol_sky, self.telescope.lmax + 1,
self.telescope.lmax + 1), dtype=np.complex128)
mlist = range(1 if self.no_m_zero else 0, self.telescope.mmax + 1)
for mi in mlist:
alm[..., mi] = alm_list[mi]
alm[:, :, 100:, 1] = 0
skymap = hputil.sphtrans_inv_sky(alm, nside)
with h5py.File(self.output_directory + '/' + mapname, 'w') as f:
f.create_dataset('/map', data=skymap)
f.attrs['frequency'] = cfreqs
f.attrs['polarization'] = np.array(['I', 'Q', 'U', 'V'])[:self.beamtransfer.telescope.num_pol_sky]
mpiutil.barrier()
def generate(self, regen=False):
"""Calculate the total Fisher matrix and bias and save to a file.
Parameters
----------
regen : boolean, optional
Force regeneration if products already exist (default `False`).
"""
if mpiutil.rank0:
st = time.time()
print "======== Starting PS calculation ========"
ffile = self.psdir + "/fisher.hdf5"
if os.path.exists(ffile) and not regen:
print ("Fisher matrix file: %s exists. Skipping..." % ffile)
return
mpiutil.barrier()
# Pre-compute all the angular power spectra for the bands
self.genbands()
# Use parallel map to distribute Fisher calculation
fisher_bias = mpiutil.parallel_map(self.fisher_bias_m, range(self.telescope.mmax + 1))
# Unpack into separate lists of the Fisher matrix and bias
fisher, bias = zip(*fisher_bias)
# Sum over all m-modes to get the over all Fisher and bias
self.fisher = np.sum(np.array(fisher), axis=0).real # Be careful of the .real here
self.bias = np.sum(np.array(bias), axis=0).real # Be careful of the .real here
# Write out all the PS estimation products
if mpiutil.rank0:
et = time.time()
print "======== Ending PS calculation (time=%f) ========" % (et - st)
# Check to see ensure that Fisher matrix isn't all zeros.
if not (self.fisher == 0).all():
# Generate derived quantities (covariance, errors..)
cv = la.inv(self.fisher)
err = cv.diagonal() ** 0.5
cr = cv / np.outer(err, err)
else:
cv = np.zeros_like(self.fisher)
err = cv.diagonal()
cr = np.zeros_like(self.fisher)
f = h5py.File(self.psdir + "/fisher.hdf5", "w")
f.attrs["bandtype"] = self.bandtype
f.create_dataset("fisher/", data=self.fisher)
f.create_dataset("bias/", data=self.bias)
f.create_dataset("covariance/", data=cv)
f.create_dataset("errors/", data=err)
f.create_dataset("correlation/", data=cr)
f.create_dataset("band_power/", data=self.band_power)
if self.bandtype == "polar":
f.create_dataset("k_start/", data=self.k_start)
f.create_dataset("k_end/", data=self.k_end)
f.create_dataset("k_center/", data=self.k_center)
f.create_dataset("theta_start/", data=self.theta_start)
f.create_dataset("theta_end/", data=self.theta_end)
f.create_dataset("theta_center/", data=self.theta_center)
f.create_dataset("k_bands", data=self.k_bands)
f.create_dataset("theta_bands", data=self.theta_bands)
elif self.bandtype == "cartesian":
f.create_dataset("kpar_start/", data=self.kpar_start)
f.create_dataset("kpar_end/", data=self.kpar_end)
f.create_dataset("kpar_center/", data=self.kpar_center)
f.create_dataset("kperp_start/", data=self.kperp_start)
f.create_dataset("kperp_end/", data=self.kperp_end)
f.create_dataset("kperp_center/", data=self.kperp_center)
f.create_dataset("kpar_bands", data=self.kpar_bands)
f.create_dataset("kperp_bands", data=self.kperp_bands)
f.close()
separator(sec, 'gather_list')
if rank == 0:
lst = [0.5, 2]
elif rank == 1:
lst = ['a', False, 'xy']
elif rank == 2:
lst = [{'x': 1}]
else:
lst = []
lst = mpiutil.gather_list(lst, root=None)
print 'rank %d has %s after gather_list' % (rank, lst)
# parallel_map
separator(sec, 'parallel_map')
glist = range(6)
result = mpiutil.parallel_map(lambda x: x * x, glist, root=0)
if rank == 0:
print 'result = %s' % result
# split_all
separator(sec, 'split_all')
print 'rank %d has: %s' % (rank, mpiutil.split_all(6))
# split_local
separator(sec, 'split_local')
print 'rank %d has: %s' % (rank, mpiutil.split_local(6))
# gather_array
separator(sec, 'gather_array')
if rank == 0:
local_ary = np.array([[0, 1], [6, 7]])
def cross_powerspectrum(timestreams, psname, psfile):
import scipy.linalg as la
if os.path.exists(psfile):
print "File %s exists. Skipping..." % psfile
return
products = timestreams[0].manager
ps = products.psestimators[psname]
ps.genbands()
nstream = len(timestreams)
def _q_estimate(mi):
qp = np.zeros((nstream, nstream, ps.nbands), dtype=np.float64)
for ti in range(nstream):
for tj in range(ti+1, nstream):
print "Making m=%i (%i, %i)" % (mi, ti, tj)
si = timestreams[ti]
sj = timestreams[tj]
qp[ti, tj] = ps.q_estimator(mi, si.mmode_kl(mi), sj.mmode_kl(mi))
qp[tj, ti] = qp[ti, tj]
return qp
# Determine whether to use m=0 or not
mlist = range(1 if timestreams[0].no_m_zero else 0, products.telescope.mmax + 1)
qvals = mpiutil.parallel_map(_q_estimate, mlist)
qtotal = np.array(qvals).sum(axis=0)
fisher, bias = ps.fisher_bias()
# Subtract bias and reshape into new array
qtotal = (qtotal - bias).reshape(nstream**2, ps.nbands).T
powerspectrum = np.dot(la.inv(fisher), qtotal)
powerspectrum = powerspectrum.T.reshape(nstream, nstream, ps.nbands)
if mpiutil.rank0:
with h5py.File(psfile, 'w') as f:
cv = la.inv(fisher)
err = cv.diagonal()**0.5
cr = cv / np.outer(err, err)
f.create_dataset('fisher/', data=fisher)
# f.create_dataset('bias/', data=self.bias)
f.create_dataset('covariance/', data=cv)
f.create_dataset('error/', data=err)
f.create_dataset('correlation/', data=cr)
f.create_dataset('bandpower/', data=ps.band_power)
#f.create_dataset('k_start/', data=ps.k_start)
#f.create_dataset('k_end/', data=ps.k_end)
#f.create_dataset('k_center/', data=ps.k_center)
#f.create_dataset('psvalues/', data=ps.psvalues)
f.create_dataset('powerspectrum', data=powerspectrum)
# Delete cache of bands for memory reasons
del ps.clarray
ps.clarray = None
mpiutil.barrier()
return powerspectrum
