def collect_mmodes_kl(self):
def evfunc(mi):
evf = np.zeros(self.beamtransfer.ndofmax, dtype=np.complex128)
ev = self.mmode_kl(mi)
if ev.size > 0:
evf[-ev.size:] = ev
return evf
if mpiutil.rank0:
print("Creating eigenvalues file (process 0 only).")
mlist = list(range(self.telescope.mmax + 1))
shape = (self.beamtransfer.ndofmax, )
evarray = kltransform.collect_m_array(mlist, evfunc, shape,
np.complex128)
if mpiutil.rank0:
fname = self.output_directory + ("/klmodes_%s_%f.hdf5" %
(self.klname, self.klthreshold))
if os.path.exists(fname):
print("File: %s exists. Skipping..." % (fname))
return
with h5py.File(fname, "w") as f:
f.create_dataset("evals", data=evarray)
def _collect(self):
def evfunc(mi):
ta = np.zeros(shape, dtype=np.float64)
f = h5py.File(self._evfile % mi, "r")
if f["evals_full"].shape[0] > 0:
ev = f["evals_full"][:]
fev = f["f_evals"][:]
ta[0, -ev.size :] = ev
ta[1, -fev.size :] = fev
f.close()
return ta
if mpiutil.rank0:
logger.info("Creating eigenvalues file (process 0 only).")
mlist = list(range(self.telescope.mmax + 1))
shape = (2, self.beamtransfer.ndofmax)
evarray = kltransform.collect_m_array(mlist, evfunc, shape, np.float64)
if mpiutil.rank0:
fname = self.evdir + "/evals.hdf5"
if os.path.exists(fname):
logger.info("File: {fname} exists. Skipping...")
return
f = h5py.File(fname, "w")
f.create_dataset("evals", data=evarray[:, 0])
f.create_dataset("f_evals", data=evarray[:, 1])
f.close()
def collect_mmodes_kl(self):
def evfunc(mi):
evf = np.zeros(self.beamtransfer.ndofmax, dtype=np.complex128)
ev = self.mmode_kl(mi)
if ev.size > 0:
evf[-ev.size:] = ev
return evf
if mpiutil.rank0:
print "Creating eigenvalues file (process 0 only)."
mlist = range(self.telescope.mmax+1)
shape = (self.beamtransfer.ndofmax, )
evarray = kltransform.collect_m_array(mlist, evfunc, shape, np.complex128)
if mpiutil.rank0:
fname = self.output_directory + ("/klmodes_%s_%f.hdf5"% (self.klname, self.klthreshold))
if os.path.exists(fname):
print "File: %s exists. Skipping..." % (fname)
return
with h5py.File(fname, 'w') as f:
f.create_dataset('evals', data=evarray)
def _collect(self):
def evfunc(mi):
ta = np.zeros(shape, dtype=np.float64)
f = h5py.File(self._evfile % mi, 'r')
if f['evals_full'].shape[0] > 0:
ev = f['evals_full'][:]
fev = f['f_evals'][:]
ta[0, -ev.size:] = ev
ta[1, -fev.size:] = fev
f.close()
return ta
if mpiutil.rank0:
print "Creating eigenvalues file (process 0 only)."
mlist = range(self.telescope.mmax+1)
shape = (2, self.beamtransfer.ndofmax)
evarray = kltransform.collect_m_array(mlist, evfunc, shape, np.float64)
if mpiutil.rank0:
if os.path.exists(self.evdir + "/evals.hdf5"):
print "File: %s exists. Skipping..." % (self.evdir + "/evals.hdf5")
return
f = h5py.File(self.evdir + "/evals.hdf5", 'w')
f.create_dataset('evals', data=evarray[:, 0])
f.create_dataset('f_evals', data=evarray[:, 1])
f.close()
bf = bf[:, :3, :]
bf = bf.reshape(bt.ntel, 3 * (bt.telescope.lmax + 1))
# Weight by noise matrix
noisew = bt.telescope.noisepower(np.arange(bt.telescope.npairs),
fi).flatten()**(-0.5)
noisew = np.concatenate([noisew, noisew])
bf = bf * noisew[:, np.newaxis]
# Regularise me.
bf = bf + bf.max() * 1e-11 * np.eye(bf.shape[0], bf.shape[1])
u, s, v = la.svd(bf)
# s = np.linalg.svd(bf, compute_uv=False)
sv[fi] = s
return sv
svdspectrum = kltransform.collect_m_array(list(range(bt.telescope.mmax + 1)),
svd_func, (bt.nfreq, svd_len),
np.float64)
if mpiutil.rank0:
with h5py.File(bt.directory + "/allsvdspectrum.hdf5", "w") as f:
f.create_dataset("singularvalues", data=svdspectrum)
mpiutil.barrier()
bf = bf.reshape(bt.ntel, 3 * (bt.telescope.lmax+1))
# Weight by noise matrix
noisew = bt.telescope.noisepower(np.arange(bt.telescope.npairs), fi).flatten()**(-0.5)
noisew = np.concatenate([noisew, noisew])
bf = bf * noisew[:, np.newaxis]
# Regularise me.
bf = bf + bf.max() * 1e-11 * np.eye(bf.shape[0], bf.shape[1])
u, s, v = la.svd(bf)
#s = np.linalg.svd(bf, compute_uv=False)
sv[fi] = s
return sv
svdspectrum = kltransform.collect_m_array(range(bt.telescope.mmax + 1), svd_func, (bt.nfreq, svd_len,), np.float64)
if mpiutil.rank0:
with h5py.File(bt.directory + '/allsvdspectrum.hdf5', 'w') as f:
f.create_dataset('singularvalues', data=svdspectrum)
mpiutil.barrier()
def generate(self):
for mentry in self.maps:
mfile = mentry['file']
stem = mentry['stem']
if mpiutil.rank0 and not os.path.exists(os.path.dirname(stem)):
os.makedirs(os.path.dirname(stem))
mpiutil.barrier()
if self.copy_orig:
shutil.copy(mfile, stem + 'orig.hdf5')
print "============\nProjecting file %s\n============\n" % mfile
## Load map and perform spherical harmonic transform
if mpiutil.rank0:
# Calculate alm's and broadcast
print "Read in skymap."
f = h5py.File(mfile, 'r')
skymap = f['map'][:]
f.close()
nside = healpy.get_nside(skymap[0])
alm = hputil.sphtrans_sky(skymap, lmax=self.telescope.lmax)
else:
alm = None
## Function to write out a map from the collected array of alms
def _write_map_from_almarray(almp, filename, attrs=None):
if mpiutil.rank0:
almp = np.squeeze(np.transpose(almp, axes=(2, 1, 3, 0)))
almf = np.zeros(
(almp.shape[0], almp.shape[1], almp.shape[1]),
dtype=np.complex128)
almf[:, :, :almp.shape[2]] = almp
pmap = hputil.sphtrans_inv_sky(almf, self.nside)
f = h5py.File(filename, 'w')
if attrs is not None:
for key, val in attrs.items():
f.attrs[repr(key)] = val
f.create_dataset('/map', data=pmap)
f.close()
mpiutil.barrier()
## Broadcast set of alms to the world
alm = mpiutil.world.bcast(alm, root=0)
mlist = range(self.kltransform.telescope.mmax + 1)
nevals = self.beamtransfer.ntel * self.beamtransfer.nfreq
## Construct beam projection of map
if self.beam_proj:
def proj_beam(mi):
print "Projecting %i" % mi
bproj = self.beamtransfer.project_vector_forward(
mi, alm[:, :, mi]).flatten()
return self.beamtransfer.project_vector_backward(mi, bproj)
shape = (self.telescope.nfreq, self.telescope.num_pol_sky,
self.telescope.lmax + 1)
almp = kltransform.collect_m_array(mlist, proj_beam, shape,
np.complex128)
_write_map_from_almarray(almp, stem + "beam.hdf5")
mpiutil.barrier()
## Construct EV projection of map
if self.evec_proj:
def proj_evec(mi):
## Worker function for mapping over list and projecting onto signal modes.
print "Projecting %i" % mi
p2 = np.zeros(nevals, dtype=np.complex128)
if self.kltransform.modes_m(mi)[0] is not None:
p1 = self.kltransform.project_sky_vector_forward(
mi, alm[:, :, mi])
p2[-p1.size:] = p1
return p2
shape = (nevals, )
evp = kltransform.collect_m_array(mlist, proj_evec, shape,
np.complex128)
if mpiutil.rank0:
f = h5py.File(stem + "ev.hdf5", 'w')
f.create_dataset("/evec_proj", data=evp)
f.close()
mpiutil.barrier()
## Iterate over noise cuts and filter out noise.
for cut in self.thresholds:
def filt_kl(mi):
## Worker function for mapping over list and projecting onto signal modes.
print "Projecting %i" % mi
mvals, mvecs = self.kltransform.modes_m(mi, threshold=cut)
if mvals is None:
return None
ev_vec = self.kltransform.project_sky_vector_forward(
mi, alm[:, :, mi], threshold=cut)
tel_vec = self.kltransform.project_tel_vector_backward(
mi, ev_vec, threshold=cut)
alm2 = self.beamtransfer.project_vector_backward(
mi, tel_vec)
return alm2
shape = (self.telescope.nfreq, self.telescope.num_pol_sky,
self.telescope.lmax + 1)
almp = kltransform.collect_m_array(mlist, filt_kl, shape,
np.complex128)
_write_map_from_almarray(almp, stem + ("kl_%g.hdf5" % cut),
{'threshold': cut})
mpiutil.barrier()
def generate(self):
for mentry in self.maps:
mfile = mentry['file']
stem = mentry['stem']
if mpiutil.rank0 and not os.path.exists(os.path.dirname(stem)):
os.makedirs(os.path.dirname(stem))
mpiutil.barrier()
if self.copy_orig:
shutil.copy(mfile, stem + 'orig.hdf5')
print "============\nProjecting file %s\n============\n" % mfile
## Load map and perform spherical harmonic transform
if mpiutil.rank0:
# Calculate alm's and broadcast
print "Read in skymap."
f = h5py.File(mfile, 'r')
skymap = f['map'][:]
f.close()
nside = healpy.get_nside(skymap[0])
alm = hputil.sphtrans_sky(skymap, lmax=self.telescope.lmax)
else:
alm = None
## Function to write out a map from the collected array of alms
def _write_map_from_almarray(almp, filename, attrs=None):
if mpiutil.rank0:
almp = np.squeeze(np.transpose(almp, axes=(2, 1, 3, 0)))
almf = np.zeros((almp.shape[0], almp.shape[1], almp.shape[1]), dtype=np.complex128)
almf[:, :, :almp.shape[2]] = almp
pmap = hputil.sphtrans_inv_sky(almf, self.nside)
f = h5py.File(filename, 'w')
if attrs is not None:
for key, val in attrs.items():
f.attrs[repr(key)] = val
f.create_dataset('/map', data=pmap)
f.close()
mpiutil.barrier()
## Broadcast set of alms to the world
alm = mpiutil.world.bcast(alm, root=0)
mlist = range(self.kltransform.telescope.mmax+1)
nevals = self.beamtransfer.ntel * self.beamtransfer.nfreq
## Construct beam projection of map
if self.beam_proj:
def proj_beam(mi):
print "Projecting %i" % mi
bproj = self.beamtransfer.project_vector_forward(mi, alm[:, :, mi]).flatten()
return self.beamtransfer.project_vector_backward(mi, bproj)
shape = (self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax+1)
almp = kltransform.collect_m_array(mlist, proj_beam, shape, np.complex128)
_write_map_from_almarray(almp, stem + "beam.hdf5")
mpiutil.barrier()
## Construct EV projection of map
if self.evec_proj:
def proj_evec(mi):
## Worker function for mapping over list and projecting onto signal modes.
print "Projecting %i" % mi
p2 = np.zeros(nevals, dtype=np.complex128)
if self.kltransform.modes_m(mi)[0] is not None:
p1 = self.kltransform.project_sky_vector_forward(mi, alm[:, :, mi])
p2[-p1.size:] = p1
return p2
shape = (nevals,)
evp = kltransform.collect_m_array(mlist, proj_evec, shape, np.complex128)
if mpiutil.rank0:
f = h5py.File(stem + "ev.hdf5", 'w')
f.create_dataset("/evec_proj", data=evp)
f.close()
mpiutil.barrier()
## Iterate over noise cuts and filter out noise.
for cut in self.thresholds:
def filt_kl(mi):
## Worker function for mapping over list and projecting onto signal modes.
print "Projecting %i" % mi
mvals, mvecs = self.kltransform.modes_m(mi, threshold=cut)
if mvals is None:
return None
ev_vec = self.kltransform.project_sky_vector_forward(mi, alm[:, :, mi], threshold=cut)
tel_vec = self.kltransform.project_tel_vector_backward(mi, ev_vec, threshold=cut)
alm2 = self.beamtransfer.project_vector_backward(mi, tel_vec)
return alm2
shape = (self.telescope.nfreq, self.telescope.num_pol_sky, self.telescope.lmax+1)
almp = kltransform.collect_m_array(mlist, filt_kl, shape, np.complex128)
_write_map_from_almarray(almp, stem + ("kl_%g.hdf5" % cut), {'threshold' : cut})
mpiutil.barrier()
