def collect_m_arrays(mlist, func, shapes, dtype):
data = [ (mi, func(mi)) for mi in mpiutil.partition_list_mpi(mlist) ]
mpiutil.barrier()
if mpiutil.rank0 and mpiutil.size == 1:
p_all = [data]
else:
p_all = mpiutil.world.gather(data, root=0)
mpiutil.barrier() # Not sure if this barrier really does anything,
# but hoping to stop collect breaking
marrays = None
if mpiutil.rank0:
marrays = [np.zeros((len(mlist),) + shape, dtype=dtype) for shape in shapes]
for p_process in p_all:
for mi, result in p_process:
for si in range(len(shapes)):
if result[si] is not None:
marrays[si][mi] = result[si]
mpiutil.barrier()
return marrays
def project_sky(self, sky, mlist = None, threshold=None, harmonic=False):
# Set default list of m-modes (i.e. all of them), and partition
if mlist is None:
mlist = range(self.telescope.mmax + 1)
mpart = mpiutil.partition_list_mpi(mlist)
# Total number of sky modes.
nmodes = self.beamtransfer.nfreq * self.beamtransfer.ntel
# If sky is alm fine, if not perform spherical harmonic transform.
alm = sky if harmonic else hputil.sphtrans_sky(sky, lmax=self.telescope.lmax)
## Routine to project sky onto eigenmodes
def _proj(mi):
p1 = self.project_sky_vector_forward(mi, alm[:, :, mi], threshold)
p2 = np.zeros(nmodes, dtype=np.complex128)
p2[-p1.size:] = p1
return p2
# Map over list of m's and project sky onto eigenbasis
proj_sec = [(mi, _proj(mi)) for mi in mpart]
# Gather projections onto the rank=0 node.
proj_all = mpiutil.world.gather(proj_sec, root=0)
proj_arr = None
if mpiutil.rank0:
# Create array to put projections into
proj_arr = np.zeros((2*self.telescope.mmax + 1, nmodes), dtype=np.complex128)
# Iterate over all gathered projections and insert into the array
for proc_rank in proj_all:
for pm in proc_rank:
proj_arr[pm[0]] = pm[1]
# Return the projections (rank=0) or None elsewhere.
return proj_arr
sky_vec = bt.project_vector_forward(mi, alm[:, :, mi])
tau_vec = np.fft.fft(blackman_harris(cyl.nfreq)[:, np.newaxis] * sky_vec,
axis=0)
tau_vec = np.fft.ifft(tau_vec * blmask, axis=0)
alm2 = bt.project_vector_backward(mi, tau_vec)
return alm2
# Project m-modes across different processes
mlist = list(range(mmax + 1))
mpart = mpiutil.partition_list_mpi(mlist)
mproj = [[mi, projm(mi)] for mi in mpart]
if mpiutil.rank0:
print("Gather results onto root process")
p_all = mpiutil.world.gather(mproj, root=0)
# Save out results
if mpiutil.rank0:
palm = np.zeros_like(alm)
print("Combining results.")
for p_process in p_all:
for mi, proj in p_process:
mvals, mvecs = klt.modes_m(mi, threshold=cut)
if mvals is None:
return None
ev_vec = klt.project_sky_vector_forward(mi, alm[:, :, mi], threshold=cut)
tel_vec = klt.project_tel_vector_backward(mi, ev_vec, threshold=cut)
alm2 = bt.project_vector_backward(mi, tel_vec)
return alm2
# Project m-modes across different processes
mlist = range(mmax+1)
mpart = mpiutil.partition_list_mpi(mlist)
mproj = [[mi, projm(mi)] for mi in mpart]
if mpiutil.rank0:
print "Gather results onto root process"
p_all = mpiutil.world.gather(mproj, root=0)
# Save out results
if mpiutil.rank0:
palm = np.zeros_like(alm)
print "Combining results."
for p_process in p_all:
