def main():
# We are going to group our processes in a single group. This is fine
# if we have fewer processes than detectors. Otherwise we should group
# them in a reasonable size that is smaller than the number of detectors
# and which divides evenly into the total number of processes.
comm = toast.Comm(world=MPI.COMM_WORLD, groupsize=MPI.COMM_WORLD.size)
# Make a fake focalplane. Plot it just for fun (don't waste time on this
# for very large runs though).
fp = fake_focalplane()
if comm.comm_world.rank == 0:
outfile = "custom_example_focalplane.png"
set_backend()
tt.plot_focalplane(fp, 6.0, 6.0, outfile)
# Read in 2 boresight files
borefiles = [
"../data/custom_example_boresight_1.txt",
"../data/custom_example_boresight_2.txt"
]
# Set up the distributed data
rate = 100.0
data = create_observations(comm, rate, fp, borefiles)
# Configure the healpix pixelization we will use for map-making and
# also the "submap" resolution, which sets granularity of the locally
# stored pieces of the sky.
map_nside = 512
map_npix = 12 * map_nside**2
sub_nside = 4
sub_npix = 12 * sub_nside**2
# Compute a pointing matrix with healpix pixels and weights.
pointing = tt.OpPointingHpix(nside=map_nside, nest=True, mode="IQU",
pixels="pixels", weights="weights")
pointing.exec(data)
# Compute the locally hit submaps
local_submaps = pixel_dist(data, sub_npix)
# Sources of simulated data: scan from a symmetric beam convolved sky
# and then add some simulated noise.
signalmap = tm.DistPixels(comm=comm.comm_world, size=map_npix, nnz=3,
dtype=np.float64, submap=sub_npix, local=local_submaps)
signalmap.read_healpix_fits("../data/custom_example_sky.fits")
scanmap = tt.OpSimScan(distmap=signalmap, pixels='pixels',
weights='weights', out="sim")
scanmap.exec(data)
nse = tt.OpSimNoise(out="sim", realization=0)
nse.exec(data)
# Accumulate the hits and inverse diagonal pixel covariance, as well as the
# noise weighted map. Here we simply use inverse noise weighting.
detweights = {}
for d in fp.keys():
net = fp[d]["NET"]
detweights[d] = 1.0 / (rate * net * net)
invnpp = tm.DistPixels(comm=comm.comm_world, size=map_npix, nnz=6,
dtype=np.float64, submap=sub_npix, local=local_submaps)
hits = tm.DistPixels(comm=comm.comm_world, size=map_npix, nnz=1,
dtype=np.int64, submap=sub_npix, local=local_submaps)
zmap = tm.DistPixels(comm=comm.comm_world, size=map_npix, nnz=3,
dtype=np.float64, submap=sub_npix, local=local_submaps)
invnpp.data.fill(0.0)
hits.data.fill(0)
zmap.data.fill(0.0)
build_invnpp = tm.OpAccumDiag(detweights=detweights, invnpp=invnpp,
hits=hits, zmap=zmap, name="sim")
build_invnpp.exec(data)
invnpp.allreduce()
hits.allreduce()
zmap.allreduce()
# Write these products out
hits.write_healpix_fits("custom_example_hits.fits")
invnpp.write_healpix_fits("custom_example_invnpp.fits")
zmap.write_healpix_fits("custom_example_zmap.fits")
# Invert the covariance and write
tm.covariance_invert(invnpp, 1.0e-3)
invnpp.write_healpix_fits("custom_example_npp.fits")
# Apply covariance to make the binned map
tm.covariance_apply(invnpp, zmap)
zmap.write_healpix_fits("custom_example_binned.fits")
MPI.Finalize()
return
madam = tm.OpMadam(params=pars, detweights=detweights,
name="tot_signal")
madam.exec(data)
comm.comm_world.barrier()
stop = MPI.Wtime()
elapsed = stop - start
if comm.comm_world.rank == 0:
print("Mapmaking took {:.3f} s".format(elapsed), flush=True)
comm.comm_world.barrier()
stop = MPI.Wtime()
elapsed = stop - global_start
if comm.comm_world.rank == 0:
print("Total Time: {:.2f} seconds".format(elapsed), flush=True)
if __name__ == "__main__":
try:
main()
tman = timing.timing_manager()
tman.report()
MPI.Finalize()
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
lines = [ "Proc {}: {}".format(MPI.COMM_WORLD.rank, x) for x in lines ]
print("".join(lines), flush=True)
toast.raise_error(6) # typical error code for SIGABRT
MPI.COMM_WORLD.Abort(6)
