def __exit__(self, type, value, traceback):
self.time2 = time.time()
self.clock2 = time.clock()
self.mem2 = memory.current()
self.memmax = memory.max()
print("%5.2f %5.2f %5.2f %s" % (self.time2 - self.time1, self.mem2 *
1e-9, self.memmax * 1e-9, self.name))
def filter(self, record):
record.rank = self.rank
record.wtime = time.time() - self.t0
record.wmins = record.wtime / 60.
record.whours = record.wmins / 60.
record.mem = memory.current() / 1024.**3
record.resmem = memory.resident() / 1024.**3
record.memmax = memory.max() / 1024.**3
return record
def __exit__(self, type, value, traceback):
sys.stderr.write("%6.2f %6.3f %6.3f %s\n" % (time.time()-self.t1,memory.current()/1024.**3, memory.max()/1024.**3, self.desc))
def __exit__(self, type, value, traceback):
sys.stderr.write("%6.2f %6.3f %6.3f %s\n" %
(time.time() - self.t1, memory.current() / 1024.**3,
memory.max() / 1024.**3, self.desc))
def __enter__(self):
self.time1 = time.time()
self.clock1 = time.clock()
self.mem1 = memory.current()
def __exit__(self, type, value, traceback):
self.time2 = time.time()
self.clock2 = time.clock()
self.mem2 = memory.current()
stats.add(self.name, self.time2 - self.time1,
self.clock2 - self.clock1, self.mem1, self.mem2 - self.mem1)
else:
pmap = pmat.PmatMap(scan, mask, sys="sidelobe:%s" % args.objname)
# Build a tod to project onto.
tod = np.zeros((d.ndet, d.nsamp), dtype=dtype)
# And project
pmap.forward(tod, mask)
# Any nonzero samples should be cut
tod = tod != 0
cut = sampcut.from_mask(tod)
del tod
progress = 100.0 * (ind - comm.rank * ntod // comm.size) / (
(comm.rank + 1) * ntod // comm.size -
comm.rank * ntod // comm.size)
print("%3d %5.1f %s %6.4f %d %8.3f %8.3f" %
(comm.rank, progress, id, float(cut.sum()) / cut.size, visible,
memory.current() / 1024.**3, memory.max() / 1024.**3))
mystats.append([ind, float(cut.sum()) / cut.size, visible])
# Add to my work file
_, uids = actdata.split_detname(d.dets)
flags = flagrange.from_sampcut(cut, dets=uids)
flags.write(hfile, group=id)
# Merge all the individual cut files into a single big one.
comm.Barrier()
if comm.rank == 0:
with h5py.File(args.odir + "/cuts.hdf", "w") as ofile:
for i in range(comm.size):
print("Reducing %3d" % i)
with h5py.File(args.odir + "/work_%03d.hdf" % i, "r") as ifile:
for key in sorted(ifile.keys()):
ifile.copy(key, ofile)
continue
visible = np.any(object_pos[1] >= -margin)
if not visible:
cut = sampcut.empty(d.ndet, d.nsamp)
else:
pmap = pmat.PmatMap(scan, mask, sys="sidelobe:%s" % args.objname)
# Build a tod to project onto.
tod = np.zeros((d.ndet, d.nsamp), dtype=dtype)
# And project
pmap.forward(tod, mask)
# Any nonzero samples should be cut
tod = tod != 0
cut = sampcut.from_mask(tod)
del tod
progress = 100.0*(ind-comm.rank*ntod//comm.size)/((comm.rank+1)*ntod//comm.size-comm.rank*ntod//comm.size)
print("%3d %5.1f %s %6.4f %d %8.3f %8.3f" % (comm.rank, progress, id, float(cut.sum())/cut.size, visible, memory.current()/1024.**3, memory.max()/1024.**3))
mystats.append([ind, float(cut.sum())/cut.size, visible])
# Add to my work file
_, uids = actdata.split_detname(d.dets)
flags = flagrange.from_sampcut(cut, dets=uids)
flags.write(hfile, group=id)
# Merge all the individual cut files into a single big one.
comm.Barrier()
if comm.rank == 0:
with h5py.File(args.odir + "/cuts.hdf", "w") as ofile:
for i in range(comm.size):
print("Reducing %3d" % i)
with h5py.File(args.odir + "/work_%03d.hdf" % i, "r") as ifile:
for key in sorted(ifile.keys()):
ifile.copy(key, ofile)
