def __call__(self, scan, tod):
blocks = utils.find_equal_groups(scan.layout.pcb[scan.dets])
todfilter.filter_common_blockwise(tod,
blocks,
cuts=scan.cut,
niter=self.niter,
inplace=True)
def __call__(self, scan, tod):
blocks = utils.find_equal_groups(scan.layout.pcb[scan.dets])
todfilter.filter_phase_blockwise(tod,
blocks,
scan.boresight[:, 1],
daz=self.daz,
cuts=scan.cut,
niter=self.niter,
inplace=True)
def get_groups(ids, mode):
if mode == "tod":
return [[i] for i, id in enumerate(ids)]
elif mode == "array":
# Group tods across arrays
ids = np.asarray(ids)
tids, arr = np.char.partition(ids, ":").T[[0,2]]
groups = utils.find_equal_groups(tids)
return groups
else:
raise ValueError("Unknown group mode '%s'" % mode)
def read_buddies(fname, mode="auto"):
"""Read a beam decomposition of the near-sidelobe "buddies".
Each line should contain xi eta T Q U for one buddy, or
det xi eta T Q U for the detector-dependent format. The result
will be dets, [ndet][nbuddy,{xi,eta,T,Q,U}]. For the
buddy-independent format, dets is None."""
res = np.loadtxt(fname, ndmin=2)
if res.size == 0: return None, res.reshape(-1,5)
if mode == "uniform" or mode == "auto" and res.shape[-1] == 5:
# detector-independent format
return None, [res]
else:
# detector-dependent format
groups = utils.find_equal_groups(res[:,0])
dets = [int(res[g[0],0]) for g in groups]
buds = np.array([res[g,1:] for g in groups])
return dets, buds
def read_buddies(fname, mode="auto"):
"""Read a beam decomposition of the near-sidelobe "buddies".
Each line should contain xi eta T Q U for one buddy, or
det xi eta T Q U for the detector-dependent format. The result
will be dets, [ndet][nbuddy,{xi,eta,T,Q,U}]. For the
buddy-independent format, dets is None."""
res = np.loadtxt(fname, ndmin=2)
if res.size == 0: return None, res.reshape(-1,5)
if mode == "uniform" or mode == "auto" and res.shape[-1] == 5:
# detector-independent format
return None, [res]
else:
# detector-dependent format
groups = utils.find_equal_groups(res[:,0])
dets = [int(res[g[0],0]) for g in groups]
buds = np.array([res[g,1:] for g in groups])
return dets, buds
def find_pairs_blind(det_pos, tol=0.2 * utils.arcmin):
pairs = utils.find_equal_groups(det_pos, tol=tol)
# Pair must have exactly two members
pairs = [p for p in pairs if len(p) == 2]
pairs = np.array(pairs)
return pairs
only = [int(word) for word in args.only.split(",")] if args.only else []
# Should we use distributed maps?
npix = shape[-2] * shape[-1]
use_dmap = npix > 5e7
utils.mkdir(root + "log")
logfile = root + "log/log%03d.txt" % comm.rank
log_level = log.verbosity2level(config.get("verbosity"))
L = log.init(level=log_level, file=logfile, rank=comm.rank)
filedb.init()
db = filedb.scans.select(args.sel)
ids = db.ids
mjd = utils.ctime2mjd(db.data["t"])
chunks = utils.find_equal_groups(mjd // args.dt)
chunks = [np.sort(chunk) for chunk in chunks]
chunks = [chunks[i] for i in np.argsort([c[0] for c in chunks])]
corr_pos = planet9.choose_corr_points(shape, wcs,
args.corr_spacing * utils.degree)
if args.inject:
inject_params = np.loadtxt(args.inject,
ndmin=2) # [:,{ra0,dec0,R,vx,vy,flux}]
asteroids = planet9.get_asteroids(args.asteroid_file, args.asteroid_list)
# How to parallelize? Could do it over chunks. Usually there will be more chunks than
# mpi tasks. But there will still be many tods per chunk too (about 6 tods per hour
# and 72 hours per chunk gives 432 tods per chunk). That's quite a bit for one mpi
# task to do. Could paralellize over both... No, keep things simple. Parallelize over tods
def find_pairs_blind(det_pos, tol=0.2*utils.arcmin): pairs = utils.find_equal_groups(det_pos, tol=tol) # Pair must have exactly two members pairs = [p for p in pairs if len(p) == 2] pairs = np.array(pairs) return pairs