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