imap = bunch.Bunch(sys=imap_sys or None, map=enmap.read_map(fname))
for i in myinds:
id = ids[i]
entry = db[id]
ofile = "%s/%s.hdf" % (args.odir, id)
if os.path.isfile(ofile) and args.resume: continue
t = []
t.append(time.time())
try:
fields = ["gain", "tconst", "cut", "tod", "boresight", "noise_cut"]
if args.spikecut: fields.append("spikes")
if args.imap: fields += ["polangle", "point_offsets", "site"]
d = data.read(entry, fields)
t.append(time.time())
d = data.calibrate(d)
t.append(time.time())
if args.imap:
# Make a full scan object, so we can perform pointing projection
# operations
d.noise = None
scan = data.ACTScan(entry, d=d)
imap.map = imap.map.astype(d.tod.dtype, copy=False)
pmap = pmat.PmatMap(scan, imap.map, sys=imap.sys)
# Subtract input map from tod inplace
pmap.forward(d.tod, imap.map, tmul=1, mmul=-1)
utils.deslope(d.tod, w=8, inplace=True)
ft = fft.rfft(d.tod) * d.tod.shape[1]**-0.5
t.append(time.time())
spikes = d.spikes[:2].T if args.spikecut else None
if model == "old":
fref = [9.5, 10.5]
# Create a global (all tods + all dets) 1-D noise spectrum. Result
# will probably have to be highpass filtered, but that can be done elsewhere
myps = np.zeros(args.N)
mynspec = 0
filedb.init()
ids = filedb.scans[args.sel]
for si in range(comm.rank, len(ids), comm.size):
id = ids[si]
entry = filedb.data[id]
print "reading %s" % id
try:
d = data.read(entry)
d = data.calibrate(d, nofft=True)
except (IOError, errors.DataMissing) as e:
print "skipping (%s)" % e.message
continue
n = d.tod.shape[1]
ft = fft.rfft(d.tod)
del d.tod
ps = np.abs(ft)**2 / (n * srate)
inds = np.linspace(0, args.N, ps.shape[1], endpoint=False).astype(int)
for det, ps_det in zip(d.dets, ps):
ps_bin = np.bincount(inds, ps_det, minlength=args.N) / np.bincount(
inds, minlength=args.N)
weight = 1.0
if args.weight:
iref = [f * n / srate for f in fref]
parser.add_argument("--di", type=int, default=0, help="Index into array of accepted detectors to use.")
args = parser.parse_args()
dtype = np.float64
eqsys = config.get("map_eqsys")
area = enmap.read_map(args.area).astype(dtype)
area = enmap.zeros((3,)+area.shape[-2:], area.wcs, dtype)
entry = filedb.data[args.id]
# First get the raw samples
d = data.read(entry, subdets=[args.di])
raw_tod = d.tod[0,d.sample_offset:d.cutafter].copy()
raw_bore = d.boresight[:,d.sample_offset:d.cutafter].T
# Then some calibrated samples
d = data.calibrate(d)
cal_tod = d.tod[0]
cal_bore = d.boresight.T
# Apply fourier-truncation to raw data
raw_tod = raw_tod[:cal_tod.shape[0]]
raw_bore = raw_bore[:cal_bore.shape[0]]
# And a proper ACTScan
scan = data.ACTScan(entry, subdets=[args.di])
# Detector pointing
det_ipoint = scan.boresight + scan.offsets[0]
# Manual transformation
trf = pmat.pos2pix(scan, None, eqsys)
det_opoint_exact = trf(det_ipoint.T).T
toks = args.imap.split(":")
imap_sys, fname = ":".join(toks[:-1]), toks[-1]
imap = bunch.Bunch(sys=imap_sys or None, map=enmap.read_map(fname))
for i in myinds:
id = ids[i]
entry = db[id]
ofile = "%s/%s.hdf" % (args.odir, id)
if os.path.isfile(ofile) and args.resume: continue
t=[]; t.append(time.time())
try:
fields = ["gain","tconst","cut","tod","boresight", "noise_cut"]
if args.spikecut: fields.append("spikes")
if args.imap: fields += ["polangle","point_offsets","site"]
d = data.read(entry, fields) ; t.append(time.time())
d = data.calibrate(d) ; t.append(time.time())
if args.imap:
# Make a full scan object, so we can perform pointing projection
# operations
d.noise = None
scan = data.ACTScan(entry, d=d)
imap.map = imap.map.astype(d.tod.dtype, copy=False)
pmap = pmat.PmatMap(scan, imap.map, sys=imap.sys)
# Subtract input map from tod inplace
pmap.forward(d.tod, imap.map, tmul=1, mmul=-1)
utils.deslope(d.tod, w=8, inplace=True)
ft = fft.rfft(d.tod) * d.tod.shape[1]**-0.5 ; t.append(time.time())
spikes = d.spikes[:2].T if args.spikecut else None
if model == "old":
noise = nmat_measure.detvecs_old(ft, d.srate, d.dets)
elif model == "jon":