def write_scan(fname, scan):
with h5py.File(fname, "w") as hfile:
for k in ["boresight", "offsets", "comps", "sys", "mjd0", "dets"]:
hfile[k] = getattr(scan, k)
n, neach, flat = scan.cut.flatten()
# h5py has problems with zero size arrays
if flat.size == 0: flat = np.zeros([1, 2], dtype=np.int32)
hfile["cut/neach"] = neach
hfile["cut/flat"] = flat
nmat.write_nmat(hfile.create_group("noise"), scan.noise)
for k in scan.site:
hfile["site/" + k] = scan.site[k]
hfile["tod"] = scan.get_samples()
if os.path.isfile(ofile) and args.resume: continue
t = []
t.append(time.time())
try:
d = data.read(entry, ["gain", "tconst", "cut", "tod", "boresight"])
t.append(time.time())
d = data.calibrate(d)
t.append(time.time())
except errors.DataMissing as e:
print "%3d/%d %25s skip (%s)" % (i + 1, n, id, e.message)
continue
except zipfile.BadZipfile:
print "%d/%d %25s bad zip" % (i + 1, n, id)
continue
ft = fft.rfft(d.tod) * d.tod.shape[1]**-0.5
t.append(time.time())
if model == "old":
noise = nmat_measure.detvecs_old(ft, d.srate, d.dets)
elif model == "jon":
di = np.where(d.dets == 20)[0]
noise = nmat_measure.detvecs_jon(ft, d.srate, d.dets, shared)
elif model == "simple":
noise = nmat_measure.detvecs_simple(ft, d.srate, d.dets)
t.append(time.time())
nmat.write_nmat("%s/%s.hdf" % (args.odir, id), noise)
t.append(time.time())
t = np.array(t)
dt = t[1:] - t[:-1]
print("%3d/%d %25s" + " %6.3f" * len(dt)) % tuple([i + 1, n, id] +
list(dt))
elif model == "jon":
noise = nmat_measure.detvecs_jon(ft,
d.srate,
d.dets,
shared,
cut_bins=spikes)
elif model == "simple":
noise = nmat_measure.detvecs_simple(ft, d.srate, d.dets)
elif model == "joint":
noise = nmat_measure.detvecs_joint(ft,
d.srate,
d.dets,
cut_bins=spikes)
t.append(time.time())
with h5py.File("%s/%s.hdf" % (args.odir, id), "w") as hfile:
nmat.write_nmat(hfile, noise)
t.append(time.time())
if args.covtest:
# Measure full cov per bin
ndet = ft.shape[0]
bins = np.minimum(
(noise.bins * ft.shape[1] / noise.bins[-1, 1]).astype(int),
ft.shape[1] - 1)
nbin = len(bins)
cov_full = np.zeros([nbin, ndet, ndet])
for bi, b in enumerate(bins):
print "A", bi, b, np.mean(np.abs(ft[0, b[0]:b[1]])**
2)**0.5 / 20
cov_full[bi] = nmat_measure.measure_cov(ft[:, b[0]:b[1]])
cov_model = noise.covs
# Compute total noise and correlated noise per detector for the two
# 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":
noise = nmat_measure.detvecs_jon(ft, d.srate, d.dets, shared, cut_bins=spikes)
elif model == "simple":
noise = nmat_measure.detvecs_simple(ft, d.srate, d.dets)
elif model == "joint":
noise = nmat_measure.detvecs_joint(ft, d.srate, d.dets, cut_bins=spikes)
t.append(time.time())
with h5py.File("%s/%s.hdf" % (args.odir, id),"w") as hfile:
nmat.write_nmat(hfile, noise) ; t.append(time.time())
if args.covtest:
# Measure full cov per bin
ndet = ft.shape[0]
bins = np.minimum((noise.bins*ft.shape[1]/noise.bins[-1,1]).astype(int),ft.shape[1]-1)
nbin = len(bins)
cov_full = np.zeros([nbin,ndet,ndet])
for bi, b in enumerate(bins):
print "A", bi, b, np.mean(np.abs(ft[0,b[0]:b[1]])**2)**0.5/20
cov_full[bi] = nmat_measure.measure_cov(ft[:,b[0]:b[1]])
cov_model= noise.covs
# Compute total noise and correlated noise per detector for the two
pow_full_tot = np.einsum("bii->bi",cov_full)
pow_model_tot = np.einsum("bii->bi",cov_model)
pow_full_ucorr = 1/np.einsum("bii->bi",array_ops.eigpow(cov_full,-1))
pow_model_ucorr = 1/np.einsum("bii->bi",array_ops.eigpow(cov_model,-1))