verbose=100)
else:
pool = schwimmbad.choose_pool(mpi=USE_MPI, processes=n_workers)
outputs = pool.map(_run_sim, range(n_sims))
pool.close()
pres, mres = zip(*outputs)
pres, mres = cut_nones(pres, mres)
if rank == 0:
dt = [('g1p', 'f8'), ('g1m', 'f8'), ('g1', 'f8'), ('g2p', 'f8'),
('g2m', 'f8'), ('g2', 'f8')]
dplus = np.array(pres, dtype=dt)
dminus = np.array(mres, dtype=dt)
with fitsio.FITS('data.fits', 'rw') as fits:
fits.write(dplus, extname='plus')
fits.write(dminus, extname='minus')
m, msd, c, csd = estimate_m_and_c(pres, mres, 0.02, swap12=SWAP12)
print("""\
# of sims: {n_sims}
noise cancel m [1e-3, 3-sigma] : {m:f} +/- {msd:f}
noise cancel c [1e-5, 3-sigma] : {c:f} +/- {csd:f}""".format(
n_sims=len(pres),
m=m / 1e-3,
msd=msd * 3 / 1e-3,
c=c / 1e-5,
csd=csd * 3 / 1e-5),
flush=True)
dplus4 = np.array(pres4, dtype=dt)
dminus4 = np.array(mres4, dtype=dt)
with fitsio.FITS('data.fits', 'rw') as fits:
fits.write(dplus0, extname='plus0')
fits.write(dminus0, extname='minus0')
fits.write(dplus1, extname='plus1')
fits.write(dminus1, extname='minus1')
fits.write(dplus2, extname='plus2')
fits.write(dminus2, extname='minus2')
fits.write(dplus3, extname='plus3')
fits.write(dminus3, extname='minus3')
fits.write(dplus4, extname='plus4')
fits.write(dminus4, extname='minus4')
m, msd, c, csd = estimate_m_and_c(pres0, mres0, 0.02, swap12=SWAP12)
print("""\
# of sims: {n_sims}
noise cancel m : {m:f} +/- {msd:f}
noise cancel c : {c:f} +/- {csd:f}""".format(n_sims=len(pres0),
m=m,
msd=msd,
c=c,
csd=csd),
flush=True)
m, msd, c, csd = estimate_m_and_c(pres1, mres1, 0.02, swap12=SWAP12)
print("""\
# of sims: {n_sims}
noise cancel m : {m:f} +/- {msd:f}
noise cancel c : {c:f} +/- {csd:f}""".format(n_sims=len(pres1),
print('found %d outputs' % len(files))
io = [joblib.delayed(_func)(fname) for fname in files]
outputs = joblib.Parallel(verbose=10,
n_jobs=-1,
pre_dispatch='2*n_jobs',
max_nbytes=None)(io)
for i, s2n in enumerate([10, 15, 20]):
_outputs = [o[i][0] for o in outputs if o[i][0] is not None]
_wgts = [o[i][1] for o in outputs if o[i][0] is not None]
pres, mres = zip(*_outputs)
pres, mres = cut_nones(pres, mres)
m, msd, c, csd = estimate_m_and_c(pres,
mres,
0.02,
swap12=SWAP12,
weights=_wgts)
if np.abs(m) < 1e-2:
mfac = 1e-3
mstr = '[1e-3]'
else:
mfac = 1
mstr = ' '
if np.abs(c) < 1e-4:
cfac = 1e-4
cstr = '[1e-4]'
else:
cfac = 1
if rank == 0:
dt = [('g1p', 'f8'), ('g1m', 'f8'), ('g1', 'f8'),
('g2p', 'f8'), ('g2m', 'f8'), ('g2', 'f8')]
dplus = np.array(pres, dtype=dt)
dminus = np.array(mres, dtype=dt)
dplus_ex = np.array(pres_ex, dtype=dt)
dminus_ex = np.array(mres_ex, dtype=dt)
with fitsio.FITS('data.fits', 'rw') as fits:
fits.write(dplus, extname='plus')
fits.write(dminus, extname='minus')
fits.write(dplus_ex, extname='plus_ex')
fits.write(dminus_ex, extname='minus_ex')
m, msd, c, csd = estimate_m_and_c(pres, mres, 0.02, swap12=SWAP12)
m_ex, msd_ex, c_ex, csd_ex = estimate_m_and_c(pres_ex, mres_ex,
0.02, swap12=SWAP12)
print("""\
# of sims: {n_sims}
noise cancel m : {m:f} +/- {msd:f}
noise cancel c : {c:f} +/- {csd:f}""".format(
n_sims=len(pres),
m=m,
msd=msd,
c=c,
csd=csd), flush=True)
print("""\
# of sims: {n_sims}