def make_data(filename):
data = gv.dataset.avg_data(read_dataset(filename))
basis = EigenBasis(
data, keyfmt='1s0.{s1}{s2}', srcs=['l', 'g', 'd', 'e'],
t=(1,2), tdata=range(1,24),
)
return data, basis
def make_data(filename):
data = gv.dataset.avg_data(cf.read_dataset(filename))
basis = cf.EigenBasis(
data,
keyfmt='1s0.{s1}{s2}',
srcs=['l', 'g', 'd', 'e'],
t=(1, 2),
tdata=range(1, 24),
)
return basis.apply(data, keyfmt='1s0.{s1}{s2}'), basis # 3
def make_data(filename):
data = gv.dataset.avg_data(cf.read_dataset(filename, grep='1s0'))
basis = cf.EigenBasis(
data,
keyfmt=KEYFMT,
srcs=SOURCES,
t=(1, 2),
tdata=TDATA,
)
return data, basis
def main():
dset = cf.read_dataset('etas-Ds.h5')
s = gv.dataset.svd_diagnosis(dset, models=make_models())
print('svdcut =', s.svdcut)
s.plot_ratio(show=True)
# chained fit
models = make_models()
models = models[:2] + [tuple(models[2:])]
for m in models:
s = gv.dataset.svd_diagnosis(dset, models=[m])
print('svdcut (chained) =', s.svdcut)
s.plot_ratio(show=True)
def make_data(filename):
dset = gv.dataset.Dataset(filename)
data = c_hack * gv.dataset.avg_data(cf.read_dataset(filename, grep=ttag))
#data = gv.regulate(data, svdcut=0.5)
if OSC:
basis = cf.EigenBasis(data,
keyfmt=KEYFMT,
srcs=SRCs,
t=(t0, t0 + 2),
tdata=TDATA)
else:
basis = cf.EigenBasis(data,
keyfmt=KEYFMT,
srcs=SRCs,
t=(t0, t0 + 1),
tdata=TDATA)
return data, basis
def main():
data = make_data('etas-Ds.h5')
models = make_models() # 1a
models = [
models[0], models[1], # 1b
dict(nterm=(2, 1), svdcut=6.3e-5), # 1c
(models[2], models[3]) # 1d
]
fitter = cf.CorrFitter(models=models) # 1e
p0 = None
for N in [1, 2, 3, 4]:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.chained_lsqfit(data=data, prior=prior, p0=p0) # 2
print(fit.format(pstyle=None if N < 4 else 'm'))
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fit.show_plots()
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.chained_lsqfit(
data=data, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
noise=True,
)
print(noisy_fit.format(pstyle=None))
p = key_parameters(fit.p)
noisy_p = key_parameters(noisy_fit.p)
print(' fit:', p)
print('noisy fit:', noisy_p)
print(' ', gv.fmt_chi2(gv.chi2(p - noisy_p)))
# simulated fit
for sim_pdata in fitter.simulated_pdata_iter(
n=2, dataset=cf.read_dataset('etas-Ds.h5'), p_exact=fit.pmean
):
print('\n==================== simulation')
sim_fit = fitter.chained_lsqfit(
pdata=sim_pdata, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
)
print(sim_fit.format(pstyle=None))
p = key_parameters(fit.pmean)
sim_p = key_parameters(sim_fit.p)
print('simulated - exact:', sim_p - p)
print(' ', gv.fmt_chi2(gv.chi2(p - sim_p)))
def main():
data = make_data('etas-Ds.h5')
fitter = cf.CorrFitter(models=make_models())
p0 = None
prior = make_prior(8) # 1
for N in [1, 2]: # 2
print(30 * '=', 'nterm =', N)
fit = fitter.lsqfit(
data=data, prior=prior, p0=p0, nterm=(N, N), svdcut=SVDCUT # 3
)
print(fit) # 4
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fit.show_plots()
# check fit quality by adding noise
print('\n==================== add svd, prior noise')
noisy_fit = fitter.lsqfit(
data=data, prior=prior, p0=fit.pmean, svdcut=SVDCUT, nterm=(N, N),
noise=True,
)
print(noisy_fit.format(pstyle=None))
p = key_parameters(fit.p)
noisy_p = key_parameters(noisy_fit.p)
print(' fit:', p)
print('noisy fit:', noisy_p)
print(' ', gv.fmt_chi2(gv.chi2(p - noisy_p)))
# simulated fit
for sim_pdata in fitter.simulated_pdata_iter(
n=2, dataset=cf.read_dataset('etas-Ds.h5'), p_exact=fit.pmean
):
print('\n==================== simulation')
sim_fit = fitter.lsqfit(
pdata=sim_pdata, prior=prior, p0=fit.pmean, svdcut=SVDCUT,
nterm=(N, N),
)
print(sim_fit.format(pstyle=None))
p = key_parameters(fit.pmean)
sim_p = key_parameters(sim_fit.p)
print('simulated - exact:', sim_p - p)
print(' ', gv.fmt_chi2(gv.chi2(p - sim_p)))
def test_fit(fitter, datafile):
""" Test the fit with simulated data """
gv.ranseed((623738625, 435880512, 1745400596))
print('\nRandom seed:', gv.ranseed.seed)
dataset = read_dataset(datafile)
pexact = fitter.fit.pmean
prior = fitter.fit.prior
for sdata in fitter.simulated_data_iter(n=2, dataset=dataset, pexact=pexact):
print('\n============================== simulation')
sfit = fitter.lsqfit(data=sdata, prior=prior, p0=pexact, nterm=(2, 2))
diff = []
# check chi**2 for leading parameters
for k in prior:
diff.append(sfit.p[k].flat[0] - pexact[k].flat[0])
chi2_diff = gv.chi2(diff)
print(
'Leading parameter chi2/dof [dof] = %.2f' %
(chi2_diff / chi2_diff.dof),
'[%d]' % chi2_diff.dof,
' Q = %.1f' % chi2_diff.Q
)
def test_fit(fitter, datafile):
""" Test the fit with simulated data """
gv.ranseed((1487942813, 775399747, 906327435))
print('\nRandom seed:', gv.ranseed.seed)
dataset = cf.read_dataset(datafile)
pexact = fitter.fit.pmean
prior = fitter.fit.prior
for sdata in fitter.simulated_data_iter(n=2, dataset=dataset, pexact=pexact):
print('\n============================== simulation')
sfit = fitter.lsqfit(data=sdata, prior=prior, p0=pexact)
diff = []
# check chi**2 for leading parameters
for k in prior:
diff.append(sfit.p[k].flat[0] - pexact[k].flat[0])
chi2diff = gv.chi2(diff)
print(
'Leading parameter chi2/dof [dof] = %.2f' %
(chi2diff / chi2diff.dof),
'[%d]' % chi2diff.dof,
' Q = %.1f' % chi2diff.Q
)
def fit_data(filename_in, key, otherkey):
dset = cf.read_dataset(filename_in) # read data
# If we don't have many samples this next part suggests an svdcut
#s = gv.dataset.svd_diagnosis(dset, models=make_models(key, otherkey, n))
#print('svdcut =', s.svdcut) # suggested svdcut
#s.plot_ratio(show=True)
############################################################################################
data = make_data(filename_in)
fitter = cf.CorrFitter(models=make_models(key, otherkey))
p0 = None
for N in NEXP:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.lsqfit(
data=data, prior=prior,
p0=p0) # add_svdnoise=True, add_priornoise=True, svdcut=s.svdcut
p0 = fit.pmean
print(fit.format(pstyle=None if N < 10 else 'm'))
print_results(fit)
def make_data(filename):
data = c_hack*gv.dataset.avg_data(cf.read_dataset(filename, grep=ttag))
basis = cf.EigenBasis(data, keyfmt=KEYFMT, srcs=SRC, t=(t0, t0+2), tdata=TDATA)
return data, basis
def make_data(datafile):
""" Read data from datafile and average it. """
return gv.dataset.avg_data(cf.read_dataset(datafile))
def main():
dset = cf.read_dataset('Ds-Ds.h5')
s = gv.dataset.svd_diagnosis(dset, models=make_models())
print('svdcut =', s.svdcut)
s.plot_ratio(show=True)
def make_data(file):
return cf.read_dataset(file)
def make_data(filename):
""" Read data, compute averages/covariance matrix for G(t). """
return gv.dataset.avg_data(cf.read_dataset(filename))
def make_data(filename):
""" Read data, compute averages/covariance matrix for G(t). """
return gv.dataset.avg_data(cf.read_dataset(filename))
#l3248f211b580m002426m06730m8447\a\
#l3296f211b630m0074m037m440-coul-v5
ensemble = 'l3296f211b630m0074m037m440-coul-v5'
corr = 't0_onemmHy_vec_m0.450.gpl'
basedir = '../data/hybrid'
corrpath = os.path.join(basedir, ensemble, corr)
SRCs = ['H', 'h']
KEYFMT = 'onemm.{s1}{s2}'
tag = KEYFMT[:6]
ttag = tag[:-1]
otag = ttag + '_o.'
# get temporal extent of lattice
dset = gv.dataset.Dataset(corrpath)
data = gv.dataset.avg_data(cf.read_dataset(corrpath, grep=ttag))
T = data[dset.keys()[0]].size
## single channel parameters
TDATA = range(T)
TFIT = TDATA[1:20]
TP = T
NEXP = range(1, 10)
s_coeff = (1, -1)
key = tag + SRCs[1] + SRCs[1]
corrtag = corr[:-4]
otherkey = None
## flags for both single and matrix fits
OSC = True
CORRFIT = True
def make_data(datafile):
""" Read data from datafile and average it. """
dset = cf.read_dataset(datafile)
return gv.dataset.avg_data(dset)
