def test_bootstrap_lsqfit(self):
fitter = MultiFitter(models=self.make_models(ncg=1))
fit = fitter.lsqfit(data=self.data, prior=self.prior)
datalist = gv.bootstrap_iter(self.data, n=10)
ds = gv.dataset.Dataset()
for bf in fit.bootstrapped_fit_iter(datalist=datalist):
ds.append(bf.pmean)
p = gv.dataset.avg_data(ds, bstrap=True)
self.assertTrue(abs(p['a'].mean - 1.) < 5 * p['a'].sdev)
self.assertTrue(abs(p['b'].mean - 0.5) < 5 * p['b'].sdev)
self.assertEqual(ds.samplesize, 10)
pdatalist = (fitter.process_data(d, fitter.models)
for d in gv.bootstrap_iter(self.data, n=10))
ds = gv.dataset.Dataset()
for bf in fit.bootstrapped_fit_iter(pdatalist=pdatalist):
ds.append(bf.pmean)
p = gv.dataset.avg_data(ds, bstrap=True)
self.assertTrue(abs(p['a'].mean - 1.) < 5 * p['a'].sdev)
self.assertTrue(abs(p['b'].mean - 0.5) < 5 * p['b'].sdev)
self.assertEqual(ds.samplesize, 10)
ds = gv.dataset.Dataset()
for bf in fit.bootstrapped_fit_iter(n=10):
ds.append(bf.pmean)
p = gv.dataset.avg_data(ds, bstrap=True)
self.assertTrue(abs(p['a'].mean - 1.) < 5 * p['a'].sdev)
self.assertTrue(abs(p['b'].mean - 0.5) < 5 * p['b'].sdev)
self.assertEqual(ds.samplesize, 10)
def make_bs(m_N, e_NN, n=100):
''' re-sample the normal distributed energies (m_N and e_NN)
to create bootstrap samples
'''
d_dict = dict()
d_dict['m_n'] = m_N
d_dict['e_nn'] = e_NN
bs_list = list(gv.bootstrap_iter(d_dict, n=n))
return bs_list
def test_bootstrap_chained_lsqfit(self):
fitter = MultiFitter(models=self.make_models(ncg=1))
fit = fitter.chained_lsqfit(data=self.data, prior=self.prior)
datalist = gv.bootstrap_iter(self.data, n=10)
ds = gv.dataset.Dataset()
for bf in fit.bootstrapped_fit_iter(datalist=datalist):
ds.append(bf.pmean)
p = gv.dataset.avg_data(ds, bstrap=True)
self.assertTrue(abs(p['a'].mean - 1.) < 5 * p['a'].sdev)
self.assertTrue(abs(p['b'].mean - 0.5) < 5 * p['b'].sdev)
def add_noise(data, frac):
""" add noise to correlators in list corrlist; frac = rel. size """
global_noise = gv.gvar(1, frac)
ans = gv.BufferDict()
for k in data:
## add: a) uncorr. noise (smear for zeros); b) corr. noise
corr = data[k]
dcorr = np.abs(corr * frac)
dcorr[1:-1] = (dcorr[1:-1] + dcorr[2:] + dcorr[:-2]) / 3.0
dcorr = gv.gvar(np.zeros(dcorr.shape), dcorr)
dcorr = next(gv.bootstrap_iter(dcorr))
ans[k] = (corr + dcorr) * global_noise
return ans
if vs_mpi:
qsq_m = qsq / mpi**2
qcotd_m = all_qcotd[k] * mN / mpi
else:
qsq_m = qsq / mN**2
qcotd_m = all_qcotd[k]
print('%d& %3s& %s& %s& %s& %s& %s& %s& %s& %s& %s& %s\\\\' \
%(Psq, irrep, n, s1, en1, s2, en2, de_nn, e_nn, np.sqrt(E_cmSq), qsq_m, qcotd_m))
# put parent x-variable into dictionary
all_data[k] = e_nn
#print(all_data)
# Using correlated parent-x variables, create BS distribution of them for different data sets
all_data_bs = list(gv.bootstrap_iter(all_data, n=n_bs))
# create container for x-variable for plotting
qsqmn_range = np.arange(-0.04, 0.501, .001)
qsqmn_plot = {k: k for k in qsqmn_range}
# create dictionaries to hold mean and BS results
qcotd_vals_0 = dict()
qsq_result_0 = dict()
qcotd_vals_bs = dict()
qsq_result_bs = dict()
for k in all_data_bs[0]:
if k != 'm_n':
x = []
y = []
for bs in range(n_bs):