def _load_correlation_matrix(self):
filename = self.cfg.get('correlations', None)
if filename is None:
return
mat = N.loadtxt(filename)
if mat.shape[0] != mat.shape[1]:
raise Exception('Non square matrix provided:', mat.shape[0],
mat.shape[1])
if len(self._par_container) != mat.shape[0]:
raise Exception(
'Unable to set correlation to %i parameters with %ix%i matrix'
% (len(pars, mat.shape[0], mat.shape[1])))
mmin, mmax = mat.min(), mat.max()
if mmin < -1 - 1.e-12 or mmax > 1.0 + 1.e-12:
raise Exception('Invalid min/max correlation values:', mmin, mmax)
diag = mat.diagonal()
ones = diag == 1.0
if not ones.all():
raise Exception('There are values !=1 on the diagonal (d-1): ',
diag[~ones] - 1.0)
if self.cfg.get('verbose', 0) > 1:
print('Load correlation matrix from %s:' % filename)
print(mat)
from gna.parameters import covariance_helpers as ch
ch.covariate_pars(self._par_container, mat)
def _load_covariance_matrix(self):
filename = self.cfg.get('covariance', None)
assert filename
covmat = N.loadtxt(filename)
if self.cfg.get('verbose', 0) > 1:
print('Load covariance matrix from %s:' % filename)
print(covmat)
sigma_inv = N.diag(covmat.diagonal()**-0.5)
corrmat = N.matmul(N.matmul(sigma_inv, covmat), sigma_inv)
if self.cfg.get('verbose', 0) > 1:
print('Compute correlation matrix from:')
print(corrmat)
self._checkmatrix(covmat, is_correlation=False)
self.covmat = covmat
self.corrmat = corrmat
for par, sigma2 in zip(self._par_container, covmat.diagonal()):
par.setSigma(sigma2**0.5)
from gna.parameters import covariance_helpers as ch
ch.covariate_pars(self._par_container, corrmat)
def _load_correlation_matrix(self):
filename = self.cfg.get('correlations', None)
assert filename
corrmat = N.loadtxt(filename)
if self.cfg.get('verbose', 0) > 1:
print('Load correlation matrix from %s:' % filename)
print(corrmat)
self._checkmatrix(corrmat, is_correlation=True)
self.corrmat = corrmat
from gna.parameters import covariance_helpers as ch
ch.covariate_pars(self._par_container, corrmat)
def defparameter_group(self, *args, **kwargs):
import gna.parameters.covariance_helpers as ch
pars = [
self.defparameter(name, **ctor_args) for name, ctor_args in args
]
covmat_passed = kwargs.get('covmat')
if covmat_passed is not None:
ch.covariate_pars(pars, covmat_passed)
cov_from_cfg = kwargs.get('covmat_cfg')
if cov_from_cfg is not None:
ch.CovarianceHandler(cov_from_cfg, pars).covariate_pars()
return pars
def reqparameter_group(self, *args, **kwargs):
import gna.parameters.covariance_helpers as ch
args_patched = [(name, dict(ctor_args, with_status=True))
for name, ctor_args in args]
pars_with_status = [
self.reqparameter(name, **ctor_args)
for name, ctor_args in args_patched
]
statuses = [status for _, status in pars_with_status]
pars = [par for par, _ in pars_with_status]
if not any(statuses):
covmat_passed = kwargs.get('covmat')
if covmat_passed is not None:
ch.covariate_pars(pars, covmat_passed)
cov_from_cfg = kwargs.get('covmat_cfg')
if cov_from_cfg is not None:
ch.CovarianceHandler(cov_from_cfg, pars).covariate_pars()
return pars
def define_variables(self):
from gna.parameters import covariance_helpers as ch
self._par_container = []
separate_uncertainty = self.cfg.get('separate_uncertainty', False)
parname = self.cfg.parameter
labelfmt = self.cfg.get('label', '')
data = self.data
values = data['values']
names = data['names']
unc = data['uncertainties']
mode = data['mode']
if mode == 'percent':
relunc = unc / 100.
unc = values * relunc
elif mode == 'relative':
relunc = unc
unc = values * relunc
elif mode == 'absolute':
relunc = unc / values
else:
raise Exception('Invalid uncertainty mode: ' + mode)
for it_minor in self.nidx_minor:
container = []
for it_major in self.nidx_major:
major_values, = it_major.current_values()
if not major_values in names:
raise Exception(
'Variable {} is not in the list'.format(major_values))
data_idx = names.index(major_values)
it = it_major + it_minor
label = it.current_format(labelfmt) if labelfmt else ''
if separate_uncertainty:
uncpar = self.reqparameter(separate_uncertainty,
it,
central=1.0,
sigma=relunc[data_idx],
label=label + ' (norm)')
par = self.reqparameter(parname,
it,
central=values[data_idx],
fixed=True,
label=label)
container.append(uncpar)
else:
par = self.reqparameter(parname,
it,
central=values[data_idx],
sigma=unc[data_idx],
label=label)
container.append(par)
if self.cfg.get("objectize"):
trans = par.transformations.value
trans.setLabel(label)
self.set_output(parname, it, trans.single())
self._par_container.append(container)
if 'correlations' in self.data:
cormat = self.data['correlations']
self._checkmatrix(cormat, container, is_correlation=True)
cormat = self.filter_matrix(cormat, container)
if cormat is not None:
ch.covariate_pars(container, cormat)
def define_variables(self):
with entryContext(subgraph="LSNL"):
from physlib import pdg
ns = self.namespace
#
# Some constants
#
emass = ns.reqparameter("emass",
central=pdg['live']['ElectronMass'],
fixed=True,
label='Electron mass, MeV')
self.doubleme = 2 * emass.value()
ns.defparameter("ngamma",
central=2.0,
fixed=True,
label='Number of e+e- annihilation gammas')
labels = OrderedDict([('birks.Kb0', 'Kb0=1'),
('birks.Kb1', "Birk's 1st constant (E')"),
('birks.Kb2', "Birk's 2nd constant (E'')"),
('cherenkov.E_0', ''), ('cherenkov.p0', ''),
('cherenkov.p1', ''), ('cherenkov.p2', ''),
('cherenkov.p3', ''), ('cherenkov.p4', ''),
('Npescint', ''), ('kC', ''),
('normalizationEnergy', '')])
#
# Define parameters according to predefined list and input configuration
#
for name, label in labels.items():
parcfg = self.cfg.pars.get(name, None)
if parcfg is None:
if 'Kb2' in name:
continue
raise self.exception(
'Parameter {} configuration is not provided'.format(
name))
self.reqparameter(name, None, cfg=parcfg, label=label)
#
# Set the correlation matrix if provided by configuration
#
correlations_pars = self.cfg.correlations_pars
correlations = self.cfg.correlations
if not correlations_pars and not correlations:
return
if not correlations_pars or not correlations:
raise self.exception(
"Both 'correlations' and 'correlations_pars' should be defined"
)
npars = len(correlations_pars)
corrmatrix = N.array(correlations, dtype='d').reshape(npars, npars)
# Check matrix sanity
if (corrmatrix.diagonal() != 1.0).any():
raise self.exception(
'There should be no non-unitary elements on the correlation matrix'
)
if (N.fabs(corrmatrix) > 1.0).any():
raise self.exception(
'Correlation matrix values should be within -1.0 and 1.0')
if (corrmatrix != corrmatrix.T).all():
raise self.exception(
'Correlation matrix is expected to be diagonal')
# Take parameters and set correlations
from gna.parameters import covariance_helpers as ch
pars = [ns[par] for par in correlations_pars]
ch.covariate_pars(pars, corrmatrix)
def test_par_cov():
probe_1 = env.defparameter('probe1', central=0., sigma=1.)
probe_2 = env.defparameter('probe2', central=0., sigma=1.)
probe_3 = env.defparameter('probe3', central=0., sigma=1.)
test_ns = env.ns('test_ns')
test_ns.defparameter('test0', central=1., sigma=0.1)
test_ns.defparameter('test1', central=1., sigma=0.1)
test_ns.defparameter('test2', central=1., sigma=0.1)
test_ns.defparameter('test3', central=1., sigma=0.1)
extra_test_ns = env.ns('extra_test_ns')
extra1 = extra_test_ns.defparameter('extra1', central=1., sigma=0.1)
extra2 = extra_test_ns.defparameter('extra2', central=1., sigma=0.1)
extra3 = extra_test_ns.defparameter('extra3', central=1., sigma=0.1)
extra4 = extra_test_ns.defparameter('extra4', central=1., sigma=0.1)
cov1 = 0.1
print("Setting covariance of probe_1 with probe_2 to {0}".format(cov1))
probe_1.setCovariance(probe_2, cov1)
print("Check that they are mutually correlated now.")
assert probe_1.isCorrelated(probe_2) and probe_2.isCorrelated(probe_1)
print("Success")
print("Get covariance from both -- {0} and {1}\n".format(
probe_1.getCovariance(probe_2), probe_2.getCovariance(probe_1)))
print("Checks that change of one propagates to another")
cov2 = 0.2
probe_1.setCovariance(probe_2, cov2)
assert (probe_1.getCovariance(probe_2) == cov2
and probe_2.getCovariance(probe_1) == cov2)
print("Success\n")
test_pars = get_parameters(
['test_ns.test0', 'test_ns.test1', 'test_ns.test2', 'test_ns.test3'])
print("Test pars sequence is {}".format([_.name() for _ in test_pars]))
cov_matrix1 = make_fake_covmat(4)
print("Test covariance matrix is \n", cov_matrix1)
ch.covariate_pars(test_pars, cov_matrix1)
for first, second in itertools.combinations_with_replacement(
range(len(test_pars)), 2):
try:
if first != second:
assert test_pars[first].getCovariance(
test_pars[second]) == cov_matrix1[first, second]
else:
assert test_pars[first].sigma() == np.sqrt(cov_matrix1[first,
second])
except AssertionError:
print((first, second),
test_pars[first].getCovariance(test_pars[second])**2,
cov_matrix1[first, second])
raise
extra_pars = [extra1, extra2, extra3]
cov_mat_extra = make_fake_covmat(3)
cov_storage = ch.CovarianceStorage("extra_store", extra_pars,
cov_mat_extra)
ch.covariate_ns('extra_test_ns', cov_storage)
for first, second in itertools.combinations_with_replacement(
range(len(extra_pars)), 2):
try:
if first != second:
assert test_pars[first].getCovariance(
test_pars[second]) == cov_matrix1[first, second]
else:
assert test_pars[first].sigma() == np.sqrt(cov_matrix1[first,
second])
except AssertionError:
print((first, second),
test_pars[first].getCovariance(test_pars[second])**2,
cov_matrix1[first, second])
raise