def build_bundle(self, cfg):
printl_debug('build bundle', cfg.bundle)
from gna.bundle import execute_bundles
with nextlevel():
b = execute_bundles(cfg=cfg, context=self)
self.executed_bundles.append(b)
def test_offeq_correction(tmp_path):
""" Test implementation of off-equilibrium contribution to antineutrino spectra
from nuclear fission in commercial reactors. The reason that contribution
appears is the fact the electron conversion spectra measured at ILL doesn't
take into account long-lived isotopes. That correction is required for
precision measurement of oscillation parameters and is uncorrelated
between reactors. It comes in a form of ratio of true spectra to nominal
Huber-Mueller.
"""
_enu = np.linspace(1., 8.0, 500, dtype='d')
Enu = C.Points(_enu, labels='anue energy')
indices = [('i', 'isotope', ['U235', 'U238', 'Pu239', 'Pu241']),
('r', 'reactor', ['DB1', 'DB2', 'LA1', 'LA2', 'LA3', 'LA4'])]
cfg = NestedDict(
bundle=dict(name='reactor_offeq_spectra', version='v03', nidx=indices),
offeq_data=
'./data/reactor_anu_spectra/Mueller/offeq/mueller_offequilibrium_corr_{isotope}.dat',
)
ns = env.globalns('offeq')
offeq, = execute_bundles(cfg, namespace=ns)
ns.printparameters(labels=True)
for iso in offeq.context.inputs.offeq_correction.values():
try:
for _input in iso.values():
Enu >> _input.values()
except AttributeError:
Enu >> iso
fig, ax = plt.subplots()
for iso in indices[0][2]:
corrected_spectra = offeq.context.outputs.offeq_correction[iso][
'DB1'].data()
ax.plot(Enu.single().data(), corrected_spectra - 1., label=iso)
ax.set_title("Offequilibrium correction")
ax.grid()
ax.legend(loc='best')
ax.set_xlabel("Antineutrino energy, MeV")
ax.set_ylabel("(Corrected - nominal) / nominal")
suffix = 'correction'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
savefig(path.replace('.png', '.pdf'), dpi=300)
allure_attach_file(path)
path = os.path.join(str(tmp_path), suffix + '_graph.png')
savegraph(offeq.context.outputs.offeq_correction['U235']['DB1'], path)
allure_attach_file(path)
def test_geoneutrino_spectrum_v01(tmp_path):
_enu = np.arange(1., 8.0+1.e-6, 0.01, dtype='d')
Enu = C.Points(_enu, labels='anue energy')
cfg = NestedDict(
bundle = dict(name='geoneutrino_spectrum', version='v01'),
data = 'data/data-common/geo-neutrino/2006-sanshiro/geoneutrino-luminosity_{isotope}_truncated.knt'
)
ns = env.globalns('geonu')
geonu, = execute_bundles(cfg, namespace=ns)
ns.printparameters(labels=True)
Enu >> geonu.context.inputs.values(nested=True)
# Dump some info
print(geonu.context.inputs)
print(geonu.context.outputs)
geonu.interp.values()[0].printtransformations()
geonu.interp.values()[1].printtransformations()
# Plot figures and graphs
fig = plt.figure()
ax = plt.subplot(111, xlabel=r'$E_{\nu}$, MeV', ylabel='N/MeV/s', title='Geo-neutrino luminosity (truncated at 1.7 MeV)')
ax.minorticks_on()
ax.grid()
for k, v in geonu.context.outputs.items():
ax.plot(_enu, v.data(), label=k)
ax.legend()
plt.show()
savefig(os.path.join(str(tmp_path), '_spectra.png'))
savegraph(Enu, os.path.join(str(tmp_path), '_graph.png'))
ns.printparameters(labels=True)
parser.add_argument('-s',
'--show',
action='store_true',
help='show the figure')
args = parser.parse_args()
#
# Initialize bundle
#
cfg = NestedDict(bundle='detector_iav_db_root_v01',
parname='OffdiagScale.{}',
scale=uncertain(1.0, 4, 'percent'),
ndiag=1,
filename='data/dayabay/tmp/detector_iavMatrix_P14A_LS.root',
matrixname='iav_matrix')
b, = execute_bundles(cfg=cfg, namespaces=['AD1', 'AD2', 'AD3'])
(smear1, smear2, smear3) = b.transformations_out.values()
env.globalns.printparameters(labels=True)
par1, par2, par3 = (b.common_namespace('OffdiagScale')[s]
for s in ('AD1', 'AD2', 'AD3'))
par1.set(1.5)
par2.set(1.0)
par3.set(0.5)
#
# Test bundle
#
def singularities(values, edges):
# Bundle name
bundle=dict(
name='reactor_baselines',
version='v01',
nidx=indices,
major='rd' # first for reactor, second for detector
),
# Reactor positions
reactors=reactors,
# Detector positions
detectors=detectors,
# SNF pools,
snf_pools=snf_pools,
unit="meter")
#
# Initialize bundle
#
if args.make_idx:
cfg_idx = NestedDict(baselines=cfg)
context = ExpressionContext(cfg_idx, ns=env.globalns)
a.build(context)
else:
b, = execute_bundles(cfg=cfg)
env.globalns.printparameters(labels=True)
#
# Test bundle
#
#
nbins = 240
edges = N.linspace(0.0, 12.0, nbins+1, dtype='d')
points = C.Points(edges)
hists_list = ()
for eset in ( (1.025, 6.025), (2.025, 7.025), (3.025, 8.025) ):
heights = unit_bins_at( eset, edges )
hist = C.Histogram( edges, heights )
hists_list += hist,
#
# Define the chain
#
shared = NestedDict( edges=points.single() )
b, = execute_bundles(cfg=cfg.detector, namespaces=namespaces, shared=shared)
print('Parameters:')
env.globalns.printparameters(labels=True)
#
# Connect inputs
#
for inp, hist in zip(b.inputs.values(), hists_list):
inp( hist.hist )
print('\nObservables:')
env.globalns.printobservables()
#
# Dump graph
#!/usr/bin/env python
from load import ROOT as R
R.GNAObject
from gna.bundle import execute_bundles
from gna.env import env
from gna.configurator import NestedDict, uncertain
from collections import OrderedDict
cfg = NestedDict(bundle='dummy',
name='dymmubundle',
indices=[('n', 'num', ['1', '2', '3']),
('a', 'alph', ['a', 'b', 'c']),
('z', 'zyx', ['X', 'Y', 'Z'])],
format='var.{num}.{alph}.{zyx}',
input=True,
size=10,
debug=True)
ns = env.globalns
shared = NestedDict()
b, = execute_bundles(common_namespace=ns, cfg=cfg, shared=shared)
print(shared)
name = 'hist_{group}_{det}'.format( group=gr, det=det )
h = R.TH1D(name, name, 10, 0, 10 )
h.SetBinContent( it_det, 1 ); it_det+=1
file.WriteTObject( h )
print('Generated file contents')
file.ls()
file.Close()
make_sample_file( cfg.filename )
ns = env.globalns('testexp')
for det in cfg.detectors:
detns = ns(det).reqparameter('livetime', central=10, sigma=0.1, fixed=True)
b, = execute_bundles( cfg=bkg, common_namespace=ns )
bfn, = execute_bundles( cfg=bkg.bkg_fn, common_namespace=ns )
print( 'Parameters:' )
env.globalns.printparameters(labels=True)
print( 'Observables:' )
env.globalns.printobservables()
if opts.set:
for name, value in opts.set:
value = float(value)
print( 'Set', name, value )
var = findname( ns.pathto(name), ns )
var.set( value )
indices = N.digitize(values, edges) - 1
phist = N.zeros(edges.size - 1)
phist[indices] = 1.0
return phist
nbins = 240
edges = N.linspace(0.0, 12.0, nbins + 1, dtype='d')
points = C.Points(edges)
phist = singularities([1.225, 2.225, 4.025, 7.025, 9.025], edges)
hist = C.Histogram(edges, phist)
#
# Initialize bundle
#
b, = execute_bundles(edges=points.single(), cfg=cfg, namespaces=['ad1', 'ad2'])
pars = [p for k, p in b.common_namespace.items() if k.startswith('weight')]
escale1, escale2 = (b.common_namespace('escale')[ns] for ns in ['ad1', 'ad2'])
(nonlin1, nonlin2) = b.objects['nonlinearity'].values()
factor1, factor2 = b.objects['factor'].values()
factor_nominal = factor1.data().copy()
escale1.set(0.98)
escale2.set(1.02)
#
# Plot curves:
#
fig = P.figure()
ax = P.subplot(111)