from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--dot', help='write graphviz output')
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)
#
# Define the configuration
#
cfg = NestedDict()
cfg.detector = NestedDict(
# the bundle name
bundle = 'bundlechain_v01',
detectors = [ 'AD11', 'AD21', 'AD31' ],
bundlechain_list = [ 'iav', 'nonlinearity', 'eres', 'rebin' ],
)
cfg.detector.nonlinearity = NestedDict(
bundle = 'detector_nonlinearity_db_root_v01',
names = [ 'nominal', 'pull0', 'pull1', 'pull2', 'pull3' ],
filename = 'data/dayabay/tmp/detector_nl_consModel_450itr.root',
parname = 'escale.{}',
par = uncertain(1.0, 0.2, 'percent'),
)
cfg.detector.iav = 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'
)
cfg.detector.eres = NestedDict(
bundle = 'detector_eres_common3',
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
pars = uncertaindict(
[('Eres_a', 0.014764) ,
('Eres_b', 0.0869) ,
def init_configuration(self):
self.cfg = NestedDict(
integral=NestedDict(
bundle=dict(name='integral_2d1d', version='v03'),
variables=('evis', 'ctheta'),
edges=N.linspace(0.0, 12.0, 241, dtype='d'),
xorders=4,
yorder=2,
),
ibd_xsec=NestedDict(bundle=dict(name='xsec_ibd', version='v02'),
order=1,
pdg_year='dyboscar'),
oscprob=NestedDict(
bundle=dict(name='oscprob', version='v04', major='rdc'),
# pdg_year='dyboscar',
dm='23',
),
anuspec=NestedDict(
bundle=dict(name='reactor_anu_spectra', version='v04'),
name='anuspec',
filename=[
'data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'
],
free_params=True, # enable free spectral model
varmode='plain',
varname='anue_weight_{index}',
ns_name='spectral_weights',
edges=N.concatenate((N.arange(1.8, 8.7, 0.5), [12.3])),
),
offeq_correction=NestedDict(
bundle=dict(name='reactor_offeq_spectra',
version='v04',
major='ir'),
offeq_data=
'data/reactor_anu_spectra/Mueller/offeq/mueller_offequilibrium_corr_{isotope}.dat',
),
eff=NestedDict(
bundle=dict(name='efficiencies',
version='v02',
names=dict(norm='global_norm'),
major=''),
correlated=False,
uncorrelated=True,
norm=True,
efficiencies='data/dayabay/efficiency/P15A_efficiency.py'),
livetime=NestedDict(
bundle=dict(name='dayabay_livetime_hdf', version='v02'),
file=
'data/dayabay/data/P15A/dubna/dayabay_data_dubna_v15_bcw_adsimple.hdf5',
),
baselines=NestedDict(
bundle=dict(name='reactor_baselines',
version='v01',
major='rd'),
reactors=
'data/dayabay/reactor/coordinates/coordinates_docDB_9757.py',
detectors=
'data/dayabay/ad/coordinates/coordinates_docDB_9757.py',
unit='m'),
thermal_power=NestedDict(
bundle=dict(name='dayabay_reactor_burning_info_v02',
major='ri'),
reactor_info=
'data/dayabay/reactor/power/WeeklyAvg_P15A_v1.txt.npz',
fission_uncertainty_info=
'data/dayabay/reactor/fission_fraction/2013.12.05_djurcic.py',
add_ff=True,
nominal_power=False,
),
nominal_thermal_power=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='nominal_thermal_power',
label='Nominal thermal power [Gw] for {reactor}',
pars=uncertaindict(
[
('DB1', 2.895),
('DB2', 2.895),
('LA1', 2.895),
('LA2', 2.895),
('LA3', 2.895),
('LA4', 2.895),
],
uncertainty=0.5,
mode='percent',
),
),
snf_ff=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='snf_fission_fractions',
objectize=True,
label='SNF fission fraction {isotope}',
pars=uncertaindict(
[('U235', 0.563), ('U238', 0.079), ('Pu239', 0.301),
('Pu241', 0.057)],
mode='fixed',
),
),
snf_denom=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='snf_denom',
objectize=True,
label='SNF inverse weight for average energy release per decay',
pars=uncertain(snf_energy_per_decay, 'fixed'),
),
snf_correction=NestedDict(
bundle=dict(name='reactor_snf_spectra',
version='v04',
major='r'),
snf_average_spectra=
'./data/reactor_anu_spectra/SNF/kopeikin_0412.044_spent_fuel_spectrum_smooth.dat',
),
eper_fission=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter="eper_fission",
label='Energy per fission for {isotope} in MeV',
pars=uncertaindict([('Pu239', (211.12, 0.34)),
('Pu241', (214.26, 0.33)),
('U235', (202.36, 0.26)),
('U238', (205.99, 0.52))],
mode='absolute'),
),
conversion_factor=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='conversion_factor',
label='Conversion factor from GWt to MeV',
#taken from transformations/neutrino/ReactorNorm.cc
pars=uncertain(R.NeutrinoUnits.reactorPowerConversion,
'fixed'),
),
nprotons_nominal=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='nprotons_nominal',
label=
'Daya Bay nominal number of protons (20 tons x GdLS Np/ton)',
pars=uncertain(20.0 * 7.163e28, 'fixed'),
),
nprotons_corr=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='nprotons_corr',
label='Correction to number of protons per AD',
pars=uncertaindict(
[('AD11', 19941. / nominal_mass),
('AD12', 19967. / nominal_mass),
('AD21', 19891. / nominal_mass),
('AD22', 19944. / nominal_mass),
('AD31', 19917. / nominal_mass),
('AD32', 19989. / nominal_mass),
('AD33', 19892. / nominal_mass),
('AD34', 19931. / nominal_mass)],
mode='fixed',
),
),
iav=NestedDict(
bundle=dict(name='detector_iav_db_root_v03', major='d'),
parname='OffdiagScale',
scale=uncertain(1.0, 4, 'percent'),
ndiag=1,
filename='data/dayabay/tmp/detector_iavMatrix_P14A_LS.root',
matrixname='iav_matrix',
),
eres=NestedDict(
bundle=dict(name='detector_eres_normal',
version='v01',
major=''),
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
parameter='eres',
pars=uncertaindict(
[('a', 0.016), ('b', 0.081), ('c', 0.026)],
# [('a', 0.014764) ,
# ('b', 0.0869) ,
# ('c', 0.0271)],
mode='percent',
uncertainty=30),
expose_matrix=True),
lsnl=NestedDict(
bundle=dict(name='energy_nonlinearity_db_root',
version='v02',
major='dl'),
names=['nominal', 'pull0', 'pull1', 'pull2', 'pull3'],
filename='data/dayabay/tmp/detector_nl_consModel_450itr.root',
par=uncertain(1.0, 0.2, 'percent'),
edges='evis_edges',
extrapolation_strategy='extrapolate',
nonlin_range=(0.5, 12.)),
rebin=NestedDict(bundle=dict(name='rebin', version='v03',
major=''),
rounding=3,
edges=N.concatenate(
([0.7], N.arange(1.2, 8.1, 0.2), [12.0])),
name='rebin',
label='Final histogram\n {detector}'),
#
# Spectra
#
bkg_spectrum_acc=NestedDict(
bundle=dict(name='root_histograms_v03'),
filename=
'data/dayabay/data_spectra/P15A_IHEP_data/P15A_All_raw_sepctrum_coarse.root',
format='{site}_AD{adnum_local}_singleTrigEnergy',
name='bkg_spectrum_acc',
label='Accidentals {detector}\n (norm spectrum)',
groups=self.groups,
normalize=True,
),
bkg_spectrum_li=NestedDict(
bundle=dict(name='root_histograms_v03'),
filename='data/dayabay/bkg/lihe/toyli9spec_BCWmodel_v1.root',
format='h_eVisAllSmeared',
name='bkg_spectrum_li',
label='9Li spectrum\n (norm)',
normalize=True,
),
bkg_spectrum_he=NestedDict(
bundle=dict(name='root_histograms_v03'),
filename='data/dayabay/bkg/lihe/toyhe8spec_BCWmodel_v1.root',
format='h_eVisAllSmeared',
name='bkg_spectrum_he',
label='8He spectrum\n (norm)',
normalize=True,
),
bkg_spectrum_amc=NestedDict(
bundle=dict(name='root_histograms_v03'),
filename='data/dayabay/bkg/P12B_amc_expofit.root',
format='hCorrAmCPromptSpec',
name='bkg_spectrum_amc',
label='AmC spectrum\n (norm)',
normalize=True,
),
bkg_spectrum_alphan=NestedDict(
bundle=dict(name='root_histograms_v03'),
filename='data/dayabay/bkg/P12B_alphan_coarse.root',
format='AD{adnum_global_alphan_subst}',
groups=self.groups,
name='bkg_spectrum_alphan',
label='C(alpha,n) spectrum\n {detector} (norm)',
normalize=True,
),
lihe_fractions=NestedDict(
bundle=dict(name='var_fractions_v02'),
names=['li', 'he'],
format='frac_{component}',
fractions=uncertaindict(li=(0.95, 0.05, 'relative')),
),
bkg_spectrum_fastn=NestedDict(
bundle=dict(name='dayabay_fastn_power',
version='v02',
major='s'),
parameter='fastn_shape',
name='bkg_spectrum_fastn',
normalize=(0.7, 12.0),
bins='evis_edges',
order=2,
),
#
# Parameters
#
fastn_shape=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='fastn_shape',
label='Fast neutron shape parameter for {site}',
pars=uncertaindict(
[('EH1', (67.79, 0.1132)), ('EH2', (58.30, 0.0817)),
('EH3', (68.02, 0.0997))],
mode='relative',
),
),
lihe_fracs=NestedDict(
bundle=dict(name='dyb_lihe_fractions', version='v01'),
frac_file_path='./data/dayabay/fractions/{site}_fractions.txt',
mode=self.opts.lihe_fractions)
if self.opts.lihe_fractions != 'no' else NestedDict(),
#
# Rates
#
bkg_rate_acc=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='bkg_rate_acc',
label='Acc rate at {detector}',
pars=uncertaindict(
[
('AD11', 8.463602),
('AD12', 8.464611),
('AD21', 6.290756),
('AD22', 6.180896),
('AD31', 1.273798),
('AD32', 1.189542),
('AD33', 1.197807),
('AD34', 0.983096),
],
uncertainty=1.0,
mode='percent',
),
separate_uncertainty='acc_norm',
),
bkg_rate_lihe=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='bkg_rate_lihe',
label='⁹Li/⁸He rate at {site}',
pars=uncertaindict(
[('EH1', (2.46, 1.06)), ('EH2', (1.72, 0.77)),
('EH3', (0.15, 0.06))],
mode='absolute',
),
),
bkg_rate_fastn=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='bkg_rate_fastn',
label='Fast neutron rate at {site}',
pars=uncertaindict(
[('EH1', (0.792, 0.103)), ('EH2', (0.566, 0.074)),
('EH3', (0.047, 0.009))],
mode='absolute',
),
),
bkg_rate_amc=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='bkg_rate_amc',
label='AmC rate at {detector}',
pars=uncertaindict(
[
('AD11', (0.18, 0.08)),
('AD12', (0.18, 0.08)),
('AD21', (0.16, 0.07)),
('AD22', (0.15, 0.07)),
('AD31', (0.07, 0.03)),
('AD32', (0.06, 0.03)),
('AD33', (0.07, 0.03)),
('AD34', (0.05, 0.02)),
],
mode='absolute',
),
),
bkg_rate_alphan=NestedDict(
bundle=dict(name="parameters", version="v01"),
parameter='bkg_rate_alphan',
label='C(alpha,n) rate at {detector}',
pars=uncertaindict(
[
('AD11', (0.08)),
('AD12', (0.07)),
('AD21', (0.05)),
('AD22', (0.07)),
('AD31', (0.05)),
('AD32', (0.05)),
('AD33', (0.05)),
('AD34', (0.05)),
],
uncertainty=50,
mode='percent',
),
),
)
('SA.D2', 3.0),
('SB.D2', 4.0),
('SA.D3', 5.0),
('SB.D3', 6.0),
('SA.D4', 7.0),
('SB.D4', 8.0),
],
uncertainty=1.0,
mode='percent',
),
)
cfg3 = NestedDict(bundle=dict(
name='parameters',
version='v01',
),
parameter='constant',
label='some constant',
pars=uncertain(-1.0, uncertainty=4.0, mode='percent'))
b1 = execute_bundle(cfg1)
b2 = execute_bundle(cfg2)
b3 = execute_bundle(cfg3)
env.globalns.printparameters(labels=True)
print('Provides:')
print(b1.provides(cfg1))
print(b1.provides(cfg2))
print(b1.provides(cfg3))
def init_configuration(self):
edges = np.arange(0.0, 12+1.e-8, self.opts.fine_step or 0.01)
edges_final = np.concatenate( (
[0.8],
np.arange(1, 6.0, self.opts.fine_step_final or 0.02),
np.arange(6, 7.0, 0.1),
[7.0, 7.5, 12.0]
)
)
if self.opts.final_emin is not None:
print('Truncate final binning E>={}'.format(self.opts.final_emin))
edges_final = edges_final[edges_final>=self.opts.final_emin]
if self.opts.final_emax is not None:
print('Truncate final binning E<={}'.format(self.opts.final_emax))
edges_final = edges_final[edges_final<=self.opts.final_emax]
if self.opts.final_emin is not None or self.opts.final_emax is not None:
print('Final binning:', edges_final)
self.cfg = NestedDict(
#
# Numbers
#
numbers0 = OrderedDict(
bundle = dict(name='parameters', version='v05'),
state='fixed',
labels=dict(
seconds_in_year = 'Number of seconds in year',
days_in_second = 'Number of days in a second',
conversion_factor = 'Conversion factor: W[GW]/[MeV] N[fissions]->N[fissions]/T[s]',
),
pars = dict(
seconds_in_year = 365.0*24.0*60.0*60.0,
days_in_second = 1.0/(24.0*60.0*60.0),
conversion_factor = R.NeutrinoUnits.reactorPowerConversion, #taken from transformations/neutrino/ReactorNorm.cc
),
),
numbers = OrderedDict(
bundle = dict(name='parameters', version='v06'),
pars = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/juno_input_numbers.py',
skip = ('percent',),
state= 'fixed'
),
bkg_shape_unc = OrderedDict(
bundle = dict(name='parameters', version='v06'),
pars = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/bkg_bin2bin.yaml',
hooks = OrderedDict(bin_width_factor=lambda pars: (1.0/pars['bin_width'], '1/`bin width`')),
state= 'fixed'
),
# Detector and reactor
norm = OrderedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "norm",
label = 'Reactor power/detection efficiency correlated normalization',
pars = uncertain(1.0, (2**2+1**2)**0.5, 'percent')
),
baselines = OrderedDict(
bundle = dict(name='reactor_baselines', version='v02', major = 'rd'),
reactors = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/reactor_baselines.yaml',
reactors_key = 'reactor_baseline',
detectors = dict(AD1=0.0),
unit = 'km'
),
# Reactor
energy_per_fission = OrderedDict(
bundle = dict(name="parameters", version = "v06"),
pars = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/energy_per_fission.yaml',
separate_uncertainty = '{}_scale'
),
thermal_power = OrderedDict(
bundle = dict(name="parameters", version = "v06", names=dict(thermal_power='thermal_power_nominal')),
pars = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/thermal_power.yaml',
separate_uncertainty = '{}_scale'
),
# Backgrounds
bkg_rate = OrderedDict(
bundle = dict(name="parameters", version = "v06"),
pars = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/bkg_rates.yaml',
separate_uncertainty = '{}_norm'
),
#
# Transformations
#
# General
kinint2 = OrderedDict(
bundle = dict(name='integral_2d1d', version='v03', names=dict(integral='kinint2')),
variables = ('evis', 'ctheta'),
edges = edges,
# edges = np.linspace(0.0, 12.001, 601),
xorders = 4,
yorder = 5,
),
rebin = OrderedDict(
bundle = dict(name='rebin', version='v04', major=''),
rounding = 5,
edges = edges_final,
instances={
'rebin': 'Final histogram {detector}',
}
),
rebin_bkg = OrderedDict(
bundle = dict(name='rebin', version='v04', major=''),
rounding = 5,
edges = edges_final,
instances={
'rebin_acc': 'Accidentals {autoindex}',
'rebin_lihe': '9Li/8He {autoindex}',
'rebin_fastn': 'Fast neutrons {autoindex}',
'rebin_alphan': 'C(alpha,n)O {autoindex}',
'rebin_geonu': 'Geo-nu {autoindex}'
}
),
bkg_shape_uncertainty = OrderedDict(
bundle = dict(name='trans_sumsq', version='v01', major=''),
ninputs = 3,
instances={'sumsq_bkg': 'Bkg shape variance {autoindex}'}
),
bkg_edges = OrderedDict(
bundle = dict(name='trans_histedges', version='v01', major=''),
types = ('widths', ),
instances={'bkgbin': 'Bkg bins {autoindex}'}
),
variance = OrderedDict(
bundle = dict(name='trans_snapshot', version='v01', major=''),
instances={'sumsq_snapshot': 'Bkg shape variance snapshot, not corrected|{autoindex}',
'staterr2': 'Stat. errors (snapshot)'}
),
# Oscillations and detection
ibd_xsec = OrderedDict(
bundle = dict(name='xsec_ibd', version='v02'),
order = 1,
),
oscprob = OrderedDict(
bundle = dict(name='oscprob', version='v05', major='rdc', inactive=self.opts.oscprob=='matter'),
parameters = dict(
DeltaMSq23 = 2.453e-03,
DeltaMSq12 = 7.53e-05,
SinSqDouble13 = (0.08529904, 0.00267792),
SinSqDouble12 = 0.851004,
# SinSq13 = (0.0218, 0.0007),
# SinSq12 = 0.307,
)
),
# oscprob_matter = OrderedDict(
# bundle = dict(name='oscprob_matter', version='v01', major='rd', inactive=self.opts.oscprob=='vacuum',
# names=dict(oscprob='oscprob_matter')),
# density = 2.6, # g/cm3
# pdgyear = self.opts.pdgyear,
# dm = '23'
# ),
anuspec_hm = OrderedDict(
bundle = dict(name='reactor_anu_spectra', version='v05'),
name = 'anuspec',
filename = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/deprecated/JUNOInputs2020_6_26.root',
objectnamefmt = 'HuberMuellerFlux_{isotope}',
free_params=False, # enable free spectral model
varmode='log',
varname='anue_weight_{index:02d}',
ns_name='spectral_weights',
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
offeq_correction = OrderedDict(
bundle = dict(name='reactor_offeq_spectra', version='v05', major=''),
offeq_data = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/deprecated/JUNOInputs2020_6_26.root',
objectnamefmt = 'NonEq_FluxRatio',
relsigma = 0.3,
),
fission_fractions = OrderedDict(
bundle = dict(name="parameters_yaml_v01", major = 'i'),
parameter = 'fission_fractions_nominal',
separate_uncertainty = "fission_fractions_scale",
label = 'Fission fraction of {isotope} in reactor {reactor}',
objectize=True,
data = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/files/fission_fractions.yaml'
),
snf_correction = OrderedDict(
bundle = dict(name='reactor_snf_spectra', version='v05', major='r'),
snf_average_spectra = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/deprecated/JUNOInputs2020_6_26.root',
objectname = 'SNF_FluxRatio'
),
lsnl = OrderedDict(
bundle = dict(name='energy_nonlinearity_db_root', version='v03', major='l'),
names = OrderedDict( [
('nominal', 'positronScintNL'),
('pull0', 'positronScintNLpull0'),
('pull1', 'positronScintNLpull1'),
('pull2', 'positronScintNLpull2'),
('pull3', 'positronScintNLpull3'),
]),
filename = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/deprecated/JUNOInputs2020_6_26.root',
edges = 'evis_edges',
extrapolation_strategy = 'extrapolate',
nonlin_range = (0.95, 12.),
expose_matrix = True
),
shape_uncertainty = OrderedDict(
unc = uncertain(1.0, 1.0, 'percent'),
nbins = 200 # number of bins, the uncertainty is defined to
),
snf_norm = OrderedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'snf_norm',
label='SNF norm',
pars = uncertain(1.0, 'fixed'),
),
reactor_active_norm = OrderedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'reactor_active_norm',
label='Reactor nu (active norm)',
pars = uncertain(1.0, 'fixed'),
),
#
# Backgrounds
#
bkg_spectra = OrderedDict(
bundle = dict(name='root_histograms_v05'),
filename = 'data/data_juno/data-joint/2020-06-11-NMO-Analysis-Input/deprecated/JUNOInputs2020_6_26.root',
formats = ['AccBkgHistogramAD', 'Li9BkgHistogramAD', 'FnBkgHistogramAD', 'AlphaNBkgHistogramAD', 'GeoNuTh232' , 'GeoNuU238'],
names = ['acc_spectrum', 'lihe_spectrum', 'fastn_spectrum', 'alphan_spectrum', 'geonu_Th232_spectrum' , 'geonu_U238_spectrum'],
labels = ['Accidentals|(norm spectrum)', '9Li/8He|(norm spectrum)', 'Fast n|(norm spectrum)', 'AlphaN|(norm spectrum)', 'GeoNu Th232|(norm spectrum)', 'GeoNu U238|(norm spectrum)'],
normalize = True,
),
geonu_fractions=OrderedDict(
bundle = dict(name='var_fractions_v02'),
names = [ 'Th232', 'U238' ],
format = 'frac_{component}',
fractions = uncertaindict(
Th232 = ( 0.23, 'fixed' )
),
),
)
if 'eres' in self.opts.energy_model:
self.cfg.eres = OrderedDict(
bundle = dict(name='detector_eres_normal', version='v01', major=''),
# pars: sigma_e/e = sqrt(a^2 + b^2/E + c^2/E^2),
# a - non-uniformity
# b - statistical term
# c - noise
parameter = 'eres',
pars = uncertaindict([
('a_nonuniform', (0.0082, 0.0001)),
('b_stat', (0.0261, 0.0002)),
('c_noise', (0.0123, 0.0004))
],
mode='absolute'),
expose_matrix = False
)
elif 'multieres' in self.opts.energy_model:
self.cfg.subdetector_fraction = OrderedDict(
bundle = dict(name="parameters", version = "v03"),
parameter = "subdetector_fraction",
label = 'Subdetector fraction weight for {subdetector}',
pars = uncertaindict(
[(subdet_name, (0.2, 0.04, 'relative')) for subdet_name in self.subdetectors_names],
),
covariance = 'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_cov.txt'
)
self.cfg.multieres = OrderedDict(
bundle = dict(name='detector_multieres_stats', version='v01', major='s'),
# pars: sigma_e/e = sqrt(b^2/E),
parameter = 'eres',
relsigma = self.opts.eres_b_relsigma,
nph = 'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_nph.txt',
rescale_nph = self.opts.eres_npe,
expose_matrix = False
)
if not 'lsnl' in self.opts.correlation:
self.cfg.lsnl.correlations = None
self.cfg.lsnl.correlations_pars = None
if not 'subdetectors' in self.opts.correlation:
self.cfg.subdetector_fraction.correlations = None
def init_configuration(self):
if self.opts.eres_npe:
self.opts.eres_sigma = self.opts.eres_npe**-0.5
else:
self.opts.eres_npe = self.opts.eres_sigma**-2
print('Energy resolution at 1 MeV: {}% ({} pe)'.format(
self.opts.eres_sigma * 100, self.opts.eres_npe))
edges = np.arange(0.0, 12.001, 0.01) #FIXME
edges_final = np.concatenate(([0.7], np.arange(1, 6.0,
self.opts.estep),
np.arange(6, 7.0, 0.1), [7.0, 7.5,
12.0]))
if self.opts.final_emin is not None:
print('Truncate final binning E>={}'.format(self.opts.final_emin))
edges_final = edges_final[edges_final >= self.opts.final_emin]
if self.opts.final_emax is not None:
print('Truncate final binning E<={}'.format(self.opts.final_emax))
edges_final = edges_final[edges_final <= self.opts.final_emax]
if self.opts.final_emin is not None or self.opts.final_emax is not None:
print('Final binning:', edges_final)
self.cfg = NestedDict(
kinint2 = NestedDict(
bundle = dict(name='integral_2d1d', version='v03', names=dict(integral='kinint2')),
variables = ('evis', 'ctheta'),
edges = edges,
# edges = np.linspace(0.0, 12.001, 601),
xorders = 4,
yorder = 5,
),
rebin = NestedDict(
bundle = dict(name='rebin', version='v04', major=''),
rounding = 5,
edges = edges_final,
instances={
'rebin': 'Final histogram {detector}',
}
),
rebin_bkg = NestedDict(
bundle = dict(name='rebin', version='v04', major=''),
rounding = 5,
edges = edges_final,
instances={
'rebin_acc': 'Accidentals {autoindex}',
'rebin_li': '9Li {autoindex}',
'rebin_he': '8He {autoindex}',
'rebin_fastn': 'Fast neutrons {autoindex}',
'rebin_alphan': 'C(alpha,n)O {autoindex}'
}
),
ibd_xsec = NestedDict(
bundle = dict(name='xsec_ibd', version='v02'),
order = 1,
),
oscprob = NestedDict(
bundle = dict(name='oscprob', version='v04', major='rdc', inactive=self.opts.oscprob=='matter'),
pdgyear = self.opts.pdgyear,
dm = self.opts.dm
),
oscprob_matter = NestedDict(
bundle = dict(name='oscprob_matter', version='v01', major='rd', inactive=self.opts.oscprob=='vacuum',
names=dict(oscprob='oscprob_matter')),
density = 2.6, # g/cm3
pdgyear = self.opts.pdgyear,
dm = self.opts.dm
),
anuspec_hm = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v04', inactive=self.opts.flux!='huber-mueller'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
free_params=False, # enable free spectral model
varmode='log',
varname='anue_weight_{index:02d}',
ns_name='spectral_weights',
debug = True,
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
anuspec_ill = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v04', inactive=self.opts.flux!='ill-vogel'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/ILL/ILL_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Vogel/Vogel_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
free_params=False, # enable free spectral model
varmode='log',
varname='anue_weight_{index:02d}',
ns_name='spectral_weights',
debug = True,
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
offeq_correction = NestedDict(
bundle = dict(name='reactor_offeq_spectra',
version='v03', major='ir'),
offeq_data = 'data/reactor_anu_spectra/Mueller/offeq/mueller_offequilibrium_corr_{isotope}.dat',
),
geonu = NestedDict(
bundle = dict(name='geoneutrino_spectrum', version='v01'),
data = 'data/data-common/geo-neutrino/2006-sanshiro/geoneutrino-luminosity_{isotope}_truncated.knt'
),
eff = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="eff",
label='Detection efficiency',
pars = uncertain(0.8, 'fixed')
),
global_norm = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="global_norm",
label='Global normalization',
pars = uncertain(1, 'free'),
),
fission_fractions = NestedDict(
bundle = dict(name="parameters_yaml_v01", major = 'i'),
parameter = 'fission_fractions_nominal',
separate_uncertainty = "fission_fractions_scale",
label = 'Fission fraction of {isotope} in reactor {reactor}',
objectize=True,
data = 'data/data_juno/fission_fractions/2013.12.05_xubo.yaml'
),
snf_correction = NestedDict(
bundle = dict(name='reactor_snf_spectra', version='v04', major='r'),
snf_average_spectra = './data/data-common/snf/2004.12-kopeikin/kopeikin_0412.044_spent_fuel_spectrum_smooth.dat',
),
livetime = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "livetime",
label = 'Livetime of {detector} in seconds',
pars = uncertaindict(
[('AD1', (6*365*seconds_per_day, 'fixed'))],
),
),
baselines = NestedDict(
bundle = dict(name='reactor_baselines', version='v01', major = 'rd'),
reactors = 'near-equal' in self.opts.reactors \
and 'data/juno_nominal/coordinates_reactors_equal.py' \
or 'data/juno_nominal/coordinates_reactors.py',
detectors = 'data/juno_nominal/coordinates_det.py',
unit = 'km'
),
norm = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "norm",
label = 'Reactor power/detection efficiency correlated normalization',
pars = uncertain(1.0, (2**2+1**2)**0.5, 'percent')
),
thermal_power = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "thermal_power_nominal",
label = 'Thermal power of {reactor} in GWt',
pars = uncertaindict([
('TS1', 4.6),
('TS2', 4.6),
('TS3', 4.6),
('TS4', 4.6),
('YJ1', 2.9),
('YJ2', 2.9),
('YJ3', 2.9),
('YJ4', 2.9),
('YJ5', 2.9),
('YJ6', 2.9),
('DYB', 17.4),
('HZ', 17.4),
],
uncertainty=0.8,
mode='percent'
),
separate_uncertainty = 'thermal_power_scale'
),
target_protons = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "target_protons",
label = 'Number of protons in {detector}',
pars = uncertaindict(
[('AD1', (1.42e33, 'fixed'))],
),
),
conversion_factor = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter='conversion_factor',
label='Conversion factor from GWt to MeV',
#taken from transformations/neutrino/ReactorNorm.cc
pars = uncertain(R.NeutrinoUnits.reactorPowerConversion, 'fixed'),
),
days_in_second = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter='days_in_second',
label='Number of days in a second',
pars = uncertain(1.0/(24.0*60.0*60.0), 'fixed'),
),
eper_fission = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'eper_fission_nominal',
label = 'Energy per fission for {isotope} in MeV',
pars = uncertaindict(
[
('U235', (201.92, 0.46)),
('U238', (205.52, 0.96)),
('Pu239', (209.99, 0.60)),
('Pu241', (213.60, 0.65))
],
mode='absolute'
),
separate_uncertainty = 'eper_fission_scale'
),
lsnl = NestedDict(
bundle = dict( name='energy_nonlinearity_birks_cherenkov', version='v01', major=''),
stopping_power='data/data_juno/energy_model/2019_birks_cherenkov_v01/stoppingpower.txt',
annihilation_electrons=dict(
file='data/data_juno/energy_model/2019_birks_cherenkov_v01/hgamma2e.root',
histogram='hgamma2e_1KeV',
scale=1.0/50000 # events simulated
),
pars = uncertaindict(
[
('birks.Kb0', (1.0, 'fixed')),
('birks.Kb1', (15.2e-3, 0.1776)),
# ('birks.Kb2', (0.0, 'fixed')),
("cherenkov.E_0", (0.165, 'fixed')),
("cherenkov.p0", ( -7.26624e+00, 'fixed')),
("cherenkov.p1", ( 1.72463e+01, 'fixed')),
("cherenkov.p2", ( -2.18044e+01, 'fixed')),
("cherenkov.p3", ( 1.44731e+01, 'fixed')),
("cherenkov.p4", ( 3.22121e-02, 'fixed')),
("Npescint", (1341.38, 0.0059)),
("kC", (0.5, 0.4737)),
("normalizationEnergy", (11.99, 'fixed'))
],
mode='relative'
),
integration_order = 2,
correlations_pars = [ 'birks.Kb1', 'Npescint', 'kC' ],
correlations = [ 1.0, 0.94, -0.97,
0.94, 1.0, -0.985,
-0.97, -0.985, 1.0 ],
fill_matrix=True,
labels = dict(
normalizationEnergy = 'Conservative normalization point at 12 MeV'
),
),
shape_uncertainty = NestedDict(
unc = uncertain(1.0, 1.0, 'percent'),
nbins = 200 # number of bins, the uncertainty is defined to
),
snf_norm = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'snf_norm',
label='SNF norm',
pars = uncertain(1.0, 'fixed'),
),
reactor_active_norm = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'reactor_active_norm',
label='Reactor nu (active norm)',
pars = uncertain(1.0, 'fixed'),
),
#
# Backgrounds
#
acc_spectrum_db = NestedDict(
bundle = dict(name='root_histograms_v04', inactive=True),
filename = 'data/data_juno/bkg/acc/2016_acc_dayabay_p15a/dayabay_acc_spectrum_p15a.root',
format = 'accidentals',
name = 'acc_spectrum',
label = 'Accidentals|norm spectrum',
normalize = True
),
acc_spectrum = NestedDict(
bundle = dict(name='root_histograms_v04'),
filename = 'data/data_juno/bkg/acc/2019_acc_malyshkin/acc_bckg_FVcut.root',
format = 'hAcc',
name = 'acc_spectrum',
label = 'Accidentals|norm spectrum',
normalize = slice(200,-1),
xscale = 1.e-3,
),
fastn_spectrum=NestedDict(
bundle=dict(name='histogram_flat_v01'),
name='fastn_spectrum',
edges=edges,
label='Fast neutron|norm spectrum',
normalize=(0.7, 12.0),
),
li_spectrum=NestedDict(
bundle = dict(name='root_histograms_v03'),
filename = 'data/data_juno/bkg/lihe/2014_lihe_ochoa/toyli9spec_BCWmodel_v1_2400.root',
format = 'h_eVisAllSmeared',
name = 'li_spectrum',
label = '9Li spectrum|norm',
normalize = True,
),
he_spectrum= NestedDict(
bundle = dict(name='root_histograms_v03'),
filename = 'data/data_juno/bkg/lihe/2014_lihe_ochoa/toyhe8spec_BCWmodel_v1_2400.root',
format = 'h_eVisAllSmeared',
name = 'he_spectrum',
label = '8He spectrum|norm',
normalize = True,
),
alphan_spectrum = NestedDict(
bundle = dict(name='root_histograms_v03'),
filename = 'data/data_juno/bkg/alphan/2012_dayabay_alphan/P12B_alphan_2400.root',
format = 'AD1',
name = 'alphan_spectrum',
label = 'C(alpha,n) spectrum|(norm)',
normalize = True,
),
lihe_fractions=NestedDict(
bundle = dict(name='var_fractions_v02'),
names = [ 'li', 'he' ],
format = 'frac_{component}',
fractions = uncertaindict(
li = ( 0.95, 0.05, 'relative' )
),
),
# bkg_spectra = NestedDict(
# bundle = dict(name='root_histograms_v05'),
# filename = 'data/data_juno/bkg/group/2020-05-JUNO-YB/JunoBkg_evis_2400.root',
# formats = ['AccBkgHistogramAD', 'Li9BkgHistogramAD', 'FnBkgHistogramAD', 'AlphaNBkgHistogramAD', 'GeoNuHistogramAD'],
# names = ['acc_spectrum', 'lihe_spectrum', 'fn_spectrum', 'alphan_spectrum', 'geonu_spectrum'],
# labels = ['Accidentals|(norm spectrum)', '9Li|(norm spectrum)', 'Fast n|(norm spectrum)', 'AlphaN|(norm spectrum)', 'GeoNu combined|(norm spectrum)'],
# normalize = True,
# ),
acc_rate = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'acc_rate',
label='Acc rate',
pars = uncertain(0.9, 1, 'percent'),
separate_uncertainty='acc_norm',
),
fastn_rate = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'fastn_rate',
label='Fast n rate',
pars = uncertain(0.1, 100, 'percent'),
separate_uncertainty='fastn_norm',
),
lihe_rate = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'lihe_rate',
label='9Li/8He rate',
pars = uncertain(1.6, 20, 'percent'),
separate_uncertainty='lihe_norm',
),
alphan_rate = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = 'alphan_rate',
label='C(alpha,n)O rate',
pars = uncertain(0.05, 50, 'percent'),
separate_uncertainty='alphan_norm',
),
)
if 'eres' in self.opts.energy_model:
bconf = self.opts.eres_b_relsigma and (self.opts.eres_b_relsigma,
'relative') or ('fixed', )
self.cfg.eres = NestedDict(
bundle=dict(name='detector_eres_normal',
version='v01',
major=''),
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
parameter='eres',
pars=uncertaindict([('a', (0.000, 'fixed')),
('b', (self.opts.eres_sigma, ) + bconf),
('c', (0.000, 'fixed'))]),
expose_matrix=False)
elif 'multieres' in self.opts.energy_model:
self.cfg.subdetector_fraction = NestedDict(
bundle=dict(name="parameters", version="v03"),
parameter="subdetector_fraction",
label='Subdetector fraction weight for {subdetector}',
pars=uncertaindict(
[(subdet_name, (0.2, 0.04, 'relative'))
for subdet_name in self.subdetectors_names], ),
covariance=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_cov.txt'
)
self.cfg.multieres = NestedDict(
bundle=dict(name='detector_multieres_stats',
version='v01',
major='s'),
# pars: sigma_e/e = sqrt(b^2/E),
parameter='eres',
relsigma=self.opts.eres_b_relsigma,
nph=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_nph.txt',
rescale_nph=self.opts.eres_npe,
expose_matrix=False)
if not 'lsnl' in self.opts.correlation:
self.cfg.lsnl.correlations = None
self.cfg.lsnl.correlations_pars = None
if not 'subdetectors' in self.opts.correlation:
self.cfg.subdetector_fraction.correlations = None
self.cfg.eff.pars = uncertain(0.73, 'fixed')
self.cfg.livetime.pars['AD1'] = uncertain(6 * 330 * seconds_per_day,
'fixed')
cfg = NestedDict(
# Bundle name
bundle=dict(
name='energy_nonlinearity_db_root',
version='v02',
nidx=[('d', 'detector', ['D1']),
('l', 'lsnl_component',
['nominal', 'pull0', 'pull1', 'pull2', 'pull3'])],
major='dl',
),
# file to read
filename='data/data_dayabay/tmp/detector_nl_consModel_450itr.root',
# TGraph names. First curve will be used as nominal
names=['nominal', 'pull0', 'pull1', 'pull2', 'pull3'],
# The uncorrelated energy scale uncertainty type (absolute/relative)
par=uncertain(1.0, 0.2, 'percent'),
parname='escale',
extrapolation_strategy='extrapolate',
nonlin_range=(0.5, 12.),
edges='evis_edges',
)
#
# Make input histogram
#
def singularities(values, edges):
indices = N.digitize(values, edges) - 1
phist = N.zeros(edges.size - 1)
phist[indices] = 1.0
return phist
def init_configuration(self):
if self.opts.eres_npe:
self.opts.eres_sigma = self.opts.eres_npe**-0.5
else:
self.opts.eres_npe = self.opts.eres_sigma**-2
print('Energy resolution at 1 MeV: {}% ({} pe)'.format(
self.opts.eres_sigma * 100, self.opts.eres_npe))
self.cfg = NestedDict(
kinint2 = NestedDict(
bundle = dict(name='integral_2d1d', version='v03', names=dict(integral='kinint2')),
variables = ('evis', 'ctheta'),
edges = np.arange(0.0, 12.001, 0.01), #FIXME
# edges = np.linspace(0.0, 12.001, 601),
xorders = 4,
yorder = 5,
),
rebin = NestedDict(
bundle = dict(name='rebin', version='v03', major=''),
rounding = 3,
edges = np.concatenate( (
[0.7],
np.arange(1, 6.0, self.opts.estep),
np.arange(6, 7.0, 0.1),
[7.0, 7.5, 12.0]
)
),
name = 'rebin',
label = 'Final histogram {detector}'
),
ibd_xsec = NestedDict(
bundle = dict(name='xsec_ibd', version='v02'),
order = 1,
),
oscprob = NestedDict(
bundle = dict(name='oscprob', version='v04', major='rdc', inactive=self.opts.oscprob=='matter'),
pdgyear = self.opts.pdgyear,
dm = self.opts.dm
),
oscprob_matter = NestedDict(
bundle = dict(name='oscprob_matter', version='v01', major='rd', inactive=self.opts.oscprob=='vacuum',
names=dict(oscprob='oscprob_matter')),
density = 2.6, # g/cm3
pdgyear = self.opts.pdgyear,
dm = self.opts.dm
),
anuspec_hm = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v03', inactive=self.opts.flux!='huber-mueller'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
anuspec_ill = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v03', inactive=self.opts.flux!='ill-vogel'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/ILL/ILL_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Vogel/Vogel_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
offeq_correction = NestedDict(
bundle = dict(name='reactor_offeq_spectra',
version='v03', major='ir'),
offeq_data = 'data/reactor_anu_spectra/Mueller/offeq/mueller_offequilibrium_corr_{isotope}.dat',
),
eff = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="eff",
label='Detection efficiency',
pars = uncertain(0.8, 'fixed')
),
global_norm = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="global_norm",
label='Global normalization',
pars = uncertain(1, 'free'),
),
fission_fractions = NestedDict(
bundle = dict(name="parameters_yaml_v01", major = 'i'),
parameter = "fission_fractions",
label = 'Fission fraction of {isotope} in reactor {reactor}',
objectize=True,
data = 'data/data_juno/fission_fractions/2013.12.05_xubo.yaml'
),
livetime = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "livetime",
label = 'Livetime of {detector} in seconds',
pars = uncertaindict(
[('AD1', (6*365*seconds_per_day, 'fixed'))],
),
),
baselines = NestedDict(
bundle = dict(name='reactor_baselines', version='v01', major = 'rd'),
reactors = 'near-equal' in self.opts.reactors \
and 'data/juno_nominal/coordinates_reactors_equal.py' \
or 'data/juno_nominal/coordinates_reactors.py',
detectors = 'data/juno_nominal/coordinates_det.py',
unit = 'km'
),
norm = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "norm",
label = 'Reactor power/detection efficiency correlated normalization',
pars = uncertain(1.0, (2**2+1**2)**0.5, 'percent')
),
thermal_power = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "thermal_power",
label = 'Thermal power of {reactor} in GWt',
pars = uncertaindict([
('TS1', 4.6),
('TS2', 4.6),
('TS3', 4.6),
('TS4', 4.6),
('YJ1', 2.9),
('YJ2', 2.9),
('YJ3', 2.9),
('YJ4', 2.9),
('YJ5', 2.9),
('YJ6', 2.9),
('DYB', 17.4),
('HZ', 17.4),
],
uncertainty=0.8,
mode='percent'
),
),
target_protons = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "target_protons",
label = 'Number of protons in {detector}',
pars = uncertaindict(
[('AD1', (1.42e33, 'fixed'))],
),
),
conversion_factor = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter='conversion_factor',
label='Conversion factor from GWt to MeV',
#taken from transformations/neutrino/ReactorNorm.cc
pars = uncertain(R.NeutrinoUnits.reactorPowerConversion, 'fixed'),
),
eper_fission = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "eper_fission",
label = 'Energy per fission for {isotope} in MeV',
pars = uncertaindict(
[
('U235', (201.92, 0.46)),
('U238', (205.52, 0.96)),
('Pu239', (209.99, 0.60)),
('Pu241', (213.60, 0.65))
],
mode='absolute'
),
),
lsnl = NestedDict(
bundle = dict( name='energy_nonlinearity_birks_cherenkov', version='v01', major=''),
stopping_power='data/data_juno/energy_model/2019_birks_cherenkov_v01/stoppingpower.txt',
annihilation_electrons=dict(
file='data/data_juno/energy_model/2019_birks_cherenkov_v01/hgamma2e.root',
histogram='hgamma2e_1KeV',
scale=1.0/50000 # events simulated
),
pars = uncertaindict(
[
('birks.Kb0', (1.0, 'fixed')),
('birks.Kb1', (15.2e-3, 0.1776)),
# ('birks.Kb2', (0.0, 'fixed')),
("cherenkov.E_0", (0.165, 'fixed')),
("cherenkov.p0", ( -7.26624e+00, 'fixed')),
("cherenkov.p1", ( 1.72463e+01, 'fixed')),
("cherenkov.p2", ( -2.18044e+01, 'fixed')),
("cherenkov.p3", ( 1.44731e+01, 'fixed')),
("cherenkov.p4", ( 3.22121e-02, 'fixed')),
("Npescint", (1341.38, 0.0059)),
("kC", (0.5, 0.4737)),
("normalizationEnergy", (11.99, 'fixed'))
],
mode='relative'
),
integration_order = 2,
correlations_pars = [ 'birks.Kb1', 'Npescint', 'kC' ],
correlations = [ 1.0, 0.94, -0.97,
0.94, 1.0, -0.985,
-0.97, -0.985, 1.0 ],
fill_matrix=True,
labels = dict(
normalizationEnergy = 'Conservative normalization point at 12 MeV'
),
),
shape_uncertainty = NestedDict(
unc = uncertain(1.0, 1.0, 'percent'),
nbins = 200 # number of bins, the uncertainty is defined to
)
)
if 'eres' in self.opts.energy_model:
bconf = self.opts.eres_b_relsigma and (self.opts.eres_b_relsigma,
'relative') or ('fixed', )
self.cfg.eres = NestedDict(
bundle=dict(name='detector_eres_normal',
version='v01',
major=''),
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
parameter='eres',
pars=uncertaindict([('a', (0.000, 'fixed')),
('b', (self.opts.eres_sigma, ) + bconf),
('c', (0.000, 'fixed'))]),
expose_matrix=False)
elif 'multieres' in self.opts.energy_model:
if self.opts.subdetectors_number == 200:
self.cfg.subdetector_fraction = NestedDict(
bundle=dict(name="parameters", version="v02"),
parameter="subdetector_fraction",
label='Subdetector fraction weight for {subdetector}',
pars=uncertaindict(
[(subdet_name, (1.0 / self.opts.subdetectors_number,
0.04, 'relative'))
for subdet_name in self.subdetectors_names], ),
correlations=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/corrmap_xuyu.txt'
)
self.cfg.multieres = NestedDict(
bundle=dict(name='detector_multieres_stats',
version='v01',
major='s'),
# pars: sigma_e/e = sqrt(b^2/E),
parameter='eres',
relsigma=self.opts.eres_b_relsigma,
nph=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector200_nph.txt',
rescale_nph=self.opts.eres_npe,
expose_matrix=False)
elif self.opts.subdetectors_number == 5:
self.cfg.subdetector_fraction = NestedDict(
bundle=dict(name="parameters", version="v03"),
parameter="subdetector_fraction",
label='Subdetector fraction weight for {subdetector}',
pars=uncertaindict(
[(subdet_name, (1.0 / self.opts.subdetectors_number,
0.04, 'relative'))
for subdet_name in self.subdetectors_names], ),
covariance=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_cov.txt'
)
self.cfg.multieres = NestedDict(
bundle=dict(name='detector_multieres_stats',
version='v01',
major='s'),
# pars: sigma_e/e = sqrt(b^2/E),
parameter='eres',
relsigma=self.opts.eres_b_relsigma,
nph=
'data/data_juno/energy_resolution/2019_subdetector_eres_n200_proper/subdetector5_nph.txt',
rescale_nph=self.opts.eres_npe,
expose_matrix=False)
else:
assert False
if not 'lsnl' in self.opts.correlation:
self.cfg.lsnl.correlations = None
self.cfg.lsnl.correlations_pars = None
if not 'subdetectors' in self.opts.correlation:
self.cfg.subdetector_fraction.correlations = None
self.cfg.eff.pars = uncertain(0.73, 'fixed')
self.cfg.livetime.pars['AD1'] = uncertain(6 * 330 * seconds_per_day,
'fixed')
('Pu241', 0.057)
] )
cfg.anu = NestedDict(
bundle = 'reactor_anu_spectra_v01',
isotopes = [ 'U5', 'U8', 'Pu9', 'Pu1' ],
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
strategy = dict( underflow='constant', overflow='extrapolate' ),
edges = N.concatenate( ( N.arange( 1.8, 8.7, 0.5 ), [ 12.3 ] ) ),
)
cfg.snf = NestedDict(
bundle='reactor_snf_spectra_v01',
filename = 'data/reactor_anu_spectra/SNF/kopeikin_0412.044_spent_fuel_spectrum_smooth.dat',
norm=uncertain( 1.0, 100, mode='percent' ),
edges = N.concatenate( ( N.arange( 1.8, 8.7, 0.25 ), [ 12.3 ] ) )
)
cfg.offeq = NestedDict(
bundle='reactor_offeq_spectra_v01',
skip=['U238'],
filename = 'data/reactor_anu_spectra/Mueller/offeq/mueller_offequilibrium_corr_{isotope}.dat',
norm=uncertain( 1.0, 30, mode='percent' ),
edges = N.concatenate( ( N.arange( 1.8, 8.7, 0.25 ), [ 12.3 ] ) )
)
"""Init inputs"""
points = N.linspace( 0.0, 12.0, 241 )
points_t = C.Points(points)
points_t.points.setLabel('E (integr)')
def build_and_plot(expr, obj, suffix):
# Initialize the expression and indices
a = Expression(expr, indices)
# Dump the information
print(a.expressions_raw)
print(a.expressions)
# Parse the expression
a.parse()
# The next step is needed to name all the intermediate variables.
a.guessname(lib, save=True)
# Dump the tree.
a.tree.dump(True)
cfg = NestedDict(
kinint2=NestedDict(
bundle='integral_2d1d_v01',
variables=('evis', 'ctheta'),
edges=N.linspace(0.0, 12.0, 241, dtype='d'),
xorders=3,
yorder=5,
provides=['evis', 'ctheta', 'evis_edges'],
),
ibd_xsec=NestedDict(bundle='xsec_ibd_v01',
order=1,
provides=['ibd_xsec', 'ee', 'enu', 'jacobian']),
oscprob=NestedDict(bundle='oscprob_v01',
name='oscprob',
provides=['oscprob', 'pmns']),
anuspec=NestedDict(
bundle='reactor_anu_spectra_v02',
name='anuspec',
filename=[
'data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'
],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
edges=N.concatenate((N.arange(1.8, 8.7, 0.5), [12.3])),
),
eff=NestedDict(
bundle='efficiencies_v01',
correlated=False,
uncorrelated=True,
norm=True,
names=dict(norm='global_norm'),
provides=['eff', 'effunc_corr', 'effunc_uncorr', 'global_norm'],
efficiencies='data/dayabay/efficiency/P15A_efficiency.py'),
livetime=NestedDict(
bundle='dayabay_livetime_hdf_v01',
file=
'data/dayabay/data/P15A/dubna/dayabay_data_dubna_v15_bcw_adsimple.hdf5',
provides=['livetime_daily', 'efflivetime_daily']),
baselines=NestedDict(
bundle='baselines_v01',
reactors=
'data/dayabay/reactor/coordinates/coordinates_docDB_9757.py',
detectors='data/dayabay/ad/coordinates/coordinates_docDB_9757.py',
provides=['baseline', 'baselineweight'],
units="meters"),
thermal_power=NestedDict(
bundle='dayabay_reactor_burning_info_v01',
reactor_info='data/dayabay/reactor/power/WeeklyAvg_P15A_v1.txt.npz',
provides=['thermal_power', 'fission_fractions']),
iav=NestedDict(
bundle='detector_iav_db_root_v02',
parname='OffdiagScale',
scale=uncertain(1.0, 4, 'percent'),
ndiag=1,
filename='data/dayabay/tmp/detector_iavMatrix_P14A_LS.root',
matrixname='iav_matrix'),
eres=NestedDict(
bundle='detector_eres_common3_v02',
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
pars=uncertaindict([('Eres_a', 0.014764), ('Eres_b', 0.0869),
('Eres_c', 0.0271)],
mode='percent',
uncertainty=30),
provides=['eres', 'eres_matrix'],
expose_matrix=True),
lsnl=NestedDict(
bundle='detector_nonlinearity_db_root_v02',
names=['nominal', 'pull0', 'pull1', 'pull2', 'pull3'],
filename='data/dayabay/tmp/detector_nl_consModel_450itr.root',
parnames=dict(lsnl='lsnl_weight', escale='escale'),
par=uncertain(1.0, 0.2, 'percent'),
edges='evis_edges',
provides=[
'lsnl', 'lsnl_component', 'escale', 'lsnl_weight', 'lsnl_edges'
]),
rebin=NestedDict(bundle='rebin_v02',
rounding=3,
edges=N.concatenate(([0.7], N.arange(1.2, 8.1,
0.2), [12.0]))))
#
# Put the expression into context
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)
# # Print the list of outputs, stored for the expression
# from gna.bindings import OutputDescriptor
# env.globalns.materializeexpressions(True)
# env.globalns.printparameters( labels=True )
# print( 'outputs:' )
# print( context.outputs )
try:
from gna.graphviz import GNADot
obj = context.outputs[obj]
if isinstance(obj, NestedDict):
obj = obj.values()
if len(expr) < 4:
label = expr[0]
if label == 'evis()':
label = expr[1]
else:
label = ''
graph = GNADot(obj, joints=False, label=label)
name = args.dot.replace('.dot', '_' + suffix + '.dot')
graph.write(name)
print('Write output to:', name)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
def init_configuration(self):
mode_yb = self.opts.mode == 'yb'
self.cfg = NestedDict(
kinint2 = NestedDict(
bundle = dict(name='integral_2d1d', version='v03', names=dict(integral='kinint2'), inactive=mode_yb),
variables = ('evis', 'ctheta'),
edges = np.arange(0.6, 12.001, 0.01),
# edges = np.linspace(0.0, 12.001, 601),
xorders = 4,
yorder = 5,
),
rebin = NestedDict(
bundle = dict(name='rebin', version='v03', major='', inactive=mode_yb),
rounding = 3,
edges = np.concatenate(( [0.7], np.arange(1, 8.001, 0.02), [9.0, 12.0] )),
name = 'rebin',
label = 'Final histogram {detector}'
),
kinint2_enu = NestedDict(
bundle = dict(name='integral_2d1d', version='v03', names=dict(integral='kinint2'), inactive=not mode_yb),
variables = ('enu_in', 'ctheta'),
edges = np.linspace(1.8, 8.0, 601),
xorders = 4,
yorder = 5,
),
rebin_yb = NestedDict(
bundle = dict(name='rebin', version='v03', major='', inactive=not mode_yb),
rounding = 3,
edges = np.linspace(1.8, 8.0, 201),
name = 'rebin',
label = 'Final histogram {detector}'
),
ibd_xsec = NestedDict(
bundle = dict(name='xsec_ibd', version='v02'),
order = 1,
),
oscprob = NestedDict(
bundle = dict(name='oscprob', version='v03', major='rdc'),
pdgyear = self.opts.pdgyear
),
anuspec = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v03'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.025 ), [ 12.3 ] ) ),
),
anuspec_1 = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v03'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.05 ), [ 12.3 ] ) ),
),
eff = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="eff",
label='Detection efficiency',
pars = uncertain(0.8, 'fixed')
),
global_norm = NestedDict(
bundle = dict(
name='parameters',
version='v01'),
parameter="global_norm",
label='Global normalization',
pars = uncertain(1, 'free'),
),
fission_fractions = NestedDict(
bundle = dict(name="parameters",
version = "v01",
major = 'i'
),
parameter = "fission_fractions",
label = 'Fission fraction of {isotope} in reactor {reactor}',
objectize=True,
pars = uncertaindict([
('U235', 0.60),
('Pu239', 0.27),
('U238', 0.07),
('Pu241', 0.06)
],
# uncertainty = 30.0,
# mode = 'percent',
mode = 'fixed',
),
),
livetime = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "livetime",
label = 'Livetime of {detector} in seconds',
pars = uncertaindict(
[('AD1', (6*365*seconds_per_day, 'fixed'))],
),
),
baselines = NestedDict(
bundle = dict(name='reactor_baselines', version='v01', major = 'rd'),
reactors = 'near-equal' in self.opts.reactors \
and 'data/juno_nominal/coordinates_reactors_equal.py' \
or 'data/juno_nominal/coordinates_reactors.py',
detectors = 'data/juno_nominal/coordinates_det.py',
unit = 'km'
),
norm = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "norm",
label = 'Reactor power/detection efficiency correlated normalization',
pars = uncertain(1.0, (2**2+1**2)**0.5, 'percent')
),
thermal_power = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "thermal_power",
label = 'Thermal power of {reactor} in GWt',
pars = uncertaindict([
('TS1', 4.6),
('TS2', 4.6),
('TS3', 4.6),
('TS4', 4.6),
('YJ1', 2.9),
('YJ2', 2.9),
('YJ3', 2.9),
('YJ4', 2.9),
('YJ5', 2.9),
('YJ6', 2.9),
('DYB', 17.4),
('HZ', 17.4),
],
uncertainty=0.8,
mode='percent'
),
),
target_protons = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "target_protons",
label = 'Number of protons in {detector}',
pars = uncertaindict(
[('AD1', (1.42e33, 'fixed'))],
),
),
conversion_factor = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter='conversion_factor',
label='Conversion factor from GWt to MeV',
#taken from transformations/neutrino/ReactorNorm.cc
pars = uncertain(R.NeutrinoUnits.reactorPowerConversion, 'fixed'),
),
eper_fission = NestedDict(
bundle = dict(name="parameters", version = "v01"),
parameter = "eper_fission",
label = 'Energy per fission for {isotope} in MeV',
pars = uncertaindict(
[
# ('U235', (201.92, 0.46)),
# ('U238', (205.52, 0.96)),
# ('Pu239', (209.99, 0.60)),
# ('Pu241', (213.60, 0.65))
('U235', 201.92),
('U238', 205.52),
('Pu239', 209.99),
('Pu241', 213.60)
],
# mode='absolute'
mode='fixed'
),
),
lsnl = NestedDict(
bundle = dict( name='energy_nonlinearity_birks_cherenkov', version='v01', major=''),
stopping_power='data/data_juno/energy_model/2019_birks_cherenkov_v01/stoppingpower.txt',
annihilation_electrons=dict(
file='data/data_juno/energy_model/2019_birks_cherenkov_v01/hgamma2e.root',
histogram='hgamma2e_1KeV',
scale=1.0/50000 # events simulated
),
pars = uncertaindict(
[
('birks.Kb0', (1.0, 'fixed')),
('birks.Kb1', (15.2e-3, 0.1776)),
# ('birks.Kb2', (0.0, 'fixed')),
("cherenkov.E_0", (0.165, 'fixed')),
("cherenkov.p0", ( -7.26624e+00, 'fixed')),
("cherenkov.p1", ( 1.72463e+01, 'fixed')),
("cherenkov.p2", ( -2.18044e+01, 'fixed')),
("cherenkov.p3", ( 1.44731e+01, 'fixed')),
("cherenkov.p4", ( 3.22121e-02, 'fixed')),
("Npescint", (1341.38, 0.0059)),
("kC", (0.5, 0.4737)),
("normalizationEnergy", (12.0, 'fixed'))
],
mode='relative'
),
integration_order = 2,
correlations_pars = [ 'birks.Kb1', 'Npescint', 'kC' ],
correlations = [ 1.0, 0.94, -0.97,
0.94, 1.0, -0.985,
-0.97, -0.985, 1.0 ],
fill_matrix=True,
labels = dict(
normalizationEnergy = 'Conservative normalization point at 12 MeV'
),
),
eres = NestedDict(
bundle = dict(name='detector_eres_normal', version='v01', major='', inactive='multieres' in self.opts.energy_model),
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
parameter = 'eres',
pars = uncertaindict([
('a', (0.000, 'fixed')),
('b', (0.03, 'fixed')),
('c', (0.000, 'fixed'))
]),
expose_matrix = False
),
subdetector_fraction = NestedDict(
bundle = dict(name="parameters", version = "v02"),
parameter = "subdetector_fraction",
label = 'Subdetector fraction weight for {subdetector}',
pars = uncertaindict(
[(subdet_name, (1.0/self.subdetectors_number, 0.04, 'relative')) for subdet_name in self.subdetectors_names],
),
correlations = 'data/data_juno/energy_resolution/2019_subdetector_eres_n200/corrmap_xuyu.txt'
),
multieres = NestedDict(
bundle = dict(name='detector_multieres_stats', version='v01', major='s', inactive='multieres' not in self.opts.energy_model),
# pars: sigma_e/e = sqrt(b^2/E),
parameter = 'eres',
nph = 'data/data_juno/energy_resolution/2019_subdetector_eres_n200/subdetector200_nph.txt',
expose_matrix = False
),
shape_uncertainty = NestedDict(
unc = uncertain(1.0, 1.0, 'percent'),
nbins = 200 # number of bins, the uncertainty is defined to
)
)
if mode_yb:
from physlib import PhysicsConstants
pc = PhysicsConstants(2016)
shift = pc.DeltaNP - pc.ElectronMass
histshift = R.HistEdgesLinear(1.0, -shift)
self.cfg.enuToEvis0 = NestedDict(
bundle=dict(name='predefined', version='v01'),
name='enuToEvis0',
inputs=(histshift.histedges.hist_in, ),
outputs=histshift.histedges.hist,
object=histshift)
if not 'lsnl' in self.opts.correlation:
self.cfg.lsnl.correlations = None
self.cfg.lsnl.correlations_pars = None
if not 'subdetectors' in self.opts.correlation:
self.cfg.subdetector_fraction.correlations = None
if self.opts.parameters in ['yb', 'yb-noosc']:
self.cfg.eff.pars = uncertain(0.73, 'fixed')
self.cfg.livetime.pars['AD1'] = uncertain(
6 * 330 * seconds_per_day, 'fixed')
cfg = NestedDict()
cfg.detectors = [ 'AD11', 'AD12', 'AD21', 'AD22', 'AD31', 'AD32', 'AD33', 'AD34' ]
cfg.groups = OrderedDict( [
( 'EH1', ('AD11', 'AD12') ),
( 'EH2', ('AD21', 'AD22') ),
( 'EH3', ('AD31', 'AD32', 'AD33', 'AD34') ),
] )
bkg = cfg.bkg = NestedDict([
('list', [ 'acc', 'lihe', 'fastn', 'amc', 'alphan' ]),
# ('bundle', 'bundle_sum_v01')
])
bkg.acc = NestedDict(
bundle = 'bkg_weighted_hist_v01'
norm = uncertain( 1.0, 1.0, 'percent' ),
spectra = NestedDict(
bundle = 'root_histograms_v01',
file = 'data/dayabay/data_spectra/P15A_IHEP_data/data_IHEP_P15A_All.root',
format = 'accidental_pred_{}',
variants = OrderedDict([
( 'AD11', 'EH1_AD1' ), ( 'AD12', 'EH1_AD2' ),
( 'AD21', 'EH2_AD3' ), ( 'AD22', 'EH2_AD8' ),
( 'AD31', 'EH3_AD4' ), ( 'AD32', 'EH3_AD5' ), ( 'AD33', 'EH3_AD6' ), ( 'AD34', 'EH3_AD7' ),
])
)
)
cfg.lihe = NestedDict(
rates = NestedDict(
docdb = [10956],