def test_snf_spectrum_expression(tmp_path):
"""Test for new version of Daya Bay fast neutron background bundle,
updated for new integrators"""
indices = [
('s', 'site', ['EH1', 'EH2', 'EH3']),
('d', 'detector', ['AD11', 'AD12', 'AD21', 'AD22', 'AD31', 'AD32', 'AD33', 'AD34'],
dict(short='s', name='site', map=OrderedDict([('EH1', ('AD11', 'AD12')), ('EH2', ('AD21', 'AD22')), ('EH3', ('AD31', 'AD32', 'AD33', 'AD34'))]))),
]
expr = ['evis_edges()',
'fastn_shape[s]',
'bkg_spectrum_fastn[s]()'
]
a = Expression_v01(expr, indices = NIndex.fromlist(indices))
a.parse()
lib = dict()
a.guessname(lib, save=True)
ns = env.globalns('fastn')
cfg = NestedDict(
integral = NestedDict(
bundle = dict(name='integral_2d1d', version='v03'),
variables = ('evis', 'ctheta'),
edges = np.linspace(0.0, 12.0, 241, dtype='d'),
xorders = 4,
yorder = 2,
),
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,
),
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',
),
),
)
context = ExpressionContext_v01(cfg, ns=ns)
a.build(context)
])
)
bkg = cfg('bkg')
bkg.bundle = 'bundlesum_v01'
bkg.bundlesum_list = [ 'bkg1', 'bkg2', 'bkgw' ] #, 'bkg_fn'
bkg.observable = 'bkg_total'
bkg.bkg1 = NestedDict(
bundle = 'bkg_weighted_hist_v01',
formula = [ '{det}.bkg1_num', ('bkg1_norm.{det}', '{det}.bkg1_rate', '{det}.livetime') ],
groups = cfg.groups,
variants = cfg.detectors,
bkg1_norm = uncertaindict([
(det, (1.0, 1.0, 'percent')) \
for det in cfg.detectors
]),
bkg1_rate = uncertaindict(
[ ('D1', 8),
('D2', 7),
('D3', 4),
('D4', 3) ],
mode = 'fixed',
),
spectra = NestedDict(
bundle = 'root_histograms_v01',
filename = cfg.filename,
format = 'hist_{self}',
variants = OrderedDict([
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.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) ,
('Eres_c', 0.0271)],
mode='percent',
uncertainty=30
)
)
cfg.detector.rebin = NestedDict(
bundle = 'rebin',
rounding = 3,
edges = [ 0.0, 5.0, 10.0 ]
)
#
# Define namespaces
#
namespaces = cfg.detector.detectors
def main(opts):
global savefig
cfg = NestedDict(
bundle = dict(
name='energy_nonlinearity_birks_cherenkov',
version='v01',
nidx=[ ('r', 'reference', ['R1', 'R2']) ],
major=[],
),
stopping_power='stoppingpower.txt',
annihilation_electrons=dict(
file='input/hgamma2e.root',
histogram='hgamma2e_1KeV',
scale=1.0/50000 # event 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", (2.505, 'fixed'))
# ("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 = '60Co total gamma energy, MeV'
# normalizationEnergy = 'Pessimistic norm point'
),
)
ns = env.globalns('energy')
quench = execute_bundle(cfg, namespace=ns)
ns.printparameters(labels=True)
print()
normE = ns['normalizationEnergy'].value()
#
# Input bins
#
evis_edges_full_input = N.arange(0.0, 12.0+1.e-6, 0.025)
evis_edges_full_hist = C.Histogram(evis_edges_full_input, labels='Evis bin edges')
evis_edges_full_hist >> quench.context.inputs.evis_edges_hist['00']
#
# HistNonLinearity transformation
#
reference_histogram1_input = N.zeros(evis_edges_full_input.size-1)
reference_histogram2_input = reference_histogram1_input.copy()
reference_histogram1_input+=1.0
# reference_histogram2_input[[10, 20, 50, 100, 200, 300, 400]]=1.0
reference_histogram2_input[[10, 20]]=1.0
reference_histogram1 = C.Histogram(evis_edges_full_input, reference_histogram1_input, labels='Reference hist 1')
reference_histogram2 = C.Histogram(evis_edges_full_input, reference_histogram2_input, labels='Reference hist 2')
reference_histogram1 >> quench.context.inputs.lsnl.R1.values()
reference_histogram2 >> quench.context.inputs.lsnl.R2.values()
reference_smeared1 = quench.context.outputs.lsnl.R1
reference_smeared2 = quench.context.outputs.lsnl.R2
#
# Plots and tests
#
if opts.output and opts.output.endswith('.pdf'):
pdfpages = PdfPages(opts.output)
pdfpagesfilename=opts.output
savefig_old=savefig
pdf=pdfpages.__enter__()
def savefig(*args, **kwargs):
if opts.individual and args and args[0]:
savefig_old(*args, **kwargs)
pdf.savefig()
else:
pdf = None
pdfpagesfilename = ''
#
# Plots and tests
#
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( 'dE/dx' )
ax.set_title( 'Stopping power' )
quench.birks_quenching_p.points.points.plot_vs(quench.birks_e_p.points.points, '-', markerfacecolor='none', markersize=2.0, label='input')
ax.legend(loc='upper right')
savefig(opts.output, suffix='_spower')
ax.set_xlim(left=0.001)
ax.set_xscale('log')
savefig(opts.output, suffix='_spower_log')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( '' )
ax.set_title( "Birk's integrand" )
quench.birks_integrand_raw.polyratio.ratio.plot_vs(quench.birks_e_p.points.points, '-o', alpha=0.5, markerfacecolor='none', markersize=2.0, label='raw')
# birks_integrand_interpolated.plot_vs(integrator_ekin.points.x, '-', alpha=0.5, markerfacecolor='none', markersize=2.0, label='interpolated')
lines=quench.birks_integrand_interpolated.plot_vs(quench.integrator_ekin.points.x, '-', alpha=0.5, markerfacecolor='none', markersize=2.0, label='interpolated')
quench.birks_integrand_interpolated.plot_vs(quench.integrator_ekin.points.x, 'o', alpha=0.5, color='black', markersize=0.6)
ax.legend(loc='lower right')
savefig()
ax.set_xlim(left=0.0001)
ax.set_ylim(bottom=0.3)
# ax.set_yscale('log')
ax.set_xscale('log')
savefig(opts.output, suffix='_spower_int')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( '' )
ax.set_title( "Birk's integral (bin by bin)" )
quench.birks_integral.plot_hist()
savefig()
ax.set_xlim(left=0.0001)
ax.set_ylim(bottom=0.0)
# ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim(auto=True)
savefig(opts.output, suffix='_spower_intc')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( 'Evis, MeV' )
ax.set_title( 'Electron energy (Birks)' )
# birks_accumulator.reduction.out.plot_vs(integrator_evis.transformations.hist, '-', markerfacecolor='none', markersize=2.0, label='partial sum')
quench.birks_accumulator.reduction.plot_vs(quench.histoffset.histedges.points_offset)
ax.plot([0.0, 12.0], [0.0, 12.0], '--', alpha=0.5)
savefig(opts.output, suffix='_birks_evis')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( 'Evis/Edep' )
ax.set_title( 'Electron energy (Birks)' )
# birks_accumulator.reduction.out.plot_vs(integrator_evis.transformations.hist, '-', markerfacecolor='none', markersize=2.0, label='partial sum')
quench.histoffset.histedges.points_offset.vs_plot(quench.birks_accumulator.reduction.data()/quench.histoffset.histedges.points_offset.data())
ax.set_ylim(0.40, 1.0)
savefig(opts.output, suffix='_birks_evis_rel')
ax.set_xlim(1.e-3, 2.0)
ax.set_xscale('log')
savefig()
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( 'Npe' )
ax.set_title( 'Cherenkov photons' )
quench.cherenkov.cherenkov.ch_npe.plot_vs(quench.histoffset.histedges.points_offset)
savefig(opts.output, suffix='_cherenkov_npe')
ax.set_ylim(bottom=0.1)
ax.set_yscale('log')
savefig()
ax.set_xlim(0.0, 2.0)
# ax.set_ylim(0.0, 200.0)
savefig()
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( 'Npe' )
ax.set_title( 'Electron model' )
quench.electron_model.single().plot_vs(quench.histoffset.histedges.points_offset)
savefig(opts.output, suffix='_electron')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( 'Npe' )
ax.set_title( 'Electron model (low energy view)' )
annihilation_gamma_evis = quench.npe_positron_offset.normconvolution.result.data()[0]
label = 'Annihilation contribution=%.2f Npe'%annihilation_gamma_evis
quench.electron_model_lowe.plot_vs(quench.ekin_edges_lowe, 'o', markerfacecolor='none', label='data')
quench.electron_model_lowe_interpolated.single().plot_vs(quench.annihilation_electrons_centers.single(), '-', label='interpolation\n%s'%label)
ax.legend(loc='upper left')
savefig(opts.output, suffix='_electron_lowe')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( 'Npe' )
ax.set_title( 'Total Npe' )
quench.positron_model.sum.outputs[0].plot_vs(quench.histoffset.histedges.points_truncated)
savefig(opts.output, suffix='_total_npe')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( 'Evis, MeV' )
ax.set_title( 'Positron energy model' )
quench.positron_model_scaled.plot_vs(quench.histoffset.histedges.points_truncated, label='definition range')
quench.positron_model_scaled_full.plot_vs(quench.histoffset.histedges.points, '--', linewidth=1., label='full range', zorder=0.5)
ax.vlines(normE, 0.0, normE, linestyle=':')
ax.hlines(normE, 0.0, normE, linestyle=':')
ax.legend(loc='upper left')
savefig(opts.output, suffix='_total')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'Edep, MeV' )
ax.set_ylabel( 'Evis/Edep' )
ax.set_title( 'Positron energy nonlineairty' )
quench.positron_model_relative.single().plot_vs(quench.histoffset.histedges.points_truncated, label='definition range')
quench.positron_model_relative_full.plot_vs(quench.histoffset.histedges.points, '--', linewidth=1., label='full range', zorder=0.5)
ax.vlines(normE, 0.0, 1.0, linestyle=':')
ax.legend(loc='lower right')
ax.set_ylim(0.85, 1.1)
savefig(opts.output, suffix='_total_relative')
ax.set_xlim(0.75, 3)
savefig(opts.output, suffix='_total_relative1')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Etrue, MeV')
ax.set_ylabel('Edep, MeV')
ax.set_title( 'Smearing matrix' )
quench.pm_histsmear.matrix.FakeMatrix.plot_matshow(mask=0.0, extent=[evis_edges_full_input.min(), evis_edges_full_input.max(), evis_edges_full_input.max(), evis_edges_full_input.min()], colorbar=True)
ax.plot([0.0, 12.0], [0.0, 12.0], '--', alpha=0.5, linewidth=1.0, color='magenta')
ax.vlines(normE, 0.0, normE, linestyle=':')
ax.hlines(normE, 0.0, normE, linestyle=':')
savefig(opts.output, suffix='_matrix')
ax.set_xlim(0.8, 3.0)
ax.set_ylim(3.0, 0.8)
savefig(opts.output, suffix='_matrix_zoom')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( '' )
ax.set_title( 'Reference histogram 1' )
reference_histogram1.single().plot_hist(linewidth=0.5, alpha=1.0, label='original')
reference_smeared1.single().plot_hist( linewidth=0.5, alpha=1.0, label='smeared')
ax.legend(loc='upper right')
savefig(opts.output, suffix='_refsmear1')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( '' )
ax.set_title( 'Matrix projections' )
mat = quench.pm_histsmear.matrix.FakeMatrix.data()
proj0 = mat.sum(axis=0)
proj1 = mat.sum(axis=1)
plot_hist(evis_edges_full_input, proj0, alpha=0.7, linewidth=1.0, label='Projection 0: Edep view')
plot_hist(evis_edges_full_input, proj1, alpha=0.7, linewidth=1.0, label='Projection 1: Evis')
ax.legend(loc='upper right')
savefig(opts.output, suffix='_matrix_projections')
if opts.mapping:
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( '' )
ax.set_title( 'Mapping' )
positron_model_scaled_data = quench.positron_model_scaled.single().data()
for e1, e2 in zip(quench.histoffset.histedges.points_truncated.data(), positron_model_scaled_data):
if e2>12.0 or e2<1.022:
alpha = 0.05
else:
alpha = 0.7
ax.plot( [e1, e2], [1.0, 0.0], '-', linewidth=2.0, alpha=alpha )
ax.axvline(1.022, linestyle='--', linewidth=1.0)
ax.axvline(12.0, linestyle='--', linewidth=1.0)
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
# ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( '' )
ax.set_title( 'Mapping' )
positron_model_scaled_data = quench.positron_model_scaled.single().data()
for e1, e2 in zip(quench.histoffset.histedges.points_truncated.data(), positron_model_scaled_data):
if e2>12.0 or e2<1.022:
alpha = 0.05
else:
alpha = 0.7
ax.plot( [e1, e2], [1.1, 0.9], '-', linewidth=2.0, alpha=alpha )
for e1 in quench.histoffset.histedges.points.data():
ax.axvline(e1, linestyle=':', linewidth=1.0, color='black')
ax.axvline(1.022, linestyle='--', linewidth=1.0)
ax.axvline(12.0, linestyle='--', linewidth=1.0)
# ax.legend(loc='upper right')
savefig(opts.output, suffix='_mapping_bins')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( '' )
ax.set_title( 'Reference histogram 2' )
reference_histogram2.single().plot_hist(linewidth=0.5, alpha=1.0, label='original')
reference_smeared2.single().plot_hist( linewidth=0.5, alpha=1.0, label='smeared')
ax.vlines(normE, 0.0, 1.0, linestyle=':')
ax.legend(loc='upper right')
savefig(opts.output, suffix='_refsmear2')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'E, MeV' )
ax.set_ylabel( 'Entries' )
ax.set_title( 'Annihilation gamma electrons' )
plot_hist(quench.annihilation_electrons_edges_input, quench.annihilation_electrons_p_input)
ax.set_yscale('log')
savefig(opts.output, suffix='_annihilation_electrons')
ax.set_yscale('linear')
ax.set_xlim(0.0, 0.1)
savefig(opts.output, suffix='_annihilation_electrons_lin')
if pdfpages:
pdfpages.__exit__(None,None,None)
print('Write output figure to', pdfpagesfilename)
savegraph(quench.histoffset.histedges.points_truncated, opts.graph, namespace=ns)
if opts.show:
P.show()
nidx=[
('s', 'source', ['SA', 'SB']),
'name',
('d', 'detector', ['D1', 'D2', 'D3']),
],
),
parameter='rate0',
label='Flux normalization {source}->{detector}',
separate_uncertainty='norm0',
pars=uncertaindict(
[
('SA.D1', 1.0),
('SB.D1', 2.0),
('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',
),
)
cfg2 = NestedDict(
bundle=dict(name='parameters',
version='v01',
nidx=[('s', 'source', ['SA', 'SB']),
('d', 'detector', ['D1', 'D2', 'D3']),
('e', 'element', ['e0', 'e1'])],
major=('s', 'd')),
print()
# Provide the configuration. The configuration tells the Expression on how to build each variable/output.
cfg = NestedDict(
ta=NestedDict(bundle=dict(name='dummy', version='v01'),
name='ta',
input=False,
size=10,
debug=False),
tb=NestedDict(bundle=dict(name='dummy', version='v01'),
name='tb',
input=False,
size=10,
debug=False),
va=NestedDict(
bundle=dict(name='dummyvar', version='v01'),
variables=uncertaindict([('vara', (2, 0.1))], mode='percent'),
),
vb=NestedDict(
bundle=dict(name='dummyvar', version='v01'),
variables=uncertaindict([('varb', (3, 0.1))], mode='percent'),
),
)
# Build the expression for given configuration. The output is a context, that contains the inputs and outputs.
context = ExpressionContext(cfg)
a.build(context)
from gna.bindings import OutputDescriptor
# Print inputs
print('Inputs')
print(context.inputs)
debug=False),
offset=NestedDict(bundle='dummy',
name='offset',
input=False,
size=10,
debug=False),
function=NestedDict(
bundle='dummy',
name='function',
label='function\ni={index_i}, j={index_j}, k={index_k}',
input=1,
size=10,
debug=False),
scale=NestedDict(bundle='dummyvar',
variables=uncertaindict([
('scale', (2, 0.1)),
],
mode='percent')),
)
# Build the expression for given configuration. The output is a context, that contains the inputs and outputs.
context = ExpressionContext(cfg)
a.build(context)
from gna.bindings import OutputDescriptor
# Print inputs
print('Inputs')
print(context.inputs)
# Print outputs
print()
print('Outputs')
print(context.outputs)
bundle='dayabay_reactor_burning_info_v01',
reactor_info='data/dayabay/reactor/power/WeeklyAvg_P15A_v1.txt.npz',
fission_uncertainty_info=
'data/dayabay/reactor/fission_fraction/2013.12.05_xubo.py',
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,
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')
import load
from gna.bundle import execute_bundle
from gna.configurator import NestedDict, uncertaindict, uncertain
from gna.env import env
#
# Bundle configuration
#
cfg = NestedDict(
bundle=dict(
name='parameters',
version='ex01',
),
pars=uncertaindict([('par_a', (1.0, 1.0, 'percent')),
('par_b', (2.0, 0.01, 'relative')),
('par_c', (3.0, 0.5, 'absolute')),
('group.a', (1.0, 'free')),
('group.b',
(1.0, 'fixed', 'Labeled fixed parameter'))], ),
)
#
# Execute bundle configuration
#
b1 = execute_bundle(cfg)
#
# Print the parameters
#
env.globalns.printparameters(labels=True)
from gna.env import env
from gna.bindings import common
from gna import constructors as C
import numpy as np
from matplotlib import pyplot as plt
cfg = NestedDict(
bundle=dict(
name='detector_eres',
version='ex01',
),
parameter='eres',
pars=uncertaindict([
('a', 0.01),
('b', 0.09),
('c', 0.03),
],
mode='percent',
uncertainty=30.0),
)
b = execute_bundle(cfg)
env.globalns.printparameters(labels=True)
print()
#
# Prepare inputs
#
emin, emax = 0.0, 12.0
nbins = 240
edges = np.linspace(emin, emax, nbins + 1, dtype='d')
data = np.zeros(nbins, dtype='d')
'name',
('d', 'detector', ['D1', 'D2', 'D3']),
],
),
# Parameter name to be defined
parameter='rate0',
# Label format
label='Flux normalization {source}->{detector}',
# Dictionary with parameter values and uncertainties
pars=uncertaindict(
[
('SA.D1', 1.0),
('SB.D1', 2.0),
('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',
),
)
b1 = execute_bundle(cfg1, namespace=env.globalns('bundle1'))
env.globalns('bundle1').printparameters(labels=True)
print()
#
# List of parameters 2
#
cfg2 = NestedDict(
provides = [ 'eff', 'effunc_corr', 'effunc_uncorr', 'global_norm' ],
efficiencies = 'data/dayabay/efficiency/P15A_efficiency.py'
),
fission_fractions = NestedDict(
bundle = dict(name="parameters",
version = "v01",
nidx = [indices[1], indices[2]],
major = 'i'
),
parameter = "fission_fractions",
label = 'Fission fraction of {isotope} in reactor {reactor}',
pars = uncertaindict([
('U235', 0.60),
('Pu239', 0.27),
('U238', 0.07),
('Pu241', 0.06)
],
uncertainty = 30.0,
mode = 'percent',
),
provides=['fission_fractions']
),
livetime = NestedDict(
bundle = dict(name="parameters",
version = "v01",
nidx = [indices[0]]),
parameter = "livetime",
label = 'Livetime of {detector} in seconds',
pars = uncertaindict(
[('AD1', (6*365*seconds_per_day, 'fixed'))],
),
def main(opts):
global savefig
cfg = NestedDict(
bundle=dict(
name='energy_nonlinearity_birks_cherenkov',
version='v01',
nidx=[('r', 'reference', ['R1', 'R2'])],
major=[],
),
stopping_power='stoppingpower.txt',
annihilation_electrons=dict(
file='input/hgamma2e.root',
histogram='hgamma2e_1KeV',
scale=1.0 / 50000 # event 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='Pessimistic'),
)
ns = env.globalns('energy')
quench = execute_bundle(cfg, namespace=ns)
ns.printparameters(labels=True)
print()
normE = ns['normalizationEnergy'].value()
#
# Input bins
#
evis_edges_full_input = N.arange(0.0, 15.0 + 1.e-6, 0.025)
evis_edges_full_hist = C.Histogram(evis_edges_full_input,
labels='Evis bin edges')
evis_edges_full_hist >> quench.context.inputs.evis_edges_hist['00']
#
# Python energy model interpolation function
#
from scipy.interpolate import interp1d
lsnl_x = quench.histoffset.histedges.points_truncated.data()
lsnl_y = quench.positron_model_relative.single().data()
lsnl_fcn = interp1d(lsnl_x, lsnl_y, kind='quadratic')
#
# Energy resolution
#
def eres_sigma_rel(edep):
return 0.03 / edep**0.5
def eres_sigma_abs(edep):
return 0.03 * edep**0.5
#
# Energy offset
#
from physlib import pc
edep_offset = pc.DeltaNP - pc.ElectronMass
#
# Oscprob
#
baselinename = 'L'
ns = env.ns("oscprob")
import gna.parameters.oscillation
gna.parameters.oscillation.reqparameters(ns)
ns.defparameter(baselinename,
central=52.0,
fixed=True,
label='Baseline, km')
#
# Define energy range
#
enu_input = N.arange(1.8, 15.0, 0.001)
edep_input = enu_input - edep_offset
edep_lsnl = edep_input * lsnl_fcn(edep_input)
# Initialize oscillation variables
enu = C.Points(enu_input, labels='Neutrino energy, MeV')
component_names = C.stdvector(['comp0', 'comp12', 'comp13', 'comp23'])
with ns:
R.OscProbPMNSExpressions(R.Neutrino.ae(),
R.Neutrino.ae(),
component_names,
ns=ns)
labels = [
'Oscillation probability|%s' % s
for s in ('component 12', 'component 13', 'component 23', 'full',
'probsum')
]
oscprob = R.OscProbPMNS(R.Neutrino.ae(),
R.Neutrino.ae(),
baselinename,
labels=labels)
enu >> oscprob.full_osc_prob.Enu
enu >> (oscprob.comp12.Enu, oscprob.comp13.Enu, oscprob.comp23.Enu)
unity = C.FillLike(1, labels='Unity')
enu >> unity.fill.inputs[0]
with ns:
op_sum = C.WeightedSum(component_names, [
unity.fill.outputs[0], oscprob.comp12.comp12,
oscprob.comp13.comp13, oscprob.comp23.comp23
],
labels='Oscillation probability sum')
psur = op_sum.single().data()
from scipy.signal import argrelmin, argrelmax
psur_minima, = argrelmin(psur)
psur_maxima, = argrelmax(psur)
def build_extrema(x):
data_min_x = (x[psur_minima][:-1] + x[psur_minima][1:]) * 0.5
data_min_y = (x[psur_minima][1:] - x[psur_minima][:-1])
data_max_x = (x[psur_maxima][:-1] + x[psur_maxima][1:]) * 0.5
data_max_y = (x[psur_maxima][1:] - x[psur_maxima][:-1])
data_ext_x = N.vstack([data_max_x, data_min_x]).T.ravel()
data_ext_y = N.vstack([data_max_y, data_min_y]).T.ravel()
return data_ext_x, data_ext_y
psur_ext_x_enu, psur_ext_y_enu = build_extrema(enu_input)
psur_ext_x_edep, psur_ext_y_edep = build_extrema(edep_input)
psur_ext_x_edep_lsnl, psur_ext_y_edep_lsnl = build_extrema(edep_lsnl)
#
# Plots and tests
#
if opts.output and opts.output.endswith('.pdf'):
pdfpages = PdfPages(opts.output)
pdfpagesfilename = opts.output
savefig_old = savefig
pdf = pdfpages.__enter__()
def savefig(*args, **kwargs):
if opts.individual and args and args[0]:
savefig_old(*args, **kwargs)
pdf.savefig()
else:
pdf = None
pdfpagesfilename = ''
pdfpages = None
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel('Evis/Edep')
ax.set_title('Positron energy nonlineairty')
quench.positron_model_relative.single().plot_vs(
quench.histoffset.histedges.points_truncated, label='definition range')
quench.positron_model_relative_full.plot_vs(
quench.histoffset.histedges.points,
'--',
linewidth=1.,
label='full range',
zorder=0.5)
ax.vlines(normE, 0.0, 1.0, linestyle=':')
ax.legend(loc='lower right')
ax.set_ylim(0.8, 1.05)
savefig(opts.output, suffix='_total_relative')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel(r'$\sigma/E$')
ax.set_title('Energy resolution')
ax.plot(edep_input, eres_sigma_rel(edep_input), '-')
savefig(opts.output, suffix='_eres_rel')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel(r'$\sigma$')
ax.set_title('Energy resolution')
ax.plot(edep_input, eres_sigma_abs(edep_input), '-')
savefig(opts.output, suffix='_eres_abs')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Enu, MeV')
ax.set_ylabel('Psur')
ax.set_title('Survival probability')
op_sum.single().plot_vs(enu.single(), label='full')
ax.plot(enu_input[psur_minima],
psur[psur_minima],
'o',
markerfacecolor='none',
label='minima')
ax.plot(enu_input[psur_maxima],
psur[psur_maxima],
'o',
markerfacecolor='none',
label='maxima')
savefig(opts.output, suffix='_psur_enu')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel('Psur')
ax.set_title('Survival probability')
op_sum.single().plot_vs(edep_input, label='true')
op_sum.single().plot_vs(edep_lsnl, label='with LSNL')
ax.legend()
savefig(opts.output, suffix='_psur_edep')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Enu, MeV')
ax.set_ylabel('Dist, MeV')
ax.set_title('Nearest peaks distance')
ax.plot(psur_ext_x_enu, psur_ext_y_enu, 'o-', markerfacecolor='none')
savefig(opts.output, suffix='_dist_enu')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel('Dist, MeV')
ax.set_title('Nearest peaks distance')
ax.plot(psur_ext_x_edep,
psur_ext_y_edep,
'-',
markerfacecolor='none',
label='true')
ax.plot(psur_ext_x_edep_lsnl,
psur_ext_y_edep_lsnl,
'-',
markerfacecolor='none',
label='with LSNL')
ax.plot(edep_input,
eres_sigma_abs(edep_input),
'-',
markerfacecolor='none',
label=r'$\sigma$')
ax.legend(loc='upper left')
savefig(opts.output, suffix='_dist')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel(r'Dist/$\sigma$')
ax.set_title('Resolution ability')
x1, y1 = psur_ext_x_edep, psur_ext_y_edep / eres_sigma_abs(psur_ext_x_edep)
x2, y2 = psur_ext_x_edep_lsnl, psur_ext_y_edep_lsnl / eres_sigma_abs(
psur_ext_x_edep_lsnl)
ax.plot(x1, y1, '-', markerfacecolor='none', label='true')
ax.plot(x2, y2, '-', markerfacecolor='none', label='with LSNL')
ax.legend(loc='upper left')
savefig(opts.output, suffix='_ability')
ax.set_xlim(3, 4)
ax.set_ylim(5, 8)
savefig(opts.output, suffix='_ability_zoom')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('Edep, MeV')
ax.set_ylabel(r'Dist/$\sigma$')
ax.set_title('Resolution ability difference (quenching-true)')
y2fcn = interp1d(x2, y2)
y2_on_x1 = y2fcn(x1)
diff = y2_on_x1 - y1
from scipy.signal import savgol_filter
diff = savgol_filter(diff, 21, 3)
ax.plot(x1, diff)
savefig(opts.output, suffix='_ability_diff')
if pdfpages:
pdfpages.__exit__(None, None, None)
print('Write output figure to', pdfpagesfilename)
savegraph(quench.histoffset.histedges.points_truncated,
opts.graph,
namespace=ns)
if opts.show:
P.show()
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
from matplotlib import pyplot as plt
from gna.bindings import common
cfg = NestedDict(bundle=dict(name='detector_eres',
version='ex02',
nidx=[('d', 'detector', ['D1', 'D2', 'D3']),
('z', 'zone', ['z1', 'z2'])],
major=['z'],
names=dict(eres_matrix='smearing_matrix', )),
parameter='eres',
pars=uncertaindict([
('z1.a', (0.0, 'fixed')),
('z1.b', (0.05, 30, 'percent')),
('z1.c', (0.0, 'fixed')),
('z2.a', (0.0, 'fixed')),
('z2.b', (0.10, 30, 'percent')),
('z2.c', (0.0, 'fixed')),
('z3.a', (0.0, 'fixed')),
('z3.b', (0.15, 30, 'percent')),
('z3.c', (0.0, 'fixed')),
]),
labels=dict(matrix='Smearing\nmatrix\n{autoindex}',
smear='Energy\nresolution\n{autoindex}',
parameter='{description} (zone {autoindex})'),
split_transformations=True)
b = execute_bundle(cfg)
env.globalns.printparameters(labels=True)
print()
#
# Prepare inputs
def main(opts):
global savefig
if opts.output and opts.output.endswith('.pdf'):
pdfpages = PdfPages(opts.output)
pdfpagesfilename=opts.output
savefig_old=savefig
pdf=pdfpages.__enter__()
def savefig(*args, **kwargs):
close = kwargs.pop('close', False)
if opts.individual and args and args[0]:
savefig_old(*args, **kwargs)
pdf.savefig()
if close:
P.close()
else:
pdf = None
pdfpagesfilename = ''
pdfpages = None
cfg = NestedDict(
bundle = dict(
name='energy_nonlinearity_birks_cherenkov',
version='v01',
nidx=[ ('r', 'reference', ['R1', 'R2']) ],
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", (11.9999999, '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 = 'Pessimistic'
),
)
ns = env.globalns('energy')
quench = execute_bundle(cfg, namespace=ns)
print()
normE = ns['normalizationEnergy'].value()
#
# Input bins
#
evis_edges_full_input = N.arange(0.0, 15.0+1.e-6, 0.001)
evis_edges_full_hist = C.Histogram(evis_edges_full_input, labels='Evis bin edges')
evis_edges_full_hist >> quench.context.inputs.evis_edges_hist['00']
#
# Python energy model interpolation function
#
lsnl_x = quench.histoffset.histedges.points_truncated.data()
lsnl_y = quench.positron_model_relative.single().data()
lsnl_fcn = interp1d(lsnl_x, lsnl_y, kind='quadratic', bounds_error=False, fill_value='extrapolate')
#
# Energy resolution
#
def eres_sigma_rel(edep):
return 0.03/edep**0.5
def eres_sigma_abs(edep):
return 0.03*edep**0.5
#
# Oscprob
#
baselinename='L'
ns = env.ns("oscprob")
import gna.parameters.oscillation
gna.parameters.oscillation.reqparameters(ns)
ns.defparameter(baselinename, central=52.0, fixed=True, label='Baseline, km')
#
# Define energy range
#
data = Data(N.arange(1.8, 15.0, 0.001), lsnl_fcn=lsnl_fcn, eres_fcn=eres_sigma_abs)
# Initialize oscillation variables
enu = C.Points(data.enu, labels='Neutrino energy, MeV')
component_names = C.stdvector(['comp0', 'comp12', 'comp13', 'comp23'])
with ns:
R.OscProbPMNSExpressions(R.Neutrino.ae(), R.Neutrino.ae(), component_names, ns=ns)
labels=['Oscillation probability|%s'%s for s in ('component 12', 'component 13', 'component 23', 'full', 'probsum')]
oscprob = R.OscProbPMNS(R.Neutrino.ae(), R.Neutrino.ae(), baselinename, labels=labels)
enu >> oscprob.full_osc_prob.Enu
enu >> (oscprob.comp12.Enu, oscprob.comp13.Enu, oscprob.comp23.Enu)
unity = C.FillLike(1, labels='Unity')
enu >> unity.fill.inputs[0]
with ns:
op_sum = C.WeightedSum(component_names, [unity.fill.outputs[0], oscprob.comp12.comp12, oscprob.comp13.comp13, oscprob.comp23.comp23], labels='Oscillation probability sum')
oscprob.printtransformations()
env.globalns.printparameters(labels=True)
ns = env.globalns('oscprob')
data.set_dm_par(ns['DeltaMSqEE'])
data.set_nmo_par(ns['Alpha'])
data.set_psur_fcn(op_sum.single().data)
data.build()
#
# Plotting
#
xmax = 12.0
#
# Positron non-linearity
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='Evis/Edep', title='Positron energy nonlineairty')
ax.minorticks_on(); ax.grid()
quench.positron_model_relative.single().plot_vs(quench.histoffset.histedges.points_truncated, label='definition range')
quench.positron_model_relative_full.plot_vs(quench.histoffset.histedges.points, '--', linewidth=1., label='full range', zorder=0.5)
ax.vlines(normE, 0.0, 1.0, linestyle=':')
ax.legend(loc='lower right')
ax.set_ylim(0.8, 1.05)
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_total_relative', close=not opts.show_all)
#
# Positron non-linearity derivative
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='dEvis/dEdep', title='Positron energy nonlineairty derivative')
ax.minorticks_on(); ax.grid()
e = quench.histoffset.histedges.points_truncated.single().data()
f = quench.positron_model_relative.single().data()*e
ec = (e[1:] + e[:-1])*0.5
df = (f[1:] - f[:-1])
dedf = (e[1:] - e[:-1])/df
ax.plot(ec, dedf)
ax.legend(loc='lower right')
ax.set_ylim(0.975, 1.01)
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_total_derivative', close=not opts.show_all)
#
# Positron non-linearity effect
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='Evis/Edep', title='Positron energy nonlineairty')
ax.minorticks_on(); ax.grid()
es = N.arange(1.0, 3.1, 0.5)
esmod = es*lsnl_fcn(es)
esmod_shifted = esmod*(es[-1]/esmod[-1])
ax.vlines(es, 0.0, 1.0, linestyle='--', linewidth=2, alpha=0.5, color='green', label='Edep')
ax.vlines(esmod, 0.0, 1.0, linestyle='-', color='red', label='Edep quenched')
ax.legend()
savefig(opts.output, suffix='_quenching_effect_0')
ax.vlines(esmod_shifted, 0.0, 1.0, linestyle=':', color='blue', label='Edep quenched, scaled')
ax.legend()
savefig(opts.output, suffix='_quenching_effect_1', close=not opts.show_all)
#
# Energy resolution
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel=r'$\sigma/E$', title='Energy resolution')
ax.minorticks_on(); ax.grid()
ax.plot(data.edep, eres_sigma_rel(data.edep), '-')
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_eres_rel', close=not opts.show_all)
#
# Energy resolution
#
fig = P.figure()
ax = P.subplot(111, xlabel= 'Edep, MeV', ylabel= r'$\sigma$', title='Energy resolution')
ax.minorticks_on(); ax.grid()
ax.plot(data.edep, eres_sigma_abs(data.edep), '-')
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_eres_abs', close=not opts.show_all)
#
# Survival probability vs Enu
#
fig = P.figure()
ax = P.subplot(111, xlabel='Enu, MeV', ylabel='Psur', title='Survival probability')
ax.minorticks_on(); ax.grid()
ax.plot(data.enu, data.data_no.psur[data.dmmid_idx], label=r'full NO')
ax.plot(data.enu, data.data_io.psur[data.dmmid_idx], label=r'full IO')
ax.plot(data.data_no.data_enu.psur_e[data.dmmid_idx], data.data_no.data_enu.psur[data.dmmid_idx], '^', markerfacecolor='none')
ax.legend()
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_psur_enu')
ax.set_xlim(2.0, 4.5)
savefig(opts.output, suffix='_psur_enu_zoom', close=not opts.show_all)
#
# Survival probability vs Edep
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='Psur', title='Survival probability')
ax.minorticks_on(); ax.grid()
ax.plot(data.edep, data.data_no.psur[data.dmmid_idx], label=r'full NO')
ax.plot(data.edep, data.data_io.psur[data.dmmid_idx], label=r'full IO')
ax.plot(data.data_no.data_edep.psur_e[data.dmmid_idx], data.data_no.data_edep.psur[data.dmmid_idx], '^', markerfacecolor='none')
ax.legend()
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_psur_edep')
ax.set_xlim(1.2, 3.7)
savefig(opts.output, suffix='_psur_edep_zoom', close=not opts.show_all)
#
# Survival probability vs Edep_lsnl
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep quenched, MeV', ylabel='Psur', title='Survival probability')
ax.minorticks_on(); ax.grid()
ax.plot(data.edep_lsnl, data.data_no.psur[data.dmmid_idx], label=r'full NO')
ax.plot(data.edep_lsnl, data.data_io.psur[data.dmmid_idx], label=r'full IO')
ax.plot(data.data_no.data_edep_lsnl.psur_e[data.dmmid_idx], data.data_no.data_edep_lsnl.psur[data.dmmid_idx], '^', markerfacecolor='none')
ax.legend()
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_psur_edep_lsnl')
ax.set_xlim(1.2, 3.7)
savefig(opts.output, suffix='_psur_edep_lsnl_zoom', close=not opts.show_all)
#
# Distance between nearest peaks vs Enu, single
#
fig = P.figure()
ax = P.subplot(111, xlabel='Enu, MeV', ylabel='Dist, MeV', title='Nearest peaks distance')
ax.minorticks_on(); ax.grid()
ax.plot(data.data_no.data_enu.diff_x[data.dmmid_idx], data.data_no.data_enu.diff[data.dmmid_idx], label=r'NO')
ax.plot(data.data_io.data_enu.diff_x[data.dmmid_idx], data.data_io.data_enu.diff[data.dmmid_idx], label=r'IO')
ax.legend()
ax.set_xlim(0.0, xmax)
ax.set_ylim(bottom=0.0)
savefig(opts.output, suffix='_dist_enu')
ax.set_xlim(2.0, 5.0)
ax.set_ylim(top=0.5)
savefig(opts.output, suffix='_dist_enu_zoom', close=not opts.show_all)
#
# Distance between nearest peaks vs Edep, single
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='Dist, MeV', title='Nearest peaks distance')
ax.minorticks_on(); ax.grid()
ax.plot(data.data_no.data_edep.diff_x[data.dmmid_idx], data.data_no.data_edep.diff[data.dmmid_idx], label=r'NO')
ax.plot(data.data_io.data_edep.diff_x[data.dmmid_idx], data.data_io.data_edep.diff[data.dmmid_idx], label=r'IO')
ax.legend()
ax.set_xlim(0.0, xmax)
ax.set_ylim(bottom=0.0)
savefig(opts.output, suffix='_dist_edep')
ax.set_xlim(1.2, 4.2)
ax.set_ylim(top=0.5)
savefig(opts.output, suffix='_dist_edep_zoom', close=not opts.show_all)
#
# Distance between nearest peaks vs Edep, single
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep quenched, MeV', ylabel='Dist, MeV', title='Nearest peaks distance')
ax.minorticks_on(); ax.grid()
ax.plot(data.data_no.data_edep_lsnl.diff_x[data.dmmid_idx], data.data_no.data_edep_lsnl.diff[data.dmmid_idx], label=r'NO')
ax.plot(data.data_io.data_edep_lsnl.diff_x[data.dmmid_idx], data.data_io.data_edep_lsnl.diff[data.dmmid_idx], label=r'IO')
ax.legend()
ax.set_xlim(0.0, xmax)
ax.set_ylim(bottom=0.0)
savefig(opts.output, suffix='_dist_edep_lsnl')
ax.set_xlim(1.2, 4.2)
ax.set_ylim(top=0.5)
savefig(opts.output, suffix='_dist_edep_lsnl_zoom')
poly = N.polynomial.polynomial.Polynomial([0, 1, 0])
x = data.data_no.data_edep_lsnl.diff_x[data.dmmid_idx]
pf = poly.fit(x, data.data_no.data_edep_lsnl.diff[data.dmmid_idx], 2)
print(pf)
ax.plot(x, pf(x), label=r'NO fit')
ax.legend()
savefig(opts.output, suffix='_dist_edep_lsnl_fit', close=not opts.show_all)
#
# Distance between nearest peaks vs Edep, multiple
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep quenched, MeV', ylabel='Dist, MeV', title='Nearest peaks distance')
ax.minorticks_on(); ax.grid()
ax.plot(data.data_no.data_edep_lsnl.diff_x[data.dmmid_idx], data.data_no.data_edep_lsnl.diff[data.dmmid_idx], label=r'NO')
ax.plot(data.data_io.data_edep_lsnl.diff_x[data.dmmid_idx], data.data_io.data_edep_lsnl.diff[data.dmmid_idx], '--', label=r'IO')
for idx in (0, 5, 15, 20):
ax.plot(data.data_io.data_edep_lsnl.diff_x[idx], data.data_io.data_edep_lsnl.diff[idx], '--')
ax.legend()
ax.set_xlim(0.0, xmax)
savefig(opts.output, suffix='_dist_edep_lsnl_multi', close=not opts.show_all)
#
# Distance between nearest peaks difference
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='Dist(IO) - Dist(NO), MeV', title='Nearest peaks distance diff: IO-NO')
ax.minorticks_on(); ax.grid()
ax.plot(data.e, data.diffs.edep[data.dmmid_idx], '-', markerfacecolor='none', label='Edep')
ax.plot(data.e, data.diffs.edep_lsnl[data.dmmid_idx], '-', markerfacecolor='none', label='Edep quenched')
ax.plot(data.e, data.diffs.enu[data.dmmid_idx], '-', markerfacecolor='none', label='Enu')
ax.legend()
savefig(opts.output, suffix='_dist_diff')
ax.plot(data.e, data.eres, '-', markerfacecolor='none', label='Resolution $\\sigma$')
ax.legend()
savefig(opts.output, suffix='_dist_diff_1')
#
# Distance between nearest peaks difference relative to sigma
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='(Dist(IO) - Dist(NO))/$\\sigma$', title='Nearest peaks distance diff: IO-NO')
ax.minorticks_on(); ax.grid()
ax.plot(data.e, data.diffs_rel.edep[data.dmmid_idx], '-', markerfacecolor='none', label='Edep')
ax.plot(data.e, data.diffs_rel.edep_lsnl[data.dmmid_idx], '-', markerfacecolor='none', label='Edep quenched')
i_edep=N.argmax(data.diffs_rel.edep[data.dmmid_idx])
i_edep_lsnl=N.argmax(data.diffs_rel.edep_lsnl[data.dmmid_idx])
ediff = data.e[i_edep] - data.e[i_edep_lsnl]
ax.axvline(data.e[i_edep], linestyle='dashed', label='Max location: %.3f MeV'%(data.e[i_edep]))
ax.axvline(data.e[i_edep_lsnl], linestyle='dashed', label='Max location: %.3f MeV'%(data.e[i_edep_lsnl]))
ax.axvspan(data.e[i_edep], data.e[i_edep_lsnl], alpha=0.2, label='Max location diff: %.3f MeV'%(ediff))
ax.legend()
savefig(opts.output, suffix='_dist_diff_rel')
#
# Distance between nearest peaks difference relative to sigma
#
fig = P.figure()
ax = P.subplot(111, xlabel='Edep, MeV', ylabel='(Dist(IO) - Dist(NO))/$\\sigma$', title='Nearest peaks distance diff: IO-NO')
ax.minorticks_on(); ax.grid()
ledep = ax.plot(data.e, data.diffs_rel.edep[data.dmmid_idx], '--', markerfacecolor='none', label='Edep')[0]
lquench = ax.plot(data.e, data.diffs_rel.edep_lsnl[data.dmmid_idx], '-', color=ledep.get_color(), markerfacecolor='none', label='Edep quenched')[0]
kwargs=dict(alpha=0.8, linewidth=1.5, markerfacecolor='none')
for idx in (0, 5, 15, 20):
l = ax.plot(data.e, data.diffs_rel.edep[idx], '--', **kwargs)[0]
ax.plot(data.e, data.diffs_rel.edep_lsnl[idx], '-', color=l.get_color(), **kwargs)
ax.legend()
savefig(opts.output, suffix='_dist_diff_rel_multi', close=not opts.show_all)
#
# Distance between nearest peaks difference relative to sigma
#
fig = P.figure()
ax = P.subplot(111, ylabel=r'$\Delta m^2_\mathrm{ee}$', xlabel='Edep quenched, MeV',
title='Nearest peaks distance diff: IO-NO/$\\sigma$')
ax.minorticks_on(); ax.grid()
formatter = ax.yaxis.get_major_formatter()
formatter.set_useOffset(False)
formatter.set_powerlimits((-2,2))
formatter.useMathText=True
c = ax.pcolormesh(data.mesh_e.T, data.mesh_dm.T, data.diffs_rel.edep_lsnl.T)
from mpl_tools.helpers import add_colorbar
add_colorbar(c, rasterized=True)
c.set_rasterized(True)
savefig(opts.output, suffix='_dist_diff_rel_heatmap')
if pdfpages:
pdfpages.__exit__(None,None,None)
print('Write output figure to', pdfpagesfilename)
# savegraph(quench.histoffset.histedges.points_truncated, opts.graph, namespace=ns)
if opts.show or opts.show_all:
P.show()
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))
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')
expr = 'prod[a]| sum[z]| weight1[z]*weight2[a] * spec| enu()'
a = Expression(expr, indices=indices)
print(a.expressions_raw)
print(a.expressions)
a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
print()
cfg = NestedDict(
weight1=NestedDict(bundle='dummyvar',
variables=uncertaindict([
('weight1', (2, 0.1)),
],
mode='percent')),
weight2=NestedDict(bundle='dummyvar',
variables=uncertaindict([
('weight2', (3, 0.1)),
],
mode='percent')),
enu=NestedDict(bundle='dummy',
name='enu',
input=False,
size=10,
debug=False),
spec=NestedDict(bundle='dummy',
name='spec',
input=True,
size=10,
'norm1[d] * norm2[z] * eres[z,d]| hist[d]()'
],
# Configuration
bundles=NestedDict(eres=NestedDict(
bundle=dict(name='detector_eres_normal',
version='v01',
major=['z'],
names=dict(eres_matrix='smearing_matrix', )),
parameter='eres',
pars=uncertaindict([
('z1.a', (0.0, 'fixed')),
('z1.b', (0.05, 30, 'percent')),
('z1.c', (0.0, 'fixed')),
('z2.a', (0.0, 'fixed')),
('z2.b', (0.10, 30, 'percent')),
('z2.c', (0.0, 'fixed')),
('z3.a', (0.0, 'fixed')),
('z3.b', (0.15, 30, 'percent')),
('z3.c', (0.0, 'fixed')),
]),
labels=dict(matrix='Smearing\nmatrix\n{autoindex}',
smear='Energy\nresolution\n{autoindex}',
parameter='{description} (zone {autoindex})'),
split_transformations=True),
norm1=NestedDict(
bundle='parameters_v01',
parameter='norm1',
label='Normalization at detector {detector}',
pars=uncertaindict(
[
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',
),
),
)
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],
lifraction = uncertain( 0.95, 0.05, 'percent' ),
correlation = 'group',
rates = uncertaindict(
mode = 'absolute',
EH1 = (2.71, 0.90),
EH2 = (1.91, 0.73),
EH3 = (0.22, 0.07),
)
),
spectra = NestedDict(
docdb = [8772, 8860],
list = [ 'li_spectrum', 'he_spectrum' ],
li_spectrum = NestedDict(
bundle = 'root_histograms_v01',
file = 'data/background/lihe/13.09/toyli9spec_BCWmodel_v1.root',
format = 'h_eVisAllSmeared',
),
he_spectrum = NestedDict(
bundle = 'root_histograms_v01',
file = 'data/background/lihe/13.09/toyhe8spec_BCWmodel_v1.root',
format = 'h_eVisAllSmeared',
