class PdfPagesExecution(object):
def __init__(self, filename, *args, **kwargs):
self.pdfPages = None
self.filename = filename
self.args = args
self.kwargs = kwargs
def __enter__(self):
self.pdfPages = PdfPages(self.filename, *self.args, **self.kwargs)
return self.pdfPages.__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.pdfPages.__exit__(exc_type, exc_val, exc_tb)
if exc_type is not None:
os.remove(self.filename)
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 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 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()
