def build(self):
edges = self.cfg.edges
emin, emax = self.cfg.normalize
try:
(imin, imax), = N.where(((edges - emin) *
(edges - emax)).round(6) == 0.0)
except:
raise Exception(
'Was able to determine normalization region for Fast Neutrons (%f, %f)'
% (emin, emax))
data = N.zeros(edges.size - 1)
data[imin:imax] = 1.0
data /= data.sum()
label = self.cfg.get('label', 'hist {autoindex}')
for it in self.nidx.iterate():
hist = C.Histogram(edges, data, labels=it.current_format(label))
self.set_output(self.cfg.name, it, hist.single())
# """Provide the outputs and objects"""
self.context.objects[('hist', ) + it.current_values()] = hist
def test_histogram_v01_TH1D(tmp_path):
rhist = R.TH1D('testhist', 'testhist', 20, -5, 5)
rhist.FillRandom('gaus', 10000)
hist = C.Histogram(rhist)
buf = rhist.get_buffer()
res = hist.hist.hist()
# Plot
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X label')
ax.set_ylabel('entries')
ax.set_title('Histogram')
rhist.plot(alpha=0.5, linestyle='dashed', label='ROOT histogram')
hist.hist.hist.plot_hist(alpha=0.5,
linestyle='dashdot',
label='GNA histogram')
ax.legend()
suffix = 'histogram1d'
path = os.path.join(str(tmp_path), suffix + '.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
path = os.path.join(str(tmp_path), suffix + '_graph.png')
savegraph(hist.hist, path)
allure_attach_file(path)
plt.close()
# Test consistency
assert np.all(buf == res)
def test_typeclass_ndim_v01():
objects = [C.Histogram(np.arange(6), np.arange(5)), C.Points(np.arange(5))]
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt = R.TypeClasses.CheckNdimT(context.current_precision())(1)
R.SetOwnership(dt, False)
dt.dump()
print()
obj.add_typeclass(dt)
res = obj.process_types()
assert res
dt1 = R.TypeClasses.CheckNdimT(context.current_precision())(2)
R.SetOwnership(dt1, False)
dt1.dump()
print()
obj.add_typeclass(dt1)
print('Exception expected: ', end='')
res = obj.process_types()
assert not res
def _handler_root(self, source):
import mpl_tools.root2numpy as r2n
r2n.bind()
def _apply_filters(names):
if not self.opts.take:
yield names
for filt in self.opts.take:
yield fn.filter(names, filt)
roo_file = ROOT.TFile(source)
items_in_file = [_.GetName() for _ in iter(roo_file.GetListOfKeys())]
filtered = list(chain(*_apply_filters(items_in_file)))
# register observables
for name in filtered:
hist = roo_file.Get(name)
data, edges = hist.get_buffer(), hist.get_edges()
obs = C.Histogram(edges, data)
self.data.append(obs)
self.namespace.addobservable(name, obs.hist)
roo_file.Close()
def test_typeclass_passeach():
"""Last input has another edges"""
objects = [
C.Histogram2d(np.arange(4), np.arange(5)),
C.Histogram(np.arange(4)),
C.Points(np.arange(20).reshape(4,5))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
j = list(map(obj.add_input, outputs))
for i in range(5):
obj.add_output()
dt1 = R.TypeClasses.PassTypeT(context.current_precision())((2,), (0,1))
dt2 = R.TypeClasses.PassEachTypeT(context.current_precision())((0,-1), (2,-1))
R.SetOwnership(dt1, False)
R.SetOwnership(dt2, False)
dt1.dump(); print()
dt2.dump(); print()
obj.add_typeclass(dt1)
obj.add_typeclass(dt2)
res = obj.process_types();
assert res
obj.print()
dta = outputs[0].datatype()
dtb = outputs[1].datatype()
dtc = outputs[2].datatype()
doutputs = obj.transformations.back().outputs
assert doutputs[0].datatype()==dtc
assert doutputs[1].datatype()==dtc
assert doutputs[2].datatype()==dta
assert doutputs[3].datatype()==dtb
assert doutputs[4].datatype()==dtc
def test_histedges_v01(edges):
centers = 0.5*(edges[1:]+edges[:-1])
widths = edges[1:]-edges[:-1]
data = N.arange(edges.size-1)
hist = C.Histogram(edges, data)
h2e = R.HistEdges()
h2e.histedges.hist(hist.hist.hist)
out_edges = h2e.histedges.edges.data()
out_centers = h2e.histedges.centers.data()
out_widths = h2e.histedges.widths.data()
print( 'Input:' )
print( edges )
print( 'Output:' )
print( 'Edges', out_edges )
print( 'Centers', out_centers )
print( 'Widths', out_widths )
assert (edges==out_edges).all()
assert (centers==out_centers).all()
assert (widths==out_widths).all()
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()
xyg.SetParameter(1, -1 / 8.)
R.gDirectory.Add(xyg)
roothist2d.FillRandom('xyg', 10000)
# Fill TMatrixD with
for i, (i1, i2) in enumerate(
I.product(range(rootmatrix.GetNrows()), range(rootmatrix.GetNcols()))):
rootmatrix[i1, i2] = i
# Create Points
p1 = C.Points(roothist1d)
p2 = C.Points(roothist2d)
p3 = C.Points(rootmatrix)
# Create Histograms
h1d = C.Histogram(roothist1d)
h2d = C.Histogram2d(roothist2d)
# Check p1
print('Points from TH1D (underflow/overflow are ignored)')
roothist1d.Print('all')
print(p1.points.points())
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('X axis')
ax.set_ylabel('Entries')
ax.set_title('Points and TH1 comparison')
# Define namespaces
#
namespaces = cfg.detector.detectors
#
# Bin edges, required by energy nonlinearity
# Data input
#
nbins = 240
edges = N.linspace(0.0, 12.0, nbins+1, dtype='d')
points = C.Points(edges)
hists_list = ()
for eset in ( (1.025, 6.025), (2.025, 7.025), (3.025, 8.025) ):
heights = unit_bins_at( eset, edges )
hist = C.Histogram( edges, heights )
hists_list += hist,
#
# Define the chain
#
shared = NestedDict( edges=points.single() )
b, = execute_bundles(cfg=cfg.detector, namespaces=namespaces, shared=shared)
print('Parameters:')
env.globalns.printparameters(labels=True)
#
# Connect inputs
#
for inp, hist in zip(b.inputs.values(), hists_list):
centers_m = conversion_fcn_direct(centers)
edges_m = conversion_fcn_direct(edges)
edges_back = conversion_fcn_inverse(edges)
print('Edges before:', edges)
print('Edges after:', edges_m)
print('Edges back:', edges_back)
matp_a = rescale_to_matrix_a( edges, edges_m, roundto=3 )
matp_b = rescale_to_matrix_b( edges, centers_m, roundto=3 )
matp_c = rescale_to_matrix_c( edges, edges_m, centers_m, roundto=3 )
pedges_m = C.Points(edges_m)
pcenters = C.Points(centers)
ntrue = C.Histogram(edges, np.ones(edges.size-1))
nlA = C.HistNonlinearity(True)
nlA.set(ntrue.hist, pedges_m)
nlA.add_input()
# nlB = C.HistNonlinearityB(True)
# nlB.set(ntrue.hist, pcenters)
# nlB.add_input()
mat_a = nlA.matrix.FakeMatrix.data()
# mat_b = nlB.matrix.FakeMatrix.data()
print( 'Mat A and its sum' )
print( mat_a )
print( mat_a.sum( axis=0 ) )
data1 = np.zeros(nbins, dtype='d')
data1[20] = 1.0 # 1 MeV
data1[120] = 1.0 # 6 MeV
data1[200] = 1.0 # 10 MeV
data2 = np.zeros(nbins, dtype='d')
data2[40] = 1.0 # 2 MeV
data2[140] = 1.0 # 7 MeV
data2[220] = 1.0 # 11 MeV
data3 = np.zeros(nbins, dtype='d')
data3[60] = 1.0 # 3 MeV
data3[160] = 1.0 # 8 MeV
data3[239] = 1.0 # 12 MeV
hist1 = C.Histogram(edges, data1)
hist1.hist.setLabel('Input histogram 1')
hist2 = C.Histogram(edges, data2)
hist2.hist.setLabel('Input histogram 2')
hist3 = C.Histogram(edges, data3)
hist3.hist.setLabel('Input histogram 3')
#
# Bind outputs
#
suffix = '' if cfg.split_transformations else 'merged_'
savegraph(b.context.outputs.smearing_matrix.values(),
tutorial_image_name('png', suffix=suffix + 'graph0'),
rankdir='TB')
def build(self):
snf_ratio = C.Histogram(self.cfg.edges, self.ratio_spectrum )
self.objects['snf_spectra'] = snf_ratio
self.set_output(snf_ratio.single(), "snf_ratio")
def test_rebinner(tmp_path):
edges = N.array([0.0, 0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.0],
dtype='d')
edges_m = N.array([0.1, 1.2, 3.4, 4.5, 7.8], dtype='d')
ntrue = C.Histogram(edges, N.ones(edges.size - 1))
rebin = R.Rebin(edges_m.size, edges_m, 3)
ntrue >> rebin.rebin.histin
olddata = ntrue.data()
newdata = rebin.rebin.histout.data()
mat = convert(rebin.getDenseMatrix(), 'matrix')
if "pytest" not in sys.modules:
print(mat)
prj = mat.sum(axis=0)
assert ((prj == 1.0) +
(prj == 0.0)).all(), "Not matching results after rebin"
#
# Plot spectra
#
print(((prj == 1.0) + (prj == 0.0)).all() and '\033[32mOK!'
or '\033[31mFAIL!', '\033[0m')
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
# ax.grid()
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_title('Rebinner')
ax.vlines(edges, 0.0, 4.0, linestyle='--', linewidth=0.5)
plot_hist(edges, olddata, label='before')
plot_hist(edges_m, newdata, label='after')
ax.legend(loc='upper left')
path = os.path.join(str(tmp_path), 'rebinner_hist.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
#
# Plot matrix
#
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
# ax.grid()
ax.set_xlabel('Source bins')
ax.set_ylabel('Target bins')
ax.set_title('Rebinning matrix')
c = ax.matshow(N.ma.array(mat, mask=mat == 0.0),
extent=[edges[0], edges[-1], edges_m[-1], edges_m[0]])
# add_colorbar( c )
path = os.path.join(str(tmp_path), 'rebinner_matrix.png')
savefig(path, dpi=300)
allure_attach_file(path)
plt.close()
def test_energyresolutioninput_v01(tmp_path):
def axes( title, ylabel='' ):
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( L.u('evis') )
ax.set_ylabel( ylabel )
ax.set_title( title )
return ax
def singularities( values, edges ):
indices = np.digitize( values, edges )-1
phist = np.zeros( edges.size-1 )
phist[indices] = 1.0
return phist
#
# Define the parameters in the current namespace
#
wvals = [0.016, 0.081, 0.026]
#
# Define bin edges
#
binwidth=0.05
edges = np.arange( 0.0, 12.0001, binwidth )
efine = np.arange( edges[0], edges[-1]+1.e-5, 0.005 )
centers = 0.5*(edges[1:]+edges[:-1])
def RelSigma(e):
a, b, c = wvals
return (a**2+ (b**2)/e + (c/e)**2)**0.5
relsigma = RelSigma(centers)
for eset in [
[ [1.025], [3.025], [6.025], [9.025] ],
[ [ 1.025, 5.025, 9.025 ] ],
[ [ 6.025, 7.025, 8.025, 8.825 ] ],
]:
for i, e in enumerate(eset):
ax = axes( 'Energy resolution (input) impact' )
phist = singularities( e, edges )
relsigma_i = relsigma.copy()
relsigma_p = C.Points(relsigma_i)
hist = C.Histogram( edges, phist )
edges_o = R.HistEdges(hist)
eres = C.EnergyResolutionInput(True)
hist >> eres.matrix.Edges
relsigma_p >> eres.matrix.RelSigma
hist >> eres.smear.Ntrue
path = os.path.join(str(tmp_path), 'eres_graph_%i.png'%i)
savegraph(hist, path)
allure_attach_file(path)
smeared = eres.smear.Nrec.data()
diff = phist.sum()-smeared.sum()
print( 'Sum check for {} (diff): {}'.format( e, diff ) )
assert diff<1.e-9
lines = plot_hist( edges, smeared, label='default' )
color = lines[0].get_color()
ax.vlines( e, 0.0, smeared.max(), linestyle='--', color=color )
if len(e)>1:
color='green'
for e in e:
ax.plot( efine, binwidth*norm.pdf( efine, loc=e, scale=RelSigma(e)*e ), linestyle='--', color='green' )
sprev = smeared.copy()
icut = relsigma_i.size//2
relsigma_i[icut:]*=2
relsigma_p.set(relsigma_i, relsigma_i.size)
smeared = eres.smear.Nrec.data()
shouldchange = phist[icut:].any()
assert not np.all(smeared==sprev)==shouldchange
plot_hist( edges, smeared, label='modified', color='red', alpha=0.5)
ax.legend()
path = os.path.join(str(tmp_path), 'eres_test_{:02d}.png'.format(i))
savefig(path, density=300)
allure_attach_file(path)
plt.close()
relsigma_p.set(relsigma, relsigma.size)
smeared = eres.smear.Nrec.data()
ax = axes( 'Relative energy uncertainty', ylabel=L.u('eres_sigma_rel') )
ax.set_xlim(0.5, 12.0)
ax.set_ylim(0, 13.0)
ax.plot( centers, relsigma*100. )
path = os.path.join(str(tmp_path), 'eres_sigma.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
ax.set_title( 'Energy resolution convertsion matrix (class)' )
mat = convert(eres.getDenseMatrix(), 'matrix')
mat = np.ma.array( mat, mask= mat==0.0 )
c = ax.matshow( mat, extent=[ edges[0], edges[-1], edges[-1], edges[0] ] )
add_colorbar( c )
path = os.path.join(str(tmp_path), 'eres_matc.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
ax.set_title( 'Energy resolution convertsion matrix (trans)' )
eres.matrix.FakeMatrix.plot_matshow(colorbar=True, mask=0.0, extent=[edges[0], edges[-1], edges[-1], edges[0]])
path = os.path.join(str(tmp_path), 'eres_mat.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
env.defparameter('Eres_c', central=0.0, fixed=True)
print_parameters(env.globalns)
#
# Define bin edges
#
nbins = 40
edges = N.linspace(0.0, 2.0, nbins + 1)
binwidth = edges[1] - edges[0]
centers = (edges[1:] + edges[:-1]) * 0.5
efine = N.arange(edges[0], edges[-1] + 1.e-5, 0.005)
ax = axes('Energy resolution impact')
phist = N.ones(edges.size - 1)
hist = C.Histogram(edges, phist)
edges_o = R.HistEdges(hist)
eres = R.EnergyResolution(True)
eres.matrix.Edges(hist)
eres.smear.Ntrue(hist)
smeared = eres.smear.Nrec.data()
eres.smear.plot_hist(label='default')
arrays = N.eye(20, edges.size - 1, dtype='d')
objects = [C.Histogram(edges, a) for a in arrays]
outputs = [o.single() for o in objects]
for i, out in enumerate(outputs):
out = eres.add_input(out)
import gna.constructors as C
import numpy as np
from gna.bindings import common
from matplotlib import pyplot as plt
# Create numpy array for data points
nbins = 40
# Create numpy array for bin edges
edges = np.linspace(1.0, 10.0, nbins + 1)
narray1 = np.exp(edges[:-1])
narray2 = np.flip(narray1)
narray3 = narray1[-1] * np.exp(-0.5 *
(5.0 - edges[:-1])**2 / 1.0) / (2 * np.pi)**0.5
# Create a histogram instance with data, stored in `narray`
# and edges, stored in `edges`
hist1 = C.Histogram(edges, narray1)
hist2 = C.Histogram(edges, narray2)
hist3 = C.Histogram(edges, narray3)
fig = plt.figure()
ax = plt.subplot(111)
ax.set_title('Plot title (left)')
ax.minorticks_on()
ax.grid()
ax.set_xlabel('x label')
ax.set_ylabel('y label')
plt.ticklabel_format(style='sci',
axis='y',
scilimits=(-2, 2),
useMathText=True)
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 test_histedges_offset_02():
size = 13
for edges_offset in (0, 1.5):
edges = N.arange(size, dtype='d') + edges_offset
hist_in = C.Histogram(edges)
arr = N.zeros(edges.size - 1, dtype=context.current_precision_short())
for offset in range(size - 1):
threshold = edges[offset] + 0.6
print('Offset', offset)
print('Threshold', threshold)
view = C.HistEdgesOffset(hist_in, threshold)
edges_truncated = edges[offset:]
edges_threshold = edges_truncated.copy()
edges_threshold[0] = threshold
edges_offset = edges_threshold.copy()
edges_offset -= threshold
trans = view.histedges
points = trans.points.data()
points_truncated = trans.points_truncated.data()
points_threshold = trans.points_threshold.data()
points_offset = trans.points_offset.data()
hist_truncated_d = trans.hist_truncated.data()
hist_truncated = N.array(trans.hist_truncated.datatype().edges)
hist_threshold_d = trans.hist_threshold.data()
hist_threshold = N.array(trans.hist_threshold.datatype().edges)
hist_offset_d = trans.hist_offset.data()
hist_offset = N.array(trans.hist_offset.datatype().edges)
print(' Edges (expected)', edges)
print(' Edges truncated (expected)', edges_truncated)
print(' Edges threshold (expected)', edges_threshold)
print(' Edges offset (expected)', edges_offset)
print(' Points', points)
print(' Points truncated', points_truncated)
print(' Points threshold', points_threshold)
print(' Points offset', points_offset)
print(' Hist truncated', hist_truncated)
print(' Hist truncated data', hist_truncated_d)
print(' Hist threshold', hist_threshold)
print(' Hist threshold data', hist_threshold_d)
print(' Hist offset', hist_offset)
print(' Hist offset data', hist_offset_d)
assert (edges == points).all()
assert (edges_truncated == points_truncated).all()
assert (edges_threshold == points_threshold).all()
assert (edges_offset == points_offset).all()
assert (edges_truncated == hist_truncated).all()
assert (0.0 == hist_truncated_d).all()
assert (edges_threshold == hist_threshold).all()
assert (0.0 == hist_threshold_d).all()
assert (edges_offset == hist_offset).all()
assert (0.0 == hist_offset_d).all()
p2b = ns.defparameter('fcn2.b', central=0.1, free=True, label='weight 2')
p2k = ns.defparameter('fcn2.k', central=16.0, free=True, label='argument scale')
# Print the list of parameters
ns.printparameters(labels=True)
print()
# Define binning and integration orders
nbins = 20
x_edges = np.linspace(-np.pi, np.pi, nbins+1, dtype='d')
x_widths = x_edges[1:]-x_edges[:-1]
x_width_ratio = x_widths/x_widths.min()
orders = 4
# Initialize histogram
hist = C.Histogram(x_edges)
# Initialize integrator
integrator = R.IntegratorGL(nbins, orders)
integrator.points.edges(hist.hist.hist)
int_points = integrator.points.x
# Initialize sine and two cosines
sin_t = R.Sin(int_points)
cos1_arg = C.WeightedSum( ['fcn1.k'], [int_points] )
cos2_arg = C.WeightedSum( ['fcn2.k'], [int_points] )
cos1_t = R.Cos(cos1_arg.sum.sum)
cos2_t = R.Cos(cos2_arg.sum.sum)
# Initalizes both weighted sums
# Prepare inputs
#
emin, emax = 0.0, 12.0
nbins = 240
edges = np.linspace(emin, emax, nbins + 1, dtype='d')
peaks = 20, 120, 200
hists = ()
for i in range(3):
data = np.zeros(nbins, dtype='d')
for peak in peaks:
pos = np.min((peak + 20 * i, nbins - 1))
data[pos] = 1.0
hist = C.Histogram(edges, data)
hist.hist.setLabel('Input histogram %i' % i)
hists += hist,
cfg = NestedDict(
bundle=dict(
name='expression',
version='v01',
nidx=[('d', 'detector', ['D1', 'D2', 'D3']),
('z', 'zone', ['z1', 'z2'])],
),
verbose=2,
# Expression
expr=[
'smearing_matrix[z]| hist0()',
def test_energyresolution_v01(tmp_path):
def axes( title, ylabel='' ):
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( L.u('evis') )
ax.set_ylabel( ylabel )
ax.set_title( title )
return ax
def singularities( values, edges ):
indices = np.digitize( values, edges )-1
phist = np.zeros( edges.size-1 )
phist[indices] = 1.0
return phist
#
# Define the parameters in the current namespace
#
ns = env.globalns('test_energyresolution_v01')
weights = [ 'Eres_'+s for s in 'abc' ]
wvals = [0.016, 0.081, 0.026]
percent = 0.01
ns.defparameter(weights[0], central=wvals[0], relsigma=30*percent )
par = ns.defparameter(weights[1], central=wvals[1], relsigma=30*percent )
ns.defparameter(weights[2], central=wvals[2], relsigma=30*percent )
ns.printparameters()
values = []
def pop_value():
values, par
par.set(values.pop())
def push_value(v):
values, par
values.append(par.value())
par.set(v)
#
# Define bin edges
#
binwidth=0.05
edges = np.arange( 0.0, 12.0001, binwidth )
efine = np.arange( edges[0], edges[-1]+1.e-5, 0.005 )
for eset in [
[ [1.025], [3.025], [6.025], [9.025] ],
[ [ 1.025, 5.025, 9.025 ] ],
[ [ 6.025, 7.025, 8.025, 8.825 ] ],
]:
for i, e in enumerate(eset):
ax = axes( 'Energy resolution impact' )
phist = singularities( e, edges )
hist = C.Histogram( edges, phist )
edges_o = R.HistEdges(hist)
with ns:
eres = C.EnergyResolution(weights, True)
eres.matrix.Edges( hist )
eres.smear.Ntrue( hist )
path = os.path.join(str(tmp_path), 'eres_graph_%i.png'%i)
savegraph(hist, path)
allure_attach_file(path)
smeared = eres.smear.Nrec.data()
diff = phist.sum()-smeared.sum()
print( 'Sum check for {} (diff): {}'.format( e, diff ) )
assert diff<1.e-9
lines = plot_hist( edges, smeared, label='default' )
color = lines[0].get_color()
ax.vlines( e, 0.0, smeared.max(), linestyle='--', color=color )
if len(e)>1:
color='green'
for e in e:
ax.plot( efine, binwidth*norm.pdf( efine, loc=e, scale=eres.relativeSigma(e)*e ), linestyle='--', color='green' )
sprev = smeared.copy()
push_value(0.162)
assert eres.smear.tainted()
smeared = eres.smear.Nrec.data()
assert not np.all(smeared==sprev)
plot_hist( edges, smeared, label='modified', color='red', alpha=0.5)
pop_value()
ax.legend()
path = os.path.join(str(tmp_path), 'eres_test_{:02d}.png'.format(i))
savefig(path, density=300)
allure_attach_file(path)
plt.close()
smeared = eres.smear.Nrec.data()
ax = axes( 'Relative energy uncertainty', ylabel=L.u('eres_sigma_rel') )
ax.set_ylim(0, 13.0)
ax.set_xlim(0.5, 12.0)
x = np.arange( 0.5, 12.0, 0.01 )
fcn = np.frompyfunc( eres.relativeSigma, 1, 1 )
y = fcn( x )
ax.plot( x, y*100. )
path = os.path.join(str(tmp_path), 'eres_sigma.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
ax.set_title( 'Energy resolution convertsion matrix (class)' )
mat = convert(eres.getDenseMatrix(), 'matrix')
mat = np.ma.array( mat, mask= mat==0.0 )
c = ax.matshow( mat, extent=[ edges[0], edges[-1], edges[-1], edges[0] ] )
add_colorbar( c )
path = os.path.join(str(tmp_path), 'eres_matc.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
ax.set_title( 'Energy resolution convertsion matrix (trans)' )
eres.matrix.FakeMatrix.plot_matshow(colorbar=True, mask=0.0, extent=[edges[0], edges[-1], edges[-1], edges[0]])
path = os.path.join(str(tmp_path), 'eres_mat.png')
savefig(path, density=300)
allure_attach_file(path)
plt.close()
#
# Make input histogram
#
def singularities(values, edges):
indices = N.digitize(values, edges) - 1
phist = N.zeros(edges.size - 1)
phist[indices] = 1.0
return phist
nbins = 240
edges = N.linspace(0.0, 12.0, nbins + 1, dtype='d')
points = C.Points(edges, labels='Bin edges')
phist = singularities([1.225, 2.225, 4.025, 7.025, 9.025], edges)
hist = C.Histogram(edges, phist, labels='Input hist')
#
# Initialize expression
#
indices = [('l', 'lsnl_component',
['nominal', 'pull0', 'pull1', 'pull2', 'pull3'])]
lib = dict()
formulas = [
'evis_edges()',
'lsnl_coarse = sum[l]| lsnl_weight[l] * lsnl_component_y[l]()',
'lsnl_interpolator| lsnl_x(), lsnl_coarse, evis_edges() ',
'lsnl_edges| hist()',
'lsnl| hist()',
#!/usr/bin/env python from tutorial import tutorial_image_name import gna.constructors as C import numpy as np from gna.bindings import common from matplotlib import pyplot as plt # Create numpy array for data points nbins = 100 narray = np.arange(nbins)**2 * np.arange(nbins)[::-1]**2 # Create numpy array for bin edges edges = np.linspace(1.0, 40.0, nbins+1) # Create a histogram instance with data, stored in `narray` # and edges, stored in `edges` hist = C.Histogram(edges, narray) fig = plt.figure(figsize=(12, 5)) ax = plt.subplot( 121 ) ax.set_title( 'Plot title (left)' ) ax.minorticks_on() ax.grid() ax.set_xlabel( 'x label' ) ax.set_ylabel( 'y label' ) plt.ticklabel_format(style='sci', axis='y', scilimits=(-2,2), useMathText=True) hist.hist.hist.plot_hist(label='histogram 1') ax.legend() ax = plt.subplot( 122 )
orig = np.array( [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] )
orig_proj = fcn_direct(orig)
mod_proj = fcn_inverse(orig)
xfine = np.linspace(orig[0], orig[-1], 1000)
yfine = np.linspace(orig_proj[0], orig_proj[-1], 1000)
print('Edges original:', orig)
print('Edges original projected:', orig_proj)
print('Edges modified projected back:', mod_proj)
Orig = C.Points(orig)
Orig_proj = C.Points(orig_proj)
Mod_proj = C.Points(mod_proj)
ntrue = C.Histogram(orig, np.ones(orig.size-1))
nlB = C.HistNonlinearityB(True)
nlB.set(ntrue.hist, Orig_proj, Mod_proj)
nlB.add_input()
print('Get data')
mat_b = nlB.matrix.FakeMatrix.data()
print( 'Mat B and its sum' )
print( mat_b )
matbsum = mat_b.sum( axis=0 )
print( matbsum )
assert np.allclose(matbsum[1:-1], 1, rtol=0, atol=1.e-16)
ntrue.hist.hist.data()
#
# Test bundle
#
def singularities(values, edges):
indices = N.digitize(values, edges) - 1
phist = N.zeros(edges.size - 1)
phist[indices] = 1.0
return phist
binwidth = 0.05
edges = N.arange(0.0, 12.0001, binwidth)
phist = singularities([1.225, 4.025, 7.025], edges)
hist = C.Histogram(edges, phist)
smear1.inputs.Ntrue(hist.hist)
smear2.inputs.Ntrue(hist.hist)
smear3.inputs.Ntrue(hist.hist)
#
# Plot
#
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('IAV effect')
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()
