def test_chi2_v03():
n = 10
start = 10
offset = 1.0
dataa = N.arange(start, start+n, dtype='d')
theorya = dataa+offset
covmat = N.diag(dataa)+2.0
La = N.linalg.cholesky(covmat)
data = C.Points(dataa, labels='Data')
theory = C.Points(theorya, labels='Theory')
L = C.Points(La, labels='Stat errors')
chi = C.Chi2(labels='Chi2')
chi.add(theory, data, L)
if graphviz:
chi.print()
from gna.graphviz import savegraph
savegraph(data.single(), 'output/unit_chi2_v03.dot')
res = chi.chi2.chi2.data()[0]
diff = N.array(dataa-theorya).T
res_expected1 = N.matmul(diff.T, N.matmul(N.linalg.inv(covmat), diff))
ndiff = N.matmul(N.linalg.inv(La), diff)
res_expected2 = N.matmul(ndiff.T, ndiff)
assert N.allclose(res, res_expected1, rtol=0, atol=1.e-15)
assert N.allclose(res, res_expected2, rtol=0, atol=1.e-15)
def test_chi2_v01():
n = 10
start = 10
offset = 1.0
dataa = N.arange(start, start+n, dtype='d')
theorya = dataa+offset
stata = dataa**0.5
data = C.Points(dataa, labels='Data')
theory = C.Points(theorya, labels='Theory')
stat = C.Points(stata, labels='Stat errors')
chi = C.Chi2(labels='Chi2')
chi.add(theory, data, stat)
if graphviz:
chi.print()
from gna.graphviz import savegraph
savegraph(data.single(), 'output/unit_chi2_v01.dot')
res = chi.chi2.chi2.data()[0]
res_expected1 = (((dataa-theorya)/stata)**2).sum()
res_expected2 = ((offset/stata)**2).sum()
assert (res==res_expected1).all()
assert (res==res_expected2).all()
def test_chi2_v02():
n = 10
start = 10
offset = 1.0
dataa = N.arange(start, start+n, dtype='d')
theorya = dataa+offset
covmat = N.diag(dataa)
La = N.linalg.cholesky(covmat)
data = C.Points(dataa, labels='Data')
theory = C.Points(theorya, labels='Theory')
L = C.Points(La, labels='Stat errors')
chi = C.Chi2(labels='Chi2')
chi.add(theory, data, L)
if graphviz:
chi.print()
from gna.graphviz import savegraph
savegraph(data.single(), 'output/unit_chi2_v02.dot')
res = chi.chi2.chi2.data()[0]
res_expected1 = (offset**2/dataa).sum()
assert (res==res_expected1).all()
debug=False),
va=NestedDict(
bundle=dict(name='dummyvar', version='v01'),
variables=uncertaindict([('vara', (2, 0.1))], mode='percent'),
),
vb=NestedDict(
bundle=dict(name='dummyvar', version='v01'),
variables=uncertaindict([('varb', (3, 0.1))], mode='percent'),
),
)
# Build the expression for given configuration. The output is a context, that contains the inputs and outputs.
context = ExpressionContext(cfg)
a.build(context)
from gna.bindings import OutputDescriptor
# Print inputs
print('Inputs')
print(context.inputs)
# Print outputs
print()
print('Outputs')
print(context.outputs)
if args.dot:
from gna.graphviz import GNADot, savegraph
savegraph(next(context.outputs.ta.values(nested=True)),
args.dot,
rankdir='LR')
args = parser.parse_args()
indices = [('k', 'kin', ['a', 'b', 'c'])]
lib = dict(scaled_e=dict(expr='weight*evis'), )
expr = 'integral[k]'
a = Expression_v01(expr, indices)
print(a.expressions_raw)
print(a.expressions)
a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
print()
cfg = NestedDict(kinint=NestedDict(bundle=dict(name='integral_1d_v02'),
variable='evis',
edges=N.linspace(0.0, 12.0, 241, dtype='d'),
orders=3,
labels=dict(
sampler='GL Sampler',
integrator='Integrator {autoindex}')), )
context = ExpressionContext_v01(cfg, ns=env.globalns)
a.build(context)
for fname in args.dot:
from gna.graphviz import savegraph
savegraph([context.outputs.evis] + list(context.outputs.integral.values()),
fname)
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()
extrapolation_strategy='extrapolate',
nonlin_range=(0.5, 12.),
expose_matrix=True,
))
#
# Initialize bundles
#
expr.parse()
expr.guessname(lib, save=True)
expr.tree.dump()
context = ExpressionContext_v01(cfg, ns=env.globalns)
expr.build(context)
print(context.outputs)
savegraph(context.outputs.evis_edges, opts.dot)
env.globalns.printparameters(labels=True)
#
# Plot functions
#
def plot_projections(update=False, relative=False, label=''):
newfigure = not update
if newfigure:
fig = plt.figure()
ax = plt.subplot(111, xlabel='E', ylabel='E\'', title='')
ax.minorticks_on()
ax.grid()
ax.set_xlim(0.5, edges[-1])
def example():
parser = argparse.ArgumentParser()
parser.add_argument("-e",
"--energy",
metavar=('Start', 'Final', 'Step'),
default=[700., 2000., 10],
nargs=3,
type=float,
help='Set neutrino energy range and step in MeV')
parser.add_argument(
'-f',
'--flavors',
metavar=('Initial_flavor', 'Final_flavor'),
default=['mu', 'mu'],
nargs=2,
help='Set initital and final flavors for oscillation probability')
parser.add_argument('-r',
'--density',
default=2.75,
type=float,
help='Set density of matter in g/cm^3')
parser.add_argument('-L',
'--distance',
default=810.,
type=float,
help='Set distance of propagation in kilometers')
parser.add_argument('--mass-ordering',
default='normal',
choices=['normal', 'inverted'],
help='Set neutrino mass ordering (hierarchy)')
parser.add_argument('--print-pars',
action='store_true',
help='Print all parameters in namespace')
parser.add_argument('--savefig', help='Path to save figure')
parser.add_argument('--graph',
help='Path to save computational graph scheme')
opts = parser.parse_args()
# For fast usage printing load GNA specific modules only after argument
# parsing
import ROOT
ROOT.PyConfig.IgnoreCommandLineOptions = True #prevents ROOT from hijacking sys.argv
from gna.env import env
from gna.parameters.oscillation import reqparameters
from gna.bindings import common
import gna.constructors as C
_flavors = {
"e": ROOT.Neutrino.e(),
"mu": ROOT.Neutrino.mu(),
"tau": ROOT.Neutrino.tau(),
"ae": ROOT.Neutrino.ae(),
"amu": ROOT.Neutrino.amu(),
"atau": ROOT.Neutrino.atau()
}
# Parsed arguments are put into opts object (of type argparse.Namespace) as attributes and can be accessed with a '.'
# like in examples below.
# Note that argparse translate dashes '-' into underscores '_' for attribute names.
#initialize energy range
Enu = np.arange(opts.energy[0], opts.energy[1], step=opts.energy[2])
E_MeV = C.Points(Enu, labels='Neutrino energy')
# initialize initial and final flavors for neutrino after propagating in media
try:
initial_flavor, final_flavor = tuple(_flavors[flav]
for flav in opts.flavors)
except KeyError:
raise KeyError(
"Invalid flavor is requested: try something from e, mu, tau")
ns = env.ns("matter_osc")
reqparameters(
ns) # initialize oscillation parameters in namespace 'matter_osc'
ns['Delta'].set(0) # set CP-violating phase to zero
ns['SinSq23'].set(
0.6
) # change value of sin²θ₂₃, it will cause recomputation of PMNS matrix elements that depend of it
ns['Alpha'].set(opts.mass_ordering) # Choose mass ordering
ns.defparameter("L", central=opts.distance, fixed=True) # kilometres
ns.defparameter("rho", central=opts.density, fixed=True) # g/cm^3
# Print values of all parameters in namespace if asked
if opts.print_pars:
ns.printparameters()
#initialize neutrino oscillation probability in matter and in vacuum for comparison.
# All neccessary parameters such as values of mixing angles, mass splittings,
# propagation distance and density of matter are looked up in namespace ns
# upon creation of ROOT.OscProbMatter.
with ns:
oscprob_matter = ROOT.OscProbMatter(
initial_flavor,
final_flavor,
labels='Oscillation probability in matter')
oscprob_vacuum = ROOT.OscProbPMNS(
initial_flavor,
final_flavor,
labels='Oscillation probability in vacuum')
# Add output of plain array of energies as input "Enu" to oscillation
# probabilities. It means that oscillation probabilities would be computed
# for each energy in array and array of it would be
# provided as output
E_MeV.points >> oscprob_matter.oscprob.Enu
E_MeV.points >> oscprob_vacuum.full_osc_prob
oscprob_vacuum.full_osc_prob.setLabel(
'Oscillation probability in vacuum')
# Accessing the outputs of oscillation probability for plotting
matter = oscprob_matter.oscprob.oscprob.data()
vacuum = oscprob_vacuum.full_osc_prob.oscprob.data()
# Make a pair of plots for both probabilities and their ratio
# Plot probabilities
fig, (ax, ax_ratio) = plt.subplots(nrows=2, ncols=1, sharex=True)
ax.plot(Enu,
matter,
label='Matter, density={} gm/cm^3'.format(opts.density))
ax.plot(Enu, vacuum, label='Vacuum')
ax.set_title("Oscillation probabilities, {0} -> {1}".format(
opts.flavors[0], opts.flavors[1]),
fontsize=16)
ax.set_ylabel("Oscprob", fontsize=14)
ax.legend(loc='best')
ax.grid(alpha=0.5)
# Plot ratio
ax_ratio.plot(Enu, matter / vacuum, label='matter / vacuum')
ax_ratio.set_xlabel("Neutrino energy, MeV", fontsize=14)
ax_ratio.set_ylabel("Ratio", fontsize=14)
ax_ratio.legend(loc='best')
ax_ratio.grid(alpha=0.5)
# Convert path to absolute path and save figure or show
if opts.savefig:
plt.savefig(os.path.abspath(opts.savefig))
else:
plt.show()
# Save a plot of computational graph scheme
if opts.graph:
from gna.graphviz import savegraph
savegraph(oscprob_matter.oscprob.oscprob, opts.graph, namespace=ns)
b = execute_bundle(cfg)
print('Inputs')
print(b.context.inputs)
print()
print('Outputs')
print(b.context.outputs)
print()
print('Parameters')
b.namespace.printparameters(labels=True)
print()
if opts.graph:
savegraph(b.context.outputs.hist0, opts.graph)
if opts.show or opts.output:
fig = plt.figure(figsize=(12, 12))
for i, det in enumerate(['D1', 'D2', 'D3']):
ax = plt.subplot(221 + i,
xlabel='E, MeV',
ylabel='',
title='Energy smearing in ' + det)
ax.minorticks_on()
ax.grid()
hists[i].hist.hist.plot_hist(label='Original histogram')
for i, out in enumerate(
b.context.outputs.eres[det].values(nested=True)):
def test_oscprob():
baselinename = 'L'
ns = env.ns("testoscprob")
gna.parameters.oscillation.reqparameters(ns)
ns.defparameter(baselinename,
central=2.0,
fixed=True,
label='Baseline, km')
# Define energy range
enu_input = np.arange(1.0, 10.0, 0.01)
enu = C.Points(enu_input, labels='Neutrino energy, MeV')
# Initialize oscillation variables
component_names = C.stdvector(['comp0', 'comp12', 'comp13', 'comp23'])
with ns:
R.OscProbPMNSExpressions(R.Neutrino.ae(),
R.Neutrino.ae(),
component_names,
ns=ns)
# Initialize neutrino oscillations
with ns:
labels = [
'Oscillation probability|%s' % s
for s in ('component 12', 'component 13', 'component 23', 'full',
'probsum')
]
oscprob = C.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)
# Oscillation probability as single transformation
op_full = oscprob.full_osc_prob.oscprob
# Oscillation probability as weighted sum
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')
# Print some information
oscprob.print()
print()
ns.printparameters(labels=True)
# Print extended information
oscprob.print(data=True, slice=slice(None, 5))
assert np.allclose(op_full.data(), op_sum.data())
if "pytest" not in sys.modules:
fig = plt.figure()
ax = plt.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('E nu, MeV')
ax.set_ylabel('P')
ax.set_title('Oscillation probability')
op_full.plot_vs(enu.single(), '-', label='full oscprob')
op_sum.plot_vs(enu.single(), '--', label='oscprob (sum)')
ax.legend(loc='lower right')
savefig('output/test_oscprob.pdf')
savegraph(enu, 'output/test_oscprob_graph.dot', namespace=ns)
savegraph(enu, 'output/test_oscprob_graph.pdf', namespace=ns)
plt.show()
from gna.bindings import OutputDescriptor
env.globalns.printparameters(labels=True)
print('outputs:')
print(context.outputs)
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('E')
ax.set_ylabel('int f(E)')
ax.set_title('Linear function f(E)=E integrated')
edges = cfg.kinint.edges
centers = (edges[1:] + edges[:-1]) * 0.5
widths = (edges[1:] - edges[:-1])
ints = centers * widths
for i, (par, out) in enumerate(zip(pars, context.outputs.kinint.values())):
out.plot_hist(label='GL quad ' + str(i))
ax.plot(centers, ints * par.value(), '--', label='calc ' + str(i))
ax.legend(loc='upper left')
if args.show:
P.show()
if args.dot:
from gna.graphviz import savegraph
savegraph(context.outputs.evis, args.dot)
hist_output.plot_bar(label='histogram (sum=%g)' % hist.sum(), **baropts)
# plot histogram manually
plot_bar(edges, hist / widths, label='histogram/binwidth', **baropts)
# add legend
ax.legend(loc=opts.legend)
# Our function of interest is a guassian and should give 1 when integrated
# Test it by summing the histogram bins
diff = hist.sum() - integral
print('Integral (analytic)', integral)
print('Integral (analytic, sum)', integrals.sum())
print('Diff (Integral - %g):' % integral, diff)
# print('Integrals (analytic)', integrals)
# print('Integrals (calc)', hist)
adiff = N.fabs(integrals - hist).sum()
print('Diffs (abssum):', adiff)
print(
N.fabs(diff) < 1.e-8 and adiff < 1.e-8 and '\033[32mIntegration is OK!'
or '\033[31mIntegration FAILED!', '\033[0m')
if opts.dot:
try:
from gna.graphviz import savegraph
savegraph(integrator.hist, opts.dot)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
P.show()
instances={
'integral_eq': 'Quenched energy integral',
'integral_evis': 'Visible energy integral',
}))
#
# Initialize bundles
#
expr.parse()
expr.guessname(lib, save=True)
expr.tree.dump()
context = ExpressionContext_v01(cfg, ns=env.globalns)
expr.build(context)
print(context.outputs)
savegraph(context.outputs.energy_edges, opts.dot)
env.globalns.printparameters(labels=True)
#
# Plot functions
#
def plot_projections(update=False, relative=False, label=''):
newfigure = not update
if newfigure:
fig = plt.figure('proj')
fig.clf()
ax = plt.subplot(111, xlabel='E', ylabel='E\'', title='')
ax.minorticks_on()
ax.grid()
ax.set_xlim(0.5, edges[-1])
E >> (unity.fill, oscprob.comp12, oscprob.comp13, oscprob.comp23)
ws = C.WeightedSum(weights, labels, labels='OscProb')
unity >> ws.sum.comp0
oscprob.comp12 >> ws.sum.item12
oscprob.comp13 >> ws.sum.item13
oscprob.comp23 >> ws.sum.item23
ns.materializeexpressions()
pars = tuple(par.getVariable() for (name,par) in ns.walknames())
manager.setVariables(C.stdvector(pars))
if args.graph:
from gna.graphviz import savegraph
savegraph(ws.sum, args.graph)
name, ext = args.graph.rsplit('.', 1)
savegraph(ws.sum, name+'_vars.'+ext, namespace=ns)
out = ws.sum.sum
from gna.bindings import common
fig = plt.figure()
ax = plt.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_title(r'$\overline{\nu}_e$ survival probability at 52 km')
ax.set_xlabel(r'$E_{\nu}$, MeV')
ax.set_ylabel(u'$P_{ee}$')
out.plot_vs(E_arr, label='Full')
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()
