def test_rebinner():
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)
rebin.rebin.histin(ntrue)
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
#
if "pytest" not in sys.modules:
print(((prj == 1.0) + (prj == 0.0)).all() and '\033[32mOK!'
or '\033[31mFAIL!', '\033[0m')
fig = P.figure()
ax = P.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')
#
# Plot matrix
#
fig = P.figure()
ax = P.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 )
P.show()
def plot_ratio(gna, dyboscar, **kwargs):
title = kwargs.pop('title', '')
dyb_data, dyb_bins = dyboscar.numpy()
fig, (axis_spectra, axis_ratio) = plt.subplots(ncols=1,
nrows=2,
sharex=True,
gridspec_kw={'hspace': 0.},
squeeze=True)
fig.subplots_adjust(hspace=0.05)
fig.set_tight_layout(True)
gna.plot_hist(axis=axis_spectra, label='GNA')
plot_hist(dyb_bins, dyb_data, label='dybOscar', axis=axis_spectra)
axis_spectra.legend()
axis_spectra.set_title("{}".format(title), fontsize=16)
axis_spectra.minorticks_on()
axis_spectra.grid('both', alpha=0.5)
axis_spectra.set_ylabel(r"Entries", fontsize=14)
axis_spectra.set_ylim(bottom=0.)
ratio = gna.data() / dyb_data - 1
try:
scale = int(kwargs['yscale'])
ratio *= 10**(scale)
ylabel = ''.join(("Ratio$-1$, $10^{-", str(scale), "}$"))
axis_ratio.set_ylabel(r'{}'.format(ylabel), fontsize=14)
except (KeyError, TypeError):
axis_ratio.set_ylabel(r"Ratio$-1$", fontsize=14)
plot_hist(dyb_bins, ratio, axis=axis_ratio, label='GNA/dybOscar')
ylims = kwargs.pop('ylims')
if ylims is not None:
axis_ratio.set_ylim(ylims[0], ylims[1])
xlims = kwargs.pop('xlims')
axis_ratio.axhline(y=0.0, linestyle='--', color='grey', alpha=0.5)
axis_ratio.legend()
axis_ratio.minorticks_on()
axis_ratio.grid(alpha=0.5)
axis_ratio.grid('minor', alpha=0.5)
axis_ratio.set_xlabel(r"$E_{\mathrm{vis}}$, MeV", fontsize=14)
plt.setp(axis_ratio.get_yticklabels()[-1], visible=False)
axis_ratio.get_yaxis().get_major_formatter().set_useOffset(True)
if xlims is not None:
axis_spectra.set_xlim(xlims[0], xlims[1])
axis_ratio.set_xlim(xlims[0], xlims[1])
printer_to_file = kwargs.get('pp', None)
if printer_to_file:
printer_to_file.savefig(fig)
plt.close('all')
def plot_hist1(h, *args, **kwargs):
"""Plot 1-dimensinal histogram using pyplot.plot
executes pyplot.plot(x, y, *args, **kwargs) with first two arguments overridden
all other arguments passes as is.
Options:
autolabel=True guesses plot label with histogram's title
returns pyplot.plot() result
"""
if kwargs.pop('autolabel', None):
kwargs['label'] = h.GetTitle()
lims = R2N.get_bin_edges_axis(h.GetXaxis())
height = R2N.get_buffer_hist1(h).copy()
return helpers.plot_hist(lims, height, *args, **kwargs)
def plot_hist(output, *args, **kwargs):
"""Plot 1-dimensinal output using pyplot.plot
executes pyplot.plot(x, y, *args, **kwargs) with first two arguments overridden
all other arguments are passed as is.
Options:
scale=float or 'width' - multiply bin by a scale or divide by bin width
returns pyplot.plot() result
"""
scale = kwargs.pop('scale', None)
height, lims, _ = get_1d_data(output, scale=scale)
diff = kwargs.pop('diff', None)
if diff is not None:
ifSameType(output, diff)
height1, lims1, _ = get_1d_data(diff, scale)
height -= height1
ratio = kwargs.pop('ratio', None)
if ratio is not None:
ifSameType(output, ratio)
height1, lims1, _ = get_1d_data(ratio, scale)
height /= height1
height[N.isnan(height)] = 1.0
offset_ratio = kwargs.pop('offset_ratio', None)
if offset_ratio is not None:
ifSameType(output, offset_ratio)
height1, lims1, _ = get_1d_data(offset_ratio, scale)
height /= height1
height -= 1.0
height[N.isnan(height)] = 0.0
return helpers.plot_hist(lims, height, *args, **kwargs)
from mpl_tools.helpers import plot_hist
from gna.labelfmt import formatter as L
axes=()
for i, hist in enumerate(hists_list):
P.figure(),
ax = P.subplot( 111 )
axes+=ax,
ax.minorticks_on()
ax.grid()
ax.set_xlabel( L.u('evis') )
ax.set_ylabel( 'Entries' )
ax.set_title( cfg.detector.detectors[i] )
data = hist.hist.hist.data()
print( 'Sum data %i=%i'%( i, data.sum() ) )
plot_hist(edges, data, label='original')
for bundle in b.bundles.values():
for i, (oname, out) in enumerate( bundle.outputs.items() ):
P.sca(axes[i])
data = out.data()
print( 'Sum data %s (%s) %i=%f'%( type(bundle).__name__, oname, i, data.sum() ) )
plot_hist(out.datatype().edges, data, label=type(bundle).__name__)
for i, hist in enumerate(hists_list):
ax=axes[i]
P.sca(ax)
ax.legend( loc='upper right' )
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()
#
# Plot hists
#
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('Non-linearity effect')
smeared = smear.Nrec.data().copy()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared.sum()))
plot_hist(edges, phist, label='original')
lines = plot_hist(edges, smeared, label='smeared: nominal')
ax.legend(loc='upper right')
savefig(args.output)
#
# Plot matrix
#
fig = P.figure()
ax1 = P.subplot(111)
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('Source bins')
ax1.set_ylabel('Target bins')
ax1.set_title('Daya Bay LSNL matrix')
print(a.expressions_raw)
print(a.expressions)
a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
edges = np.linspace(0., 12, 240 + 1)
print()
cfg = NestedDict(snf=NestedDict(
bundle="reactor_snf_spectra_v02",
data_path=
"data/reactor_anu_spectra/SNF/kopeikin_0412.044_spent_fuel_spectrum_smooth.dat",
edges=edges))
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)
from gna.bindings import OutputDescriptor
env.globalns.printparameters(labels=True)
print('outputs:')
print(context.outputs)
if args.show:
import matplotlib.pyplot as plt
import mpl_tools.helpers as mplh
snf = context.outputs['snf_ratio']
bin_centers = (edges[1:] + edges[:-1]) / 2
mplh.plot_hist(edges, snf.data())
plt.show()
rd = R.RenormalizeDiag(ndiag, int(opts.offdiag), int(opts.upper))
rd.renorm.inmat(pmat.points)
esmear = R.HistSmear(opts.upper)
esmear.smear.inputs.SmearMatrix(rd.renorm)
esmear.smear.inputs.Ntrue(hist.hist)
for i, value in enumerate([1.0, 0.5, 2.0]):
par.set(value)
smeared = esmear.smear.Nrec.data()
print('Sum check for {} (diff): {}'.format(value,
phist.sum() - smeared.sum()))
# bars = P.bar( edges[:-1], phist, binwidth, align='edge' )
P.sca(ax)
lines = plot_hist(edges, smeared, label='%.2f' % value)
color = lines[0].get_color()
fig = P.figure()
ax1 = P.subplot(111)
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('')
ax1.set_ylabel('')
ax1.set_title('Synthetic energy leak matrix (diag scale=%.2f, ndiag=%i)' %
(value, ndiag))
mat = rd.renorm.outmat.data()
mat = N.ma.array(mat, mask=mat == 0.0)
c = ax1.matshow(mat, extent=[edges[0], edges[-1], edges[-1], edges[0]])
add_colorbar(c)
#
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( r'$E_\nu$, MeV' )
ax.set_ylabel( 'entries' )
ax.set_title( 'IAV effect' )
smeared = smear.data().copy()
par.set( 2.0 )
smeared2 = smear.data().copy()
print( 'Sum check for {} (diff): {}'.format( 1.0, phist.sum()-smeared.sum() ) )
print( 'Sum check for {} (diff): {}'.format( 2.0, phist.sum()-smeared2.sum() ) )
lines = plot_hist( edges, smeared, label='nominal' )
lines = plot_hist( edges, smeared2, linewidth=1.0, label='diag scale $s=2$' )
ax.legend( loc='upper right' )
if args.xlim:
ax.set_xlim( *args.xlim )
savefig( args.output )
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
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()
#
# Test bundle
#
nonlin1.smear.Ntrue(hist.hist)
nonlin2.smear.Ntrue(hist.hist)
#
# Plot hists
#
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('Non-linearity effect')
smeared1 = nonlin1.smear.Nrec.data().copy()
smeared2 = nonlin2.smear.Nrec.data().copy()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared1.sum()))
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared2.sum()))
lines = plot_hist(edges, smeared1, label='Smeared 1')
lines = plot_hist(edges, smeared2, label='Smeared 2')
plot_hist(edges, phist, label='original')
ax.legend(loc='upper right')
P.show()
ax = axes('Energy resolution impact')
for i, e in enumerate(eset):
phist = singularities(e, edges)
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()
print('Sum check for {} (diff): {}'.format(e,
phist.sum() -
smeared.sum()))
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=color)
push_value(0.162)
smeared = eres.smear.Nrec.data()
nl.set(hist.hist, pedges_m.points)
nl.add_input(hist)
smeared = nl.smear.Nrec.data()
print('Sum check (diff): {}'.format(phist.sum() - smeared.sum()))
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel(L.u('evis'))
ax.set_ylabel('Entries')
ax.set_title('Non-linearity correction')
lines = plot_hist(edges, smeared)
color = lines[0].get_color()
_, ev_m = nlfcn(ev)
heights = smeared[smeared > 0.45]
ax.vlines(ev, 0.0, heights, alpha=0.7, color='red', linestyle='--')
ax.vlines(ev_m, 0.0, heights, alpha=0.7, color='green', linestyle='--')
savefig(opts.output, suffix='_evis')
fig = P.figure()
ax1 = P.subplot(111)
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('Source bins')
ax1.set_ylabel('Target bins')
ax1.set_title('Energy non-linearity matrix')
for bundle in b.bundles.values()+[b, bfn]:
bkgname=bundle.cfg.get('name', 'total')
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( 'x axis' )
ax.set_ylabel( 'entries' )
ax.set_title(bkgname)
for i, (name, output) in enumerate(bundle.outputs.items()):
pack=None
if opts.pack:
if bkgname=='bkg2':
group = bundle.groups.get_group(name, 'site')
pack = (group.index(name), len(group))
elif bkgname=='bkgw':
pack = (i, len(bundle.outputs))
if bkgname=='bkg_fn':
plot_hist( output.datatype().edges, output.data(), label=name )
else:
plot_bar( output.datatype().edges, output.data(), label=name, pack=pack, alpha=0.8 )
ax.legend( loc='upper right' )
P.show()
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('IAV effect')
smeared1 = smear1.Nrec.data()
smeared2 = smear2.Nrec.data()
smeared3 = smear3.Nrec.data()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared1.sum()))
print('Sum check for {} (diff): {}'.format(2.0, phist.sum() - smeared2.sum()))
print('Sum check for {} (diff): {}'.format(2.0, phist.sum() - smeared3.sum()))
lines = plot_hist(edges, smeared1, linewidth=1.0, label='IAV 1')
lines = plot_hist(edges, smeared2, linewidth=1.0, label='IAV 2')
lines = plot_hist(edges, smeared3, linewidth=1.0, label='IAV 3')
ax.legend(loc='upper right')
#
# Dump graph
#
if args.dot:
try:
from gna.graphviz import GNADot
graph = GNADot(b.transformations_out.values()[0])
graph.write(args.dot)
print('Write output to:', args.dot)
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()