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
mydict = dict( mylabel = 'my text', anotherlabel = 'another text')
reg_dictionary( 'mydict', mydict )
print("Printing labels")
print( L(' my label: {mylabel}') )
print( L(' two labels: {mylabel} and {anotherlabel}') )
print( L(' unknown key representation: {unknown}') )
print( L(' {1} {0} arguments: {mylabel}', 'positional', 'using'))
print()
print("Simple acecss to labels")
print(' by short key:', L.s('mylabel'))
print(' by short key:', L.base('mylabel'))
print()
print("Simple modifications")
print( L(' capitalize: {^mylabel}') )
print( L(' indirect access to label from key "var": {$var}', var='mylabel') )
print( L(' capitalize indirect: {^$var}', var='mylabel') )
print()
print( 'Units and labels:' )
print(' name, unit, default offset:', L.u('dm32'))
print(' name, unit, custom offset:', L.u('dm32', offset=-5))
print(' name, unit, force no offset:', L.u('dm32', offset=0))
print()
print(' name, no unit:', L.u('theta13'))
print(' name, no unit, custom offset:', L.u('theta13', offset=-2))
enew[reg2] = edges[reg2] * corr[reg2]
enew[reg3] = edges[reg3] * corr[reg3]
return corr, enew
binwidth = 0.05
edges = N.arange(0.0, 12.0001, binwidth)
corr, edges_m = nlfcn(edges)
edges_m_plot = N.ma.array(edges_m, mask=edges_m <= -1.e100)
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel(L.u('edep'))
ax.set_ylabel('Correction')
ax.set_title('Non-linearity correction')
ax.plot(edges, corr)
savefig(opts.output, suffix='_corr')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel(L.u('edep'))
ax.set_ylabel(L.u('evis'))
ax.set_title('Non-linearity correction')
except Exception as e:
print( '\033[31mFailed to plot dot\033[0m' )
#
# Make plots
#
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__)
points_t.points.setLabel('E (integr)')
shared=NestedDict( points=points_t.single() )
ns = env.globalns('testexp')
"""Execute bundle"""
b, = execute_bundles( cfg=cfg, common_namespace=ns, shared=shared )
env.globalns.printparameters( labels=True )
"""Plot result"""
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( L.u('enu') )
ax.set_ylabel( L.u('anu_yield') )
ax.set_title( '' )
# ax.vlines(cfg.edges, 0.0, 2.5, linestyles='--', alpha=0.5, colors='blue')
for name, output in b.outputs.items():
data=output.data().copy()
ax.plot( points, N.ma.array(data, mask=data==0.0), label=L.s(name) )
if opts.set or opts.rset:
for var, value in opts.set:
par=ns[var]
par.set(float(value))
for var, value in opts.rset:
par=ns[var]
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()
lines = plot_hist(edges, smeared)
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)
savefig(opts.output, suffix='_test_%i' % i)
ax = axes('Relative energy uncertainty', ylabel=L.u('eres_sigma_rel'))
x = N.arange(0.5, 12.0, 0.01)
fcn = N.frompyfunc(eres.relativeSigma, 1, 1)
y = fcn(x)
ax.plot(x, y * 100.)
savefig(opts.output, suffix='_sigma')
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('Energy resolution convertsion matrix')
ns = env.globalns('testexp')
"""Execute bundle"""
bundles = execute_bundles( cfg=cfg, common_namespace=ns, shared=shared )
snf = bundles[-1]
offeq = bundles[-2]
env.globalns.printparameters( labels=True )
"""Plot result"""
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( L.u('enu') )
ax.set_ylabel( '' )
ax.set_title( '' )
ax.plot( snf.edges, snf.ratio )
# ax.vlines(cfg.edges, 0.0, 2.5, linestyles='--', alpha=0.5, colors='blue')
# for name, output in b.outputs.items():
# ax.plot( points, output.data().copy(), label=L.s(name) )
# if opts.set or opts.rset:
# for var, value in opts.set:
# par=ns[var]
# par.set(float(value))
# for var, value in opts.rset:
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()