def test_viewrear_hist_04_auto_threshold():
"""Test ViewRear on Histogram (auto start and len), enable threshold"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size-1, dtype=context.current_precision_short())
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6)]
for rng in ranges:
hist_main = C.Histogram(edges, arr)
start, len = rng
pedges = edges[start:start+len+1].copy()
print('Original edges', pedges)
pedges[0] = 0.5*(pedges[:2].sum())
pedges[-1] = 0.5*(pedges[-2:].sum())
print('Modified edges', pedges)
arr_sub = N.arange(start, start+len, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub, True)
view = C.ViewRear(hist_main);
view.allowThreshold()
hist_sub >> view.view.rear
res = view.view.result.data()
expect_edges = N.array(view.view.result.datatype().edges)
expect = arr.copy()
expect[start:start+len]=arr_sub
print('Range', rng)
print('Result', res)
print('Expect', expect)
print('Result (edges)', edges)
print('Expect (edges)', expect_edges)
print()
assert (res==expect).all()
assert (edges==expect_edges).all()
assert (hist_main.single().data()==arr).all()
def test_viewrear_hist_02_auto():
"""Test ViewRear on Histogram (determine start and length)"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size-1, dtype=context.current_precision_short())
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
print('Range', rng)
hist_main = C.Histogram(edges, arr)
start, len = rng
pedges = edges[start:start+len+1]
arr_sub = N.arange(start, start+len, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub, True)
view = C.ViewRear(hist_main);
hist_sub >> view.view.rear
res = view.view.result.data()
expect_edges = N.array(view.view.result.datatype().edges)
expect = arr.copy()
expect[start:start+len]=arr_sub
print('Result', res)
print('Expect', expect)
print('Result (edges)', edges)
print('Expect (edges)', expect_edges)
print()
assert (res==expect).all()
assert (edges==expect_edges).all()
assert (hist_main.single().data()==arr).all()
def test_viewrear_points_04_auto_fromhist_edges():
"""Test ViewRear on Histogram (determine start and length)"""
edges = N.arange(13, dtype='d')
arr = N.zeros(edges.size, dtype=context.current_precision_short())
points_main = C.Points(arr)
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
print('Range', rng)
hist_main = C.Histogram(edges, arr[:-1])
start, len = rng
pedges = edges[start:start+len+1]
arr_sub = N.arange(start, start+len+1, dtype=arr.dtype)
hist_sub=C.Histogram(pedges, arr_sub[:-1], True)
points_sub=C.Points(arr_sub, True)
view = C.ViewRear()
view.determineOffset(hist_main, hist_sub, True)
points_main >> view.view.original
points_sub >> view.view.rear
res = view.view.result.data()
expect = arr.copy()
expect[start:start+len+1]=arr_sub
print('Result', res)
print('Expect', expect)
print()
assert (res==expect).all()
def test_viewrear_points_01_start_len(function_name):
"""Test ViewRear on Points (start, len)"""
arr = N.zeros(12, dtype=context.current_precision_short())
ns = env.env.globalns(function_name)
names=[]
for i in range(arr.size):
name='val_%02i'%i
names.append(name)
ns.defparameter( name, central=i, fixed=True, label='Value %i'%i )
ranges = [ (0, 3), (0, 12), (1, 3), (6, 6), (6, 1)]
for rng in ranges:
start, len = rng
pview = arr[start:start+len]
points = C.Points(arr)
view = C.ViewRear(points, start, len);
cnames = names[start:start+len]
with ns:
vararray = C.VarArray(cnames)
vararray >> view.view.rear
print('Range', rng)
for ichange, iname in enumerate(['']+cnames, -1):
if iname:
print(' Change', ichange)
par=ns[iname]
par.set(par.value()+1.0)
res0 = vararray.single().data()
res = view.view.result.data()
expect = arr.copy()
for i in range(start, start+len):
expect[i] = ns[names[i]].value()
expect0 = []
for i in range(start, start+len):
expect0.append(ns[names[i]].value())
print(' Result 0', res0)
print(' Expect 0', expect0)
print(' Result', res)
print(' Expect', expect)
print(' Original data', points.single().data())
print(' Original data (expect)', arr)
assert (res==expect).all()
assert (res0==expect0).all()
assert (res[start:start+len]==res0).all()
assert (points.single().data()==arr).all()
print()
def build(self):
with entryContext(subgraph='LSNL'):
self.init_data()
#
# Initialize bin edges
#
self.histoffset = C.HistEdgesOffset(
self.doubleme, labels='Offset/threshold bin edges (Evis, Te+)')
histedges = self.histoffset.histedges
# Declare open input
self.set_input('evis_edges_hist',
None,
histedges.hist_in,
argument_number=0)
histedges.points.setLabel('Evis (full range)')
histedges.points_truncated.setLabel('Evis (truncated)')
histedges.points_threshold.setLabel('Evis (threshold)')
histedges.points_offset.setLabel('Te+')
histedges.hist_truncated.setLabel('Hist Evis (truncated)')
histedges.hist_threshold.setLabel('Hist Evis (threshold)')
histedges.hist_offset.setLabel('Hist Te+')
#
# Birk's model integration
#
birks_e_input, birks_quenching_input = self.stopping_power[
'e'], self.stopping_power['dedx']
self.birks_e_p, self.birks_quenching_p = C.Points(
birks_e_input,
labels='Te (input)'), C.Points(birks_quenching_input,
labels='Stopping power (dE/dx)')
birksns = self.namespace('birks')
with birksns:
self.birks_integrand_raw = C.PolyRatio(
[],
list(sorted(birksns.storage.keys())),
labels="Birk's integrand")
self.birks_quenching_p >> self.birks_integrand_raw.polyratio.points
self.doubleemass_point = C.Points([-self.doubleme],
labels='2me offset')
self.integrator_ekin = C.IntegratorGL(
self.histoffset.histedges.hist_offset,
self.cfg.integration_order,
labels=('Te sampler (GL)', "Birk's integrator (GL)"))
self.birks_integrand_interpolator = C.InterpLogx(
self.birks_e_p,
self.integrator_ekin.points.x,
labels=("Birk's InSegment", "Birk's interpolator"))
self.birks_integrand_interpolated = self.birks_integrand_interpolator.add_input(
self.birks_integrand_raw.polyratio.ratio)
self.birks_integral = self.integrator_ekin.add_input(
self.birks_integrand_interpolated)
self.birks_accumulator = C.PartialSum(0.,
labels="Birk's Evis|[MeV]")
self.birks_integral >> self.birks_accumulator.reduction
#
# Cherenkov model
#
with self.namespace('cherenkov'):
self.cherenkov = C.Cherenkov_Borexino(labels='Npe Cherenkov')
self.histoffset.histedges.points_offset >> self.cherenkov.cherenkov
#
# Electron energy model
#
with self.namespace:
self.electron_model = C.WeightedSum(
['kC', 'Npescint'], [
self.cherenkov.cherenkov.ch_npe,
self.birks_accumulator.reduction.out
],
labels='Npe: electron responce')
#
# 2 511 keV gamma model
#
self.annihilation_electrons_centers = C.Points(
self.annihilation_electrons_centers_input,
labels='Annihilation gamma E centers')
self.annihilation_electrons_p = C.Points(
self.annihilation_electrons_p_input,
labels='Annihilation gamma weights')
self.view_lowe = C.ViewHistBased(
self.histoffset.histedges.hist_offset,
0.0,
self.annihilation_electrons_edges_input[-1],
labels=('Low E indices', 'Low E view'))
self.ekin_edges_lowe = self.view_lowe.add_input(
self.histoffset.histedges.points_offset)
self.electron_model_lowe = self.view_lowe.add_input(
self.electron_model.single())
self.ekin_edges_lowe.setLabel('Te+ edges (low Te view)')
self.electron_model_lowe.setLabel(
'Npe: electron responce (low Te view)')
self.electron_model_lowe_interpolator = C.InterpLinear(
self.ekin_edges_lowe,
self.annihilation_electrons_centers,
labels=('Annihilation E InSegment',
'Annihilation gamma interpolator'))
self.electron_model_lowe_interpolated = self.electron_model_lowe_interpolator.add_input(
self.electron_model_lowe)
with self.namespace:
self.npe_positron_offset = C.Convolution(
'ngamma', labels='e+e- annihilation Evis [MeV]')
self.electron_model_lowe_interpolated >> self.npe_positron_offset.normconvolution.fcn
self.annihilation_electrons_p >> self.npe_positron_offset.normconvolution.weights
#
# Total positron model
#
self.positron_model = C.SumBroadcast(
outputs=[
self.electron_model.sum.sum,
self.npe_positron_offset.normconvolution.result
],
labels='Npe: positron responce')
self.positron_model_scaled = C.FixedPointScale(
self.histoffset.histedges.points_truncated,
self.namespace['normalizationEnergy'],
labels=('Fixed point index',
'Positron energy model|Evis, MeV'))
self.positron_model_scaled = self.positron_model_scaled.add_input(
self.positron_model.sum.outputs[0])
self.positron_model_scaled_full_view = C.ViewRear(
-1.0, labels='Positron Energy nonlinearity|full range')
self.positron_model_scaled_full_view.determineOffset(
self.histoffset.histedges.hist,
self.histoffset.histedges.hist_truncated, True)
self.histoffset.histedges.points >> self.positron_model_scaled_full_view.view.original
self.positron_model_scaled >> self.positron_model_scaled_full_view.view.rear
self.positron_model_scaled_full = self.positron_model_scaled_full_view.view.result
#
# Relative positron model
#
self.positron_model_relative = C.Ratio(
self.positron_model_scaled,
self.histoffset.histedges.points_truncated,
labels='Positron energy nonlinearity')
self.positron_model_relative_full_view = C.ViewRear(
0.0, labels='Positron Energy nonlinearity|full range')
self.positron_model_relative_full_view.determineOffset(
self.histoffset.histedges.hist,
self.histoffset.histedges.hist_truncated, True)
self.histoffset.histedges.points >> self.positron_model_relative_full_view.view.original
self.positron_model_relative >> self.positron_model_relative_full_view.view.rear
self.positron_model_relative_full = self.positron_model_relative_full_view.view.result
#
# Hist Smear
#
self.pm_histsmear = C.HistNonlinearity(
self.cfg.get('fill_matrix', False),
labels=('Nonlinearity matrix', 'Nonlinearity smearing'))
self.pm_histsmear.set_range(-0.5, 20.0)
self.positron_model_scaled_full >> self.pm_histsmear.matrix.EdgesModified
self.histoffset.histedges.hist >> self.pm_histsmear.matrix.Edges
trans = self.pm_histsmear.transformations.back()
for i, it in enumerate(self.nidx.iterate()):
# if i:
# trans = self.pm_histsmear.add_transformation()
inp = self.pm_histsmear.add_input()
trans.setLabel(
it.current_format('Nonlinearity smearing {autoindex}'))
self.set_input('lsnl', it, inp, argument_number=0)
self.set_output('lsnl', it, trans.outputs.back())