def test_MatrixProductDVDt(n1, n2, sym, tmp_path):
'''Matrix product of D V Dt'''
size1, size2 = n1 * n2, n2 * n2
left_n = np.arange(size1, dtype='d').reshape(n1, n2) + 1.0
square_n = 40.0 + size2 - np.arange(size2, dtype='d').reshape(n2, n2)
if sym:
square_n = 0.5 * (square_n + square_n.T)
left = C.Points(left_n)
square = C.Points(square_n)
dvdt = R.MatrixProductDVDt(left, square)
result = dvdt.product.product()
check = np.matmul(np.matmul(left_n, square_n), left_n.transpose())
diff = np.fabs(result - check)
# print('Max diff ({}, {})'.format(n1, n2), diff.max())
assert (result == check).all()
if sym:
checksym = result - result.T
assert not checksym.any()
path = os.path.join(str(tmp_path), 'graph_{:d}_{:d}.png'.format(n1, n2))
savegraph(dvdt.product, path, verbose=False)
allure_attach_file(path)
def build(self):
for rit in self.nidx_reactor:
core_name, = rit.current_values()
core = self.core_info_daily[core_name]
days_in_period = np.array(core['days']).astype(int)
thermal_power_daily = np.repeat(core['power'], days_in_period)
if self.cfg.nominal_power:
thermal_power_daily = np.ones(len(thermal_power_daily))
thermal_power_per_core = C.Points(thermal_power_daily, labels=rit.current_format('Thermal power\n{autoindex}'))
self.context.objects[(core_name, 'thermal_power')] = thermal_power_per_core
self.set_output('thermal_power', rit, thermal_power_per_core.single())
for oit in self.nidx_isotope:
it = rit+oit
iso_name, = oit.current_values()
# map fission fractions and thermal powers to days instead of weeks
fission_fractions_daily = np.repeat(core['fission_fractions'], days_in_period)
label=it.current_format('Fission fractions\n{autoindex}')
fission_per_iso = C.Points(fission_fractions_daily[iso_name], labels=label)
self.context.objects[it.current_values(name='fission_fractions')] = fission_per_iso
self.set_output('fission_fractions', it, fission_per_iso.single())
if self.cfg.add_ff:
label_add=it.current_format('Additional set of fission fractions for norm calc\n{autoindex}')
fission_per_iso_add = C.Points(fission_fractions_daily[iso_name], labels=label_add)
self.context.objects[it.current_values(name='fission_fractions_add')] = fission_per_iso_add
self.set_output('fission_fractions_add', it, fission_per_iso_add.single())
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()
def build(self):
self.inputs = {}
self.outputs = {}
self.points = {}
self.interp = {}
for k, v in self.data.items():
x = C.Points(v[0], labels='{} geo-nu X0'.format(k))
y = C.Points(v[1], labels='{} geo-nu Y0'.format(k))
interp = C.InterpLinear(
labels=('{} X insegment'.format(k),
'{} geo-neutrino spectra'.format(k)))
interp.set_underflow_strategy(
R.GNA.Interpolation.Strategy.Constant)
interp.set_overflow_strategy(R.GNA.Interpolation.Strategy.Constant)
interp.set_fill_value(0.0)
interp.setXY(x, y)
self.points[k] = (x, y)
self.interp[k] = interp
for idx in self.nidx.iterate():
for k in self.data.keys():
interp = self.interp[k]
self.set_input('geonu_spectrum_{}'.format(k),
idx,
(interp.insegment.points, interp.interp.newx),
argument_number=0)
self.set_output('geonu_spectrum_{}'.format(k), idx,
interp.interp.interp)
def normalization(self, reactor, detector, normtype):
"""Returns normalization object for given reactor/detector pair and norm type"""
def vec(lst):
v = ROOT.vector("std::string")()
for s in lst:
v.push_back(s)
return v
if normtype == 'calc':
bindings = {}
for isoname in reactor.fission_fractions:
bindings["EnergyPerFission_{0}".format(isoname)] = self.ns(
"isotopes")(isoname)["EnergyPerFission"]
norm = ROOT.ReactorNorm(vec(reactor.fission_fractions.keys()),
bindings=bindings)
lt = C.Points(detector.livetime)
lt.points.setLabel('livetime: | ' + detector.name)
norm.isotopes.livetime(lt)
pr = C.Points(reactor.power_rate)
pr.points.setLabel('power: | ' + reactor.name)
norm.isotopes.power_rate(pr)
norm.isotopes.setLabel('norm: | {} to {}'.format(
reactor.name, detector.name))
for isoname, frac in reactor.fission_fractions.items():
ff = C.Points(frac)
ff.points.setLabel('fission frac: | {} at {}'.format(
isoname, reactor.name))
norm.isotopes['fission_fraction_{0}'.format(isoname)](ff)
elif normtype == 'manual':
norm = ROOT.ReactorNormAbsolute(
vec(reactor.fission_fractions.keys()))
for isoname, frac in reactor.fission_fractions.items():
norm.isotopes['fission_fraction_{0}'.format(isoname)](frac)
return norm
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_typeclass_kind_v02():
objects = [C.Points(np.arange(6)), C.Points(np.arange(6).reshape(3, 2))]
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt_points = R.TypeClasses.CheckKindT(context.current_precision())(1)
R.SetOwnership(dt_points, False)
dt_points.dump()
print()
dt_hist = R.TypeClasses.CheckKindT(context.current_precision())(2)
R.SetOwnership(dt_hist, False)
dt_hist.dump()
print()
obj.add_typeclass(dt_points)
res = obj.process_types()
assert res
obj.add_typeclass(dt_hist)
print('Exception expected: ', end='')
res = obj.process_types()
assert not res
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_typeclass_ndim_v03():
objects = [
C.Histogram(np.arange(6), np.arange(5)),
C.Points(np.arange(5)),
C.Histogram2d(np.arange(6), np.arange(7)),
C.Points(np.arange(12).reshape(3, 4))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt = R.TypeClasses.CheckNdimT(context.current_precision())(1, (0, 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, (-2, -1))
R.SetOwnership(dt1, False)
dt1.dump()
print()
obj.add_typeclass(dt1)
res = obj.process_types()
assert res
def build(self):
reac, other = self.idx.split(('r'))
for rit in reac.iterate():
core_name, = rit.current_values()
core = self.core_info_daily[core_name]
days_in_period = np.array(core['days'])
thermal_power_daily = np.repeat(core['power'], days_in_period)
thermal_power_per_core = C.Points(thermal_power_daily)
thermal_power_per_core.points.setLabel(
rit.current_format('Thermal power\n{autoindex}'))
self.objects[(core_name, 'thermal_power')] = thermal_power_per_core
self.set_output(thermal_power_per_core.single(), 'thermal_power',
rit)
for oit in other.iterate():
it = rit + oit
iso_name = it.indices['i'].current
# map fission fractions and thermal powers to days instead of weeks
fission_fractions_daily = np.repeat(core['fission_fractions'],
days_in_period)
fission_per_iso = C.Points(fission_fractions_daily[iso_name])
fission_per_iso.points.setLabel(
it.current_format('Fission fractions\n{autoindex}'))
self.objects[(core_name, 'fission_fractions',
iso_name)] = fission_per_iso
self.set_output(fission_per_iso.single(), 'fission_fractions',
it)
def build(self):
model_edges_t = C.Points( self.model_edges, ns=self.namespace )
model_edges_t.points.setLabel('Spectra interpolation edges')
self.context.objects['edges'] = model_edges_t
self.shared.reactor_anu_edges = model_edges_t.single()
if self.cfg.free_params:
with self.reac_ns:
tmp = C.VarArray(self.variables, ns=self.reac_ns, labels='Spec pars:\nlog(n_i)')
if self.cfg.varmode == 'log':
self.context.objects['npar_log'] = tmp
self.free_weights = R.Exp(ns=self.reac_ns)
self.free_weights.exp.points( tmp )
self.free_weights.exp.setLabel('n_i')
else:
tmp.vararray.setLabel('n_i')
self.free_weights = tmp
self.interp_expo = interp_expo = R.InterpExpo(ns=self.reac_ns)
sampler = interp_expo.transformations.front()
model_edges_t >> sampler.inputs.edges
sampler_input = sampler.inputs.points
interp_expo_t = interp_expo.transformations.back()
for i, it in enumerate(self.nidx_major):
isotope, = it.current_values()
spectrum_raw_t = C.Points( self.spectra[isotope], ns=self.reac_ns )
spectrum_raw_t.points.setLabel('%s spectrum, original'%isotope)
self.context.objects[('spectrum_raw', isotope)] = spectrum_raw_t
if self.cfg.free_params:
spectrum_t = C.Product(ns=self.reac_ns)
spectrum_t.multiply( spectrum_raw_t )
spectrum_t.multiply( self.free_weights.single() )
spectrum_t.product.setLabel('%s spectrum, corrected'%isotope)
else:
spectrum_t = spectrum_raw_t
if i>0:
interp_expo_t = interp_expo.add_transformation()
model_edges_t >> interp_expo_t.inputs.x
interp_output = interp_expo.add_input(spectrum_t)
interp_input = interp_expo_t.inputs.newx
if i>0:
self.set_input(self.cfg.name, it, interp_input, argument_number=0)
else:
self.set_input(self.cfg.name, it, (sampler_input, interp_input), argument_number=0)
interp_expo_t.setLabel('%s spectrum, interpolated'%isotope)
"""Store data"""
self.set_output(self.cfg.name, it, interp_output)
self.context.objects[('spectrum', isotope)] = spectrum_t
def build(self):
self.load_data()
model_edges_t = C.Points(self.model_edges, ns=self.namespace)
model_edges_t.points.setLabel('Spectra interpolation edges')
self.context.objects['edges'] = model_edges_t
self.shared.reactor_anu_edges = model_edges_t.single()
self.corrections = None
if self.cfg.get('corrections', None):
self.corrections, = execute_bundles(cfg=self.cfg.corrections,
shared=self.shared)
self.interp_expo = interp_expo = R.InterpExpo(ns=self.namespace)
sampler = interp_expo.transformations.front()
model_edges_t >> sampler.inputs.edges
sampler_input = sampler.inputs.points
interp_expo_t = interp_expo.transformations.back()
for i, it in enumerate(self.nidx_major):
isotope, = it.current_values()
spectrum_raw_t = C.Points(self.spectra[isotope], ns=self.namespace)
spectrum_raw_t.points.setLabel('%s spectrum, original' % isotope)
self.context.objects[('spectrum_raw', isotope)] = spectrum_raw_t
if self.corrections:
spectrum_t = R.Product(ns=self.namespace)
spectrum_t.multiply(spectrum_raw_t)
for corr in self.corrections.bundles.values():
spectrum_t.multiply(corr.outputs[isotope])
spectrum_t.product.setLabel('%s spectrum, corrected' % isotope)
else:
spectrum_t = spectrum_raw_t
if i > 0:
interp_expo_t = interp_expo.add_transformation()
model_edges_t >> interp_expo_t.inputs.x
interp_output = interp_expo.add_input(spectrum_t)
interp_input = interp_expo_t.inputs.newx
if i > 0:
self.set_input(self.cfg.name,
it,
interp_input,
argument_number=0)
else:
self.set_input(self.cfg.name,
it, (sampler_input, interp_input),
argument_number=0)
interp_expo_t.setLabel('%s spectrum, interpolated' % isotope)
"""Store data"""
self.set_output(self.cfg.name, it, interp_output)
self.context.objects[('spectrum', isotope)] = spectrum_t
def test_view_01():
start = 2.0
size = 12
edges = N.arange(start, start + 12, dtype='d')
arr_centers = N.arange(-2, -2 + size - 1, dtype='d')
arr_edges = N.arange(-1, -1 + size, dtype='d')
print('Input edges', edges)
print('Input arr (centers)', arr_centers)
print('Input arr (edges)', arr_edges)
hist = C.Histogram(edges)
hist_in = C.Histogram(edges, arr_centers)
points_in = C.Points(arr_centers)
points_e_in = C.Points(arr_edges)
lims = [(-1.0, 2.0), (0, 3), (0, 12), (1, 3), (6, 6.1), (1, 6), (10, 11),
(0.1, 1.5), (1.1, 1.8), (5.5, 10.9), (10.1, 10.9), (10.1, 13.0)]
for (threshold, ceiling) in lims:
print('Limits0 ', threshold, ceiling)
threshold += start
ceiling += start
print(' Limits', threshold, ceiling)
view = C.ViewHistBased(hist, threshold, ceiling)
hist_out = view.add_input(hist_in)
points_out = view.add_input(points_in)
points_e_out = view.add_input(points_e_in)
idx_thresh = N.max((int(N.floor(threshold - start)), 0))
idx_ceiling = int(N.ceil(ceiling - start))
res_expected = edges[idx_thresh:idx_ceiling + 1]
print(' Indices', idx_thresh, idx_ceiling)
hist_out_res = hist_out.data()
hist_out_exp = arr_centers[idx_thresh:idx_ceiling]
points_out_res = points_out.data()
points_out_exp = arr_centers[idx_thresh:idx_ceiling]
points_e_out_res = points_e_out.data()
points_e_out_exp = arr_edges[idx_thresh:idx_ceiling + 1]
res = N.array(view.view.view.datatype().edges)
print(' Edges result', res)
print(' Edges expect', res_expected)
print(' Hist result', hist_out_res)
print(' Hist expect', hist_out_exp)
print(' Poits result', points_out_res)
print(' Poits expect', points_out_exp)
print(' Poits (edges) result', points_e_out_res)
print(' Poits (edges) expect', points_e_out_exp)
print()
assert (res == res_expected).all()
assert (hist_out_res == hist_out_exp).all()
assert (points_out_res == points_out_exp).all()
assert (points_e_out_res == points_e_out_exp).all()
def define_variables(self):
daq = self.common_namespace('daq')
daq.reqparameter('first_day',
central=self.info['first_day'],
fixed=True,
sigma=0.01,
label='First DAQ day start: %s' %
self.info['str_first_day'])
daq.reqparameter('last_day',
central=self.info['last_day'],
fixed=True,
sigma=0.01,
label='Last DAQ day end: %s' %
self.info['str_last_day'])
daq.reqparameter('ndays',
central=self.info['days'],
fixed=True,
sigma=0.01,
label='Total number of days')
data_lt = 0.0
for it in self.idx.iterate():
ad, = it.current_values()
data = self.data.get(ad, None)
if data is None:
raise self.exception('Failed to retrieve data for %s from %s' %
(ad, self.cfg.file))
data_lt = data['livetime'] + data_lt
livetime = C.Points(data['livetime'])
livetime.points.setLabel(
it.current_format('Livetime\n{autoindex}'))
eff = C.Points(data['eff'])
eff.points.setLabel(
it.current_format('Efficiency (mu*mult)\n{autoindex}'))
efflivetime = R.Product(livetime, eff)
efflivetime.product.setLabel(
it.current_format('Livetime (eff)\n{autoindex}'))
self.objects[('livetime', ad)] = livetime
self.objects[('eff', ad)] = eff
self.objects[('efflivetime', ad)] = efflivetime
self.set_output(livetime.single(), 'livetime_daily', it)
self.set_output(eff.single(), 'eff_daily', it)
self.set_output(efflivetime.single(), 'efflivetime_daily', it)
ndays_daq = (data_lt > 0.0).sum()
daq.reqparameter('ndays_daq',
central=ndays_daq,
fixed=True,
sigma=0.01,
label='Total number of DAQ days (exclude no DAQ)')
def make_prediction(self, from_neutrino, to_neutrino, Enu_arr, L):
with self.ns:
Enu = C.Points(Enu_arr)
oscprob_classes = {
'standard': ROOT.OscProbPMNS,
'decoh': ROOT.OscProbPMNSDecoh,
}
oscprobs = {}
data = {}
for name, cls in oscprob_classes.items():
oscprob = cls(from_neutrino, to_neutrino)
for compname in list(oscprob.probsum.inputs.keys()):
if compname != 'comp0':
for tname, tf in oscprob.transformations.items():
if compname in tf.outputs:
oscprob.probsum[compname](tf[compname])
break
else:
oscprob.probsum[compname](C.Points(
np.ones_like(Enu_arr)))
for tf in oscprob.transformations.values():
if 'Enu' in tf.inputs:
tf.inputs.Enu(Enu)
self.ns.addobservable('probability_{0}'.format(name),
oscprob.probsum)
oscprobs[name] = oscprob
data[name] = oscprob.probsum
data_stand, data_decoh = data['standard'], data['decoh']
self.ns["L"].set(L)
self.ns["Delta"].set(0.0)
sigma_arr = [1.e-17, 1e-1, 2e-1, 5e-1]
nu_names = ['nu_e', 'nu_mu', 'nu_tau']
filename = 'oscprob_' + nu_names[
from_neutrino.flavor] + '_' + nu_names[
to_neutrino.flavor] + '.pdf'
self.open_pdf(filename, oscprobs['standard'].__class__.__name__)
plt.plot(Enu_arr,
data_stand.data(),
label=r"$P_{PW}$",
linewidth=3)
for sigma in sigma_arr:
self.ns["sigma"].set(sigma)
plt.plot(Enu_arr,
data_decoh.data(),
label=r"$\sigma={0}$".format(sigma))
#plt.show()
nu_tex = [r"$\nu_e$", r"$\nu_\mu$", r"$\nu_\tau$"]
self.close_pdf(
'E, [MeV]', r'$P($' + nu_tex[from_neutrino.flavor] + r'$\to$' +
nu_tex[to_neutrino.flavor] + r'$)$')
def build(self):
model_edges_t = C.Points( self.model_edges, ns=self.common_namespace )
model_edges_t.points.setLabel('E0 (bin edges)')
self.objects['edges'] = model_edges_t
self.shared.reactor_anu_edges = model_edges_t.single()
self.corrections=None
if self.cfg.get('corrections', None):
self.corrections, = execute_bundles(cfg=self.cfg.corrections, shared=self.shared)
self.interp_expo = interp_expo = R.InterpExpo(ns=self.common_namespace)
sampler = interp_expo.transformations.front()
model_edges_t >> sampler.inputs.edges
sampler_input = sampler.inputs.points
interp_expo.bind_transformations(False)
interp_expo_t = interp_expo.transformations.back()
for i, it in enumerate(self.idx):
isotope = it.current_values()[0]
spectrum_raw_t = C.Points( self.spectra[isotope], ns=self.common_namespace )
spectrum_raw_t.points.setLabel('S0(E0):\n'+isotope)
self.objects[('spectrum_raw', isotope)] = spectrum_raw_t
if self.corrections:
spectrum_t = R.Product(ns=self.common_namespace)
spectrum_t.multiply( spectrum_raw_t )
for corr in self.corrections.bundles.values():
spectrum_t.multiply( corr.outputs[isotope] )
spectrum_t.product.setLabel('S(E0):\n'+isotope)
else:
spectrum_t = spectrum_raw_t
if i>0:
interp_expo_t = interp_expo.add_transformation(False)
interp_expo.bind_transformations(False)
model_edges_t >> interp_expo_t.inputs.x
interp_output = interp_expo.add_input(spectrum_t)
interp_input = interp_expo_t.inputs.newx
if i>0:
self.set_input(interp_input, self.cfg.name, it, clone=0)
else:
self.set_input((sampler_input, interp_input), self.cfg.name, it, clone=0)
interp_expo_t.setLabel('S(E):\n'+isotope)
"""Store data"""
self.set_output(interp_output, self.cfg.name, it)
self.objects[('spectrum', isotope)] = spectrum_t
def make(nsources, ntargets):
sources = [C.Points([constant]) for i in range(nsources)]
targets = [
R.DebugTransformation('debug_%02d' % i) for i in range(ntargets)
]
return sources + targets
def test_points_v01():
mat = N.arange(12, dtype='d').reshape(3, 4)
print('Input matrix (numpy)')
print(mat)
print()
#
# Create transformations
#
points = C.Points(mat)
identity = C.Identity()
identity.identity.source(points.points.points)
res = identity.identity.target.data()
dt = identity.identity.target.datatype()
#
# Dump
#
print('Eigen dump (C++)')
identity.dump()
print()
print('Points output')
print(points.points.points.data())
print('Result (C++ Data to numpy)')
print(res, res.dtype)
print()
print('Datatype:', str(dt))
assert N.allclose(mat, res), "C++ and Python results doesn't match"
def test_typeclass_same_v02():
"""Last input has another shape"""
arrays = [np.arange(12, dtype='d').reshape(3,4) for i in range(5)]
arrays[-1]=arrays[-1].reshape(4,3)
points = [C.Points(a) for a in arrays]
outputs = [p.points.points for p in points]
print(outputs)
obj = C.DummyType()
list(map(obj.add_input, outputs))
dt = R.TypeClasses.CheckSameTypesT(context.current_precision())((1,-2))
R.SetOwnership(dt, False)
dt.dump(); print()
obj.add_typeclass(dt)
res = obj.process_types();
assert res
dt1 = R.TypeClasses.CheckSameTypesT(context.current_precision())((-2,-1))
R.SetOwnership(dt1, False)
dt1.dump(); print()
obj.add_typeclass(dt1)
print('Exception expected: ',end='')
res = obj.process_types();
assert not res
def test_typeclass_passeach_02():
"""Pass with step 2"""
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()
i = list(map(obj.add_input, outputs))
i1 = list(map(obj.add_input, outputs))
for i in range(3):
obj.add_output()
dt1 = R.TypeClasses.PassEachTypeT(context.current_precision())((0,-1,2), (0,-1))
R.SetOwnership(dt1, False)
dt1.dump(); print()
obj.add_typeclass(dt1)
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()==dta
assert doutputs[1].datatype()==dtc
assert doutputs[2].datatype()==dtb
def test_typeclass_passtype():
"""Last input has another edges"""
objects = [
C.Histogram2d(np.arange(4), np.arange(5)),
C.Histogram(np.arange(4)),
C.Points(np.arange(12).reshape(3,4))
]
outputs = [p.single() for p in objects]
obj = C.DummyType()
k = list(map(obj.add_input, outputs))
for i in range(5):
obj.add_output()
dt1 = R.TypeClasses.PassTypeT(context.current_precision())((0,), (0,1))
dt2 = R.TypeClasses.PassTypeT(context.current_precision())((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()
doutputs = obj.transformations.back().outputs
assert doutputs[0].datatype()==dta
assert doutputs[1].datatype()==dta
assert doutputs[2].datatype()==dtb
assert doutputs[3].datatype()==dtb
assert doutputs[4].datatype()==dtb
def test_anue_free_spectra(tmp_path):
""" Test implementation of a model of antineutrino spectra with free
parameters in exponential parametrization.
"""
_enu = np.linspace(1.8, 8.0, 500, dtype='d')
Enu = C.Points(_enu, labels='anue energy')
indices = [
('i', 'isotope', ['U235', 'U238', 'Pu239', 'Pu241'])
]
expr = ['anuspec[i,r](enu())']
a = Expression_v01(expr, indices = NIndex.fromlist(indices))
a.parse()
lib = dict()
a.guessname(lib, save=True)
ns_anuexpr = env.globalns('anue_expr')
cfg = NestedDict(
anuspec = NestedDict(
bundle = dict(name='reactor_anu_spectra', version='v04'),
name = 'anuspec',
filename = ['data/reactor_anu_spectra/Huber/Huber_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat',
'data/reactor_anu_spectra/Mueller/Mueller_smooth_extrap_{isotope}_13MeV0.01MeVbin.dat'],
# strategy = dict( underflow='constant', overflow='extrapolate' ),
varmode='log',
varname='anu_weight_{index}',
free_params=True,
ns_name='spectral_weights',
edges = np.concatenate( ( np.arange( 1.8, 8.7, 0.5 ), [ 12.3 ] ) ),
),
enu = NestedDict(
bundle = NestedDict(name='predefined', version='v01', major=''),
name = 'enu',
inputs = None,
outputs = Enu.single(),
),
)
context = ExpressionContext_v01(cfg, ns=ns_anuexpr)
a.build(context)
ns_anuexpr.printparameters(labels=True)
u235_spec = context.outputs.anuspec.U235
u235_spec.plot_vs(Enu.single(), label='default pars')
ns_anuexpr['spectral_weights.anu_weight_5'].set(0.3)
ns_anuexpr['spectral_weights.anu_weight_7'].set(-0.3)
plt.rcParams.update({'font.size': 14})
plt.rcParams.update({'text.usetex': True})
u235_spec.plot_vs(Enu.single(), label='update pars')
plt.yscale('log')
plt.xlabel(r'$E_{\nu}$, MeV')
plt.ylabel('Anue per MeV')
plt.legend()
plt.title('Antineutrino spectrum')
path = os.path.join(str(tmp_path), 'anuspec.png')
savefig(path, dpi=300)
allure_attach_file(path)
def test_filllike_v02(function_name):
"""Initialize inpnuts"""
size = 5
arr1 = N.arange(0, size)
"""Initialize environment"""
ns = env.globalns(function_name)
p1 = ns.defparameter('w1', central=1.0, sigma=0.1)
points1 = C.Points(arr1)
with ns:
ws = C.WeightedSum(['w1'], [points1.points.points])
ws.print()
print()
flvalue = 2.0
fl = C.FillLike(flvalue)
ws >> fl.fill.inputs[0]
out = fl.fill.outputs[0]
data = out.data()
print('data:', data)
print()
compare_filllike(data, [flvalue] * size, 'Data output failed')
print('Change parameter')
p1.set(-1.0)
taintflag = fl.fill.tainted()
print('data:', data)
print('taintflag:', taintflag)
compare_filllike(data, [flvalue] * size, 'Data output failed')
compare_filllike(taintflag, False, 'Taintflag should be false')
def gpuargs_make(nsname, mat1, mat2):
from gna.env import env
ns = env.globalns(nsname)
ns.reqparameter('par1', central=1.0, fixed=True, label='Dummy parameter 1')
ns.reqparameter('par2', central=1.5, fixed=True, label='Dummy parameter 2')
ns.reqparameter('par3',
central=1.01e5,
fixed=True,
label='Dummy parameter 3')
ns.printparameters(labels=True)
points1, points2 = C.Points(mat1), C.Points(mat2)
with ns:
dummy = C.Dummy(4, "dummy", ['par1', 'par2', 'par3'])
return dummy, points1, points2, ns
def test_sumaxis_01(kind, axis):
"""Test ViewRear on Points (start, len)"""
size = 4
inp = np.arange(12.0).reshape(3, 4)
shouldbe = inp.sum(axis=axis)
xedges = np.arange(inp.shape[0] + 1)
yedges = np.arange(inp.shape[1] + 1)
edges = (xedges, yedges)
if kind == 'points':
Inp = C.Points(inp)
else:
Inp = C.Histogram2d(xedges, yedges, inp)
sum = C.SumAxis(axis, Inp)
sum.printtransformations()
res = sum.sumaxis.result.data()
print('Input', inp)
print('Result ({})'.format(axis), res)
print('Should be', shouldbe)
assert np.allclose(res, shouldbe, atol=0, rtol=0)
if kind == 'hist':
newedges = sum.sumaxis.result.datatype().edges
select = 1 if axis == 0 else 0
print('Original edges', edges)
print('New edges', newedges)
assert np.allclose(edges[select], newedges)
def _pointize(self, obj):
"""Given object checks whether it is C++ type (perhaps better to
refactor it) and if not make a Points out of it. Use case -- turning
numpy array into points.
"""
try:
# check if we are in modern PyROOT
cppyy.typeid(obj)
except KeyError:
# not C++ object, trying convert to Points
obj = C.Points(obj)
except AttributeError:
# we are in legacy PyROOT
if not isinstance(type(obj), ROOT.PyRootType):
obj = C.Points(obj)
return obj
def init(self):
ns = env.ns(self.opts.name)
ns.reqparameter('BackgroundRate', central=0, sigma=0.1)
ns.reqparameter('Mu', central=1, sigma=1)
ns.reqparameter('E0', central=2, sigma=0.05)
ns.reqparameter('Width', central=0.2, sigma=0.005)
edges = np.linspace(self.opts.Emin, self.opts.Emax,
self.opts.nbins + 1)
orders = np.array([self.opts.order] * (len(edges) - 1), dtype=int)
integrator = ROOT.GaussLegendre(edges, orders, len(orders))
hist = ROOT.GaussLegendreHist(integrator)
signal = ROOT.Sum()
n = self.opts.PoissonOrder
model = {}
#ff = np.arange(1,n+1)
#ff = 1/scipy.misc.factorial(ff)*np.exp(-self.opts.PoissionMean)
#ff_points = C.Points(ff)
#print(ff, ff_points)
with ns:
for i in range(1, n + 1):
print(i, n)
model[i] = ROOT.GaussianPeakWithBackground(i)
model[i].rate.E(integrator.points.x)
# print(model[i].rate,model[i].rate.rate,ff_points[i])
prod = ROOT.Product()
prod.multiply(model[i].rate.rate)
poisson_factor = poisson.pmf(i, self.opts.PoissonMean)
poisson_factor_prod = C.Points([poisson_factor])
print(type(model[i].rate), poisson_factor, poisson_factor_prod)
prod.multiply(poisson_factor_prod)
signal.add(prod)
hist.hist.f(signal)
ns.addobservable('spectrum', hist.hist)
def check_condproduct(function_name, arrays):
print('Test ', function_name, len(arrays), ':', sep='')
for array in arrays:
print(array)
print()
nprod = len(arrays)-1
truth1=1.0
truth2=1.0
for i, a in enumerate(arrays):
truth1*=a
if i<nprod:
truth2*=a
ns = env.globalns(function_name)
condition = ns.defparameter('condition', central=1.0, fixed=True)
points = [C.Points(array) for array in arrays]
with ns:
prod = C.ConditionalProduct(nprod, 'condition', outputs=[p.points.points for p in points])
calc1 = prod.single().data().copy()
print('Result (1)', condition.value(), calc1, end='\n\n')
condition.set(0.0)
calc2 = prod.single().data().copy()
print('Result (0)', condition.value(), calc2, end='\n\n')
assert (calc1==truth1).all()
assert (calc2==truth2).all()
def prepare_inputs(self):
if self.mctype=='NormalToyMC':
self.input_err = C.Points(self.err_stat)
self.inputs = (self.hist, self.input_err)
elif self.mctype=='CovarianceToyMC':
self.inputs = (self.hist, self.input_L)
else:
self.inputs = (self.hist,)