# Connect inputs
#
for inp, hist in zip(b.inputs.values(), hists_list):
inp( hist.hist )
print('\nObservables:')
env.globalns.printobservables()
#
# Dump graph
#
if args.dot:
try:
from gna.graphviz import GNADot
graph = GNADot( b.transformations_out[0][0] )
graph.write(args.dot)
print( 'Write output to:', args.dot )
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,
# plot function versus sample points
ax.plot(gl_int.points.x.data(), fcn.rate.rate.data(), label='function')
# plot histogram using GNA to matplotlib interface
hist_output.plot_bar(label='histogram (sum=%g)' % hist.sum(), alpha=0.5)
# plot histogram manually
plot_bar(edges, hist / widths, label='histogram/binwidth', alpha=0.5)
# 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() - 1
print('Integral-1:', diff)
print(
N.fabs(diff) < 1.e-8 and '\033[32mIntegration is OK!'
or '\033[31mIntegration FAILED!', '\033[0m')
if opts.dot:
try:
from gna.graphviz import GNADot
graph = GNADot(gl_hist.hist)
graph.write(opts.dot)
print('Write output to:', opts.dot)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
P.show()
args=(1.0, 10, 1)), )
# Build the expression for given configuration. The output is a context, that contains the inputs and outputs.
context = ExpressionContext(cfg)
a.build(context)
print('Variables')
context.ns.printparameters(labels=True)
from gna.bindings import OutputDescriptor
# Print inputs
print('Inputs')
print(context.inputs)
# Print outputs
print()
print('Outputs')
print(context.outputs)
print('The sum should be:', context.outputs['arr.a'].data().sum())
print('The sum is:', context.outputs['testsum.a'].data())
if args.dot:
# try:
from gna.graphviz import GNADot
graph = GNADot(context.outputs.ta)
graph.write(args.dot)
print('Write output to:', args.dot)
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )
],
mode='percent')),
enu=NestedDict(bundle='dummy',
name='enu',
input=False,
size=10,
debug=False),
spec=NestedDict(bundle='dummy',
name='spec',
input=True,
size=10,
debug=False),
)
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)
from gna.bindings import OutputDescriptor
env.globalns.printparameters()
print('outputs:')
print(context.outputs)
if args.dot:
# try:
from gna.graphviz import GNADot
graph = GNADot(context.outputs.totalprod)
graph.write(args.dot)
print('Write output to:', args.dot)
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )
name = 'epos',
input = False,
size = 10,
debug = False
),
spec = NestedDict(
bundle = 'dummy',
name = 'spec',
input = 2,
size = 10,
debug = False
),
)
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)
from gna.bindings import OutputDescriptor
env.globalns.printparameters()
print( 'outputs:' )
print( context.outputs )
if args.dot:
# try:
from gna.graphviz import GNADot
graph = GNADot( context.outputs.spec['1'], joints=False )
graph.write(args.dot)
print( 'Write output to:', args.dot )
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )
print('Parameters after modification')
env.globalns.printparameters()
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.log:
ax.set_yscale('log')
ax.legend( loc='upper right' )
if opts.dot:
try:
from gna.graphviz import GNADot
kwargs=dict(
# splines='ortho'
joints=False,
)
graph = GNADot( b.transformations_out.values()[0], **kwargs )
graph.write(opts.dot)
print( 'Write output to:', opts.dot )
except Exception as e:
print( '\033[31mFailed to plot dot\033[0m' )
savefig(opts.output)
if opts.show:
P.show()
debug = False
)
expr = 'sum[n]| '+expr
a = Expression(expr, indices=indices)
print(a.expressions_raw)
print(a.expressions)
a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
print()
context = ExpressionContext( cfg )
a.build(context)
from gna.bindings import OutputDescriptor
print( 'outputs:' )
print( context.outputs )
if args.dot:
# try:
from gna.graphviz import GNADot
graph = GNADot( context.outputs.finalsum )
graph.write(args.dot)
print( 'Write output to:', args.dot )
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )
name = 'enu',
input = False,
size = 10,
debug = False
),
oscprob = NestedDict(
bundle = 'oscprob_v01',
name = 'oscprob',
input = True,
size = 10,
debug = False
),
)
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.dot:
try:
from gna.graphviz import GNADot
graph = GNADot( context.outputs.enu, joints=False )
graph.write(args.dot)
print( 'Write output to:', args.dot )
except Exception as e:
print( '\033[31mFailed to plot dot\033[0m' )
# par.setNormalValue(float(value))
# print()
# print('Parameters after modification')
# env.globalns.printparameters()
# for name, output in b.outputs.items():
# ax.plot( points, output.data().copy(), '--', label=L.s(name) )
# if opts.log:
# ax.set_yscale('log')
# ax.legend( loc='upper right' )
if opts.dot:
if True:
from gna.graphviz import GNADot
kwargs=dict(
# splines='ortho'
)
ts = [ snf.transformations_out.values()[0] ]+list(offeq.transformations_out.values()[0].values())
graph = GNADot( ts, **kwargs )
graph.write(opts.dot)
print( 'Write output to:', opts.dot )
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )
if opts.show:
P.show()
# 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 GNADot
graph = GNADot(integrator.hist)
graph.write(opts.dot)
print( 'Write output to:', opts.dot )
except Exception as e:
print( '\033[31mFailed to plot dot\033[0m' )
raise
P.show()
def build_and_plot(expr, obj, suffix):
# Initialize the expression and indices
a = Expression(expr, indices)
# Dump the information
print(a.expressions_raw)
print(a.expressions)
# Parse the expression
a.parse()
# The next step is needed to name all the intermediate variables.
a.guessname(lib, save=True)
# Dump the tree.
a.tree.dump(True)
cfg = NestedDict(
kinint2=NestedDict(
bundle='integral_2d1d_v01',
variables=('evis', 'ctheta'),
edges=N.linspace(0.0, 12.0, 241, dtype='d'),
xorders=3,
yorder=5,
provides=['evis', 'ctheta', 'evis_edges'],
),
ibd_xsec=NestedDict(bundle='xsec_ibd_v01',
order=1,
provides=['ibd_xsec', 'ee', 'enu', 'jacobian']),
oscprob=NestedDict(bundle='oscprob_v01',
name='oscprob',
provides=['oscprob', 'pmns']),
anuspec=NestedDict(
bundle='reactor_anu_spectra_v02',
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' ),
edges=N.concatenate((N.arange(1.8, 8.7, 0.5), [12.3])),
),
eff=NestedDict(
bundle='efficiencies_v01',
correlated=False,
uncorrelated=True,
norm=True,
names=dict(norm='global_norm'),
provides=['eff', 'effunc_corr', 'effunc_uncorr', 'global_norm'],
efficiencies='data/dayabay/efficiency/P15A_efficiency.py'),
livetime=NestedDict(
bundle='dayabay_livetime_hdf_v01',
file=
'data/dayabay/data/P15A/dubna/dayabay_data_dubna_v15_bcw_adsimple.hdf5',
provides=['livetime_daily', 'efflivetime_daily']),
baselines=NestedDict(
bundle='baselines_v01',
reactors=
'data/dayabay/reactor/coordinates/coordinates_docDB_9757.py',
detectors='data/dayabay/ad/coordinates/coordinates_docDB_9757.py',
provides=['baseline', 'baselineweight'],
units="meters"),
thermal_power=NestedDict(
bundle='dayabay_reactor_burning_info_v01',
reactor_info='data/dayabay/reactor/power/WeeklyAvg_P15A_v1.txt.npz',
provides=['thermal_power', 'fission_fractions']),
iav=NestedDict(
bundle='detector_iav_db_root_v02',
parname='OffdiagScale',
scale=uncertain(1.0, 4, 'percent'),
ndiag=1,
filename='data/dayabay/tmp/detector_iavMatrix_P14A_LS.root',
matrixname='iav_matrix'),
eres=NestedDict(
bundle='detector_eres_common3_v02',
# pars: sigma_e/e = sqrt( a^2 + b^2/E + c^2/E^2 ),
pars=uncertaindict([('Eres_a', 0.014764), ('Eres_b', 0.0869),
('Eres_c', 0.0271)],
mode='percent',
uncertainty=30),
provides=['eres', 'eres_matrix'],
expose_matrix=True),
lsnl=NestedDict(
bundle='detector_nonlinearity_db_root_v02',
names=['nominal', 'pull0', 'pull1', 'pull2', 'pull3'],
filename='data/dayabay/tmp/detector_nl_consModel_450itr.root',
parnames=dict(lsnl='lsnl_weight', escale='escale'),
par=uncertain(1.0, 0.2, 'percent'),
edges='evis_edges',
provides=[
'lsnl', 'lsnl_component', 'escale', 'lsnl_weight', 'lsnl_edges'
]),
rebin=NestedDict(bundle='rebin_v02',
rounding=3,
edges=N.concatenate(([0.7], N.arange(1.2, 8.1,
0.2), [12.0]))))
#
# Put the expression into context
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)
# # Print the list of outputs, stored for the expression
# from gna.bindings import OutputDescriptor
# env.globalns.materializeexpressions(True)
# env.globalns.printparameters( labels=True )
# print( 'outputs:' )
# print( context.outputs )
try:
from gna.graphviz import GNADot
obj = context.outputs[obj]
if isinstance(obj, NestedDict):
obj = obj.values()
if len(expr) < 4:
label = expr[0]
if label == 'evis()':
label = expr[1]
else:
label = ''
graph = GNADot(obj, joints=False, label=label)
name = args.dot.replace('.dot', '_' + suffix + '.dot')
graph.write(name)
print('Write output to:', name)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
# step('_03_lsnl')
# outputs.eres.AD11.plot_hist(label='+eres')
if args.mode == 'dyboscar':
out = outputs.sum_sum_sum_eres_cw
else:
out = outputs.eres
out.AD11.plot_hist(label='EH1 AD1')
step('_04_eres')
if args.show:
P.show()
#
# Dump the histogram to a dot graph
#
if args.dot:
try:
from gna.graphviz import GNADot
graph = GNADot(context.outputs.ee, joints=False)
graph.write(args.dot)
print('Write output to:', args.dot)
graph = GNADot(context.outputs.thermal_power.values(), joints=False)
name = args.dot.replace('.dot', '_lt.dot')
graph.write(name)
print('Write output to:', name)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
def init(self):
head = self.opts.plot[0]
kwargs = dict(self.opts.options, joints=self.opts.joints)
kwargs.setdefault('rankdir', 'LR')
kwargs['subgraph']=self.opts.subgraph
kwargs['include_only']=self.opts.include_only
if self.opts.splines:
kwargs['splines']=self.opts.splines
if self.opts.namespace is not self.undefined:
kwargs['namespace']=env.globalns(self.opts.namespace)
graph = GNADot( head, **kwargs )
for output in self.opts.outputs:
print( 'Write graph to:', output )
if output.endswith('.dot'):
graph.write(output)
else:
graph.layout(prog='dot')
graph.draw(output)
if self.opts.stdout:
graph.write()
if self.opts.stderr:
import sys
graph.write( sys.stderr )
# fig = P.figure()
# ax = P.subplot( 111 )
# ax.minorticks_on()
# ax.grid()
# ax.set_xlabel(L.u('enu'))
# ax.set_ylabel(L('{enu} step'))
# ax.set_title(L('Check {enu} step'))
# idx = N.arange(enu.shape[0])
# for i, e in enumerate(enu.T):
# ax.plot(idx, e, '-', label='Slice %i'%i)
# ax.legend(loc='upper left')
if args.show:
P.show()
#
# Dump the histogram to a dot graph
#
if args.dot:
try:
from gna.graphviz import GNADot
graph = GNADot(context.outputs.ee, joints=True)
graph.write(args.dot)
print('Write output to:', args.dot)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
# ax.vlines(cfg.edges, 0.0, 2.5, linestyles='--', alpha=0.5, colors='blue')
# for name, output in context.outputs.anuspec.items():
# data=output.data().copy()
# ax.plot( context.outputs.enu.data(), N.ma.array(data, mask=data==0.0), label=L.s(name) )
if opts.log:
ax.set_yscale('log')
ax.legend(loc='upper right')
if opts.dot:
try:
from gna.graphviz import GNADot
kwargs = dict(
# splines='ortho'
joints=False, )
graph = GNADot(context.outputs.anuspec.U235, **kwargs)
graph.write(opts.dot)
print('Write output to:', opts.dot)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
raise
savefig(opts.output)
if opts.show:
P.show()
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)
except Exception as e:
print('\033[31mFailed to plot dot\033[0m')
if args.show:
P.show()
)
a = Expression(expr, indices=indices)
print(a.expressions_raw)
print(a.expressions)
a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
print()
context = ExpressionContext(cfg)
a.build(context)
from gna.bindings import OutputDescriptor
print('outputs:')
print(context.outputs)
print('Ratio (should be 0.5):', context.outputs['ratio.1.A'].data())
if args.dot:
# try:
from gna.graphviz import GNADot
graph = GNADot(context.outputs.ratio['1.A'])
graph.write(args.dot)
print('Write output to:', args.dot)
# except Exception as e:
# print( '\033[31mFailed to plot dot\033[0m' )