def observables(self, _prediction, parameters):
"""Creates a list of eos.Observable objects for the named set of predictions."""
if _prediction not in self.predictions:
raise RuntimeError(
'Cannot create observables for unknown set of predictions: \'{}\''
.format(_prediction))
prediction = self.predictions[_prediction]
options = eos.Options(**prediction['global_options'])
observables = [
eos.Observable.make(
o['name'], parameters,
eos.Kinematics(
**(o['kinematics'] if 'kinematics' in o else {})), options)
for o in prediction['observables']
]
if None in observables:
unknown_observables = set()
for p, o in zip(prediction['observables'], observables):
if o is None:
unknown_observables.add(p['name'])
raise RuntimeError(
'Prediction \'{}\' contains unknown observable names: {}'.
format(_prediction, unknown_observables))
return observables
def plot_observable(self, item):
oname = item['observable']
info(' plotting EOS observable "{}"'.format(oname))
# create parameters
parameters = eos.Parameters.Defaults()
if 'parameters' in item and 'parameters-from-file' in item:
warn(
' overriding values read from \'parameters-from-file\' with explicit values in \'parameters\''
)
if 'parameters-from-file' in item and type(
item['parameters-from-file']) is str:
parameters.override_from_file(item['parameters-from-file'])
if 'parameters' in item and type(item['parameters']) is dict:
for key, value in item['parameters'].items():
parameters.set(key, value)
# create kinematics
kinematics = eos.Kinematics()
if not 'kinematic' in item:
raise KeyError(
'kinematic not found; do not know how to map x to a kinematic variable'
)
kvar = kinematics.declare(item['kinematic'], np.nan)
# create (empty) options
options = eos.Options()
# create observable
observable = eos.Observable.make(oname, parameters, kinematics,
options)
xlo, xhi = self.xrange
if 'range' in item:
xlo, xhi = item['range']
samples = 100
if 'samples' in item:
samples = item['samples']
xvalues = np.linspace(xlo, xhi, samples + 1)
ovalues = np.array([])
for xvalue in xvalues:
kvar.set(xvalue)
ovalues = np.append(ovalues, observable.evaluate())
color = 'black'
if 'color' in item:
color = item['color']
plt.plot(xvalues, ovalues, color=color)
def test_log_000(self):
"Computation of specific observable should log error"
import eos
with self.assertLogs('EOS', level='ERROR') as cm:
eos.Observable.make(
"B->pilnu::BR", eos.Parameters.Defaults(),
eos.Kinematics(q2_min=1, q2_max=6),
eos.Options(**{'U': 'c'})) # will be overwritten by the
# implementation of the observable
self.assertEqual(cm.output, [
r"""ERROR:EOS:[ConcreteObservableEntry.make] Observable 'B->pilnu::BR' forces option key 'U' to value 'u', overriding user-provided value 'c'"""
])
def plot(self):
item = self.item
oname = item['observable']
info(' plotting EOS observable "{}"'.format(oname))
# create parameters
parameters = eos.Parameters.Defaults()
if 'parameters' in item and 'parameters-from-file' in item:
warn(' overriding values read from \'parameters-from-file\' with explicit values in \'parameters\'')
if 'parameters-from-file' in item and type(item['parameters-from-file']) is str:
warn(' overriding parameters from file')
parameters.override_from_file(item['parameters-from-file'])
if 'parameters' in item and type(item['parameters']) is dict:
for key, value in item['parameters'].items():
parameters.set(key, value)
# create kinematics
kinematics = eos.Kinematics()
if not ('kinematic' in item or 'variable' in item) and not 'parameter' in item:
raise KeyError('neither kinematic variable nor parameter found; do not know how to map x to a variable')
if ('kinematic' in item or 'variable' in item) and 'parameter' in item:
raise KeyError('both kinematic variable and parameter found; do not know how to map x to a variable')
if 'kinematic' in item:
var = kinematics.declare(item['kinematic'], np.nan)
elif 'variable' in item:
var = kinematics.declare(item['variable'], np.nan)
else:
var = parameters.declare(item['parameter'], np.nan)
if 'kinematics' in item:
for k, v in item['kinematics'].items():
kinematics.declare(k, v)
# create (empty) options
options = eos.Options()
# create observable
observable = eos.Observable.make(oname, parameters, kinematics, options)
xvalues = np.linspace(self.xlo, self.xhi, self.xsamples + 1)
ovalues = np.array([])
for xvalue in xvalues:
var.set(xvalue)
ovalues = np.append(ovalues, observable.evaluate())
plt.plot(xvalues, ovalues, alpha=self.alpha, color=self.color, label=self.label, ls=self.style)