def data_source_dict(model, input_spec, **options):
if input_spec == 'data':
source_dict = {'type': 'histo_source', 'name': 'source'}
for o in model.observables:
source_dict[o] = model.data_histos[o].get_cfg(False)
if len(model.data_rvobsvalues) > 0:
source_dict['rvobs-values'] = dict(model.data_rvobsvalues)
return (source_dict,
theta_interface.delta_distribution(beta_signal=0.0))
elif input_spec.startswith('toys:') or input_spec.startswith(
'toys-asimov:'):
beta_signal_value = float(input_spec[input_spec.find(':') + 1:])
seed = 1
if 'toydata_seed' in options: seed = int(options['toydata_seed'])
result = {
'type': 'model_source',
'name': 'source',
'model': '@model',
'rnd_gen': {
'seed': seed
}
}
if input_spec.startswith('toys-asimov:'):
result['dice_poisson'] = False
result['dice_template_uncertainties'] = False
return (result,
theta_interface.delta_distribution(
beta_signal=beta_signal_value))
else:
raise RuntimeError, 'data_source dict: not implemented for input = "%s"' % input_spec
def signal_prior_dict(spec):
if type(spec) == str:
if spec.startswith('flat'):
if spec.startswith('flat:'):
res = re.match('flat:\[([^,]+),(.*)\]', spec)
if res is None:
raise RuntimeError, "signal_prior specification '%s' invalid (does not match range specification syntax)" % spec
xmin, xmax = float(res.group(1)), float(res.group(2))
else:
if spec != 'flat':
raise RuntimeError, "signal_prior specification '%s' invalid" % spec
xmin, xmax = 0.0, float("inf")
value = 0.5 * (xmax - xmin)
if value == float("inf"): value = 1.0
signal_prior_dict = {
'type': 'flat_distribution',
'beta_signal': {
'range': [xmin, xmax],
'fix-sample-value': value
}
}
elif spec.startswith('fix:'):
v = float(spec[4:])
signal_prior_dict = theta_interface.delta_distribution(
beta_signal=v)
else:
raise RuntimeError, "signal_prior specification '%s' unknown" % spec
else:
if type(spec) != dict:
raise RuntimeError, "signal_prior specification has to be a string ('flat' / 'fix:X') or a dictionary!"
signal_prior_dict = spec
return signal_prior_dict
def data_source_dict(model, input_spec, **options):
if input_spec == 'data':
source_dict = {'type': 'histo_source', 'name': 'source'}
for o in model.observables:
source_dict[o] = model.data_histos[o].get_cfg(False)
if len(model.data_rvobsvalues) > 0:
source_dict['rvobs-values'] = dict(model.data_rvobsvalues)
return (source_dict, theta_interface.delta_distribution(beta_signal = 0.0))
elif input_spec.startswith('toys:') or input_spec.startswith('toys-asimov:'):
beta_signal_value = float(input_spec[input_spec.find(':') + 1:])
seed = 1
if 'toydata_seed' in options: seed = int(options['toydata_seed'])
result = {'type': 'model_source', 'name': 'source', 'model': '@model', 'rnd_gen': {'seed': seed}}
if input_spec.startswith('toys-asimov:'):
result['dice_poisson'] = False
result['dice_template_uncertainties'] = False
return (result, theta_interface.delta_distribution(beta_signal = beta_signal_value))
else: raise RuntimeError, 'data_source dict: not implemented for input = "%s"' % input_spec
def signal_prior_dict(spec):
if type(spec) == str:
if spec.startswith('flat'):
if spec.startswith('flat:'):
res = re.match('flat:\[([^,]+),(.*)\]', spec)
if res is None: raise RuntimeError, "signal_prior specification '%s' invalid (does not match range specification syntax)" % spec
xmin, xmax = float(res.group(1)), float(res.group(2))
else:
if spec!='flat': raise RuntimeError, "signal_prior specification '%s' invalid" % spec
xmin, xmax = 0.0, float("inf")
value = 0.5 * (xmax - xmin)
if value==float("inf"): value = 1.0
signal_prior_dict = {'type': 'flat_distribution', 'beta_signal': {'range': [xmin, xmax], 'fix-sample-value': value}}
elif spec.startswith('fix:'):
v = float(spec[4:])
signal_prior_dict = theta_interface.delta_distribution(beta_signal = v)
else: raise RuntimeError, "signal_prior specification '%s' unknown" % spec
else:
if type(spec) != dict: raise RuntimeError, "signal_prior specification has to be a string ('flat' / 'fix:X') or a dictionary!"
signal_prior_dict = spec
return signal_prior_dict