class TEST_CONFIGS(object):
"""Default configurations for stages in a minimal test pipeline."""
pipe_cfg = OrderedDict(
pipeline={
"name": "muons",
"output_binning": TEST_BINNING,
"output_key": ("weights"),
"detector_name": None,
}
)
event_generator_cfg = {
"calc_mode": "events",
"apply_mode": "events",
"output_names": ["muon"],
"params": ParamSet(
[
Param(name="n_events", value=1e3, **PARAM_DEFAULTS),
Param(name="seed", value=0, **PARAM_DEFAULTS),
Param(name="random", value=False, **PARAM_DEFAULTS),
]
),
}
aeff_cfg = {
"calc_mode": "events",
"apply_mode": "events",
"params": ParamSet(
[
Param(name="livetime", value=12345 * ureg["seconds"], **PARAM_DEFAULTS),
Param(name="weight_scale", value=1.0, **PARAM_DEFAULTS),
]
),
}
set_variance_cfg = {
"calc_mode": TEST_BINNING,
"apply_mode": TEST_BINNING,
"divide_total_mc": True,
# expected number of unweighted MC events including events that fall outside of
# the analysis binning
"expected_total_mc": 1000,
"variance_scale": 0.1,
}
fix_error_cfg = {
"calc_mode": TEST_BINNING,
"apply_mode": TEST_BINNING,
}
kde_cfg = {
"calc_mode": "events",
"apply_mode": TEST_BINNING,
"bootstrap": False,
"bootstrap_seed": 0,
"bootstrap_niter": 6,
"linearize_log_dims": True,
"stash_hists": False,
"coszen_name": "true_coszen",
"stack_pid": False,
"oversample": 1,
}
def create_mc_template(toymc_params, config_file=None, seed=None):
'''
Create MC template out of a pisa pipeline
'''
if seed is not None:
np.random.seed(seed)
Config = parse_pipeline_config(config_file)
new_n_events_data = Param(name='n_events_data',
value=toymc_params.n_data,
prior=None,
range=None,
is_fixed=True)
new_sig_frac = Param(name='signal_fraction',
value=toymc_params.signal_fraction,
prior=None,
range=None,
is_fixed=True)
new_stats_factor = Param(name='stats_factor',
value=toymc_params.stats_factor,
prior=None,
range=None,
is_fixed=True)
# These should match the values of the config file, but we override them just in case we need to change these later
new_mu = Param(name='mu',
value=toymc_params.mu,
prior=None,
range=[0, 100],
is_fixed=False)
new_sigma = Param(name='sigma',
value=toymc_params.sigma,
prior=None,
range=None,
is_fixed=True)
Config[('data', 'pi_simple_signal')]['params'].update(p=ParamSet([
new_n_events_data, new_sig_frac, new_stats_factor, new_mu, new_sigma
]))
MCtemplate = DistributionMaker(Config)
return MCtemplate
mean_perpe = []
mean_perbin = []
for idx, lt in enumerate(livetimes):
print '==========='
print 'livetime = {0}'.format(lt)
print '==========='
mean_perpe.append([])
lt_param.value = lt
pipeline.update_params(lt_param)
gen_pipe.update_params(lt_param)
u_pipe = Param(name='unfold_pipeline_cfg',
value=gen_pipe,
is_fixed=True,
prior=None,
range=None)
unfold_pipeline_cfg = u_pipe
pipeline.update_params(unfold_pipeline_cfg)
# Get nominal
re_param.value = 0 * ureg.dimensionless
pipeline.update_params(re_param)
nom_out = pipeline.get_outputs().pop()
re_param.value = 2 * ureg.dimensionless
sf_param.value = 1234 * ureg.dimensionless
pipeline.update_params(re_param)
pipeline.update_params(sf_param)
def parse_param(config, section, selector, fullname, pname, value):
"""Parse a param specification from a PISA config file.
Note that if the param sepcification does not include ``fixed``,
``prior``, and/or ``range``, the defaults for these are:
``fixed = True``, ``prior = None``, and ``range = None``.
If a prior is specified explicitly via ``.prior``, this takes precendence,
but if no ``.prior`` is specified and the param's value is parsed to be a
:class:`uncertainties.AffineScalarFunc` (i.e. have `std_dev` attribute), a
Gaussian prior is constructed from that and then the AffineScalarFunc is
stripped out of the param's value (such that it is just a
:class:`~pint.quantity.Quantity`).
Parameters
----------
config : pisa.utils.config_parser.PISAConfigParser
section : string
selector : string or None
fullname : string
pname : string
value : string
Returns
-------
param : pisa.core.param.Param
"""
# Note: imports placed here to avoid circular imports
from pisa.core.param import Param
from pisa.core.prior import Prior
kwargs = dict(name=pname, is_fixed=True, prior=None, range=None)
try:
value = parse_quantity(value)
kwargs['value'] = value.nominal_value * value.units
except ValueError:
value = parse_string_literal(value)
kwargs['value'] = value
# Search for explicit attr specifications
if config.has_option(section, fullname + '.fixed'):
kwargs['is_fixed'] = config.getboolean(section, fullname + '.fixed')
if config.has_option(section, fullname + '.unique_id'):
kwargs['unique_id'] = config.get(section, fullname + '.unique_id')
if config.has_option(section, fullname + '.range'):
range_ = config.get(section, fullname + '.range')
# Note: `nominal` and `sigma` are called out in the `range_` string
if 'nominal' in range_:
nominal = value.n * value.units # pylint: disable=unused-variable
if 'sigma' in range_:
sigma = value.s * value.units # pylint: disable=unused-variable
range_ = range_.replace('[', 'np.array([')
range_ = range_.replace(']', '])')
# Strip out uncertainties from value itself (as we will rely on the
# prior from here on out)
kwargs['range'] = eval(range_).to(value.units) # pylint: disable=eval-used
if config.has_option(section, fullname + '.prior'):
prior = str(config.get(section, fullname + '.prior')).strip().lower()
if prior == 'uniform':
kwargs['prior'] = Prior(kind='uniform')
elif prior == 'jeffreys':
kwargs['prior'] = Prior(kind='jeffreys',
A=kwargs['range'][0],
B=kwargs['range'][1])
elif prior == 'spline':
priorname = pname
if selector is not None:
priorname += '_' + selector
data = config.get(section, fullname + '.prior.data')
data = from_file(data)
data = data[priorname]
knots = ureg.Quantity(np.asarray(data['knots']), data['units'])
knots = knots.to(value.units)
coeffs = np.asarray(data['coeffs'])
deg = data['deg']
kwargs['prior'] = Prior(kind='spline',
knots=knots,
coeffs=coeffs,
deg=deg)
elif prior == 'none':
kwargs['prior'] = None
elif 'gauss' in prior:
raise Exception('Please use new style +/- notation for gaussian'
' priors in config')
else:
raise Exception('Prior type unknown')
elif hasattr(value, 'std_dev') and value.std_dev != 0:
kwargs['prior'] = Prior(kind='gaussian',
mean=value.nominal_value * value.units,
stddev=value.std_dev * value.units)
# Strip out any uncertainties from value itself (an explicit ``.prior``
# specification takes precedence over this)
if hasattr(value, 'std_dev'):
value = value.nominal_value * value.units
try:
param = Param(**kwargs)
except:
logging.error('Failed to instantiate new Param object with kwargs %s',
kwargs)
raise
return param
def create_mc_template(toymc_params,
config_file=None,
seed=None,
keep_same_weight=True):
'''
Create MC template out of a pisa pipeline
'''
if seed is not None:
np.random.seed(seed)
Config = parse_pipeline_config(config_file)
# Change binning
Config[('data', 'pi_simple_signal')]['output_specs'] = toymc_params.binning
Config[(
'likelihood',
'pi_generalized_llh_params')]['output_specs'] = toymc_params.binning
# If keep_same_weight is True, turn off the mean adjust and pseudo weight of pi_generalized_llh
if keep_same_weight:
Config[('likelihood',
'pi_generalized_llh_params')]['with_mean_adjust'] = False
Config[('likelihood',
'pi_generalized_llh_params')]['with_pseudo_weight'] = False
else:
Config[('likelihood',
'pi_generalized_llh_params')]['with_mean_adjust'] = True
Config[('likelihood',
'pi_generalized_llh_params')]['with_pseudo_weight'] = True
new_n_events_data = Param(name='n_events_data',
value=toymc_params.n_data,
prior=None,
range=None,
is_fixed=True)
new_sig_frac = Param(name='signal_fraction',
value=toymc_params.signal_fraction,
prior=None,
range=None,
is_fixed=True)
new_stats_factor = Param(name='stats_factor',
value=toymc_params.stats_factor,
prior=None,
range=None,
is_fixed=True)
# These should match the values of the config file, but we override them just in case we need to change these later
new_mu = Param(name='mu',
value=toymc_params.mu,
prior=None,
range=[0, 100],
is_fixed=False)
new_sigma = Param(name='sigma',
value=toymc_params.sigma,
prior=None,
range=None,
is_fixed=True)
Config[('data', 'pi_simple_signal')]['params'].update(p=ParamSet([
new_n_events_data, new_sig_frac, new_stats_factor, new_mu, new_sigma
]))
MCtemplate = DistributionMaker(Config)
return MCtemplate