import logging
from . import modifier
from .. import get_backend, events
from .. import interpolators
from ..parameters import constrained_by_normal, ParamViewer
log = logging.getLogger(__name__)
[docs]@modifier(name='normsys', constrained=True, op_code='multiplication')
class normsys(object):
[docs] @classmethod
def required_parset(cls, n_parameters):
return {
'paramset_type': constrained_by_normal,
'n_parameters': 1,
'modifier': cls.__name__,
'is_constrained': cls.is_constrained,
'is_shared': True,
'inits': (0.0,),
'bounds': ((-5.0, 5.0),),
'auxdata': (0.0,),
}
class normsys_combined(object):
def __init__(
self, normsys_mods, pdfconfig, mega_mods, interpcode='code1', batch_size=None
):
self.interpcode = interpcode
assert self.interpcode in ['code1', 'code4']
keys = ['{}/{}'.format(mtype, m) for m, mtype in normsys_mods]
normsys_mods = [m for m, _ in normsys_mods]
self.batch_size = batch_size
parfield_shape = (
(self.batch_size, pdfconfig.npars)
if self.batch_size
else (pdfconfig.npars,)
)
self.param_viewer = ParamViewer(parfield_shape, pdfconfig.par_map, normsys_mods)
self._normsys_histoset = [
[
[
mega_mods[m][s]['data']['lo'],
mega_mods[m][s]['data']['nom_data'],
mega_mods[m][s]['data']['hi'],
]
for s in pdfconfig.samples
]
for m in keys
]
self._normsys_mask = [
[[mega_mods[m][s]['data']['mask']] for s in pdfconfig.samples] for m in keys
]
if normsys_mods:
self.interpolator = getattr(interpolators, self.interpcode)(
self._normsys_histoset
)
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
def _precompute(self):
if not self.param_viewer.index_selection:
return
tensorlib, _ = get_backend()
self.normsys_mask = tensorlib.tile(
tensorlib.astensor(self._normsys_mask, dtype="bool"),
(1, 1, self.batch_size or 1, 1),
)
self.normsys_default = tensorlib.ones(self.normsys_mask.shape)
if self.batch_size is None:
self.indices = tensorlib.reshape(
self.param_viewer.indices_concatenated, (-1, 1)
)
def apply(self, pars):
'''
Returns:
modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
'''
if not self.param_viewer.index_selection:
return
tensorlib, _ = get_backend()
if self.batch_size is None:
normsys_alphaset = self.param_viewer.get(pars, self.indices)
else:
normsys_alphaset = self.param_viewer.get(pars)
results_norm = self.interpolator(normsys_alphaset)
# either rely on numerical no-op or force with line below
results_norm = tensorlib.where(
self.normsys_mask, results_norm, self.normsys_default
)
return results_norm