import logging
from . import modifier
from ..paramsets import constrained_by_normal
from .. import get_backend, events
from .. import interpolators
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'):
self._parindices = list(range(len(pdfconfig.suggested_init())))
self.interpcode = interpcode
assert self.interpcode in ['code1', 'code4']
pnames = [pname for pname, _ in normsys_mods]
keys = ['{}/{}'.format(mtype, m) for m, mtype in normsys_mods]
normsys_mods = [m for m, _ in normsys_mods]
self._normsys_indices = [
self._parindices[pdfconfig.par_slice(p)] for p in pnames
]
self._normsys_histoset = [
[
[
mega_mods[s][m]['data']['lo'],
mega_mods[s][m]['data']['nom_data'],
mega_mods[s][m]['data']['hi'],
]
for s in pdfconfig.samples
]
for m in keys
]
self._normsys_mask = [
[[mega_mods[s][m]['data']['mask']] for s in pdfconfig.samples] for m in keys
]
if len(normsys_mods):
self.interpolator = getattr(interpolators, self.interpcode)(
self._normsys_histoset
)
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
def _precompute(self):
tensorlib, _ = get_backend()
self.normsys_mask = tensorlib.astensor(self._normsys_mask)
self.normsys_default = tensorlib.ones(self.normsys_mask.shape)
self.normsys_indices = tensorlib.astensor(self._normsys_indices, dtype='int')
def apply(self, pars):
tensorlib, _ = get_backend()
if not tensorlib.shape(self.normsys_indices)[0]:
return
normsys_alphaset = tensorlib.gather(pars, self.normsys_indices)
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