import logging
from . import modifier
from .. import get_backend, default_backend, events
from ..parameters import constrained_by_poisson, ParamViewer
log = logging.getLogger(__name__)
[docs]@modifier(
name='shapesys', constrained=True, pdf_type='poisson', op_code='multiplication'
)
class shapesys(object):
[docs] @classmethod
def required_parset(cls, n_parameters):
return {
'paramset_type': constrained_by_poisson,
'n_parameters': n_parameters,
'modifier': cls.__name__,
'is_constrained': cls.is_constrained,
'is_shared': False,
'inits': (1.0,) * n_parameters,
'bounds': ((1e-10, 10.0),) * n_parameters,
# nb: auxdata/factors set by finalize. Set to non-numeric to crash
# if we fail to set auxdata/factors correctly
'auxdata': (None,) * n_parameters,
'factors': (None,) * n_parameters,
}
class shapesys_combined(object):
def __init__(self, shapesys_mods, pdfconfig, mega_mods, batch_size=None):
self.batch_size = batch_size
keys = ['{}/{}'.format(mtype, m) for m, mtype in shapesys_mods]
self._shapesys_mods = [m for m, _ in shapesys_mods]
parfield_shape = (self.batch_size or 1, pdfconfig.npars)
self.param_viewer = ParamViewer(
parfield_shape, pdfconfig.par_map, self._shapesys_mods
)
self._shapesys_mask = [
[[mega_mods[m][s]['data']['mask']] for s in pdfconfig.samples] for m in keys
]
self.__shapesys_uncrt = default_backend.astensor(
[
[
[
mega_mods[m][s]['data']['uncrt'],
mega_mods[m][s]['data']['nom_data'],
]
for s in pdfconfig.samples
]
for m in keys
]
)
self.finalize(pdfconfig)
global_concatenated_bin_indices = [
[[j for c in pdfconfig.channels for j in range(pdfconfig.channel_nbins[c])]]
]
self._access_field = default_backend.tile(
global_concatenated_bin_indices,
(len(shapesys_mods), self.batch_size or 1, 1),
)
# access field is shape (sys, batch, globalbin)
for s, syst_access in enumerate(self._access_field):
for t, batch_access in enumerate(syst_access):
selection = self.param_viewer.index_selection[s][t]
for b, bin_access in enumerate(batch_access):
self._access_field[s, t, b] = (
selection[bin_access] if bin_access < len(selection) else 0
)
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
def _precompute(self):
tensorlib, _ = get_backend()
if not self.param_viewer.index_selection:
return
self.shapesys_mask = tensorlib.astensor(self._shapesys_mask, dtype="bool")
self.shapesys_mask = tensorlib.tile(
self.shapesys_mask, (1, 1, self.batch_size or 1, 1)
)
self.access_field = tensorlib.astensor(self._access_field, dtype='int')
self.sample_ones = tensorlib.ones(tensorlib.shape(self.shapesys_mask)[1])
self.shapesys_default = tensorlib.ones(tensorlib.shape(self.shapesys_mask))
def finalize(self, pdfconfig):
for uncert_this_mod, pname in zip(self.__shapesys_uncrt, self._shapesys_mods):
unc_nom = default_backend.astensor(
[x for x in uncert_this_mod[:, :, :] if any(x[0][x[0] > 0])]
)
unc = unc_nom[0, 0]
nom = unc_nom[0, 1]
unc_sq = default_backend.power(unc, 2)
nom_sq = default_backend.power(nom, 2)
# the below tries to filter cases in which
# this modifier is not used by checking non
# zeroness.. shoudl probably use mask
numerator = default_backend.where(
unc_sq > 0, nom_sq, default_backend.zeros(unc_sq.shape)
)
denominator = default_backend.where(
unc_sq > 0, unc_sq, default_backend.ones(unc_sq.shape)
)
factors = numerator / denominator
factors = factors[factors > 0]
assert len(factors) == pdfconfig.param_set(pname).n_parameters
pdfconfig.param_set(pname).factors = default_backend.tolist(factors)
pdfconfig.param_set(pname).auxdata = default_backend.tolist(factors)
def apply(self, pars):
'''
Returns:
modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
'''
tensorlib, _ = get_backend()
if not self.param_viewer.index_selection:
return
tensorlib, _ = get_backend()
if self.batch_size is None:
flat_pars = pars
else:
flat_pars = tensorlib.reshape(pars, (-1,))
shapefactors = tensorlib.gather(flat_pars, self.access_field)
results_shapesys = tensorlib.einsum(
'mab,s->msab', shapefactors, self.sample_ones
)
results_shapesys = tensorlib.where(
self.shapesys_mask, results_shapesys, self.shapesys_default
)
return results_shapesys