import logging
from pyhf import get_backend, events
from pyhf import interpolators
from pyhf.parameters import ParamViewer
log = logging.getLogger(__name__)
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def required_parset(sample_data, modifier_data):
return {
'paramset_type': 'constrained_by_normal',
'n_parameters': 1,
'is_scalar': True,
'inits': (0.0,),
'bounds': ((-5.0, 5.0),),
'fixed': False,
'auxdata': (0.0,),
}
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class normsys_builder:
"""Builder class for collecting normsys modifier data"""
is_shared = True
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def __init__(self, config):
self.builder_data = {}
self.config = config
self.required_parsets = {}
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def collect(self, thismod, nom):
maskval = True if thismod else False
lo_factor = thismod['data']['lo'] if thismod else 1.0
hi_factor = thismod['data']['hi'] if thismod else 1.0
nom_data = [1.0] * len(nom)
lo = [lo_factor] * len(nom) # broadcasting
hi = [hi_factor] * len(nom)
mask = [maskval] * len(nom)
return {'lo': lo, 'hi': hi, 'mask': mask, 'nom_data': nom_data}
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def append(self, key, channel, sample, thismod, defined_samp):
self.builder_data.setdefault(key, {}).setdefault(sample, {}).setdefault(
'data', {'hi': [], 'lo': [], 'nom_data': [], 'mask': []}
)
nom = (
defined_samp['data']
if defined_samp
else [0.0] * self.config.channel_nbins[channel]
)
moddata = self.collect(thismod, nom)
self.builder_data[key][sample]['data']['nom_data'] += moddata['nom_data']
self.builder_data[key][sample]['data']['lo'] += moddata['lo']
self.builder_data[key][sample]['data']['hi'] += moddata['hi']
self.builder_data[key][sample]['data']['mask'] += moddata['mask']
if thismod:
self.required_parsets.setdefault(
thismod['name'],
[required_parset(defined_samp['data'], thismod['data'])],
)
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def finalize(self):
return self.builder_data
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class normsys_combined:
name = 'normsys'
op_code = 'multiplication'
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def __init__(
self, modifiers, pdfconfig, builder_data, interpcode='code1', batch_size=None
):
self.interpcode = interpcode
assert self.interpcode in ['code1', 'code4']
keys = [f'{mtype}/{m}' for m, mtype in modifiers]
normsys_mods = [m for m, _ in modifiers]
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 = [
[
[
builder_data[m][s]['data']['lo'],
builder_data[m][s]['data']['nom_data'],
builder_data[m][s]['data']['hi'],
]
for s in pdfconfig.samples
]
for m in keys
]
self._normsys_mask = [
[[builder_data[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)
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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)
)
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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