"""Piecewise-linear Interpolation. (Code 0)."""
import logging
import pyhf
from pyhf.tensor.manager import get_backend
from pyhf import events
from pyhf.interpolators import _slow_interpolator_looper
log = logging.getLogger(__name__)
[docs]
class code0:
r"""
The piecewise-linear interpolation strategy.
.. math::
\sigma_{sb} (\vec{\alpha}) = \sigma_{sb}^0(\vec{\alpha}) + \underbrace{\sum_{p \in \text{Syst}} I_\text{lin.} (\alpha_p; \sigma_{sb}^0, \sigma_{psb}^+, \sigma_{psb}^-)}_\text{deltas to calculate}
with
.. math::
I_\text{lin.}(\alpha; I^0, I^+, I^-) = \begin{cases} \alpha(I^+ - I^0) \qquad \alpha \geq 0\\ \alpha(I^0 - I^-) \qquad \alpha < 0 \end{cases}
"""
[docs]
def __init__(self, histogramssets, subscribe=True):
"""Piecewise-linear Interpolation."""
default_backend = pyhf.default_backend
self._histogramssets = default_backend.astensor(histogramssets)
# initial shape will be (nsysts, 1)
self.alphasets_shape = (self._histogramssets.shape[0], 1)
# precompute terms that only depend on the histogramssets
self._deltas_up = self._histogramssets[:, :, 2] - self._histogramssets[:, :, 1]
self._deltas_dn = self._histogramssets[:, :, 1] - self._histogramssets[:, :, 0]
self._broadcast_helper = default_backend.ones(
default_backend.shape(self._deltas_up)
)
self._precompute()
if subscribe:
events.subscribe('tensorlib_changed')(self._precompute)
[docs]
def _precompute(self):
tensorlib, _ = get_backend()
self.deltas_up = tensorlib.astensor(self._deltas_up)
self.deltas_dn = tensorlib.astensor(self._deltas_dn)
self.broadcast_helper = tensorlib.astensor(self._broadcast_helper)
self.mask_on = tensorlib.ones(self.alphasets_shape)
self.mask_off = tensorlib.zeros(self.alphasets_shape)
[docs]
def _precompute_alphasets(self, alphasets_shape):
if alphasets_shape == self.alphasets_shape:
return
tensorlib, _ = get_backend()
self.alphasets_shape = alphasets_shape
self.mask_on = tensorlib.ones(self.alphasets_shape)
self.mask_off = tensorlib.zeros(self.alphasets_shape)
def __call__(self, alphasets):
"""Compute Interpolated Values."""
tensorlib, _ = get_backend()
self._precompute_alphasets(tensorlib.shape(alphasets))
where_alphasets_positive = tensorlib.where(
alphasets > 0, self.mask_on, self.mask_off
)
# s: set under consideration (i.e. the modifier)
# a: alpha variation
# h: histogram affected by modifier
# b: bin of histogram
alphas_times_deltas_up = tensorlib.einsum(
'sa,shb->shab', alphasets, self.deltas_up
)
alphas_times_deltas_dn = tensorlib.einsum(
'sa,shb->shab', alphasets, self.deltas_dn
)
masks = tensorlib.astensor(
tensorlib.einsum(
'sa,shb->shab', where_alphasets_positive, self.broadcast_helper
),
dtype="bool",
)
return tensorlib.where(masks, alphas_times_deltas_up, alphas_times_deltas_dn)
class _slow_code0:
def summand(self, down, nom, up, alpha):
delta_up = up - nom
delta_down = nom - down
if alpha > 0:
delta = delta_up * alpha
else:
delta = delta_down * alpha
return delta
def __init__(self, histogramssets, subscribe=True):
self._histogramssets = histogramssets
def __call__(self, alphasets):
tensorlib, _ = get_backend()
return tensorlib.astensor(
_slow_interpolator_looper(
self._histogramssets, tensorlib.tolist(alphasets), self.summand
)
)