from __future__ import annotations
from typing import cast
import jax
import jax.numpy as jnp
from jaxtyping import Array, Bool, Float, Scalar
from evermore.binned.effect import H, Identity, OffsetAndScale
from evermore.binned.modifier import Modifier, ModifierBase, Where
from evermore.parameters.parameter import NormalParameter
from evermore.util import float_array
__all__ = [
"StatErrors",
]
def __dir__():
return __all__
[docs]
class StatErrors(ModifierBase):
"""Creates per-bin Barlow-Beeston statistical uncertainty modifiers.
Args:
hist: Nominal histogram bin contents.
variance: Estimated variance per bin.
Examples:
>>> import jax.numpy as jnp
>>> import evermore as evm
>>> hist = jnp.array([10.0, 20.0, 30.0])
>>> var = jnp.array([12.0, 21.0, 29.0])
>>> staterrors = evm.staterror.StatErrors(hist, var)
>>> staterrors(hist)
Array([10. , 20. , 29.999998], dtype=float32)
"""
eps: Float[Scalar, ""]
n_entries: Float[Array, "..."] # noqa: UP037
non_empty_mask: Bool[Array, "..."] # noqa: UP037
relative_error: Float[Array, "..."] # noqa: UP037
parameter: NormalParameter[Float[Array, "..."]] # noqa: UP037
def __init__(
self,
hist: Float[Array, "..."], # noqa: UP037
variance: Float[Array, "..."], # noqa: UP037
):
# make sure they are of dtype float
hist, variance = jax.tree.map(float_array, (hist, variance))
self.eps = cast(Float[Scalar, ""], jnp.finfo(variance.dtype).eps)
self.n_entries = jnp.where(
variance != 0.0,
(hist**2 / (variance + jnp.where(variance != 0.0, 0.0, self.eps))),
0.0,
)
self.non_empty_mask = self.n_entries != 0.0
self.relative_error = jnp.where(
self.non_empty_mask,
1.0
/ jnp.sqrt(self.n_entries + jnp.where(self.non_empty_mask, 0.0, self.eps)),
1.0,
)
self.parameter = NormalParameter(jnp.zeros_like(self.n_entries))
def offset_and_scale(self, hist: H) -> OffsetAndScale:
modifier = Where(
self.non_empty_mask,
self.parameter.scale(slope=self.relative_error, offset=1.0),
Modifier(value=self.parameter.get_value(), effect=Identity()),
)
return modifier.offset_and_scale(hist)
