Source code for zfit.models.histmodifier

#  Copyright (c) 2023 zfit

from __future__ import annotations

from typing import TYPE_CHECKING

    import zfit

from import Mapping

from .binned_functor import BaseBinnedFunctorPDF
from import supports
from ..core.interfaces import ZfitBinnedPDF
import tensorflow as tf
import zfit.z.numpy as znp
from ..util import ztyping
from ..util.exception import SpecificFunctionNotImplemented

[docs] class BinwiseScaleModifier(BaseBinnedFunctorPDF): def __init__( self, pdf: ZfitBinnedPDF, modifiers: bool | Mapping[str, ztyping.ParamTypeInput] = None, extended: ztyping.ExtendedInputType = None, norm: ztyping.NormInputType = None, name: str | None = "BinnedTemplatePDF", ) -> None: """Modifier that scales each bin separately of the *pdf*. Binwise modification can be used to account for uncorrelated or correlated uncertainties. Args: pdf: Binned pdf to be modified. modifiers: Modifiers for each bin. extended: |@doc:pdf.init.extended| The overall yield of the PDF. If this is parameter-like, it will be used as the yield, the expected number of events, and the PDF will be extended. An extended PDF has additional functionality, such as the ``ext_*`` methods and the ``counts`` (for binned PDFs). |@docend:pdf.init.extended| norm: |@doc:pdf.init.norm| Normalization of the PDF. By default, this is the same as the default space of the PDF. |@docend:pdf.init.norm| name: || Human-readable name or label of the PDF for better identification. Has no programmatical functional purpose as identification. || """ obs = if not isinstance(pdf, ZfitBinnedPDF): raise TypeError("pdf must be a BinnedPDF") if extended is None: extended = pdf.is_extended if modifiers is None: modifiers = True if modifiers is True: import zfit modifiers = { f"sysshape_{i}": zfit.Parameter(f"auto_sysshape_{self}_{i}", 1.0) for i in range(pdf.counts(obs).shape.num_elements()) } if not isinstance(modifiers, dict): raise TypeError("modifiers must be a dict-like object or True or None") params = modifiers.copy() self._binwise_modifiers = modifiers if extended is True: self._automatically_extended = True if modifiers: import zfit def sumfunc(params): values = self.counts() return znp.sum(values) from zfit.core.parameter import get_auto_number params_sumfunc = modifiers.copy() dep_params = {} for p in pdf.get_params(): while True: i = get_auto_number() name_tmp = f"DUMMPY_PARAM_{i}" if name_tmp not in params_sumfunc: dep_params[name_tmp] = p break params_sumfunc.update(dep_params) extended = zfit.ComposedParameter( f"AUTO_binwise_modifier_{get_auto_number()}", sumfunc, params=params_sumfunc, ) else: extended = pdf.get_yield() elif extended is not False: self._automatically_extended = False super().__init__( obs=obs, name=name, params=params, models=pdf, extended=extended, norm=norm ) @supports(norm=True) def _counts(self, x, norm=None): if not self._automatically_extended: raise SpecificFunctionNotImplemented values = self._counts_with_modifiers(x, norm) return values def _counts_with_modifiers(self, x, norm): values = self.pdfs[0].counts(x, norm=norm) modifiers = list(self._binwise_modifiers.values()) if modifiers: sysshape_flat = tf.stack(modifiers) modifiers = znp.reshape(sysshape_flat, values.shape) values = values * modifiers return values @supports(norm="space") def _rel_counts(self, x, norm=None): values = self._counts_with_modifiers(x, norm) return values / znp.sum(values)