# Copyright (c) 2019 zfit
import itertools
from typing import Dict, Union, Callable, Iterable
import tensorflow as tf
from zfit.core.basefunc import BaseFunc
from zfit.core.basemodel import SimpleModelSubclassMixin
from zfit.util import ztyping
from ..core.basefunc import BaseFunc
from ..core.interfaces import ZfitModel, ZfitFunc
from ..models.basefunctor import FunctorMixin
from ..util import ztyping
from ..util.container import convert_to_container
[docs]class SimpleFunc(BaseFunc):
def __init__(self, func: Callable, obs: ztyping.ObsTypeInput, name: str = "Function", **params):
"""Create a simple function out of of `func` with the observables `obs` depending on `parameters`.
Args:
func (function):
obs (Union[str, Tuple[str]]):
name (str):
**params (): The parameters as keyword arguments. E.g. `mu=Parameter(...)`
"""
super().__init__(name=name, obs=obs, params=params)
self._value_func = self._check_input_x_function(func)
def _func(self, x):
return self._value_func(self, x)
[docs]class BaseFunctorFunc(FunctorMixin, BaseFunc):
def __init__(self, funcs, name="BaseFunctorFunc", **kwargs):
funcs = convert_to_container(funcs)
params = {}
for func in funcs:
params.update(func.params)
self.funcs = funcs
super().__init__(name=name, models=self.funcs, params=params, **kwargs)
def _get_dependents(self): # TODO: change recursive to `only_floating`?
dependents = super()._get_dependents() # get the own parameter dependents
func_dependents = self._extract_dependents(self.funcs) # flatten
return dependents.union(func_dependents)
@property
def _models(self) -> Dict[Union[float, int, str], ZfitModel]:
return self.funcs
[docs]class SumFunc(BaseFunctorFunc):
def __init__(self, funcs: Iterable[ZfitFunc], obs: ztyping.ObsTypeInput = None, name: str = "SumFunc", **kwargs):
super().__init__(funcs=funcs, obs=obs, name=name, **kwargs)
def _func(self, x):
# sum_funcs = tf.add_n([func.value(x) for func in self.funcs])
funcs = [func.func(x) for func in self.funcs]
sum_funcs = tf.math.accumulate_n(funcs)
return sum_funcs
[docs]class ProdFunc(BaseFunctorFunc):
def __init__(self, funcs: Iterable[ZfitFunc], obs: ztyping.ObsTypeInput = None, name: str = "SumFunc", **kwargs):
super().__init__(funcs=funcs, obs=obs, name=name, **kwargs)
def _func(self, x):
value = self.funcs[0].func(x)
for func in self.funcs[1:]:
value *= func.func(x)
return value
[docs]class ZFunc(SimpleModelSubclassMixin, BaseFunc):
def __init__(self, obs: ztyping.ObsTypeInput, name: str = "ZFunc", **params):
super().__init__(obs=obs, name=name, **params)
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
cls._check_simple_model_subclass()