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[Chore] Simplify flaot typehints

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Fabian Joswig 2025-06-23 21:36:32 +02:00
parent ac7e98d1af
commit 356a3967fd
1 changed files with 14 additions and 14 deletions
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28
pyerrors/obs.py
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@ -159,11 +159,11 @@ class Obs:
self.tag = None
@property
def value(self) -> Union[float, int64, float64, int]:
def value(self) -> float:
return self._value
@property
def dvalue(self) -> Union[float, float64]:
def dvalue(self) -> float:
return self._dvalue
@property
@ -481,7 +481,7 @@ class Obs:
"""
return reweight(weight, [self])[0]
def is_zero_within_error(self, sigma: Union[float, int]=1) -> Union[bool, bool]:
def is_zero_within_error(self, sigma: float=1) -> bool:
"""Checks whether the observable is zero within 'sigma' standard errors.
Parameters
@ -493,7 +493,7 @@ class Obs:
"""
return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
def is_zero(self, atol: float=1e-10) -> Union[bool, bool]:
def is_zero(self, atol: float=1e-10) -> bool:
"""Checks whether the observable is zero within a given tolerance.
Parameters
@ -867,7 +867,7 @@ class Obs:
else:
return derived_observable(lambda x, **kwargs: x[0] / y, [self], man_grad=[1 / y])
def __rtruediv__(self, y: Union[float, int]) -> Obs:
def __rtruediv__(self, y: float) -> Obs:
if isinstance(y, Obs):
return derived_observable(lambda x, **kwargs: x[0] / x[1], [y, self], man_grad=[1 / self.value, - y.value / self.value ** 2])
else:
@ -878,13 +878,13 @@ class Obs:
else:
return derived_observable(lambda x, **kwargs: y / x[0], [self], man_grad=[-y / self.value ** 2])
def __pow__(self, y: Union[Obs, float, int]) -> Obs:
def __pow__(self, y: Union[Obs, float]) -> Obs:
if isinstance(y, Obs):
return derived_observable(lambda x, **kwargs: x[0] ** x[1], [self, y], man_grad=[y.value * self.value ** (y.value - 1), self.value ** y.value * np.log(self.value)])
else:
return derived_observable(lambda x, **kwargs: x[0] ** y, [self], man_grad=[y * self.value ** (y - 1)])
def __rpow__(self, y: Union[float, int]) -> Obs:
def __rpow__(self, y: float) -> Obs:
return derived_observable(lambda x, **kwargs: y ** x[0], [self], man_grad=[y ** self.value * np.log(y)])
def __abs__(self) -> Obs:
@ -941,7 +941,7 @@ class CObs:
"""Class for a complex valued observable."""
__slots__ = ['_real', '_imag', 'tag']
def __init__(self, real: Obs, imag: Union[Obs, float, int]=0.0):
def __init__(self, real: Obs, imag: Union[Obs, float]=0.0):
self._real = real
self._imag = imag
self.tag = None
@ -951,7 +951,7 @@ class CObs:
return self._real
@property
def imag(self) -> Union[Obs, float, int]:
def imag(self) -> Union[Obs, float]:
return self._imag
def gamma_method(self, **kwargs):
@ -979,7 +979,7 @@ class CObs:
else:
return CObs(self.real + other, self.imag)
def __radd__(self, y: Union[complex, float, Obs, int]) -> "CObs":
def __radd__(self, y: Union[complex, Obs]) -> "CObs":
return self + y
def __sub__(self, other: Any) -> Union[CObs, ndarray]:
@ -990,7 +990,7 @@ class CObs:
else:
return CObs(self.real - other, self.imag)
def __rsub__(self, other: Union[complex, float, Obs, int]) -> "CObs":
def __rsub__(self, other: Union[complex, Obs]) -> "CObs":
return -1 * (self - other)
def __mul__(self, other: Any) -> Union[CObs, ndarray]:
@ -1012,7 +1012,7 @@ class CObs:
else:
return CObs(self.real * other, self.imag * other)
def __rmul__(self, other: Union[complex, Obs, CObs, float, int]) -> "CObs":
def __rmul__(self, other: Union[complex, Obs, CObs]) -> "CObs":
return self * other
def __truediv__(self, other: Any) -> Union[CObs, ndarray]:
@ -1024,7 +1024,7 @@ class CObs:
else:
return CObs(self.real / other, self.imag / other)
def __rtruediv__(self, other: Union[complex, float, Obs, CObs, int]) -> CObs:
def __rtruediv__(self, other: Union[complex, Obs, CObs]) -> CObs:
r = self.real ** 2 + self.imag ** 2
if hasattr(other, 'real') and hasattr(other, 'imag'):
return CObs((self.real * other.real + self.imag * other.imag) / r, (self.real * other.imag - self.imag * other.real) / r)
@ -1164,7 +1164,7 @@ def _intersection_idx(idl: list[Union[range, list[int]]]) -> Union[range, list[i
return idinter
def _expand_deltas_for_merge(deltas: ndarray, idx: Union[range, list[int]], shape: int, new_idx: Union[range, list[int]], scalefactor: Union[float, int]) -> ndarray:
def _expand_deltas_for_merge(deltas: ndarray, idx: Union[range, list[int]], shape: int, new_idx: Union[range, list[int]], scalefactor: float) -> ndarray:
"""Expand deltas defined on idx to the list of configs that is defined by new_idx.
New, empty entries are filled by 0. If idx and new_idx are of type range, the smallest
common divisor of the step sizes is used as new step size.