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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 self.tag = None
@property @property
def value(self) -> Union[float, int64, float64, int]: def value(self) -> float:
return self._value return self._value
@property @property
def dvalue(self) -> Union[float, float64]: def dvalue(self) -> float:
return self._dvalue return self._dvalue
@property @property
@ -481,7 +481,7 @@ class Obs:
""" """
return reweight(weight, [self])[0] 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. """Checks whether the observable is zero within 'sigma' standard errors.
Parameters Parameters
@ -493,7 +493,7 @@ class Obs:
""" """
return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue 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. """Checks whether the observable is zero within a given tolerance.
Parameters Parameters
@ -867,7 +867,7 @@ class Obs:
else: else:
return derived_observable(lambda x, **kwargs: x[0] / y, [self], man_grad=[1 / y]) 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): 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]) return derived_observable(lambda x, **kwargs: x[0] / x[1], [y, self], man_grad=[1 / self.value, - y.value / self.value ** 2])
else: else:
@ -878,13 +878,13 @@ class Obs:
else: else:
return derived_observable(lambda x, **kwargs: y / x[0], [self], man_grad=[-y / self.value ** 2]) 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): 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)]) 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: else:
return derived_observable(lambda x, **kwargs: x[0] ** y, [self], man_grad=[y * self.value ** (y - 1)]) 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)]) return derived_observable(lambda x, **kwargs: y ** x[0], [self], man_grad=[y ** self.value * np.log(y)])
def __abs__(self) -> Obs: def __abs__(self) -> Obs:
@ -941,7 +941,7 @@ class CObs:
"""Class for a complex valued observable.""" """Class for a complex valued observable."""
__slots__ = ['_real', '_imag', 'tag'] __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._real = real
self._imag = imag self._imag = imag
self.tag = None self.tag = None
@ -951,7 +951,7 @@ class CObs:
return self._real return self._real
@property @property
def imag(self) -> Union[Obs, float, int]: def imag(self) -> Union[Obs, float]:
return self._imag return self._imag
def gamma_method(self, **kwargs): def gamma_method(self, **kwargs):
@ -979,7 +979,7 @@ class CObs:
else: else:
return CObs(self.real + other, self.imag) 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 return self + y
def __sub__(self, other: Any) -> Union[CObs, ndarray]: def __sub__(self, other: Any) -> Union[CObs, ndarray]:
@ -990,7 +990,7 @@ class CObs:
else: else:
return CObs(self.real - other, self.imag) 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) return -1 * (self - other)
def __mul__(self, other: Any) -> Union[CObs, ndarray]: def __mul__(self, other: Any) -> Union[CObs, ndarray]:
@ -1012,7 +1012,7 @@ class CObs:
else: else:
return CObs(self.real * other, self.imag * other) 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 return self * other
def __truediv__(self, other: Any) -> Union[CObs, ndarray]: def __truediv__(self, other: Any) -> Union[CObs, ndarray]:
@ -1024,7 +1024,7 @@ class CObs:
else: else:
return CObs(self.real / other, self.imag / other) 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 r = self.real ** 2 + self.imag ** 2
if hasattr(other, 'real') and hasattr(other, 'imag'): 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) 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 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. """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 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. common divisor of the step sizes is used as new step size.