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[Fix] Start fixing remaining type hints

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This commit is contained in:
Fabian Joswig 2025-01-03 19:01:20 +01:00
parent 23d4f4c320
commit 1916de15ec
3 changed files with 27 additions and 28 deletions
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50
pyerrors/correlators.py
View file

@ -76,7 +76,7 @@ class Corr:
T = data_input[0, 0].T
N = data_input.shape[0]
input_as_list = []
input_as_list: list[Union[None, ndarray]] = []
for t in range(T):
if any([(item.content[t] is None) for item in data_input.flatten()]):
if not all([(item.content[t] is None) for item in data_input.flatten()]):
@ -100,7 +100,7 @@ class Corr:
if all([isinstance(item, (Obs, CObs)) or item is None for item in data_input]):
_assert_equal_properties([o for o in data_input if o is not None])
self.content = [np.asarray([item]) if item is not None else None for item in data_input]
self.content: list[Union[None, ndarray]] = [np.asarray([item]) if item is not None else None for item in data_input]
self.N = 1
elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
self.content = data_input
@ -124,12 +124,13 @@ class Corr:
def __getitem__(self, idx: Union[slice, int]) -> Union[CObs, Obs, ndarray, List[ndarray]]:
"""Return the content of timeslice idx"""
if self.content[idx] is None:
idx_content = self.content[idx]
if idx_content is None:
return None
elif len(self.content[idx]) == 1:
return self.content[idx][0]
elif len(idx_content) == 1:
return idx_content[0]
else:
return self.content[idx]
return idx_content
@property
def reweighted(self):
@ -154,7 +155,7 @@ class Corr:
gm = gamma_method
def projected(self, vector_l: Optional[Union[ndarray, List[Optional[ndarray]]]]=None, vector_r: None=None, normalize: bool=False) -> "Corr":
def projected(self, vector_l: Optional[Union[ndarray, List[Optional[ndarray]]]]=None, vector_r: Optional[Union[ndarray, List[Optional[ndarray]]]]=None, normalize: bool=False) -> "Corr":
"""We need to project the Correlator with a Vector to get a single value at each timeslice.
The method can use one or two vectors.
@ -166,7 +167,7 @@ class Corr:
if vector_l is None:
vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
elif (vector_r is None):
elif vector_r is None:
vector_r = vector_l
if isinstance(vector_l, list) and not isinstance(vector_r, list):
if len(vector_l) != self.T:
@ -177,7 +178,7 @@ class Corr:
raise ValueError("Length of vector list must be equal to T")
vector_l = [vector_l] * self.T
if not isinstance(vector_l, list):
if isinstance(vector_l, ndarray) and isinstance(vector_r, ndarray):
if not vector_l.shape == vector_r.shape == (self.N,):
raise ValueError("Vectors are of wrong shape!")
if normalize:
@ -239,7 +240,7 @@ class Corr:
newcontent.append(None)
else:
newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
if (all([x is None for x in newcontent])):
if all([x is None for x in newcontent]):
raise ValueError("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange)
@ -284,7 +285,7 @@ class Corr:
"""Calculates the per-timeslice trace of a correlator matrix."""
if self.N == 1:
raise ValueError("Only works for correlator matrices.")
newcontent = []
newcontent: list[Union[None, float]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
newcontent.append(None)
@ -486,7 +487,7 @@ class Corr:
offset : int
Offset the equal spacing
"""
new_content = []
new_content: list[Union[None, list, ndarray]] = []
for t in range(self.T):
if (offset + t) % spacing != 0:
new_content.append(None)
@ -506,7 +507,7 @@ class Corr:
"""
if self.N != 1:
raise ValueError("Only one-dimensional correlators can be safely correlated.")
new_content = []
new_content: list[Union[None, ndarray]] = []
for x0, t_slice in enumerate(self.content):
if _check_for_none(self, t_slice):
new_content.append(None)
@ -538,7 +539,7 @@ class Corr:
"""
if self.N != 1:
raise Exception("Reweighting only implemented for one-dimensional correlators.")
new_content = []
new_content: list[Union[None, ndarray]] = []
for t_slice in self.content:
if _check_for_none(self, t_slice):
new_content.append(None)
@ -660,8 +661,8 @@ class Corr:
"""
if self.N != 1:
raise ValueError("second_deriv only implemented for one-dimensional correlators.")
newcontent: list[Union[None, ndarray, Obs]] = []
if variant == "symmetric":
newcontent = []
for t in range(1, self.T - 1):
if (self.content[t - 1] is None) or (self.content[t + 1] is None):
newcontent.append(None)
@ -671,7 +672,6 @@ class Corr:
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[1, 1])
elif variant == "big_symmetric":
newcontent = []
for t in range(2, self.T - 2):
if (self.content[t - 2] is None) or (self.content[t + 2] is None):
newcontent.append(None)
@ -681,7 +681,6 @@ class Corr:
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2])
elif variant == "improved":
newcontent = []
for t in range(2, self.T - 2):
if (self.content[t - 2] is None) or (self.content[t - 1] is None) or (self.content[t] is None) or (self.content[t + 1] is None) or (self.content[t + 2] is None):
newcontent.append(None)
@ -691,7 +690,6 @@ class Corr:
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
for t in range(self.T):
if (self.content[t] is None) or (self.content[t] <= 0):
newcontent.append(None)
@ -859,7 +857,7 @@ class Corr:
else:
raise ValueError("Unsupported plateau method: " + method)
def set_prange(self, prange: List[Union[int, float]]):
def set_prange(self, prange: List[int]):
"""Sets the attribute prange of the Corr object."""
if not len(prange) == 2:
raise ValueError("prange must be a list or array with two values")
@ -1098,7 +1096,7 @@ class Corr:
if isinstance(y, Corr):
if ((self.N != y.N) or (self.T != y.T)):
raise ValueError("Addition of Corrs with different shape")
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
newcontent.append(None)
@ -1107,7 +1105,7 @@ class Corr:
return Corr(newcontent)
elif isinstance(y, (Obs, int, float, CObs, complex)):
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
newcontent.append(None)
@ -1126,7 +1124,7 @@ class Corr:
if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
newcontent.append(None)
@ -1156,7 +1154,7 @@ class Corr:
raise ValueError("Can only multiply correlators by square matrices.")
if not self.N == y.shape[0]:
raise ValueError("matmul: mismatch of matrix dimensions")
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
newcontent.append(None)
@ -1183,7 +1181,7 @@ class Corr:
raise ValueError("Can only multiply correlators by square matrices.")
if not self.N == y.shape[0]:
raise ValueError("matmul: mismatch of matrix dimensions")
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
newcontent.append(None)
@ -1197,7 +1195,7 @@ class Corr:
if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
newcontent.append(None)
@ -1232,7 +1230,7 @@ class Corr:
elif isinstance(y, (int, float)):
if y == 0:
raise ValueError('Division by zero will return undefined correlator')
newcontent = []
newcontent: list[Union[None, ndarray, Obs]] = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
newcontent.append(None)

1
pyerrors/linalg.py
View file

@ -48,6 +48,7 @@ def matmul(*operands) -> ndarray:
Nr = derived_observable(multi_dot_r, extended_operands, array_mode=True)
Ni = derived_observable(multi_dot_i, extended_operands, array_mode=True)
assert isinstance(Nr, ndarray) and isinstance(Ni, ndarray)
res = np.empty_like(Nr)
for (n, m), entry in np.ndenumerate(Nr):
res[n, m] = CObs(Nr[n, m], Ni[n, m])

4
pyerrors/mpm.py
View file

@ -3,10 +3,10 @@ import numpy as np
import scipy.linalg
from .obs import Obs
from .linalg import svd, eig
from typing import List
from typing import Optional
def matrix_pencil_method(corrs: List[Obs], k: int=1, p: None=None, **kwargs) -> List[Obs]:
def matrix_pencil_method(corrs: list[Obs], k: int=1, p: Optional[int]=None, **kwargs) -> list[Obs]:
"""Matrix pencil method to extract k energy levels from data
Implementation of the matrix pencil method based on